Group4_5002_Feature&Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Group4 5002 ---Feature&Model.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "Nxeb12yxHYjS",
        "colab_type": "code",
        "outputId": "6cf4ac5d-4621-4179-eec0-f024e481f299",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import time\n",
        "import datetime\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as son\n",
        "from scipy.stats import skew\n",
        "from sklearn.model_selection import cross_val_score, train_test_split\n",
        "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, AdaBoostRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import MinMaxScaler, Normalizer\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "import math\n",
        "from fancyimpute import BiScaler, KNN, NuclearNormMinimization, SoftImpute, IterativeImputer"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "HrWuZgJyQUOY",
        "colab_type": "code",
        "outputId": "54bab405-7407-43a7-bc87-a3dfcb4ed796",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "cell_type": "code",
      "source": [
        "# install necessary packages to access google drive.\n",
        "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "!apt-get update -qq 2>&1 > /dev/null\n",
        "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
        "\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "from oauth2client.client import GoogleCredentials\n",
        "creds = GoogleCredentials.get_application_default()\n",
        "import getpass\n",
        "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
        "vcode = getpass.getpass()\n",
        "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}\n",
        "\n",
        "!mkdir -p drive\n",
        "!google-drive-ocamlfuse drive"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "E: Package 'python-software-properties' has no installation candidate\n",
            "Selecting previously unselected package libfuse2:amd64.\n",
            "(Reading database ... 26397 files and directories currently installed.)\n",
            "Preparing to unpack .../libfuse2_2.9.7-1ubuntu1_amd64.deb ...\n",
            "Unpacking libfuse2:amd64 (2.9.7-1ubuntu1) ...\n",
            "Selecting previously unselected package fuse.\n",
            "Preparing to unpack .../fuse_2.9.7-1ubuntu1_amd64.deb ...\n",
            "Unpacking fuse (2.9.7-1ubuntu1) ...\n",
            "Selecting previously unselected package google-drive-ocamlfuse.\n",
            "Preparing to unpack .../google-drive-ocamlfuse_0.7.1-0ubuntu2~ubuntu18.04.1_amd64.deb ...\n",
            "Unpacking google-drive-ocamlfuse (0.7.1-0ubuntu2~ubuntu18.04.1) ...\n",
            "Setting up libfuse2:amd64 (2.9.7-1ubuntu1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Setting up fuse (2.9.7-1ubuntu1) ...\n",
            "Setting up google-drive-ocamlfuse (0.7.1-0ubuntu2~ubuntu18.04.1) ...\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "··········\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "Please enter the verification code: Access token retrieved correctly.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "G6XhQrfADMaL",
        "colab_type": "code",
        "outputId": "5fc0c008-03fd-4312-b16b-cba536244b52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "cell_type": "code",
      "source": [
        "! pip install lightgbm"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting lightgbm\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/3b/4ae113193b4ee01387ed76d5eea32788aec0589df9ae7378a8b7443eaa8b/lightgbm-2.2.2-py2.py3-none-manylinux1_x86_64.whl (1.2MB)\n",
            "\u001b[K    100% |████████████████████████████████| 1.2MB 7.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.1.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.14.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from lightgbm) (0.19.2)\n",
            "Installing collected packages: lightgbm\n",
            "Successfully installed lightgbm-2.2.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BSLz9zuCDQQl",
        "colab_type": "code",
        "outputId": "0f71f1e5-c646-4cde-d4a1-0e35cdf5d106",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1159
        }
      },
      "cell_type": "code",
      "source": [
        "! pip install fancyimpute"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting fancyimpute\n",
            "  Downloading https://files.pythonhosted.org/packages/9d/ce/8ec5d52478b137442f5719f6fd87a2cc398c28b173d662b0cbfaa07fd82e/fancyimpute-0.4.2.tar.gz\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from fancyimpute) (1.11.0)\n",
            "Collecting knnimpute (from fancyimpute)\n",
            "  Downloading https://files.pythonhosted.org/packages/e8/35/e9ab006430d36aed71f623db8d194a723a14d40b52b3405cdd5b94d9de04/knnimpute-0.1.0.tar.gz\n",
            "Requirement already satisfied: numpy>=1.10 in /usr/local/lib/python3.6/dist-packages (from fancyimpute) (1.14.6)\n",
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            "Collecting cvxpy>=1.0.6 (from fancyimpute)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/3c/4314c56be5b069f4d542046912d503a07c96b42c0b075ef0e32b48f8579f/cvxpy-1.0.10.tar.gz (900kB)\n",
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            "Collecting np_utils (from fancyimpute)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1d/8b/dc2a4871fe81413bd07b811c26a6931e54980eee18708330868fe41ae84b/np_utils-0.5.7.0.tar.gz (61kB)\n",
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            "Collecting osqp (from cvxpy>=1.0.6->fancyimpute)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/43/f2/bbeb83c0da6fd89a6d835b98d85ec76c04f39a476c065e3c99b6b709c493/osqp-0.4.1-cp36-cp36m-manylinux1_x86_64.whl (146kB)\n",
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            "\u001b[?25hCollecting ecos>=2 (from cvxpy>=1.0.6->fancyimpute)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/55/ed/d131ff51f3a8f73420eb1191345eb49f269f23cadef515172e356018cde3/ecos-2.0.7.post1-cp36-cp36m-manylinux1_x86_64.whl (147kB)\n",
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            "\u001b[?25hCollecting scs>=1.1.3 (from cvxpy>=1.0.6->fancyimpute)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b3/fd/6e01c4f4a69fcc6c3db130ba55572089e78e77ea8c0921a679f9da1ec04c/scs-2.0.2.tar.gz (133kB)\n",
            "\u001b[K    100% |████████████████████████████████| 143kB 9.0MB/s \n",
            "\u001b[?25hCollecting multiprocess (from cvxpy>=1.0.6->fancyimpute)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7a/ee/b9bf3e171f936743758ef924622d8dd00516c5532b00a1210a09bce68325/multiprocess-0.70.6.1.tar.gz (1.4MB)\n",
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            "\u001b[?25hCollecting fastcache (from cvxpy>=1.0.6->fancyimpute)\n",
            "  Downloading https://files.pythonhosted.org/packages/fb/98/93f2d36738868e8dd5a8dbfc918169b24658f63e5fa041fe000c22ae4f8b/fastcache-1.0.2.tar.gz\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.6/dist-packages (from cvxpy>=1.0.6->fancyimpute) (0.9.0)\n",
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            "Requirement already satisfied: dill>=0.2.8.1 in /usr/local/lib/python3.6/dist-packages (from multiprocess->cvxpy>=1.0.6->fancyimpute) (0.2.8.2)\n",
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            "Requirement already satisfied: werkzeug>=0.11.10 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow->fancyimpute) (0.14.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow->fancyimpute) (3.0.1)\n",
            "Building wheels for collected packages: fancyimpute, knnimpute, cvxpy, np-utils, scs, multiprocess, fastcache\n",
            "  Running setup.py bdist_wheel for fancyimpute ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/4c/dc/65/3b721b985a3810d645829367fa42007e97db12573e6bb27f9f\n",
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            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/a3/92/31/c3f8864714e9938396c3a68d8c542531f7e2d7862bb750b2e3\n",
            "  Running setup.py bdist_wheel for cvxpy ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\n",
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            "  Running setup.py bdist_wheel for np-utils ... \u001b[?25l-\b \bdone\n",
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            "  Running setup.py bdist_wheel for scs ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\n",
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            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/8b/36/e5/96614ab62baf927e9bc06889ea794a8e87552b84bb6bf65e3e\n",
            "  Running setup.py bdist_wheel for fastcache ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/b7/90/c0/da92ac52d188d9ebca577044e89a14d0e6ff333c1bcd1ebc14\n",
            "Successfully built fancyimpute knnimpute cvxpy np-utils scs multiprocess fastcache\n",
            "Installing collected packages: knnimpute, osqp, ecos, scs, multiprocess, fastcache, cvxpy, np-utils, fancyimpute\n",
            "Successfully installed cvxpy-1.0.10 ecos-2.0.7.post1 fancyimpute-0.4.2 fastcache-1.0.2 knnimpute-0.1.0 multiprocess-0.70.6.1 np-utils-0.5.7.0 osqp-0.4.1 scs-2.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Yte80b5PU-b4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def smape(actual, predicted):\n",
        "    dividend= np.abs(np.array(actual) - np.array(predicted))\n",
        "    c = np.array(actual) + np.array(predicted)\n",
        "    \n",
        "    return (2 / len(predicted)) * np.mean(np.divide(dividend, c, out=np.zeros_like(dividend), where=c!=0, casting='unsafe'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Xc8WomY4fdZT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def smape_new(x,y):\n",
        "    x = x.tolist()\n",
        "    y = y.tolist()\n",
        "    if len(x) != len(y):\n",
        "        return('error of length')\n",
        "    else:\n",
        "        n = len(x)\n",
        "        diff = []\n",
        "        for i in range(n):\n",
        "            diff.append(abs(x[i] - y[i]))\n",
        "        value = 2 * sum(diff)/ (sum(x) + sum(y))\n",
        "        return value/n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4d4JDXxOHa3K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "final_train = pd.read_csv('drive/final.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lSlV-L_6jmcB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predict_final = pd.read_csv('drive/prediction_final.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EaRxBR9Wjzc6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction_data = predict_final[248185:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RSm_VU1lRAon",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "holiday = pd.read_csv('drive/holiday.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cGmSC4IvNYTd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "final_train.drop(['a'], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HrMIUYzvN2C7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "now_stamp = time.time()\n",
        "local_time = datetime.datetime.fromtimestamp(now_stamp)\n",
        "utc_time = datetime.datetime.utcfromtimestamp(now_stamp)\n",
        "offset = local_time - utc_time\n",
        "\n",
        "def utc2local(utc_str):\n",
        "    # UTC时间转本地时间（+8:00       \n",
        "    UTC_FORMAT = '%Y-%m-%d %H:%M:%S'\n",
        "    utc = datetime.datetime.strptime(utc_str, UTC_FORMAT)\n",
        "    local = utc + offset\n",
        "    local_str = local.strftime('%Y-%m-%d %H:%M:%S')\n",
        "    return local_str"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9tNhLcG2Q87p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "final_train['utc_time'] = final_train['utc_time'].astype(str)\n",
        "final_train['local_time'] = final_train['utc_time'].apply(lambda x:utc2local(x))\n",
        "final_train['year'] = final_train['local_time'].apply(lambda x:x[:4]).astype(int)\n",
        "final_train['month'] = final_train['local_time'].apply(lambda x:x[5:7]).astype(int)\n",
        "final_train['day'] = final_train['local_time'].apply(lambda x:x[8:10]).astype(int)\n",
        "final_train['hour'] = final_train['local_time'].apply(lambda x:x[11:13]).astype(int)\n",
        "\n",
        "final_train['local_time'] = pd.to_datetime(final_train['local_time'])\n",
        "final_train['date'] = final_train['local_time'].apply(lambda x: x.strftime(format = '%Y%m%d')).astype(int)\n",
        "final_train = pd.merge(final_train,holiday,how = 'left', on = 'date')\n",
        "final_train['holiday'].fillna(1,inplace = True)\n",
        "final_train['holiday'] = final_train['holiday'].astype(int)\n",
        "final_train['dayofweek'] = final_train['local_time'].dt.dayofweek.astype(int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bCok9RqbsBJj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predict_final['utc_time'] = predict_final['utc_time'].astype(str)\n",
        "predict_final['local_time'] = predict_final['utc_time'].apply(lambda x:utc2local(x))\n",
        "predict_final['year'] = predict_final['local_time'].apply(lambda x:x[:4]).astype(int)\n",
        "predict_final['month'] = predict_final['local_time'].apply(lambda x:x[5:7]).astype(int)\n",
        "predict_final['day'] = predict_final['local_time'].apply(lambda x:x[8:10]).astype(int)\n",
        "predict_final['hour'] = predict_final['local_time'].apply(lambda x:x[11:13]).astype(int)\n",
        "\n",
        "predict_final['local_time'] = pd.to_datetime(predict_final['local_time'])\n",
        "predict_final['date'] = predict_final['local_time'].apply(lambda x: x.strftime(format = '%Y%m%d')).astype(int)\n",
        "predict_final = pd.merge(predict_final,holiday,how = 'left', on = 'date')\n",
        "predict_final['holiday'].fillna(1,inplace = True)\n",
        "predict_final['holiday'] = predict_final['holiday'].astype(int)\n",
        "predict_final['dayofweek'] = predict_final['local_time'].dt.dayofweek.astype(int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rIgJ9y4hRKbM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "station_name = final_train.groupby('stationId').agg('count').index.tolist()\n",
        "station_list = []\n",
        "for name in station_name:\n",
        "  station_train = final_train[final_train['stationId'] == name]\n",
        "  station_list.append(station_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WfR_Qm0xstqt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "station_name = predict_final.groupby('stationId').agg('count').index.tolist()\n",
        "prediction_list = []\n",
        "for name in station_name:\n",
        "  station_train = predict_final[predict_final['stationId'] == name]\n",
        "  prediction_list.append(station_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nXhO7szgN2Fi",
        "colab_type": "code",
        "outputId": "62804df7-d8a7-4418-9d9a-bdad01005f3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2264
        }
      },
      "cell_type": "code",
      "source": [
        "new_list = []\n",
        "name_list = ['PM2.5','PM10','O3']\n",
        "\n",
        "for df in station_list:\n",
        "  for name in name_list:\n",
        "    df[name + '_' + '1'] = df[name].shift(1)\n",
        "    df[name + '_' + '24'] = df[name].shift(24)\n",
        "    df[name + '_' + '48'] = df[name].shift(48)\n",
        "    df[name + '_' + '48_mean'] = df[name].rolling(window = 48).mean()\n",
        "    df[name + '_' + '48_mean'] = df[name + '_' + '48_mean'].shift(1)\n",
        "    df[name + '_' + '24_mean'] = df[name].rolling(window = 24).mean()\n",
        "    df[name + '_' + '24_mean'] = df[name + '_' + '24_mean'].shift(1)\n",
        "    df[name + '_' + '48_max'] = df[name].rolling(window = 48).max()\n",
        "    df[name + '_' + '48_max'] = df[name + '_' + '48_max'].shift(1)\n",
        "    df[name + '_' + '24_max'] = df[name].rolling(window = 24).max()\n",
        "    df[name + '_' + '24_max'] = df[name + '_' + '24_max'].shift(1)\n",
        "    df[name + '_' + '48_std'] = df[name].rolling(window = 48).std()\n",
        "    df[name + '_' + '48_std'] = df[name + '_' + '48_std'].shift(1)\n",
        "    df[name + '_' + '24_std'] = df[name].rolling(window = 24).std()\n",
        "    df[name + '_' + '24_std'] = df[name + '_' + '24_std'].shift(1)\n",
        "    df[name + '_' + '48_min'] = df[name].rolling(window = 48).min()\n",
        "    df[name + '_' + '48_min'] = df[name + '_' + '48_min'].shift(1)\n",
        "    df[name + '_' + '24_min'] = df[name].rolling(window = 24).min()\n",
        "    df[name + '_' + '24_min'] = df[name + '_' + '24_min'].shift(1)\n",
        "    df[name + '_' + '2'] = df[name].shift(2)\n",
        "    df[name + '_' + '1_diff'] = df[name + '_' + '1'] - df[name + '_' + '2']\n",
        "    df[name + '_' + '3'] = df[name].shift(3)\n",
        "    df[name + '_' + '12'] = df[name].shift(12)\n",
        "    df[name + '_' + '8'] = df[name].shift(8)\n",
        "  new_list.append(df)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  app.launch_new_instance()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "a50Q4441sotP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2264
        },
        "outputId": "8a538326-5a6d-4e17-fc18-7d8ec7233bf3"
      },
      "cell_type": "code",
      "source": [
        "final_list = []\n",
        "name_list = ['PM2.5','PM10','O3']\n",
        "\n",
        "for df in prediction_list:\n",
        "  for name in name_list:\n",
        "    df[name + '_' + '1'] = df[name].shift(1)\n",
        "    df[name + '_' + '24'] = df[name].shift(24)\n",
        "    df[name + '_' + '48'] = df[name].shift(48)\n",
        "    df[name + '_' + '48_mean'] = df[name].rolling(window = 48).mean()\n",
        "    df[name + '_' + '48_mean'] = df[name + '_' + '48_mean'].shift(1)\n",
        "    df[name + '_' + '24_mean'] = df[name].rolling(window = 24).mean()\n",
        "    df[name + '_' + '24_mean'] = df[name + '_' + '24_mean'].shift(1)\n",
        "    df[name + '_' + '48_max'] = df[name].rolling(window = 48).max()\n",
        "    df[name + '_' + '48_max'] = df[name + '_' + '48_max'].shift(1)\n",
        "    df[name + '_' + '24_max'] = df[name].rolling(window = 24).max()\n",
        "    df[name + '_' + '24_max'] = df[name + '_' + '24_max'].shift(1)\n",
        "    df[name + '_' + '48_std'] = df[name].rolling(window = 48).std()\n",
        "    df[name + '_' + '48_std'] = df[name + '_' + '48_std'].shift(1)\n",
        "    df[name + '_' + '24_std'] = df[name].rolling(window = 24).std()\n",
        "    df[name + '_' + '24_std'] = df[name + '_' + '24_std'].shift(1)\n",
        "    df[name + '_' + '48_min'] = df[name].rolling(window = 48).min()\n",
        "    df[name + '_' + '48_min'] = df[name + '_' + '48_min'].shift(1)\n",
        "    df[name + '_' + '24_min'] = df[name].rolling(window = 24).min()\n",
        "    df[name + '_' + '24_min'] = df[name + '_' + '24_min'].shift(1)\n",
        "    df[name + '_' + '2'] = df[name].shift(2)\n",
        "    df[name + '_' + '1_diff'] = df[name + '_' + '1'] - df[name + '_' + '2']\n",
        "    df[name + '_' + '3'] = df[name].shift(3)\n",
        "    df[name + '_' + '12'] = df[name].shift(12)\n",
        "    df[name + '_' + '8'] = df[name].shift(8)\n",
        "  final_list.append(df)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  app.launch_new_instance()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Xn-2twUCRI20",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "new_train = pd.DataFrame()\n",
        "\n",
        "for df in new_list:\n",
        "  new_train = pd.concat([new_train, df], axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "i_9QHyzatZUh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction_new = pd.DataFrame()\n",
        "\n",
        "for df in final_list:\n",
        "  prediction_new = pd.concat([prediction_new, df], axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s3ct52lVtiFo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "1ae9c707-ec00-4112-f9ee-b97b9cde3a62"
      },
      "cell_type": "code",
      "source": [
        "prediction_new = prediction_new.sort_values('utc_time').reset_index()\n",
        "del prediction_new['index']\n",
        "prediction_new.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O3</th>\n",
              "      <th>PM10</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>humidity</th>\n",
              "      <th>pressure</th>\n",
              "      <th>stationId</th>\n",
              "      <th>temperature</th>\n",
              "      <th>utc_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>...</th>\n",
              "      <th>O3_24_max</th>\n",
              "      <th>O3_48_std</th>\n",
              "      <th>O3_24_std</th>\n",
              "      <th>O3_48_min</th>\n",
              "      <th>O3_24_min</th>\n",
              "      <th>O3_2</th>\n",
              "      <th>O3_1_diff</th>\n",
              "      <th>O3_3</th>\n",
              "      <th>O3_12</th>\n",
              "      <th>O3_8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214.0</td>\n",
              "      <td>50.898794</td>\n",
              "      <td>63.0</td>\n",
              "      <td>56.7600</td>\n",
              "      <td>990.8150</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>27.2225</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>190.8400</td>\n",
              "      <td>6.5675</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>192.0</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>72.0</td>\n",
              "      <td>60.3950</td>\n",
              "      <td>993.8275</td>\n",
              "      <td>tongzhou_aq</td>\n",
              "      <td>27.5975</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>161.5575</td>\n",
              "      <td>11.1550</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>199.0</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>59.0</td>\n",
              "      <td>55.1375</td>\n",
              "      <td>992.7150</td>\n",
              "      <td>daxing_aq</td>\n",
              "      <td>28.0275</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>183.7500</td>\n",
              "      <td>8.1825</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>72.0</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>56.0</td>\n",
              "      <td>54.3125</td>\n",
              "      <td>993.3050</td>\n",
              "      <td>yufa_aq</td>\n",
              "      <td>28.1550</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>182.3775</td>\n",
              "      <td>5.5850</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>96.0</td>\n",
              "      <td>263.092184</td>\n",
              "      <td>153.0</td>\n",
              "      <td>54.6575</td>\n",
              "      <td>980.3600</td>\n",
              "      <td>donggaocun_aq</td>\n",
              "      <td>28.1275</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>164.1625</td>\n",
              "      <td>10.1825</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 68 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      O3        PM10  PM2.5  humidity  pressure        stationId  temperature  \\\n",
              "0  214.0   50.898794   63.0   56.7600  990.8150  aotizhongxin_aq      27.2225   \n",
              "1  192.0  143.000000   72.0   60.3950  993.8275      tongzhou_aq      27.5975   \n",
              "2  199.0  113.000000   59.0   55.1375  992.7150        daxing_aq      28.0275   \n",
              "3   72.0   83.000000   56.0   54.3125  993.3050          yufa_aq      28.1550   \n",
              "4   96.0  263.092184  153.0   54.6575  980.3600    donggaocun_aq      28.1275   \n",
              "\n",
              "              utc_time  wind_direction  wind_speed  ...   O3_24_max  \\\n",
              "0  2017-07-09 13:00:00        190.8400      6.5675  ...         NaN   \n",
              "1  2017-07-09 13:00:00        161.5575     11.1550  ...         NaN   \n",
              "2  2017-07-09 13:00:00        183.7500      8.1825  ...         NaN   \n",
              "3  2017-07-09 13:00:00        182.3775      5.5850  ...         NaN   \n",
              "4  2017-07-09 13:00:00        164.1625     10.1825  ...         NaN   \n",
              "\n",
              "   O3_48_std O3_24_std  O3_48_min  O3_24_min  O3_2  O3_1_diff  O3_3  O3_12  \\\n",
              "0        NaN       NaN        NaN        NaN   NaN        NaN   NaN    NaN   \n",
              "1        NaN       NaN        NaN        NaN   NaN        NaN   NaN    NaN   \n",
              "2        NaN       NaN        NaN        NaN   NaN        NaN   NaN    NaN   \n",
              "3        NaN       NaN        NaN        NaN   NaN        NaN   NaN    NaN   \n",
              "4        NaN       NaN        NaN        NaN   NaN        NaN   NaN    NaN   \n",
              "\n",
              "   O3_8  \n",
              "0   NaN  \n",
              "1   NaN  \n",
              "2   NaN  \n",
              "3   NaN  \n",
              "4   NaN  \n",
              "\n",
              "[5 rows x 68 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "n4uWR1RjPvCl",
        "colab_type": "code",
        "outputId": "f16634bb-b0b7-4ef9-c4cc-21f602f887f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "cell_type": "code",
      "source": [
        "new_train = new_train.sort_values('utc_time').reset_index()\n",
        "del new_train['index']\n",
        "new_train.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O3</th>\n",
              "      <th>PM10</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>humidity</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>month</th>\n",
              "      <th>pressure</th>\n",
              "      <th>...</th>\n",
              "      <th>O3_24_max</th>\n",
              "      <th>O3_48_std</th>\n",
              "      <th>O3_24_std</th>\n",
              "      <th>O3_48_min</th>\n",
              "      <th>O3_24_min</th>\n",
              "      <th>O3_2</th>\n",
              "      <th>O3_1_diff</th>\n",
              "      <th>O3_3</th>\n",
              "      <th>O3_12</th>\n",
              "      <th>O3_8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214.0</td>\n",
              "      <td>50.898794</td>\n",
              "      <td>63.0</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>56.7600</td>\n",
              "      <td>39.982</td>\n",
              "      <td>116.397</td>\n",
              "      <td>7</td>\n",
              "      <td>990.8150</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>199.0</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>59.0</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>55.1375</td>\n",
              "      <td>39.718</td>\n",
              "      <td>116.404</td>\n",
              "      <td>7</td>\n",
              "      <td>992.7150</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>248.0</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>59.0</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>50.0750</td>\n",
              "      <td>40.499</td>\n",
              "      <td>116.911</td>\n",
              "      <td>7</td>\n",
              "      <td>963.8225</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>208.0</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>68.0</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>56.9000</td>\n",
              "      <td>39.929</td>\n",
              "      <td>116.339</td>\n",
              "      <td>7</td>\n",
              "      <td>989.7250</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>167.0</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>81.0</td>\n",
              "      <td>9</td>\n",
              "      <td>13</td>\n",
              "      <td>58.4275</td>\n",
              "      <td>39.987</td>\n",
              "      <td>116.287</td>\n",
              "      <td>7</td>\n",
              "      <td>985.9100</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 68 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      O3        PM10  PM2.5  day  hour  humidity  latitude  longitude  month  \\\n",
              "0  214.0   50.898794   63.0    9    13   56.7600    39.982    116.397      7   \n",
              "1  199.0  113.000000   59.0    9    13   55.1375    39.718    116.404      7   \n",
              "2  248.0   88.000000   59.0    9    13   50.0750    40.499    116.911      7   \n",
              "3  208.0  114.000000   68.0    9    13   56.9000    39.929    116.339      7   \n",
              "4  167.0  197.000000   81.0    9    13   58.4275    39.987    116.287      7   \n",
              "\n",
              "   pressure  ...  O3_24_max  O3_48_std O3_24_std  O3_48_min  O3_24_min  O3_2  \\\n",
              "0  990.8150  ...        NaN        NaN       NaN        NaN        NaN   NaN   \n",
              "1  992.7150  ...        NaN        NaN       NaN        NaN        NaN   NaN   \n",
              "2  963.8225  ...        NaN        NaN       NaN        NaN        NaN   NaN   \n",
              "3  989.7250  ...        NaN        NaN       NaN        NaN        NaN   NaN   \n",
              "4  985.9100  ...        NaN        NaN       NaN        NaN        NaN   NaN   \n",
              "\n",
              "  O3_1_diff  O3_3  O3_12  O3_8  \n",
              "0       NaN   NaN    NaN   NaN  \n",
              "1       NaN   NaN    NaN   NaN  \n",
              "2       NaN   NaN    NaN   NaN  \n",
              "3       NaN   NaN    NaN   NaN  \n",
              "4       NaN   NaN    NaN   NaN  \n",
              "\n",
              "[5 rows x 68 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "6Q2ja8DHS4ta",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction_data = prediction_new[248185:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n7WvyLOzS8Ii",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train = new_train[:222985]\n",
        "valid = new_train[222985:246505]\n",
        "test = new_train[246505:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "u9U-tvgiUb7A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_X_PM25 = train[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                      'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday',\n",
        "                       'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean',\n",
        "                      'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                      'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', 'PM2.5_1_diff', \n",
        "                      'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2', 'PM10_1_diff', \n",
        "                      'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "\n",
        "\n",
        "\n",
        "train_X_PM10 = train[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "       'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_2',\n",
        "       'O3_1_diff']]\n",
        "\n",
        "train_X_O3 = train[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "\n",
        "\n",
        "valid_X_PM25 = valid[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
        "       'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
        "       'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1',\n",
        "       'PM10_2', 'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "\n",
        "\n",
        "\n",
        "valid_X_PM10 = valid[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "       'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_2',\n",
        "       'O3_1_diff']]\n",
        "\n",
        "\n",
        "\n",
        "valid_X_O3 = valid[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "test_X_PM25 = test[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
        "       'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
        "       'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1',\n",
        "       'PM10_2', 'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "\n",
        "test_X_PM10 = test[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "       'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_2',\n",
        "       'O3_1_diff']]\n",
        "\n",
        "\n",
        "test_X_O3 = test[['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "train_pm25_y = train['PM2.5']\n",
        "valid_pm25_y = valid['PM2.5']\n",
        "test_pm25_y = test['PM2.5']\n",
        "train_pm10_y = train['PM10']\n",
        "valid_pm10_y = valid['PM10']\n",
        "test_pm10_y = test['PM10']\n",
        "train_o3_y = train['O3']\n",
        "valid_o3_y = valid['O3']\n",
        "test_o3_y = test['O3']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hd3XJUWWUm4G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lgb_PM25_model = lgb.LGBMRegressor(learning_rate = 0.06,n_estimators=600,max_depth=4, reg_lambda = 0.3,reg_alpha=0.2, random_state = 66)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "944J0RmSUqsB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lgb_PM10_model = lgb.LGBMRegressor(learning_rate = 0.06,n_estimators=800,max_depth=4, reg_lambda = 0.3,reg_alpha=0.2, random_state = 66)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s3v9MnFkUq14",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lgb_O3_model = lgb.LGBMRegressor(learning_rate = 0.05,n_estimators=800,max_depth=4, reg_lambda = 0.3,reg_alpha=0.2, random_state = 66)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5isXvWpiUv3V",
        "colab_type": "code",
        "outputId": "545f199d-7d6f-4b5d-f74c-b22d34f2e8ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "lgb_PM25_model.fit(train_X_PM25, train_pm25_y)\n",
        "print(mean_squared_error(train_pm25_y, lgb_PM25_model.predict(train_X_PM25)))\n",
        "print(mean_squared_error(valid_pm25_y, lgb_PM25_model.predict(valid_X_PM25)))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "143.39550784191613\n",
            "311.9120841476577\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aCtIy4E3Uxmc",
        "colab_type": "code",
        "outputId": "71a67926-7745-4d3a-fa52-3b26dc6e0f54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "lgb_PM10_model.fit(train_X_PM10, train_pm10_y)\n",
        "print(mean_squared_error(train_pm10_y, lgb_PM10_model.predict(train_X_PM10)))\n",
        "print(mean_squared_error(valid_pm10_y, lgb_PM10_model.predict(valid_X_PM10)))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "704.7198221577602\n",
            "2325.960808061772\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TeDQYXrAUxpd",
        "colab_type": "code",
        "outputId": "e031529b-8bab-4c13-c88e-fc58e2212d27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "lgb_O3_model.fit(train_X_O3, train_o3_y)\n",
        "print(mean_squared_error(train_o3_y, lgb_O3_model.predict(train_X_O3)))\n",
        "print(mean_squared_error(valid_o3_y, lgb_O3_model.predict(valid_X_O3)))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "109.57040485405032\n",
            "214.53399330743227\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9quZakbIdjI4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "f070a75d-9b89-46ca-82d5-e3bfc22f99d0"
      },
      "cell_type": "code",
      "source": [
        "prediction_xgb_PM25_test = lgb_PM25_model.predict(test_X_PM25)\n",
        "prediction_xgb_PM10_test = lgb_PM10_model.predict(test_X_PM10)\n",
        "prediction_xgb_O3_test = lgb_O3_model.predict(test_X_O3)\n",
        "\n",
        "print('PM2.5 MSE:',mean_squared_error(test_pm25_y, prediction_xgb_PM25_test))\n",
        "print('PM10 MSE:',mean_squared_error(test_pm10_y, prediction_xgb_PM10_test))\n",
        "print('O3 MSE:',mean_squared_error(test_o3_y, prediction_xgb_O3_test))\n",
        "\n",
        "print('PM2.5 SMAPE:',smape(test_pm25_y, prediction_xgb_PM25_test))\n",
        "print('PM10 SMAPE:',smape(test_pm10_y, prediction_xgb_PM10_test))\n",
        "print('O3 SMAPE:',smape(test_o3_y, prediction_xgb_O3_test))"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PM2.5 MSE: 471.2590828011186\n",
            "PM10 MSE: 6220.254115453265\n",
            "O3 MSE: 251.06484111177502\n",
            "PM2.5 SMAPE: 0.00012754126468671369\n",
            "PM10 SMAPE: 0.00012662311945294322\n",
            "O3 SMAPE: 0.00012217640322299025\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aNvX63w1LFz3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**CV K = 5**"
      ]
    },
    {
      "metadata": {
        "id": "qubgw1keNZh6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import KFold\n",
        "import copy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZyTaHsokLa7p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = new_train[:246505]\n",
        "data = shuffle(data)\n",
        "kf = KFold(n_splits = 5)\n",
        "train_list = []\n",
        "test_list = []\n",
        "for train, test in kf.split(data):\n",
        "  train_list.append(train)\n",
        "  test_list.append(test)\n",
        "  \n",
        "train_data_0 = data.drop([i for i in test_list[0]],axis = 0)\n",
        "test_data_0 = data.drop([i for i in train_list[0]],axis = 0)\n",
        "train_data_1 = data.drop([i for i in test_list[1]],axis = 0)\n",
        "test_data_1 = data.drop([i for i in train_list[1]],axis = 0)\n",
        "train_data_2 = data.drop([i for i in test_list[2]],axis = 0)\n",
        "test_data_2 = data.drop([i for i in train_list[2]],axis = 0)\n",
        "train_data_3 = data.drop([i for i in test_list[3]],axis = 0)\n",
        "test_data_3 = data.drop([i for i in train_list[3]],axis = 0)\n",
        "train_data_4 = data.drop([i for i in test_list[4]],axis = 0)\n",
        "test_data_4 = data.drop([i for i in train_list[4]],axis = 0)\n",
        "\n",
        "train_data = [train_data_0,train_data_1,train_data_2,train_data_3,train_data_4]\n",
        "test_data = [test_data_0,test_data_1,test_data_2,test_data_3,test_data_4]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d4MzL-xoPO80",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_25_X = train_data[0][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "train_10_X = train_data[0][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "train_3_X = train_data[0][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "test_25_X = test_data[0][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "test_10_X = test_data[0][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "test_3_X = test_data[0][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "train_25_y = train_data[0]['PM2.5']\n",
        "test_25_y = test_data[0]['PM2.5']\n",
        "train_10_y = train_data[0]['PM10']\n",
        "test_10_y = test_data[0]['PM10']\n",
        "train_3_y = train_data[0]['O3']\n",
        "test_3_y = test_data[0]['O3']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XRI0ySDfPO5j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "573ea556-9aa1-496d-f47a-bd31d37ee3d4"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM25_model_0 = lgb.LGBMRegressor(learning_rate = 0.01,n_estimators=400,max_depth=4, \n",
        "                                   reg_lambda = 0.3,reg_alpha=0.2, \n",
        "                                   random_state = 66).fit(train_25_X,train_25_y)\n",
        "print(smape(train_25_y, lgb_PM25_model_0.predict(train_25_X)) * 1000000)\n",
        "print(smape(test_25_y, lgb_PM25_model_0.predict(test_25_X))* 1000000)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.2844016156831959\n",
            "4.184091825527878\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tCehgiq4PO3M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b7cbec3d-f045-49a7-97a0-29ec41c2893f"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM10_model_0 = lgb.LGBMRegressor(learning_rate = 0.01,n_estimators=300,max_depth=5, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.1, \n",
        "                                   random_state = 66).fit(train_10_X,train_10_y)\n",
        "print(smape(train_10_y, lgb_PM10_model_0.predict(train_10_X)) * 1000000)\n",
        "print(smape(test_10_y, lgb_PM10_model_0.predict(test_10_X))* 1000000)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.067794826439752\n",
            "4.287505829939902\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "s_nwlvkjad0v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8bd8d46d-9f81-4ff5-82c5-5774aeaefa6c"
      },
      "cell_type": "code",
      "source": [
        "lgb_O3_model_0 = lgb.LGBMRegressor(learning_rate = 0.01,n_estimators=500,max_depth=4, \n",
        "                                   reg_lambda = 0.2,reg_alpha=0.1, \n",
        "                                   random_state = 66).fit(train_3_X,train_3_y)\n",
        "print(smape(train_3_y, lgb_O3_model_0.predict(train_3_X)) * 1000000)\n",
        "print(smape(test_3_y, lgb_O3_model_0.predict(test_3_X))* 1000000)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.440746790041064\n",
            "4.772794538717608\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "E_-CpxJrUek5",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_25_X = train_data[1][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "train_10_X = train_data[1][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "train_3_X = train_data[1][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "test_25_X = test_data[1][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "test_10_X = test_data[1][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "test_3_X = test_data[1][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "train_25_y = train_data[1]['PM2.5']\n",
        "test_25_y = test_data[1]['PM2.5']\n",
        "train_10_y = train_data[1]['PM10']\n",
        "test_10_y = test_data[1]['PM10']\n",
        "train_3_y = train_data[1]['O3']\n",
        "test_3_y = test_data[1]['O3']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZzVpeENWc3E8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "71516124-86de-4f32-ad1c-e8ceffae7e8a"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM25_model_1 = lgb.LGBMRegressor(learning_rate = 0.03,n_estimators=400,max_depth=4, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.2, \n",
        "                                   random_state = 66).fit(train_25_X,train_25_y)\n",
        "print(smape(train_25_y, lgb_PM25_model_1.predict(train_25_X)) * 1000000)\n",
        "print(smape_new(test_25_y, lgb_PM25_model_1.predict(test_25_X))* 1000000)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0784008723370784\n",
            "3.831804387559398\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RBcnkLl0EbaC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nUVFMze1dDPG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4c00c74b-1a7a-4d93-d3fb-5c43b1fb9f6a"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM10_model_1 = lgb.LGBMRegressor(learning_rate = 0.03,n_estimators=400,max_depth=5, \n",
        "                                   reg_lambda = 0.05,reg_alpha=0.05, \n",
        "                                   random_state = 66).fit(train_10_X,train_10_y)\n",
        "print(smape_new(train_10_y, lgb_PM10_model_1.predict(train_10_X)) * 1000000)\n",
        "print(smape_new(test_10_y, lgb_PM10_model_1.predict(test_10_X))* 1000000)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8303784218107834\n",
            "3.8080968174094383\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SEqL_X03dDMK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2ed70cf8-d8a4-4cfb-cf9e-7bb66be9a49f"
      },
      "cell_type": "code",
      "source": [
        "lgb_O3_model_1 = lgb.LGBMRegressor(learning_rate = 0.05,n_estimators=150,max_depth=5, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.2, \n",
        "                                   random_state = 66).fit(train_3_X,train_3_y)\n",
        "print(smape(train_3_y, lgb_O3_model_1.predict(train_3_X)) * 1000000)\n",
        "print(smape(test_3_y, lgb_O3_model_1.predict(test_3_X))* 1000000)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.2328084277327795\n",
            "6.981240644987842\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "gc9y0uSaUfmG",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_25_X = train_data[2][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "train_10_X = train_data[2][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "train_3_X = train_data[2][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "test_25_X = test_data[2][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "test_10_X = test_data[2][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "test_3_X = test_data[2][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "train_25_y = train_data[2]['PM2.5']\n",
        "test_25_y = test_data[2]['PM2.5']\n",
        "train_10_y = train_data[2]['PM10']\n",
        "test_10_y = test_data[2]['PM10']\n",
        "train_3_y = train_data[2]['O3']\n",
        "test_3_y = test_data[2]['O3']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f3lMDa7Pgt0N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5d2c978e-8ecc-40f0-a5c2-26efe9b118e0"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM25_model_2 = lgb.LGBMRegressor(learning_rate = 0.03,n_estimators=250,max_depth=4, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.02, \n",
        "                                   random_state = 66).fit(train_25_X,train_25_y)\n",
        "print(smape(train_25_y, lgb_PM25_model_2.predict(train_25_X)) * 1000000)\n",
        "print(smape_new(test_25_y, lgb_PM25_model_2.predict(test_25_X))* 1000000)"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0241518762418291\n",
            "2.831646715131342\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AumLC8c3gtw9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "81bbf054-0d17-47cf-b853-a3af3ea85ab5"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM10_model_2 = lgb.LGBMRegressor(learning_rate = 0.03,n_estimators=600,max_depth=3, \n",
        "                                   reg_lambda = 0.05,reg_alpha=0.05, \n",
        "                                   random_state = 66).fit(train_10_X,train_10_y)\n",
        "print(smape_new(train_10_y, lgb_PM10_model_2.predict(train_10_X)) * 1000000)\n",
        "print(smape_new(test_10_y, lgb_PM10_model_2.predict(test_10_X))* 1000000)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8863556728990906\n",
            "3.3899518708857923\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q3UBeMhPgtss",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b572f5c2-dfab-4a79-d00a-0a3e30028fc7"
      },
      "cell_type": "code",
      "source": [
        "lgb_O3_model_2 = lgb.LGBMRegressor(learning_rate = 0.1,n_estimators=150,max_depth=5, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.2, \n",
        "                                   random_state = 66).fit(train_3_X,train_3_y)\n",
        "print(smape(train_3_y, lgb_O3_model_2.predict(train_3_X)) * 1000000)\n",
        "print(smape(test_3_y, lgb_O3_model_2.predict(test_3_X))* 1000000)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.251017316810606\n",
            "5.240172668044495\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "0hZCiShdUfz-",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_25_X = train_data[3][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "train_10_X = train_data[3][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "train_3_X = train_data[3][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "test_25_X = test_data[3][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "test_10_X = test_data[3][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "test_3_X = test_data[3][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "train_25_y = train_data[3]['PM2.5']\n",
        "test_25_y = test_data[3]['PM2.5']\n",
        "train_10_y = train_data[3]['PM10']\n",
        "test_10_y = test_data[3]['PM10']\n",
        "train_3_y = train_data[3]['O3']\n",
        "test_3_y = test_data[3]['O3']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qT2ZDf7UFVP8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j22-6EcQj3Zu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "13ee490e-c872-4a21-bea2-b75379b8c0fd"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM25_model_3 = lgb.LGBMRegressor(learning_rate = 0.03,n_estimators=350,max_depth=3, \n",
        "                                   reg_lambda = 0.2,reg_alpha=0.2, \n",
        "                                   random_state = 66).fit(train_25_X,train_25_y)\n",
        "print(smape_new(train_25_y, lgb_PM25_model_3.predict(train_25_X)) * 1000000)\n",
        "print(smape_new(test_25_y, lgb_PM25_model_3.predict(test_25_X))* 1000000)"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6288285798105631\n",
            "3.3164642707162355\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lPBFyIRJj3Wk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "681a4241-16d3-4e8b-b7ff-5a9194c2dc55"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM10_model_3 = lgb.LGBMRegressor(learning_rate = 0.03,n_estimators=600,max_depth=3, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.1, \n",
        "                                   random_state = 66).fit(train_10_X,train_10_y)\n",
        "print(smape_new(train_10_y, lgb_PM10_model_3.predict(train_10_X)) * 1000000)\n",
        "print(smape_new(test_10_y, lgb_PM10_model_3.predict(test_10_X))* 1000000)"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8696920097983079\n",
            "3.731741192184835\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_RbhObVdj3Sp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d0846f4c-9e6b-4cad-e066-7657cd727d11"
      },
      "cell_type": "code",
      "source": [
        "lgb_O3_model_3 = lgb.LGBMRegressor(learning_rate = 0.07,n_estimators=350,max_depth=5, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.2, \n",
        "                                   random_state = 66).fit(train_3_X,train_3_y)\n",
        "print(smape_new(train_3_y, lgb_O3_model_3.predict(train_3_X)) * 1000000)\n",
        "print(smape_new(test_3_y, lgb_O3_model_3.predict(test_3_X))* 1000000)"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6566588261526499\n",
            "2.5276828919789733\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "i2XfvA6QUgFG",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_25_X = train_data[4][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "train_10_X = train_data[4][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "train_3_X = train_data[4][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "test_25_X = test_data[4][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48','PM2.5_48_mean', \n",
        "                          'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max','PM2.5_48_std', \n",
        "                          'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min','PM2.5_2', \n",
        "                          'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1','PM10_2',\n",
        "                          'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]\n",
        "test_10_X = test_data[4][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                          'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                          'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1','PM10_24',\n",
        "                          'PM10_48', 'PM10_48_mean', 'PM10_24_mean','PM10_48_max','PM10_24_max',\n",
        "                          'PM10_48_std', 'PM10_24_std', 'PM10_48_min','PM10_24_min', 'PM10_2',\n",
        "                          'PM10_1_diff','O3_1', 'O3_2','O3_1_diff']]\n",
        "test_3_X = test_data[4][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
        "                         'pressure','temperature', 'wind_direction', 'wind_speed', 'holiday', \n",
        "                         'dayofweek', 'PM2.5_1','PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', \n",
        "                         'PM10_1_diff','O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', \n",
        "                         'O3_48_max','O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', \n",
        "                         'O3_24_min', 'O3_2','O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]\n",
        "\n",
        "train_25_y = train_data[4]['PM2.5']\n",
        "test_25_y = test_data[4]['PM2.5']\n",
        "train_10_y = train_data[4]['PM10']\n",
        "test_10_y = test_data[4]['PM10']\n",
        "train_3_y = train_data[4]['O3']\n",
        "test_3_y = test_data[4]['O3']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1TvYESw5PO0L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ee5ceee7-258d-44d5-cd0e-8a4294edd6ca"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM25_model_4 = lgb.LGBMRegressor(learning_rate = 0.03,n_estimators=350,max_depth=3, \n",
        "                                   reg_lambda = 0.2,reg_alpha=0.1, \n",
        "                                   random_state = 66).fit(train_25_X,train_25_y)\n",
        "print(smape_new(train_25_y, lgb_PM25_model_4.predict(train_25_X)) * 1000000)\n",
        "print(smape_new(test_25_y, lgb_PM25_model_4.predict(test_25_X))* 1000000)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6845191501182692\n",
            "2.5665872448631224\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "T3X_QkjGPOxW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4e46501b-8fc5-41f0-b127-c9aa95ecf3d5"
      },
      "cell_type": "code",
      "source": [
        "lgb_PM10_model_4 = lgb.LGBMRegressor(learning_rate = 0.05,n_estimators=600,max_depth=3, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.1, \n",
        "                                   random_state = 66).fit(train_10_X,train_10_y)\n",
        "print(smape_new(train_10_y, lgb_PM10_model_4.predict(train_10_X)) * 1000000)\n",
        "print(smape_new(test_10_y, lgb_PM10_model_4.predict(test_10_X))* 1000000)"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8941670379920257\n",
            "4.625278244159377\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lJj6OlUEmeRm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c29d5470-933d-454f-b2f6-a6e24ca52ab4"
      },
      "cell_type": "code",
      "source": [
        "lgb_O3_model_4 = lgb.LGBMRegressor(learning_rate = 0.03,n_estimators=350,max_depth=4, \n",
        "                                   reg_lambda = 0.1,reg_alpha=0.1, \n",
        "                                   random_state = 66).fit(train_3_X,train_3_y)\n",
        "print(smape_new(train_3_y, lgb_O3_model_4.predict(train_3_X)) * 1000000)\n",
        "print(smape_new(test_3_y, lgb_O3_model_4.predict(test_3_X))* 1000000)"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.704707329393425\n",
            "2.681750188154826\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "snQ9MbSUmeNr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fae6cb09-5ce0-4dea-bdef-1d007db28441"
      },
      "cell_type": "code",
      "source": [
        "result_25_0 = pd.Series(lgb_PM25_model_0.predict(test_X_PM25))\n",
        "result_25_1 = pd.Series(lgb_PM25_model_1.predict(test_X_PM25))\n",
        "result_25_2 = pd.Series(lgb_PM25_model_2.predict(test_X_PM25))\n",
        "result_25_3 = pd.Series(lgb_PM25_model_3.predict(test_X_PM25))\n",
        "result_25_4 = pd.Series(lgb_PM25_model_4.predict(test_X_PM25))\n",
        "result = pd.concat([result_25_0,result_25_1,result_25_2,result_25_3,result_25_4],axis = 1)\n",
        "result.columns = ['pred0','pred1','pred2','pred3','pred4']\n",
        "result['pred'] = (result['pred0'] + result['pred1'] + result['pred2'] + result['pred3'] +result['pred4'])/5\n",
        "res25 = result['pred'].tolist()\n",
        "print(smape(res25,test_pm25_y))"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.00010135084047449947\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "y3YGAealmeI-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "54a31117-c247-49a2-99b5-445a0fdefb46"
      },
      "cell_type": "code",
      "source": [
        "result_10_0 = pd.Series(lgb_PM10_model_0.predict(test_X_PM10))\n",
        "result_10_1 = pd.Series(lgb_PM10_model_1.predict(test_X_PM10))\n",
        "result_10_2 = pd.Series(lgb_PM10_model_2.predict(test_X_PM10))\n",
        "result_10_3 = pd.Series(lgb_PM10_model_3.predict(test_X_PM10))\n",
        "result_10_4 = pd.Series(lgb_PM10_model_4.predict(test_X_PM10))\n",
        "result = pd.concat([result_10_0,result_10_1,result_10_2,result_10_3,result_10_4],axis = 1)\n",
        "result.columns = ['pred0','pred1','pred2','pred3','pred4']\n",
        "result['pred'] = (result['pred0'] + result['pred1'] + result['pred2'] + result['pred3'] +result['pred4'])/5\n",
        "res10 = result['pred'].tolist()\n",
        "print(smape(res10,test_pm10_y))"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.00012382295572195513\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qptVuiUirrxk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2a513ab9-2b8a-44e8-8753-a732476e6963"
      },
      "cell_type": "code",
      "source": [
        "result_3_0 = pd.Series(lgb_O3_model_0.predict(test_X_O3))\n",
        "result_3_1 = pd.Series(lgb_O3_model_1.predict(test_X_O3))\n",
        "result_3_2 = pd.Series(lgb_O3_model_2.predict(test_X_O3))\n",
        "result_3_3 = pd.Series(lgb_O3_model_3.predict(test_X_O3))\n",
        "result_3_4 = pd.Series(lgb_O3_model_4.predict(test_X_O3))\n",
        "result = pd.concat([result_10_0,result_10_1,result_10_2,result_10_3,result_10_4],axis = 1)\n",
        "result.columns = ['pred0','pred1','pred2','pred3','pred4']\n",
        "result['pred'] = (result['pred0'] + result['pred1'] + result['pred2'] + result['pred3'] +result['pred4'])/5\n",
        "res3 = result['pred'].tolist()\n",
        "print(smape(res3,test_o3_y))"
      ],
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.00032846187082476023\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "h1JKVGwSrrud",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h2tR0eVBrrqt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H5SkdRaJrrn2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t7fFmq8drrks",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jq4onbEWrrf6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9rqNUEv5U6SJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "result = pd.Series(prediction_xgb_O3_test,name='O3_c')\n",
        "res_test = test.copy().reset_index()\n",
        "result = pd.concat([res_test,result],axis=1)\n",
        "\n",
        "fix_station = result[result['stationId'] == 'aotizhongxin_aq']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "m8PPJ3nVhfHo",
        "colab_type": "code",
        "outputId": "6010d2b9-d944-41b2-d11b-73aa190e21aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "fix_station['utc_time'] = pd.to_datetime(fix_station['utc_time'])\n",
        "fix_station = fix_station.set_index(['utc_time'])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "yil5QMKWVJh3",
        "colab_type": "code",
        "outputId": "6cf3c1a7-cfc2-4775-e4cd-0187210a69cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "cell_type": "code",
      "source": [
        "plt.title('aotizhongxin_O3',fontsize = 15)\n",
        "plt.xticks(rotation = 45)\n",
        "plt.xlabel('time',fontsize = 10)\n",
        "plt.ylabel('concentration',fontsize = 10)\n",
        "plt.plot(fix_station['O3'], 'r-')\n",
        "plt.plot(fix_station['O3_c'], 'b-')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd7dd25a828>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zAeZTifuuFTPSnqLLl9oCBd+e6/0oKNTxyTFjxpCQkADApk2bCA8Pp0mTJuza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\nCCF82P8D6K+ex+YqVZYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd7dd24d208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "YMukFN9mWGC0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "3294748b-4608-471b-d4dc-1f7ce8bee440"
      },
      "cell_type": "code",
      "source": [
        "result = pd.Series(station_train['PM2.5'],name='PM25_c')\n",
        "res_test = prediction_data.copy().reset_index()\n",
        "result = pd.concat([res_test,result],axis=1)\n",
        "\n",
        "fix_station = result[result['stationId'] == 'aotizhongxin_aq']\n",
        "fix_station['utc_time'] = pd.to_datetime(fix_station['utc_time'])\n",
        "fix_station = fix_station.set_index(['utc_time'])\n",
        "plt.title('aotizhongxin_PM25',fontsize = 15)\n",
        "plt.xticks(rotation = 45)\n",
        "plt.xlabel('time',fontsize = 10)\n",
        "plt.ylabel('concentration',fontsize = 10)\n",
        "# plt.plot(fix_station['O3'], 'r-')\n",
        "plt.plot(fix_station['PM25_c'], 'b-')"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd7db775278>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QZzeCIIjzhNWrWc34DTe0A4BPyGtrScj1DAk5QRDEeUBLC/Dqq2YUFEj43vc8AID8fPLI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\nkCEnCIIgiADm/wE2ksOXcFwTBQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd7db755940>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rMx5rMn88tPY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "feILF5g8T5OO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "ad0ff0d3-dfac-4895-8525-89a5dd5e1f12"
      },
      "cell_type": "code",
      "source": [
        "for i in range(48):\n",
        "    pre_X_PM25['PM2.5_1'][248185+i] = prediction_new[248184+i]\n",
        "    pre_X_PM25['O3_1'][248185+i] = prediction_new[248184+i]\n",
        "    pre_X_PM25['PM10_1'][248185+i] = prediction_new[248184+i]\n",
        "    pre_X_PM25['O3_2'][248185+i] = prediction_new[248183+i]\n",
        "    pre_X_PM25['PM2.5_2'][248185+i] = prediction_new[248183+i]\n",
        "    pre_X_PM25['PM10_2'][248185+i] = prediction_new[248183+i]\n",
        "    pre_X_PM25['O3_1_diff'][248185+i] = pre_X_PM25['O3_1'][248185+i] - pre_X_PM25['O3_2'][248185+i]\n",
        "    pre_X_PM25['PM10_1_diff'][248185+i] = pre_X_PM25['PM10_1'][248185+i] - pre_X_PM25['PM10_2'][248185+i]\n",
        "    pre_X_PM25['PM2.5_1_diff'][248185+i] = pre_X_PM25['PM2.5_1'][248185+i] - pre_X_PM25['PM2.5_2'][248185+i]\n",
        "    pre_X_PM25['PM2.5_24'][248185+i] = prediction_new[248161+i]\n",
        "    pre_X_PM25['PM2.5_48'][248185+i] = prediction_new[248137+i]\n",
        "    pre_X_PM25['PM2.5_24_mean'][248185+i] = sum(prediction_new['PM2.5'][248161+i: 248185+i]) / 24\n",
        "    pre_X_PM25['PM2.5_48_mean'][248185+i] = sum(prediction_new['PM2.5'][248137+i: 248185+i]) / 48\n",
        "    pre_X_PM25['PM2.5_48_max'][248185+i] = max(prediction_new['PM2.5'][248137+i: 248185+i])\n",
        "    pre_X_PM25['PM2.5_24_max'][248185+i] = max(prediction_new['PM2.5'][248161+i: 248185+i])\n",
        "    pre_X_PM25['PM2.5_48_min'][248185+i] = min(prediction_new['PM2.5'][248137+i: 248185+i])\n",
        "    pre_X_PM25['PM2.5_24_min'][248185+i] = min(prediction_new['PM2.5'][248161+i: 248185+i])\n",
        "    pre_X_PM25['PM2.5_48_std'][248185+i] = np.std(prediction_new['PM2.5'][248137+i: 248185+i])\n",
        "    pre_X_PM25['PM2.5_24_std'][248185+i] = np.std(prediction_new['PM2.5'][248161+i: 248185+i])\n",
        "    pre_X_PM25[1926+i] = lgb_PM25_model.predict(pre_X_PM25[1926+i:1927+i])"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-93-e9a88d4d5c88>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m48\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mpre_X_PM25\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PM2.5_1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m248185\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhour_y_valid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m248184\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mpre_X_PM25\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PM2.5_1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m248185\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhour_y_valid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m248184\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mpre_X_PM25\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PM2.5_1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m248185\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhour_y_valid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m248184\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mpre_X_PM25\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PM2.5_1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m248185\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhour_y_valid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m248184\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'hour_y_valid' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "94NiOcWV2XaM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "66a37548-5a8f-4f1b-ad51-e605ec9c5c9a"
      },
      "cell_type": "code",
      "source": [
        "pre_X_PM25.columns"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity',\n",
              "       'pressure', 'temperature', 'wind_direction', 'wind_speed', 'holiday',\n",
              "       'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48', 'PM2.5_48_mean',\n",
              "       'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max', 'PM2.5_48_std',\n",
              "       'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min', 'PM2.5_2',\n",
              "       'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1', 'PM10_2',\n",
              "       'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "metadata": {
        "id": "2eJQiDHl25Uu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3063
        },
        "outputId": "1cea5506-dabb-4afb-f736-44cac1b563c7"
      },
      "cell_type": "code",
      "source": [
        "final_prediction = pd.DataFrame()\n",
        "for station in station_name:\n",
        "  station_train = prediction_new[prediction_new['stationId'] == station].sort_values('utc_time').reset_index()\n",
        "  del station_train['index']\n",
        "  \n",
        "  for i in range(48):\n",
        "    station_train['PM2.5_1'][7091+i] = station_train['PM2.5'][7090+i]\n",
        "    station_train['O3_1'][7091+i] = station_train['O3'][7090+i]\n",
        "    station_train['PM10_1'][7091+i] = station_train['PM10'][7090+i]\n",
        "    station_train['O3_2'][7091+i] = station_train['O3'][7089+i]\n",
        "    station_train['PM2.5_2'][7091+i] = station_train['PM2.5'][7089+i]\n",
        "    station_train['PM10_2'][7091+i] = station_train['PM10'][7089+i]\n",
        "    station_train['O3_1_diff'][7091+i] = station_train['O3_1'][7091+i] - station_train['O3_2'][7091+i]\n",
        "    station_train['PM10_1_diff'][7091+i] = station_train['PM10_1'][7091+i] - station_train['PM10_2'][7091+i]\n",
        "    station_train['PM2.5_1_diff'][7091+i] = station_train['PM2.5_1'][7091+i] - station_train['PM2.5_2'][7091+i]\n",
        "    station_train['PM2.5_24'][7091+i] = station_train['PM2.5'][7067+i]\n",
        "    station_train['PM10_24'][7091+i] = station_train['PM10'][7067+i]\n",
        "    station_train['O3_24'][7091+i] = station_train['O3'][7067+i]\n",
        "    station_train['PM2.5_48'][7091+i] = station_train['PM2.5'][7043+i]\n",
        "    station_train['PM10_48'][7091+i] = station_train['PM10'][7043+i]\n",
        "    station_train['O3_48'][7091+i] = station_train['O3'][7043+i]\n",
        "    \n",
        "    station_train['PM2.5_24_mean'][7091+i] = sum(station_train['PM2.5'][7067+i: 7091+i]) / 24\n",
        "    station_train['PM2.5_48_mean'][7091+i] = sum(station_train['PM2.5'][7043+i: 7091+i]) / 48\n",
        "    station_train['PM2.5_48_max'][7091+i] = max(station_train['PM2.5'][7043+i: 7091+i])\n",
        "    station_train['PM2.5_24_max'][7091+i] = max(station_train['PM2.5'][7067+i: 7091+i])\n",
        "    station_train['PM2.5_48_min'][7091+i] = min(station_train['PM2.5'][7043+i: 7091+i])\n",
        "    station_train['PM2.5_24_min'][7091+i] = min(station_train['PM2.5'][7067+i: 7091+i])\n",
        "    station_train['PM2.5_48_std'][7091+i] = np.std(station_train['PM2.5'][7043+i: 7091+i])\n",
        "    station_train['PM2.5_24_std'][7091+i] = np.std(station_train['PM2.5'][7067+i: 7091+i])\n",
        "    \n",
        "    station_train['PM10_24_mean'][7091+i] = sum(station_train['PM10'][7067+i: 7091+i]) / 24\n",
        "    station_train['PM10_48_mean'][7091+i] = sum(station_train['PM10'][7043+i: 7091+i]) / 48\n",
        "    station_train['PM10_48_max'][7091+i] = max(station_train['PM10'][7043+i: 7091+i])\n",
        "    station_train['PM10_24_max'][7091+i] = max(station_train['PM10'][7067+i: 7091+i])\n",
        "    station_train['PM10_48_min'][7091+i] = min(station_train['PM10'][7043+i: 7091+i])\n",
        "    station_train['PM10_24_min'][7091+i] = min(station_train['PM10'][7067+i: 7091+i])\n",
        "    station_train['PM10_48_std'][7091+i] = np.std(station_train['PM10'][7043+i: 7091+i])\n",
        "    station_train['PM10_24_std'][7091+i] = np.std(station_train['PM10'][7067+i: 7091+i])\n",
        "    \n",
        "    station_train['O3_24_mean'][7091+i] = sum(station_train['O3'][7067+i: 7091+i]) / 24\n",
        "    station_train['O3_48_mean'][7091+i] = sum(station_train['O3'][7043+i: 7091+i]) / 48\n",
        "    station_train['O3_48_max'][7091+i] = max(station_train['O3'][7043+i: 7091+i])\n",
        "    station_train['O3_24_max'][7091+i] = max(station_train['O3'][7067+i: 7091+i])\n",
        "    station_train['O3_48_min'][7091+i] = min(station_train['O3'][7043+i: 7091+i])\n",
        "    station_train['O3_24_min'][7091+i] = min(station_train['O3'][7067+i: 7091+i])\n",
        "    station_train['O3_48_std'][7091+i] = np.std(station_train['O3'][7043+i: 7091+i])\n",
        "    station_train['O3_24_std'][7091+i] = np.std(station_train['O3'][7067+i: 7091+i])\n",
        "    \n",
        "    station_train['PM2.5'][7091+i] = (lgb_PM25_model_0.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
        "       'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
        "       'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1',\n",
        "       'PM10_2', 'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]) + lgb_PM25_model_1.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
        "       'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
        "       'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1',\n",
        "       'PM10_2', 'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]) + lgb_PM25_model_2.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
        "       'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
        "       'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1',\n",
        "       'PM10_2', 'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]) + lgb_PM25_model_3.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
        "       'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
        "       'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1',\n",
        "       'PM10_2', 'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']]) + lgb_PM25_model_4.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
        "       'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
        "       'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1',\n",
        "       'PM10_2', 'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']])) / 5\n",
        "\n",
        "    \n",
        "    station_train['PM10'][7091+i] = (lgb_PM10_model_0.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "       'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_2',\n",
        "       'O3_1_diff']]) + lgb_PM10_model_1.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "       'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_2',\n",
        "       'O3_1_diff']]) + lgb_PM10_model_2.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "       'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_2',\n",
        "       'O3_1_diff']]) + lgb_PM10_model_3.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "       'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_2',\n",
        "       'O3_1_diff']]) + lgb_PM10_model_4.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "       'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_2',\n",
        "       'O3_1_diff']])) / 5\n",
        "    \n",
        "    station_train['O3'][7091+i] = (lgb_O3_model_0.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]) + lgb_O3_model_1.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]) + lgb_O3_model_2.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]) + lgb_O3_model_3.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']]) + lgb_O3_model_4.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "                 'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "       'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']])) / 5\n",
        "    \n",
        "  final_prediction = pd.concat([final_prediction, station_train], axis=0)\n",
        "    \n",
        "    "
      ],
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  app.launch_new_instance()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:33: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:34: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:35: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:37: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:38: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:39: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:41: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:42: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:43: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:44: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:45: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:46: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:47: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:48: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:75: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:113: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:140: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "ziJXastDAB3c",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "station_train = prediction_new[prediction_new['stationId'] == 'zhiwuyuan_aq'].sort_values('utc_time').reset_index()\n",
        "del station_train['index']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jrAlLr0fA3Hn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2402
        },
        "outputId": "d5caa244-31c6-4a2e-bd85-a282486da57d"
      },
      "cell_type": "code",
      "source": [
        "station_train[7091:]"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O3</th>\n",
              "      <th>PM10</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>humidity</th>\n",
              "      <th>pressure</th>\n",
              "      <th>stationId</th>\n",
              "      <th>temperature</th>\n",
              "      <th>utc_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>...</th>\n",
              "      <th>O3_24_max</th>\n",
              "      <th>O3_48_std</th>\n",
              "      <th>O3_24_std</th>\n",
              "      <th>O3_48_min</th>\n",
              "      <th>O3_24_min</th>\n",
              "      <th>O3_2</th>\n",
              "      <th>O3_1_diff</th>\n",
              "      <th>O3_3</th>\n",
              "      <th>O3_12</th>\n",
              "      <th>O3_8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7091</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.75</td>\n",
              "      <td>1000.725325</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>19.000</td>\n",
              "      <td>2018-05-01 00:00:00</td>\n",
              "      <td>13.2450</td>\n",
              "      <td>12.0225</td>\n",
              "      <td>...</td>\n",
              "      <td>124.267541</td>\n",
              "      <td>23.036932</td>\n",
              "      <td>23.775082</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>74.157838</td>\n",
              "      <td>9.998439</td>\n",
              "      <td>57.707688</td>\n",
              "      <td>93.019501</td>\n",
              "      <td>71.556270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7092</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.75</td>\n",
              "      <td>1000.208575</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>20.500</td>\n",
              "      <td>2018-05-01 01:00:00</td>\n",
              "      <td>24.1025</td>\n",
              "      <td>14.0050</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84.156276</td>\n",
              "      <td>NaN</td>\n",
              "      <td>74.157838</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>63.833614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7093</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.25</td>\n",
              "      <td>1000.737375</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>21.000</td>\n",
              "      <td>2018-05-01 02:00:00</td>\n",
              "      <td>33.2500</td>\n",
              "      <td>16.0000</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84.156276</td>\n",
              "      <td>78.515250</td>\n",
              "      <td>53.690975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7094</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.50</td>\n",
              "      <td>1000.606450</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>21.500</td>\n",
              "      <td>2018-05-01 03:00:00</td>\n",
              "      <td>37.6050</td>\n",
              "      <td>17.2250</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>73.510277</td>\n",
              "      <td>60.893632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7095</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.25</td>\n",
              "      <td>999.737700</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.000</td>\n",
              "      <td>2018-05-01 04:00:00</td>\n",
              "      <td>42.6875</td>\n",
              "      <td>17.1250</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>71.556270</td>\n",
              "      <td>63.908903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7096</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.25</td>\n",
              "      <td>998.086000</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.500</td>\n",
              "      <td>2018-05-01 05:00:00</td>\n",
              "      <td>46.0225</td>\n",
              "      <td>16.6200</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>63.833614</td>\n",
              "      <td>57.707688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7097</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.00</td>\n",
              "      <td>999.657725</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.500</td>\n",
              "      <td>2018-05-01 06:00:00</td>\n",
              "      <td>47.2400</td>\n",
              "      <td>15.2950</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.690975</td>\n",
              "      <td>74.157838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7098</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.25</td>\n",
              "      <td>999.622000</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>22.250</td>\n",
              "      <td>2018-05-01 07:00:00</td>\n",
              "      <td>46.1625</td>\n",
              "      <td>12.0125</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60.893632</td>\n",
              "      <td>84.156276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7099</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.50</td>\n",
              "      <td>999.235925</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>21.000</td>\n",
              "      <td>2018-05-01 08:00:00</td>\n",
              "      <td>41.9650</td>\n",
              "      <td>8.3075</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>63.908903</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.75</td>\n",
              "      <td>1000.009900</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>20.000</td>\n",
              "      <td>2018-05-01 09:00:00</td>\n",
              "      <td>37.8625</td>\n",
              "      <td>5.9550</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.707688</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7101</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.50</td>\n",
              "      <td>1001.495575</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>19.750</td>\n",
              "      <td>2018-05-01 10:00:00</td>\n",
              "      <td>51.0150</td>\n",
              "      <td>3.2450</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>74.157838</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7102</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.00</td>\n",
              "      <td>1000.945475</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>17.250</td>\n",
              "      <td>2018-05-01 11:00:00</td>\n",
              "      <td>91.4750</td>\n",
              "      <td>5.1250</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84.156276</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7103</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>46.75</td>\n",
              "      <td>1001.316775</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>15.500</td>\n",
              "      <td>2018-05-01 12:00:00</td>\n",
              "      <td>105.7075</td>\n",
              "      <td>6.7175</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7104</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50.50</td>\n",
              "      <td>1001.171075</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>15.250</td>\n",
              "      <td>2018-05-01 13:00:00</td>\n",
              "      <td>119.0725</td>\n",
              "      <td>4.8525</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7105</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.00</td>\n",
              "      <td>1001.020675</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>15.250</td>\n",
              "      <td>2018-05-01 14:00:00</td>\n",
              "      <td>177.1700</td>\n",
              "      <td>1.8450</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7106</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.50</td>\n",
              "      <td>1002.445400</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>15.000</td>\n",
              "      <td>2018-05-01 15:00:00</td>\n",
              "      <td>266.0825</td>\n",
              "      <td>3.4375</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7107</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>59.50</td>\n",
              "      <td>1003.305250</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.675</td>\n",
              "      <td>2018-05-01 16:00:00</td>\n",
              "      <td>256.4800</td>\n",
              "      <td>3.0050</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7108</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56.75</td>\n",
              "      <td>1002.470175</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.375</td>\n",
              "      <td>2018-05-01 17:00:00</td>\n",
              "      <td>301.1200</td>\n",
              "      <td>4.4450</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7109</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.00</td>\n",
              "      <td>1003.093750</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.150</td>\n",
              "      <td>2018-05-01 18:00:00</td>\n",
              "      <td>319.5100</td>\n",
              "      <td>6.6175</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7110</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50.25</td>\n",
              "      <td>1003.479700</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.100</td>\n",
              "      <td>2018-05-01 19:00:00</td>\n",
              "      <td>326.2450</td>\n",
              "      <td>8.0575</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7111</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.50</td>\n",
              "      <td>1003.589325</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.325</td>\n",
              "      <td>2018-05-01 20:00:00</td>\n",
              "      <td>329.3525</td>\n",
              "      <td>8.7300</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7112</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.75</td>\n",
              "      <td>1003.099400</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.900</td>\n",
              "      <td>2018-05-01 21:00:00</td>\n",
              "      <td>333.5150</td>\n",
              "      <td>9.1775</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7113</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.00</td>\n",
              "      <td>1003.444750</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>12.750</td>\n",
              "      <td>2018-05-01 22:00:00</td>\n",
              "      <td>344.1925</td>\n",
              "      <td>9.8825</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7114</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.75</td>\n",
              "      <td>1003.236275</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>14.250</td>\n",
              "      <td>2018-05-01 23:00:00</td>\n",
              "      <td>351.9400</td>\n",
              "      <td>10.0375</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7115</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.25</td>\n",
              "      <td>1003.338225</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>17.750</td>\n",
              "      <td>2018-05-02 00:00:00</td>\n",
              "      <td>267.3000</td>\n",
              "      <td>10.3300</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7116</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.50</td>\n",
              "      <td>1003.729325</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>20.250</td>\n",
              "      <td>2018-05-02 01:00:00</td>\n",
              "      <td>89.0075</td>\n",
              "      <td>10.6675</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7117</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.75</td>\n",
              "      <td>1004.041025</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>21.500</td>\n",
              "      <td>2018-05-02 02:00:00</td>\n",
              "      <td>266.9400</td>\n",
              "      <td>10.7850</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7118</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.25</td>\n",
              "      <td>1004.724150</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>22.000</td>\n",
              "      <td>2018-05-02 03:00:00</td>\n",
              "      <td>351.9775</td>\n",
              "      <td>10.9525</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7119</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.25</td>\n",
              "      <td>1002.659175</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>22.500</td>\n",
              "      <td>2018-05-02 04:00:00</td>\n",
              "      <td>348.5125</td>\n",
              "      <td>10.9400</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7120</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.25</td>\n",
              "      <td>1002.419750</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.250</td>\n",
              "      <td>2018-05-02 05:00:00</td>\n",
              "      <td>342.7250</td>\n",
              "      <td>11.0150</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7121</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.00</td>\n",
              "      <td>1001.924875</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>24.750</td>\n",
              "      <td>2018-05-02 06:00:00</td>\n",
              "      <td>343.3800</td>\n",
              "      <td>11.2250</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7122</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.50</td>\n",
              "      <td>998.495500</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.750</td>\n",
              "      <td>2018-05-02 07:00:00</td>\n",
              "      <td>352.4150</td>\n",
              "      <td>12.2575</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7123</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.00</td>\n",
              "      <td>999.067800</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>22.750</td>\n",
              "      <td>2018-05-02 08:00:00</td>\n",
              "      <td>6.2600</td>\n",
              "      <td>13.0075</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7124</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.75</td>\n",
              "      <td>999.322800</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.750</td>\n",
              "      <td>2018-05-02 09:00:00</td>\n",
              "      <td>18.1250</td>\n",
              "      <td>12.6525</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7125</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.50</td>\n",
              "      <td>999.261800</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.250</td>\n",
              "      <td>2018-05-02 10:00:00</td>\n",
              "      <td>41.2000</td>\n",
              "      <td>8.5750</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7126</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.25</td>\n",
              "      <td>999.160175</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>21.750</td>\n",
              "      <td>2018-05-02 11:00:00</td>\n",
              "      <td>50.0025</td>\n",
              "      <td>5.8350</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7127</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.25</td>\n",
              "      <td>1001.030000</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>20.000</td>\n",
              "      <td>2018-05-02 12:00:00</td>\n",
              "      <td>51.9750</td>\n",
              "      <td>3.1125</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7128</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.25</td>\n",
              "      <td>1000.753175</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>19.750</td>\n",
              "      <td>2018-05-02 13:00:00</td>\n",
              "      <td>251.5850</td>\n",
              "      <td>2.1725</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7129</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.50</td>\n",
              "      <td>999.425400</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>18.500</td>\n",
              "      <td>2018-05-02 14:00:00</td>\n",
              "      <td>311.4175</td>\n",
              "      <td>5.0175</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7130</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.75</td>\n",
              "      <td>1000.455750</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>18.250</td>\n",
              "      <td>2018-05-02 15:00:00</td>\n",
              "      <td>315.3700</td>\n",
              "      <td>7.6300</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7131</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.75</td>\n",
              "      <td>1000.637775</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.850</td>\n",
              "      <td>2018-05-02 16:00:00</td>\n",
              "      <td>267.4975</td>\n",
              "      <td>9.7250</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7132</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.25</td>\n",
              "      <td>1002.680875</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.650</td>\n",
              "      <td>2018-05-02 17:00:00</td>\n",
              "      <td>264.1225</td>\n",
              "      <td>7.8200</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7133</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.25</td>\n",
              "      <td>1001.021050</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.300</td>\n",
              "      <td>2018-05-02 18:00:00</td>\n",
              "      <td>341.9925</td>\n",
              "      <td>6.2075</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7134</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.75</td>\n",
              "      <td>1002.308425</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.125</td>\n",
              "      <td>2018-05-02 19:00:00</td>\n",
              "      <td>327.5225</td>\n",
              "      <td>5.9250</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7135</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.75</td>\n",
              "      <td>1002.989550</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.025</td>\n",
              "      <td>2018-05-02 20:00:00</td>\n",
              "      <td>321.4175</td>\n",
              "      <td>7.1725</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7136</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.50</td>\n",
              "      <td>1002.600300</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.000</td>\n",
              "      <td>2018-05-02 21:00:00</td>\n",
              "      <td>327.4700</td>\n",
              "      <td>9.2650</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7137</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.25</td>\n",
              "      <td>1002.987400</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>11.000</td>\n",
              "      <td>2018-05-02 22:00:00</td>\n",
              "      <td>339.6325</td>\n",
              "      <td>12.5075</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7138</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.25</td>\n",
              "      <td>1004.976725</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>14.250</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>349.9075</td>\n",
              "      <td>15.8975</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48 rows × 68 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      O3  PM10  PM2.5  humidity     pressure     stationId  temperature  \\\n",
              "7091 NaN   NaN    NaN     33.75  1000.725325  zhiwuyuan_aq       19.000   \n",
              "7092 NaN   NaN    NaN     28.75  1000.208575  zhiwuyuan_aq       20.500   \n",
              "7093 NaN   NaN    NaN     25.25  1000.737375  zhiwuyuan_aq       21.000   \n",
              "7094 NaN   NaN    NaN     22.50  1000.606450  zhiwuyuan_aq       21.500   \n",
              "7095 NaN   NaN    NaN     20.25   999.737700  zhiwuyuan_aq       23.000   \n",
              "7096 NaN   NaN    NaN     20.25   998.086000  zhiwuyuan_aq       23.500   \n",
              "7097 NaN   NaN    NaN     21.00   999.657725  zhiwuyuan_aq       23.500   \n",
              "7098 NaN   NaN    NaN     22.25   999.622000  zhiwuyuan_aq       22.250   \n",
              "7099 NaN   NaN    NaN     24.50   999.235925  zhiwuyuan_aq       21.000   \n",
              "7100 NaN   NaN    NaN     26.75  1000.009900  zhiwuyuan_aq       20.000   \n",
              "7101 NaN   NaN    NaN     39.50  1001.495575  zhiwuyuan_aq       19.750   \n",
              "7102 NaN   NaN    NaN     43.00  1000.945475  zhiwuyuan_aq       17.250   \n",
              "7103 NaN   NaN    NaN     46.75  1001.316775  zhiwuyuan_aq       15.500   \n",
              "7104 NaN   NaN    NaN     50.50  1001.171075  zhiwuyuan_aq       15.250   \n",
              "7105 NaN   NaN    NaN     53.00  1001.020675  zhiwuyuan_aq       15.250   \n",
              "7106 NaN   NaN    NaN     53.50  1002.445400  zhiwuyuan_aq       15.000   \n",
              "7107 NaN   NaN    NaN     59.50  1003.305250  zhiwuyuan_aq       13.675   \n",
              "7108 NaN   NaN    NaN     56.75  1002.470175  zhiwuyuan_aq       13.375   \n",
              "7109 NaN   NaN    NaN     53.00  1003.093750  zhiwuyuan_aq       13.150   \n",
              "7110 NaN   NaN    NaN     50.25  1003.479700  zhiwuyuan_aq       13.100   \n",
              "7111 NaN   NaN    NaN     48.50  1003.589325  zhiwuyuan_aq       13.325   \n",
              "7112 NaN   NaN    NaN     44.75  1003.099400  zhiwuyuan_aq       13.900   \n",
              "7113 NaN   NaN    NaN     34.00  1003.444750  zhiwuyuan_aq       12.750   \n",
              "7114 NaN   NaN    NaN     27.75  1003.236275  zhiwuyuan_aq       14.250   \n",
              "7115 NaN   NaN    NaN     22.25  1003.338225  zhiwuyuan_aq       17.750   \n",
              "7116 NaN   NaN    NaN     18.50  1003.729325  zhiwuyuan_aq       20.250   \n",
              "7117 NaN   NaN    NaN     15.75  1004.041025  zhiwuyuan_aq       21.500   \n",
              "7118 NaN   NaN    NaN     14.25  1004.724150  zhiwuyuan_aq       22.000   \n",
              "7119 NaN   NaN    NaN     13.25  1002.659175  zhiwuyuan_aq       22.500   \n",
              "7120 NaN   NaN    NaN     12.25  1002.419750  zhiwuyuan_aq       23.250   \n",
              "7121 NaN   NaN    NaN     12.00  1001.924875  zhiwuyuan_aq       24.750   \n",
              "7122 NaN   NaN    NaN     11.50   998.495500  zhiwuyuan_aq       23.750   \n",
              "7123 NaN   NaN    NaN     12.00   999.067800  zhiwuyuan_aq       22.750   \n",
              "7124 NaN   NaN    NaN     13.75   999.322800  zhiwuyuan_aq       23.750   \n",
              "7125 NaN   NaN    NaN     15.50   999.261800  zhiwuyuan_aq       23.250   \n",
              "7126 NaN   NaN    NaN     19.25   999.160175  zhiwuyuan_aq       21.750   \n",
              "7127 NaN   NaN    NaN     22.25  1001.030000  zhiwuyuan_aq       20.000   \n",
              "7128 NaN   NaN    NaN     25.25  1000.753175  zhiwuyuan_aq       19.750   \n",
              "7129 NaN   NaN    NaN     26.50   999.425400  zhiwuyuan_aq       18.500   \n",
              "7130 NaN   NaN    NaN     26.75  1000.455750  zhiwuyuan_aq       18.250   \n",
              "7131 NaN   NaN    NaN     23.75  1000.637775  zhiwuyuan_aq       13.850   \n",
              "7132 NaN   NaN    NaN     25.25  1002.680875  zhiwuyuan_aq       13.650   \n",
              "7133 NaN   NaN    NaN     27.25  1001.021050  zhiwuyuan_aq       13.300   \n",
              "7134 NaN   NaN    NaN     27.75  1002.308425  zhiwuyuan_aq       13.125   \n",
              "7135 NaN   NaN    NaN     26.75  1002.989550  zhiwuyuan_aq       13.025   \n",
              "7136 NaN   NaN    NaN     25.50  1002.600300  zhiwuyuan_aq       13.000   \n",
              "7137 NaN   NaN    NaN     22.25  1002.987400  zhiwuyuan_aq       11.000   \n",
              "7138 NaN   NaN    NaN     19.25  1004.976725  zhiwuyuan_aq       14.250   \n",
              "\n",
              "                 utc_time  wind_direction  wind_speed    ...       O3_24_max  \\\n",
              "7091  2018-05-01 00:00:00         13.2450     12.0225    ...      124.267541   \n",
              "7092  2018-05-01 01:00:00         24.1025     14.0050    ...             NaN   \n",
              "7093  2018-05-01 02:00:00         33.2500     16.0000    ...             NaN   \n",
              "7094  2018-05-01 03:00:00         37.6050     17.2250    ...             NaN   \n",
              "7095  2018-05-01 04:00:00         42.6875     17.1250    ...             NaN   \n",
              "7096  2018-05-01 05:00:00         46.0225     16.6200    ...             NaN   \n",
              "7097  2018-05-01 06:00:00         47.2400     15.2950    ...             NaN   \n",
              "7098  2018-05-01 07:00:00         46.1625     12.0125    ...             NaN   \n",
              "7099  2018-05-01 08:00:00         41.9650      8.3075    ...             NaN   \n",
              "7100  2018-05-01 09:00:00         37.8625      5.9550    ...             NaN   \n",
              "7101  2018-05-01 10:00:00         51.0150      3.2450    ...             NaN   \n",
              "7102  2018-05-01 11:00:00         91.4750      5.1250    ...             NaN   \n",
              "7103  2018-05-01 12:00:00        105.7075      6.7175    ...             NaN   \n",
              "7104  2018-05-01 13:00:00        119.0725      4.8525    ...             NaN   \n",
              "7105  2018-05-01 14:00:00        177.1700      1.8450    ...             NaN   \n",
              "7106  2018-05-01 15:00:00        266.0825      3.4375    ...             NaN   \n",
              "7107  2018-05-01 16:00:00        256.4800      3.0050    ...             NaN   \n",
              "7108  2018-05-01 17:00:00        301.1200      4.4450    ...             NaN   \n",
              "7109  2018-05-01 18:00:00        319.5100      6.6175    ...             NaN   \n",
              "7110  2018-05-01 19:00:00        326.2450      8.0575    ...             NaN   \n",
              "7111  2018-05-01 20:00:00        329.3525      8.7300    ...             NaN   \n",
              "7112  2018-05-01 21:00:00        333.5150      9.1775    ...             NaN   \n",
              "7113  2018-05-01 22:00:00        344.1925      9.8825    ...             NaN   \n",
              "7114  2018-05-01 23:00:00        351.9400     10.0375    ...             NaN   \n",
              "7115  2018-05-02 00:00:00        267.3000     10.3300    ...             NaN   \n",
              "7116  2018-05-02 01:00:00         89.0075     10.6675    ...             NaN   \n",
              "7117  2018-05-02 02:00:00        266.9400     10.7850    ...             NaN   \n",
              "7118  2018-05-02 03:00:00        351.9775     10.9525    ...             NaN   \n",
              "7119  2018-05-02 04:00:00        348.5125     10.9400    ...             NaN   \n",
              "7120  2018-05-02 05:00:00        342.7250     11.0150    ...             NaN   \n",
              "7121  2018-05-02 06:00:00        343.3800     11.2250    ...             NaN   \n",
              "7122  2018-05-02 07:00:00        352.4150     12.2575    ...             NaN   \n",
              "7123  2018-05-02 08:00:00          6.2600     13.0075    ...             NaN   \n",
              "7124  2018-05-02 09:00:00         18.1250     12.6525    ...             NaN   \n",
              "7125  2018-05-02 10:00:00         41.2000      8.5750    ...             NaN   \n",
              "7126  2018-05-02 11:00:00         50.0025      5.8350    ...             NaN   \n",
              "7127  2018-05-02 12:00:00         51.9750      3.1125    ...             NaN   \n",
              "7128  2018-05-02 13:00:00        251.5850      2.1725    ...             NaN   \n",
              "7129  2018-05-02 14:00:00        311.4175      5.0175    ...             NaN   \n",
              "7130  2018-05-02 15:00:00        315.3700      7.6300    ...             NaN   \n",
              "7131  2018-05-02 16:00:00        267.4975      9.7250    ...             NaN   \n",
              "7132  2018-05-02 17:00:00        264.1225      7.8200    ...             NaN   \n",
              "7133  2018-05-02 18:00:00        341.9925      6.2075    ...             NaN   \n",
              "7134  2018-05-02 19:00:00        327.5225      5.9250    ...             NaN   \n",
              "7135  2018-05-02 20:00:00        321.4175      7.1725    ...             NaN   \n",
              "7136  2018-05-02 21:00:00        327.4700      9.2650    ...             NaN   \n",
              "7137  2018-05-02 22:00:00        339.6325     12.5075    ...             NaN   \n",
              "7138  2018-05-02 23:00:00        349.9075     15.8975    ...             NaN   \n",
              "\n",
              "      O3_48_std  O3_24_std  O3_48_min  O3_24_min       O3_2  O3_1_diff  \\\n",
              "7091  23.036932  23.775082  49.747964  49.747964  74.157838   9.998439   \n",
              "7092        NaN        NaN        NaN        NaN  84.156276        NaN   \n",
              "7093        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7094        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7095        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7096        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7097        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7098        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7099        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7100        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7101        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7102        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7103        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7104        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7105        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7106        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7107        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7108        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7109        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7110        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7111        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7112        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7113        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7114        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7115        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7116        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7117        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7118        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7119        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7120        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7121        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7122        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7123        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7124        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7125        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7126        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7127        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7128        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7129        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7130        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7131        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7132        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7133        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7134        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7135        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7136        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7137        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "7138        NaN        NaN        NaN        NaN        NaN        NaN   \n",
              "\n",
              "           O3_3      O3_12       O3_8  \n",
              "7091  57.707688  93.019501  71.556270  \n",
              "7092  74.157838  49.747964  63.833614  \n",
              "7093  84.156276  78.515250  53.690975  \n",
              "7094        NaN  73.510277  60.893632  \n",
              "7095        NaN  71.556270  63.908903  \n",
              "7096        NaN  63.833614  57.707688  \n",
              "7097        NaN  53.690975  74.157838  \n",
              "7098        NaN  60.893632  84.156276  \n",
              "7099        NaN  63.908903        NaN  \n",
              "7100        NaN  57.707688        NaN  \n",
              "7101        NaN  74.157838        NaN  \n",
              "7102        NaN  84.156276        NaN  \n",
              "7103        NaN        NaN        NaN  \n",
              "7104        NaN        NaN        NaN  \n",
              "7105        NaN        NaN        NaN  \n",
              "7106        NaN        NaN        NaN  \n",
              "7107        NaN        NaN        NaN  \n",
              "7108        NaN        NaN        NaN  \n",
              "7109        NaN        NaN        NaN  \n",
              "7110        NaN        NaN        NaN  \n",
              "7111        NaN        NaN        NaN  \n",
              "7112        NaN        NaN        NaN  \n",
              "7113        NaN        NaN        NaN  \n",
              "7114        NaN        NaN        NaN  \n",
              "7115        NaN        NaN        NaN  \n",
              "7116        NaN        NaN        NaN  \n",
              "7117        NaN        NaN        NaN  \n",
              "7118        NaN        NaN        NaN  \n",
              "7119        NaN        NaN        NaN  \n",
              "7120        NaN        NaN        NaN  \n",
              "7121        NaN        NaN        NaN  \n",
              "7122        NaN        NaN        NaN  \n",
              "7123        NaN        NaN        NaN  \n",
              "7124        NaN        NaN        NaN  \n",
              "7125        NaN        NaN        NaN  \n",
              "7126        NaN        NaN        NaN  \n",
              "7127        NaN        NaN        NaN  \n",
              "7128        NaN        NaN        NaN  \n",
              "7129        NaN        NaN        NaN  \n",
              "7130        NaN        NaN        NaN  \n",
              "7131        NaN        NaN        NaN  \n",
              "7132        NaN        NaN        NaN  \n",
              "7133        NaN        NaN        NaN  \n",
              "7134        NaN        NaN        NaN  \n",
              "7135        NaN        NaN        NaN  \n",
              "7136        NaN        NaN        NaN  \n",
              "7137        NaN        NaN        NaN  \n",
              "7138        NaN        NaN        NaN  \n",
              "\n",
              "[48 rows x 68 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "metadata": {
        "id": "0gd-Hv6WBCf2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3046
        },
        "outputId": "1a7e7da3-9a58-4208-a397-0458358717d9"
      },
      "cell_type": "code",
      "source": [
        "station_train = prediction_new[prediction_new['stationId'] == 'aotizhongxin_aq'].sort_values('utc_time').reset_index()\n",
        "del station_train['index']\n",
        "\n",
        "for i in range(48):\n",
        "  station_train['PM2.5_1'][7091+i] = station_train['PM2.5'][7090+i]\n",
        "  station_train['O3_1'][7091+i] = station_train['O3'][7090+i]\n",
        "  station_train['PM10_1'][7091+i] = station_train['PM10'][7090+i]\n",
        "  \n",
        "  station_train['O3_2'][7091+i] = station_train['O3'][7089+i]\n",
        "  station_train['PM2.5_2'][7091+i] = station_train['PM2.5'][7089+i]\n",
        "  station_train['PM10_2'][7091+i] = station_train['PM10'][7089+i]\n",
        "  \n",
        "  station_train['O3_1_diff'][7091+i] = station_train['O3_1'][7091+i] - station_train['O3_2'][7091+i]\n",
        "  station_train['PM10_1_diff'][7091+i] = station_train['PM10_1'][7091+i] - station_train['PM10_2'][7091+i]\n",
        "  station_train['PM2.5_1_diff'][7091+i] = station_train['PM2.5_1'][7091+i] - station_train['PM2.5_2'][7091+i]\n",
        "  \n",
        "  station_train['PM2.5_24'][7091+i] = station_train['PM2.5'][7067+i]\n",
        "  station_train['PM10_24'][7091+i] = station_train['PM10'][7067+i]\n",
        "  station_train['O3_24'][7091+i] = station_train['O3'][7067+i]\n",
        "  \n",
        "  station_train['PM2.5_48'][7091+i] = station_train['PM2.5'][7043+i]\n",
        "  station_train['PM10_48'][7091+i] = station_train['PM10'][7043+i]\n",
        "  station_train['O3_48'][7091+i] = station_train['O3'][7043+i]\n",
        "\n",
        "  station_train['PM2.5_24_mean'][7091+i] = sum(station_train['PM2.5'][7067+i: 7091+i]) / 24\n",
        "  station_train['PM2.5_48_mean'][7091+i] = sum(station_train['PM2.5'][7043+i: 7091+i]) / 48\n",
        "  station_train['PM2.5_48_max'][7091+i] = max(station_train['PM2.5'][7043+i: 7091+i])\n",
        "  station_train['PM2.5_24_max'][7091+i] = max(station_train['PM2.5'][7067+i: 7091+i])\n",
        "  station_train['PM2.5_48_min'][7091+i] = min(station_train['PM2.5'][7043+i: 7091+i])\n",
        "  station_train['PM2.5_24_min'][7091+i] = min(station_train['PM2.5'][7067+i: 7091+i])\n",
        "  station_train['PM2.5_48_std'][7091+i] = np.std(station_train['PM2.5'][7043+i: 7091+i])\n",
        "  station_train['PM2.5_24_std'][7091+i] = np.std(station_train['PM2.5'][7067+i: 7091+i])\n",
        "\n",
        "  station_train['PM10_24_mean'][7091+i] = sum(station_train['PM10'][7067+i: 7091+i]) / 24\n",
        "  station_train['PM10_48_mean'][7091+i] = sum(station_train['PM10'][7043+i: 7091+i]) / 48\n",
        "  station_train['PM10_48_max'][7091+i] = max(station_train['PM10'][7043+i: 7091+i])\n",
        "  station_train['PM10_24_max'][7091+i] = max(station_train['PM10'][7067+i: 7091+i])\n",
        "  station_train['PM10_48_min'][7091+i] = min(station_train['PM10'][7043+i: 7091+i])\n",
        "  station_train['PM10_24_min'][7091+i] = min(station_train['PM10'][7067+i: 7091+i])\n",
        "  station_train['PM10_48_std'][7091+i] = np.std(station_train['PM10'][7043+i: 7091+i])\n",
        "  station_train['PM10_24_std'][7091+i] = np.std(station_train['PM10'][7067+i: 7091+i])\n",
        "\n",
        "  station_train['O3_24_mean'][7091+i] = sum(station_train['O3'][7067+i: 7091+i]) / 24\n",
        "  station_train['O3_48_mean'][7091+i] = sum(station_train['O3'][7043+i: 7091+i]) / 48\n",
        "  station_train['O3_48_max'][7091+i] = max(station_train['O3'][7043+i: 7091+i])\n",
        "  station_train['O3_24_max'][7091+i] = max(station_train['O3'][7067+i: 7091+i])\n",
        "  station_train['O3_48_min'][7091+i] = min(station_train['O3'][7043+i: 7091+i])\n",
        "  station_train['O3_24_min'][7091+i] = min(station_train['O3'][7067+i: 7091+i])\n",
        "  station_train['O3_48_std'][7091+i] = np.std(station_train['O3'][7043+i: 7091+i])\n",
        "  station_train['O3_24_std'][7091+i] = np.std(station_train['O3'][7067+i: 7091+i])\n",
        "\n",
        "  station_train['PM2.5'][7091+i] = lgb_PM25_model.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "               'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
        "     'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
        "     'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
        "     'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1', 'PM10_2', 'PM10_1_diff', 'O3_1', 'O3_2', 'O3_1_diff']])\n",
        "\n",
        "  station_train['PM10'][7091+i] = lgb_PM10_model.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "               'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_2', 'PM2.5_1_diff', 'PM10_1',\n",
        "     'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
        "     'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
        "     'PM10_24_min', 'PM10_2', 'PM10_1_diff',\n",
        "     'O3_1', 'O3_2',\n",
        "     'O3_1_diff']])\n",
        "\n",
        "  station_train['O3'][7091+i] = lgb_O3_model.predict(station_train[7091+i:7092+i][['longitude', 'latitude', 'year', 'month', 'day', 'hour', 'humidity','pressure',\n",
        "               'temperature', 'wind_direction', 'wind_speed', 'holiday', 'dayofweek', 'PM2.5_1',\n",
        "     'PM2.5_2', 'PM2.5_1_diff', 'PM10_1', 'PM10_2', 'PM10_1_diff',\n",
        "     'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
        "     'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
        "     'O3_1_diff', 'O3_3', 'O3_12', 'O3_8']])\n",
        "\n"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:34: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:35: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:37: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:38: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:39: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:40: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:41: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:43: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:44: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:45: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:46: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:47: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:48: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:49: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:50: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:56: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:64: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:71: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "K8P00hSoGviF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "91734137-6310-400d-8346-9cf1404c568d"
      },
      "cell_type": "code",
      "source": [
        "station_train.columns"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['O3', 'PM10', 'PM2.5', 'humidity', 'pressure', 'stationId',\n",
              "       'temperature', 'utc_time', 'wind_direction', 'wind_speed', 'longitude',\n",
              "       'latitude', 'local_time', 'year', 'month', 'day', 'hour', 'date',\n",
              "       'holiday', 'dayofweek', 'PM2.5_1', 'PM2.5_24', 'PM2.5_48',\n",
              "       'PM2.5_48_mean', 'PM2.5_24_mean', 'PM2.5_48_max', 'PM2.5_24_max',\n",
              "       'PM2.5_48_std', 'PM2.5_24_std', 'PM2.5_48_min', 'PM2.5_24_min',\n",
              "       'PM2.5_2', 'PM2.5_1_diff', 'PM2.5_3', 'PM2.5_12', 'PM2.5_8', 'PM10_1',\n",
              "       'PM10_24', 'PM10_48', 'PM10_48_mean', 'PM10_24_mean', 'PM10_48_max',\n",
              "       'PM10_24_max', 'PM10_48_std', 'PM10_24_std', 'PM10_48_min',\n",
              "       'PM10_24_min', 'PM10_2', 'PM10_1_diff', 'PM10_3', 'PM10_12', 'PM10_8',\n",
              "       'O3_1', 'O3_24', 'O3_48', 'O3_48_mean', 'O3_24_mean', 'O3_48_max',\n",
              "       'O3_24_max', 'O3_48_std', 'O3_24_std', 'O3_48_min', 'O3_24_min', 'O3_2',\n",
              "       'O3_1_diff', 'O3_3', 'O3_12', 'O3_8'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "metadata": {
        "id": "pPiYcsppHoBy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3009
        },
        "outputId": "6b29e54f-d7b2-4c69-f6bc-4722658525f9"
      },
      "cell_type": "code",
      "source": [
        "station_train"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O3</th>\n",
              "      <th>PM10</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>humidity</th>\n",
              "      <th>pressure</th>\n",
              "      <th>stationId</th>\n",
              "      <th>temperature</th>\n",
              "      <th>utc_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>...</th>\n",
              "      <th>O3_24_max</th>\n",
              "      <th>O3_48_std</th>\n",
              "      <th>O3_24_std</th>\n",
              "      <th>O3_48_min</th>\n",
              "      <th>O3_24_min</th>\n",
              "      <th>O3_2</th>\n",
              "      <th>O3_1_diff</th>\n",
              "      <th>O3_3</th>\n",
              "      <th>O3_12</th>\n",
              "      <th>O3_8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214.000000</td>\n",
              "      <td>50.898794</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>56.7600</td>\n",
              "      <td>990.815000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>27.2225</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>190.8400</td>\n",
              "      <td>6.5675</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>179.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>64.4100</td>\n",
              "      <td>991.247500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>26.5650</td>\n",
              "      <td>2017-07-09 14:00:00</td>\n",
              "      <td>166.8825</td>\n",
              "      <td>4.6950</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>152.000000</td>\n",
              "      <td>-15.045633</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>72.0550</td>\n",
              "      <td>991.680000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>25.9075</td>\n",
              "      <td>2017-07-09 15:00:00</td>\n",
              "      <td>120.7425</td>\n",
              "      <td>4.5600</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>-35.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>140.000000</td>\n",
              "      <td>-22.377722</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>72.8325</td>\n",
              "      <td>991.427500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>25.7100</td>\n",
              "      <td>2017-07-09 16:00:00</td>\n",
              "      <td>68.0950</td>\n",
              "      <td>2.4400</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>179.000000</td>\n",
              "      <td>-27.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>124.000000</td>\n",
              "      <td>-25.741962</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>73.6100</td>\n",
              "      <td>991.170000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>25.5125</td>\n",
              "      <td>2017-07-09 17:00:00</td>\n",
              "      <td>182.9550</td>\n",
              "      <td>4.2450</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>-12.000000</td>\n",
              "      <td>179.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>109.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>74.3925</td>\n",
              "      <td>990.917500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>25.3150</td>\n",
              "      <td>2017-07-09 18:00:00</td>\n",
              "      <td>348.4350</td>\n",
              "      <td>7.6150</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>-16.000000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>79.9450</td>\n",
              "      <td>991.075000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>24.7900</td>\n",
              "      <td>2017-07-09 19:00:00</td>\n",
              "      <td>260.7625</td>\n",
              "      <td>6.6950</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>-15.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>59.779736</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>85.5050</td>\n",
              "      <td>991.235000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>24.1600</td>\n",
              "      <td>2017-07-09 20:00:00</td>\n",
              "      <td>263.7625</td>\n",
              "      <td>5.7950</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>-14.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>91.0600</td>\n",
              "      <td>991.395000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>23.5125</td>\n",
              "      <td>2017-07-09 21:00:00</td>\n",
              "      <td>267.6975</td>\n",
              "      <td>4.9275</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>-35.220264</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>214.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>38.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>79.6075</td>\n",
              "      <td>991.520000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>25.3350</td>\n",
              "      <td>2017-07-09 22:00:00</td>\n",
              "      <td>346.7150</td>\n",
              "      <td>5.5525</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>59.779736</td>\n",
              "      <td>-26.779736</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>179.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>68.1600</td>\n",
              "      <td>991.652500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>26.7100</td>\n",
              "      <td>2017-07-09 23:00:00</td>\n",
              "      <td>338.2975</td>\n",
              "      <td>6.3725</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>59.779736</td>\n",
              "      <td>NaN</td>\n",
              "      <td>152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>52.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>56.7125</td>\n",
              "      <td>991.780000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>28.0850</td>\n",
              "      <td>2017-07-10 00:00:00</td>\n",
              "      <td>332.0250</td>\n",
              "      <td>7.3150</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>140.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>63.712452</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>45.2675</td>\n",
              "      <td>991.975000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>29.4200</td>\n",
              "      <td>2017-07-10 01:00:00</td>\n",
              "      <td>330.1150</td>\n",
              "      <td>9.1650</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>124.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>71.648972</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>33.8225</td>\n",
              "      <td>992.167500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>30.7475</td>\n",
              "      <td>2017-07-10 02:00:00</td>\n",
              "      <td>328.8975</td>\n",
              "      <td>11.0275</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>179.000000</td>\n",
              "      <td>109.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>118.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>22.3800</td>\n",
              "      <td>992.360000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>32.0825</td>\n",
              "      <td>2017-07-10 03:00:00</td>\n",
              "      <td>328.0550</td>\n",
              "      <td>12.9025</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>95.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>151.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>20.5500</td>\n",
              "      <td>992.182500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>32.6450</td>\n",
              "      <td>2017-07-10 04:00:00</td>\n",
              "      <td>327.3250</td>\n",
              "      <td>12.4050</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>59.779736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>168.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>18.7225</td>\n",
              "      <td>992.002500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>33.2075</td>\n",
              "      <td>2017-07-10 05:00:00</td>\n",
              "      <td>326.5400</td>\n",
              "      <td>11.9150</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>33.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>178.000000</td>\n",
              "      <td>67.285425</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.8900</td>\n",
              "      <td>991.827500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>33.7775</td>\n",
              "      <td>2017-07-10 06:00:00</td>\n",
              "      <td>325.6850</td>\n",
              "      <td>11.4275</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>151.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>162.000000</td>\n",
              "      <td>68.529992</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>18.5650</td>\n",
              "      <td>991.685000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>33.6825</td>\n",
              "      <td>2017-07-10 07:00:00</td>\n",
              "      <td>301.9700</td>\n",
              "      <td>4.7400</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>151.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>49.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>190.000000</td>\n",
              "      <td>47.894068</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>20.2400</td>\n",
              "      <td>991.545000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>33.5875</td>\n",
              "      <td>2017-07-10 08:00:00</td>\n",
              "      <td>199.9375</td>\n",
              "      <td>4.7500</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>-16.000000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>59.779736</td>\n",
              "      <td>52.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>230.000000</td>\n",
              "      <td>54.278438</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>21.9175</td>\n",
              "      <td>991.402500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>33.4900</td>\n",
              "      <td>2017-07-10 09:00:00</td>\n",
              "      <td>175.9375</td>\n",
              "      <td>11.4075</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>162.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>62.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>209.000000</td>\n",
              "      <td>51.152098</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>26.8925</td>\n",
              "      <td>991.417500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>31.5575</td>\n",
              "      <td>2017-07-10 10:00:00</td>\n",
              "      <td>187.5850</td>\n",
              "      <td>10.4900</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>162.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>180.000000</td>\n",
              "      <td>40.128340</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>31.8575</td>\n",
              "      <td>991.430000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>29.6200</td>\n",
              "      <td>2017-07-10 11:00:00</td>\n",
              "      <td>200.8300</td>\n",
              "      <td>10.0975</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>-21.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>118.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>156.000000</td>\n",
              "      <td>47.571495</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>36.8275</td>\n",
              "      <td>991.442500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>27.6850</td>\n",
              "      <td>2017-07-10 12:00:00</td>\n",
              "      <td>214.3325</td>\n",
              "      <td>10.2925</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>209.000000</td>\n",
              "      <td>-29.000000</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>151.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>140.000000</td>\n",
              "      <td>46.487518</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>41.9600</td>\n",
              "      <td>992.190000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>26.7025</td>\n",
              "      <td>2017-07-10 13:00:00</td>\n",
              "      <td>217.6325</td>\n",
              "      <td>8.3550</td>\n",
              "      <td>...</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60.149918</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.00000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>-24.000000</td>\n",
              "      <td>209.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>168.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>127.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>47.0875</td>\n",
              "      <td>992.937500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>25.7175</td>\n",
              "      <td>2017-07-10 14:00:00</td>\n",
              "      <td>223.0850</td>\n",
              "      <td>6.4850</td>\n",
              "      <td>...</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.503660</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.00000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>-16.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>178.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>113.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>52.2175</td>\n",
              "      <td>993.687500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>24.5875</td>\n",
              "      <td>2017-07-10 15:00:00</td>\n",
              "      <td>233.0325</td>\n",
              "      <td>4.7725</td>\n",
              "      <td>...</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56.431751</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.00000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>-13.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>162.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>74.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>53.3100</td>\n",
              "      <td>993.797500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>24.1075</td>\n",
              "      <td>2017-07-10 16:00:00</td>\n",
              "      <td>245.4425</td>\n",
              "      <td>3.0050</td>\n",
              "      <td>...</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56.178631</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.00000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>-14.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>151.000000</td>\n",
              "      <td>190.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>54.4050</td>\n",
              "      <td>993.915000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>23.6075</td>\n",
              "      <td>2017-07-10 17:00:00</td>\n",
              "      <td>271.1850</td>\n",
              "      <td>1.8350</td>\n",
              "      <td>...</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56.935003</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.00000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>-39.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>230.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>55.4975</td>\n",
              "      <td>994.025000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>23.0975</td>\n",
              "      <td>2017-07-10 18:00:00</td>\n",
              "      <td>182.6550</td>\n",
              "      <td>2.5625</td>\n",
              "      <td>...</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61.302211</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.00000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>-65.000000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>209.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7109</th>\n",
              "      <td>39.584773</td>\n",
              "      <td>95.785607</td>\n",
              "      <td>69.396656</td>\n",
              "      <td>55.2500</td>\n",
              "      <td>1010.790725</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2018-05-01 18:00:00</td>\n",
              "      <td>321.1225</td>\n",
              "      <td>6.9625</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>41.403267</td>\n",
              "      <td>49.462713</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>57.820254</td>\n",
              "      <td>-10.340369</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7110</th>\n",
              "      <td>35.250458</td>\n",
              "      <td>91.819546</td>\n",
              "      <td>68.485943</td>\n",
              "      <td>53.2500</td>\n",
              "      <td>1011.141875</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2018-05-01 19:00:00</td>\n",
              "      <td>330.7975</td>\n",
              "      <td>8.4050</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>41.778147</td>\n",
              "      <td>47.676498</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>47.479885</td>\n",
              "      <td>-7.895112</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7111</th>\n",
              "      <td>34.755179</td>\n",
              "      <td>87.351281</td>\n",
              "      <td>64.171921</td>\n",
              "      <td>51.0000</td>\n",
              "      <td>1011.277550</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2018-05-01 20:00:00</td>\n",
              "      <td>336.7350</td>\n",
              "      <td>9.5475</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>42.113082</td>\n",
              "      <td>45.928777</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>39.584773</td>\n",
              "      <td>-4.334315</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7112</th>\n",
              "      <td>33.826693</td>\n",
              "      <td>82.505682</td>\n",
              "      <td>59.679347</td>\n",
              "      <td>47.2500</td>\n",
              "      <td>1011.210625</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>14.2000</td>\n",
              "      <td>2018-05-01 21:00:00</td>\n",
              "      <td>340.9900</td>\n",
              "      <td>10.4600</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>42.336036</td>\n",
              "      <td>44.089272</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>35.250458</td>\n",
              "      <td>-0.495279</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7113</th>\n",
              "      <td>33.889867</td>\n",
              "      <td>79.081810</td>\n",
              "      <td>52.697630</td>\n",
              "      <td>34.0000</td>\n",
              "      <td>1011.902075</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>12.7500</td>\n",
              "      <td>2018-05-01 22:00:00</td>\n",
              "      <td>350.1750</td>\n",
              "      <td>11.9600</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>42.726650</td>\n",
              "      <td>42.168346</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>34.755179</td>\n",
              "      <td>-0.928485</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7114</th>\n",
              "      <td>33.706790</td>\n",
              "      <td>81.092241</td>\n",
              "      <td>53.408513</td>\n",
              "      <td>27.2500</td>\n",
              "      <td>1012.411750</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>14.2500</td>\n",
              "      <td>2018-05-01 23:00:00</td>\n",
              "      <td>265.8200</td>\n",
              "      <td>12.1725</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>43.105525</td>\n",
              "      <td>40.108631</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.00000</td>\n",
              "      <td>33.826693</td>\n",
              "      <td>0.063174</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7115</th>\n",
              "      <td>40.986230</td>\n",
              "      <td>83.482690</td>\n",
              "      <td>53.269937</td>\n",
              "      <td>22.0000</td>\n",
              "      <td>1013.258400</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>17.5000</td>\n",
              "      <td>2018-05-02 00:00:00</td>\n",
              "      <td>269.4225</td>\n",
              "      <td>12.4400</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>43.452764</td>\n",
              "      <td>39.967555</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>33.889867</td>\n",
              "      <td>-0.183077</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7116</th>\n",
              "      <td>57.776443</td>\n",
              "      <td>85.153517</td>\n",
              "      <td>58.928797</td>\n",
              "      <td>19.0000</td>\n",
              "      <td>1013.056975</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>19.5000</td>\n",
              "      <td>2018-05-02 01:00:00</td>\n",
              "      <td>180.0825</td>\n",
              "      <td>12.2625</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>43.640087</td>\n",
              "      <td>40.155618</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>33.706790</td>\n",
              "      <td>7.279440</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7117</th>\n",
              "      <td>75.461782</td>\n",
              "      <td>83.309249</td>\n",
              "      <td>54.269766</td>\n",
              "      <td>16.0000</td>\n",
              "      <td>1012.529175</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>21.2500</td>\n",
              "      <td>2018-05-02 02:00:00</td>\n",
              "      <td>269.5025</td>\n",
              "      <td>11.9250</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>43.650241</td>\n",
              "      <td>40.254767</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>40.986230</td>\n",
              "      <td>16.790214</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7118</th>\n",
              "      <td>92.891571</td>\n",
              "      <td>71.317451</td>\n",
              "      <td>43.874152</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>1012.551350</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>21.7500</td>\n",
              "      <td>2018-05-02 03:00:00</td>\n",
              "      <td>355.3725</td>\n",
              "      <td>11.1775</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>43.604142</td>\n",
              "      <td>40.261254</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>57.776443</td>\n",
              "      <td>17.685339</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7119</th>\n",
              "      <td>108.923776</td>\n",
              "      <td>61.235536</td>\n",
              "      <td>33.466371</td>\n",
              "      <td>12.7500</td>\n",
              "      <td>1010.968000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>22.2500</td>\n",
              "      <td>2018-05-02 04:00:00</td>\n",
              "      <td>349.7275</td>\n",
              "      <td>10.6125</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>43.667737</td>\n",
              "      <td>40.247200</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>75.461782</td>\n",
              "      <td>17.429788</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7120</th>\n",
              "      <td>123.049225</td>\n",
              "      <td>53.210684</td>\n",
              "      <td>33.164123</td>\n",
              "      <td>11.7500</td>\n",
              "      <td>1010.517450</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>22.2500</td>\n",
              "      <td>2018-05-02 05:00:00</td>\n",
              "      <td>344.0325</td>\n",
              "      <td>9.7125</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>43.826959</td>\n",
              "      <td>40.126138</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>92.891571</td>\n",
              "      <td>16.032205</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7121</th>\n",
              "      <td>135.997379</td>\n",
              "      <td>49.637121</td>\n",
              "      <td>40.961803</td>\n",
              "      <td>11.5000</td>\n",
              "      <td>1010.029125</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>23.7500</td>\n",
              "      <td>2018-05-02 06:00:00</td>\n",
              "      <td>346.8250</td>\n",
              "      <td>9.8275</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>44.116909</td>\n",
              "      <td>39.706705</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>108.923776</td>\n",
              "      <td>14.125449</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7122</th>\n",
              "      <td>147.325286</td>\n",
              "      <td>54.174432</td>\n",
              "      <td>47.439099</td>\n",
              "      <td>11.7500</td>\n",
              "      <td>1008.745825</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>23.7500</td>\n",
              "      <td>2018-05-02 07:00:00</td>\n",
              "      <td>179.3425</td>\n",
              "      <td>11.4125</td>\n",
              "      <td>...</td>\n",
              "      <td>144.997943</td>\n",
              "      <td>44.457939</td>\n",
              "      <td>39.166632</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>123.049225</td>\n",
              "      <td>12.948154</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7123</th>\n",
              "      <td>146.158364</td>\n",
              "      <td>58.239789</td>\n",
              "      <td>49.881094</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1008.081625</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>23.2500</td>\n",
              "      <td>2018-05-02 08:00:00</td>\n",
              "      <td>11.7625</td>\n",
              "      <td>13.6975</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>44.998997</td>\n",
              "      <td>39.327186</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>135.997379</td>\n",
              "      <td>11.327907</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7124</th>\n",
              "      <td>143.124808</td>\n",
              "      <td>64.888573</td>\n",
              "      <td>51.135323</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>1008.253050</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>23.0000</td>\n",
              "      <td>2018-05-02 09:00:00</td>\n",
              "      <td>18.6400</td>\n",
              "      <td>14.0850</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.347759</td>\n",
              "      <td>39.532874</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>-1.166922</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7125</th>\n",
              "      <td>134.289323</td>\n",
              "      <td>69.761813</td>\n",
              "      <td>54.735776</td>\n",
              "      <td>15.2500</td>\n",
              "      <td>1007.667500</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>23.0000</td>\n",
              "      <td>2018-05-02 10:00:00</td>\n",
              "      <td>31.9425</td>\n",
              "      <td>8.2175</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.548554</td>\n",
              "      <td>39.936642</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>146.158364</td>\n",
              "      <td>-3.033556</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7126</th>\n",
              "      <td>114.106916</td>\n",
              "      <td>77.118509</td>\n",
              "      <td>57.922586</td>\n",
              "      <td>18.5000</td>\n",
              "      <td>1008.620625</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>2018-05-02 11:00:00</td>\n",
              "      <td>36.2525</td>\n",
              "      <td>5.0675</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.555962</td>\n",
              "      <td>40.400995</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>143.124808</td>\n",
              "      <td>-8.835485</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7127</th>\n",
              "      <td>93.749902</td>\n",
              "      <td>86.636952</td>\n",
              "      <td>62.025516</td>\n",
              "      <td>21.7500</td>\n",
              "      <td>1008.798075</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>20.2500</td>\n",
              "      <td>2018-05-02 12:00:00</td>\n",
              "      <td>107.9650</td>\n",
              "      <td>2.5475</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.776540</td>\n",
              "      <td>40.438055</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>134.289323</td>\n",
              "      <td>-20.182407</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7128</th>\n",
              "      <td>75.199574</td>\n",
              "      <td>93.627068</td>\n",
              "      <td>59.990815</td>\n",
              "      <td>23.0000</td>\n",
              "      <td>1010.014200</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>20.0000</td>\n",
              "      <td>2018-05-02 13:00:00</td>\n",
              "      <td>328.2000</td>\n",
              "      <td>2.3825</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.759784</td>\n",
              "      <td>40.330347</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>114.106916</td>\n",
              "      <td>-20.357015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7129</th>\n",
              "      <td>65.160381</td>\n",
              "      <td>90.998362</td>\n",
              "      <td>55.668604</td>\n",
              "      <td>24.5000</td>\n",
              "      <td>1009.853100</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>19.7500</td>\n",
              "      <td>2018-05-02 14:00:00</td>\n",
              "      <td>321.2975</td>\n",
              "      <td>4.3900</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.673080</td>\n",
              "      <td>40.328542</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>93.749902</td>\n",
              "      <td>-18.550328</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7130</th>\n",
              "      <td>59.551034</td>\n",
              "      <td>85.072359</td>\n",
              "      <td>53.448223</td>\n",
              "      <td>25.7500</td>\n",
              "      <td>1010.126225</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>18.7500</td>\n",
              "      <td>2018-05-02 15:00:00</td>\n",
              "      <td>330.6700</td>\n",
              "      <td>6.4750</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.694180</td>\n",
              "      <td>40.453992</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>75.199574</td>\n",
              "      <td>-10.039193</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7131</th>\n",
              "      <td>56.155343</td>\n",
              "      <td>79.923116</td>\n",
              "      <td>52.170842</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>1009.758400</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.8000</td>\n",
              "      <td>2018-05-02 16:00:00</td>\n",
              "      <td>29.7675</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.760416</td>\n",
              "      <td>40.575647</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>65.160381</td>\n",
              "      <td>-5.609346</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7132</th>\n",
              "      <td>54.378088</td>\n",
              "      <td>76.580856</td>\n",
              "      <td>52.658423</td>\n",
              "      <td>28.2500</td>\n",
              "      <td>1010.834750</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.6000</td>\n",
              "      <td>2018-05-02 17:00:00</td>\n",
              "      <td>22.7675</td>\n",
              "      <td>7.2375</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.679488</td>\n",
              "      <td>40.615628</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>59.551034</td>\n",
              "      <td>-3.395691</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7133</th>\n",
              "      <td>49.646589</td>\n",
              "      <td>76.632073</td>\n",
              "      <td>53.162517</td>\n",
              "      <td>30.7500</td>\n",
              "      <td>1011.081200</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.2000</td>\n",
              "      <td>2018-05-02 18:00:00</td>\n",
              "      <td>183.7675</td>\n",
              "      <td>5.0750</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>45.237532</td>\n",
              "      <td>40.405865</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>56.155343</td>\n",
              "      <td>-1.777255</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7134</th>\n",
              "      <td>42.611372</td>\n",
              "      <td>73.469591</td>\n",
              "      <td>50.681207</td>\n",
              "      <td>31.0000</td>\n",
              "      <td>1011.494700</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.1000</td>\n",
              "      <td>2018-05-02 19:00:00</td>\n",
              "      <td>343.7250</td>\n",
              "      <td>4.5425</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>44.066074</td>\n",
              "      <td>40.028171</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>54.378088</td>\n",
              "      <td>-4.731499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7135</th>\n",
              "      <td>37.036368</td>\n",
              "      <td>71.512724</td>\n",
              "      <td>48.679929</td>\n",
              "      <td>30.5000</td>\n",
              "      <td>1011.515950</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2018-05-02 20:00:00</td>\n",
              "      <td>336.5800</td>\n",
              "      <td>5.6450</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>42.955121</td>\n",
              "      <td>39.702897</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>49.646589</td>\n",
              "      <td>-7.035217</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7136</th>\n",
              "      <td>35.098036</td>\n",
              "      <td>68.923240</td>\n",
              "      <td>46.875503</td>\n",
              "      <td>28.7500</td>\n",
              "      <td>1012.411975</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2018-05-02 21:00:00</td>\n",
              "      <td>342.2900</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>41.910831</td>\n",
              "      <td>39.593783</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>42.611372</td>\n",
              "      <td>-5.575004</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7137</th>\n",
              "      <td>35.477388</td>\n",
              "      <td>67.897034</td>\n",
              "      <td>44.844080</td>\n",
              "      <td>24.5000</td>\n",
              "      <td>1012.490325</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>2018-05-02 22:00:00</td>\n",
              "      <td>349.1050</td>\n",
              "      <td>11.5800</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>40.871515</td>\n",
              "      <td>39.530821</td>\n",
              "      <td>2.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>37.036368</td>\n",
              "      <td>-1.938332</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7138</th>\n",
              "      <td>36.882029</td>\n",
              "      <td>72.119172</td>\n",
              "      <td>43.016181</td>\n",
              "      <td>20.2500</td>\n",
              "      <td>1013.490875</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>15.2500</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>355.4450</td>\n",
              "      <td>15.5200</td>\n",
              "      <td>...</td>\n",
              "      <td>147.325286</td>\n",
              "      <td>39.783852</td>\n",
              "      <td>39.452331</td>\n",
              "      <td>31.0</td>\n",
              "      <td>33.70679</td>\n",
              "      <td>35.098036</td>\n",
              "      <td>0.379353</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7139 rows × 68 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              O3       PM10      PM2.5  humidity     pressure  \\\n",
              "0     214.000000  50.898794  63.000000   56.7600   990.815000   \n",
              "1     179.000000  68.000000  62.000000   64.4100   991.247500   \n",
              "2     152.000000 -15.045633  23.000000   72.0550   991.680000   \n",
              "3     140.000000 -22.377722  18.000000   72.8325   991.427500   \n",
              "4     124.000000 -25.741962  11.000000   73.6100   991.170000   \n",
              "5     109.000000  22.000000  12.000000   74.3925   990.917500   \n",
              "6      95.000000  34.000000  16.000000   79.9450   991.075000   \n",
              "7      59.779736  58.000000  21.000000   85.5050   991.235000   \n",
              "8      33.000000  40.000000  27.000000   91.0600   991.395000   \n",
              "9      38.000000  45.000000  28.000000   79.6075   991.520000   \n",
              "10     49.000000  49.000000  27.000000   68.1600   991.652500   \n",
              "11     52.000000  68.000000  38.000000   56.7125   991.780000   \n",
              "12     62.000000  63.712452  34.000000   45.2675   991.975000   \n",
              "13     75.000000  71.648972  25.000000   33.8225   992.167500   \n",
              "14    118.000000  31.000000  18.000000   22.3800   992.360000   \n",
              "15    151.000000  28.000000  24.000000   20.5500   992.182500   \n",
              "16    168.000000  20.000000  11.000000   18.7225   992.002500   \n",
              "17    178.000000  67.285425  16.000000   16.8900   991.827500   \n",
              "18    162.000000  68.529992  23.000000   18.5650   991.685000   \n",
              "19    190.000000  47.894068  19.000000   20.2400   991.545000   \n",
              "20    230.000000  54.278438  25.000000   21.9175   991.402500   \n",
              "21    209.000000  51.152098  24.000000   26.8925   991.417500   \n",
              "22    180.000000  40.128340  17.000000   31.8575   991.430000   \n",
              "23    156.000000  47.571495  22.000000   36.8275   991.442500   \n",
              "24    140.000000  46.487518  28.000000   41.9600   992.190000   \n",
              "25    127.000000  59.000000  28.000000   47.0875   992.937500   \n",
              "26    113.000000  53.000000  29.000000   52.2175   993.687500   \n",
              "27     74.000000  59.000000  31.000000   53.3100   993.797500   \n",
              "28      9.000000  82.000000  42.000000   54.4050   993.915000   \n",
              "29      2.000000  93.000000  43.000000   55.4975   994.025000   \n",
              "...          ...        ...        ...       ...          ...   \n",
              "7109   39.584773  95.785607  69.396656   55.2500  1010.790725   \n",
              "7110   35.250458  91.819546  68.485943   53.2500  1011.141875   \n",
              "7111   34.755179  87.351281  64.171921   51.0000  1011.277550   \n",
              "7112   33.826693  82.505682  59.679347   47.2500  1011.210625   \n",
              "7113   33.889867  79.081810  52.697630   34.0000  1011.902075   \n",
              "7114   33.706790  81.092241  53.408513   27.2500  1012.411750   \n",
              "7115   40.986230  83.482690  53.269937   22.0000  1013.258400   \n",
              "7116   57.776443  85.153517  58.928797   19.0000  1013.056975   \n",
              "7117   75.461782  83.309249  54.269766   16.0000  1012.529175   \n",
              "7118   92.891571  71.317451  43.874152   14.0000  1012.551350   \n",
              "7119  108.923776  61.235536  33.466371   12.7500  1010.968000   \n",
              "7120  123.049225  53.210684  33.164123   11.7500  1010.517450   \n",
              "7121  135.997379  49.637121  40.961803   11.5000  1010.029125   \n",
              "7122  147.325286  54.174432  47.439099   11.7500  1008.745825   \n",
              "7123  146.158364  58.239789  49.881094   13.0000  1008.081625   \n",
              "7124  143.124808  64.888573  51.135323   15.0000  1008.253050   \n",
              "7125  134.289323  69.761813  54.735776   15.2500  1007.667500   \n",
              "7126  114.106916  77.118509  57.922586   18.5000  1008.620625   \n",
              "7127   93.749902  86.636952  62.025516   21.7500  1008.798075   \n",
              "7128   75.199574  93.627068  59.990815   23.0000  1010.014200   \n",
              "7129   65.160381  90.998362  55.668604   24.5000  1009.853100   \n",
              "7130   59.551034  85.072359  53.448223   25.7500  1010.126225   \n",
              "7131   56.155343  79.923116  52.170842   26.0000  1009.758400   \n",
              "7132   54.378088  76.580856  52.658423   28.2500  1010.834750   \n",
              "7133   49.646589  76.632073  53.162517   30.7500  1011.081200   \n",
              "7134   42.611372  73.469591  50.681207   31.0000  1011.494700   \n",
              "7135   37.036368  71.512724  48.679929   30.5000  1011.515950   \n",
              "7136   35.098036  68.923240  46.875503   28.7500  1012.411975   \n",
              "7137   35.477388  67.897034  44.844080   24.5000  1012.490325   \n",
              "7138   36.882029  72.119172  43.016181   20.2500  1013.490875   \n",
              "\n",
              "            stationId  temperature             utc_time  wind_direction  \\\n",
              "0     aotizhongxin_aq      27.2225  2017-07-09 13:00:00        190.8400   \n",
              "1     aotizhongxin_aq      26.5650  2017-07-09 14:00:00        166.8825   \n",
              "2     aotizhongxin_aq      25.9075  2017-07-09 15:00:00        120.7425   \n",
              "3     aotizhongxin_aq      25.7100  2017-07-09 16:00:00         68.0950   \n",
              "4     aotizhongxin_aq      25.5125  2017-07-09 17:00:00        182.9550   \n",
              "5     aotizhongxin_aq      25.3150  2017-07-09 18:00:00        348.4350   \n",
              "6     aotizhongxin_aq      24.7900  2017-07-09 19:00:00        260.7625   \n",
              "7     aotizhongxin_aq      24.1600  2017-07-09 20:00:00        263.7625   \n",
              "8     aotizhongxin_aq      23.5125  2017-07-09 21:00:00        267.6975   \n",
              "9     aotizhongxin_aq      25.3350  2017-07-09 22:00:00        346.7150   \n",
              "10    aotizhongxin_aq      26.7100  2017-07-09 23:00:00        338.2975   \n",
              "11    aotizhongxin_aq      28.0850  2017-07-10 00:00:00        332.0250   \n",
              "12    aotizhongxin_aq      29.4200  2017-07-10 01:00:00        330.1150   \n",
              "13    aotizhongxin_aq      30.7475  2017-07-10 02:00:00        328.8975   \n",
              "14    aotizhongxin_aq      32.0825  2017-07-10 03:00:00        328.0550   \n",
              "15    aotizhongxin_aq      32.6450  2017-07-10 04:00:00        327.3250   \n",
              "16    aotizhongxin_aq      33.2075  2017-07-10 05:00:00        326.5400   \n",
              "17    aotizhongxin_aq      33.7775  2017-07-10 06:00:00        325.6850   \n",
              "18    aotizhongxin_aq      33.6825  2017-07-10 07:00:00        301.9700   \n",
              "19    aotizhongxin_aq      33.5875  2017-07-10 08:00:00        199.9375   \n",
              "20    aotizhongxin_aq      33.4900  2017-07-10 09:00:00        175.9375   \n",
              "21    aotizhongxin_aq      31.5575  2017-07-10 10:00:00        187.5850   \n",
              "22    aotizhongxin_aq      29.6200  2017-07-10 11:00:00        200.8300   \n",
              "23    aotizhongxin_aq      27.6850  2017-07-10 12:00:00        214.3325   \n",
              "24    aotizhongxin_aq      26.7025  2017-07-10 13:00:00        217.6325   \n",
              "25    aotizhongxin_aq      25.7175  2017-07-10 14:00:00        223.0850   \n",
              "26    aotizhongxin_aq      24.5875  2017-07-10 15:00:00        233.0325   \n",
              "27    aotizhongxin_aq      24.1075  2017-07-10 16:00:00        245.4425   \n",
              "28    aotizhongxin_aq      23.6075  2017-07-10 17:00:00        271.1850   \n",
              "29    aotizhongxin_aq      23.0975  2017-07-10 18:00:00        182.6550   \n",
              "...               ...          ...                  ...             ...   \n",
              "7109  aotizhongxin_aq      13.0000  2018-05-01 18:00:00        321.1225   \n",
              "7110  aotizhongxin_aq      13.0000  2018-05-01 19:00:00        330.7975   \n",
              "7111  aotizhongxin_aq      13.0000  2018-05-01 20:00:00        336.7350   \n",
              "7112  aotizhongxin_aq      14.2000  2018-05-01 21:00:00        340.9900   \n",
              "7113  aotizhongxin_aq      12.7500  2018-05-01 22:00:00        350.1750   \n",
              "7114  aotizhongxin_aq      14.2500  2018-05-01 23:00:00        265.8200   \n",
              "7115  aotizhongxin_aq      17.5000  2018-05-02 00:00:00        269.4225   \n",
              "7116  aotizhongxin_aq      19.5000  2018-05-02 01:00:00        180.0825   \n",
              "7117  aotizhongxin_aq      21.2500  2018-05-02 02:00:00        269.5025   \n",
              "7118  aotizhongxin_aq      21.7500  2018-05-02 03:00:00        355.3725   \n",
              "7119  aotizhongxin_aq      22.2500  2018-05-02 04:00:00        349.7275   \n",
              "7120  aotizhongxin_aq      22.2500  2018-05-02 05:00:00        344.0325   \n",
              "7121  aotizhongxin_aq      23.7500  2018-05-02 06:00:00        346.8250   \n",
              "7122  aotizhongxin_aq      23.7500  2018-05-02 07:00:00        179.3425   \n",
              "7123  aotizhongxin_aq      23.2500  2018-05-02 08:00:00         11.7625   \n",
              "7124  aotizhongxin_aq      23.0000  2018-05-02 09:00:00         18.6400   \n",
              "7125  aotizhongxin_aq      23.0000  2018-05-02 10:00:00         31.9425   \n",
              "7126  aotizhongxin_aq      21.0000  2018-05-02 11:00:00         36.2525   \n",
              "7127  aotizhongxin_aq      20.2500  2018-05-02 12:00:00        107.9650   \n",
              "7128  aotizhongxin_aq      20.0000  2018-05-02 13:00:00        328.2000   \n",
              "7129  aotizhongxin_aq      19.7500  2018-05-02 14:00:00        321.2975   \n",
              "7130  aotizhongxin_aq      18.7500  2018-05-02 15:00:00        330.6700   \n",
              "7131  aotizhongxin_aq      13.8000  2018-05-02 16:00:00         29.7675   \n",
              "7132  aotizhongxin_aq      13.6000  2018-05-02 17:00:00         22.7675   \n",
              "7133  aotizhongxin_aq      13.2000  2018-05-02 18:00:00        183.7675   \n",
              "7134  aotizhongxin_aq      13.1000  2018-05-02 19:00:00        343.7250   \n",
              "7135  aotizhongxin_aq      13.0000  2018-05-02 20:00:00        336.5800   \n",
              "7136  aotizhongxin_aq      13.0000  2018-05-02 21:00:00        342.2900   \n",
              "7137  aotizhongxin_aq      14.0000  2018-05-02 22:00:00        349.1050   \n",
              "7138  aotizhongxin_aq      15.2500  2018-05-02 23:00:00        355.4450   \n",
              "\n",
              "      wind_speed     ...       O3_24_max  O3_48_std  O3_24_std  O3_48_min  \\\n",
              "0         6.5675     ...             NaN        NaN        NaN        NaN   \n",
              "1         4.6950     ...             NaN        NaN        NaN        NaN   \n",
              "2         4.5600     ...             NaN        NaN        NaN        NaN   \n",
              "3         2.4400     ...             NaN        NaN        NaN        NaN   \n",
              "4         4.2450     ...             NaN        NaN        NaN        NaN   \n",
              "5         7.6150     ...             NaN        NaN        NaN        NaN   \n",
              "6         6.6950     ...             NaN        NaN        NaN        NaN   \n",
              "7         5.7950     ...             NaN        NaN        NaN        NaN   \n",
              "8         4.9275     ...             NaN        NaN        NaN        NaN   \n",
              "9         5.5525     ...             NaN        NaN        NaN        NaN   \n",
              "10        6.3725     ...             NaN        NaN        NaN        NaN   \n",
              "11        7.3150     ...             NaN        NaN        NaN        NaN   \n",
              "12        9.1650     ...             NaN        NaN        NaN        NaN   \n",
              "13       11.0275     ...             NaN        NaN        NaN        NaN   \n",
              "14       12.9025     ...             NaN        NaN        NaN        NaN   \n",
              "15       12.4050     ...             NaN        NaN        NaN        NaN   \n",
              "16       11.9150     ...             NaN        NaN        NaN        NaN   \n",
              "17       11.4275     ...             NaN        NaN        NaN        NaN   \n",
              "18        4.7400     ...             NaN        NaN        NaN        NaN   \n",
              "19        4.7500     ...             NaN        NaN        NaN        NaN   \n",
              "20       11.4075     ...             NaN        NaN        NaN        NaN   \n",
              "21       10.4900     ...             NaN        NaN        NaN        NaN   \n",
              "22       10.0975     ...             NaN        NaN        NaN        NaN   \n",
              "23       10.2925     ...             NaN        NaN        NaN        NaN   \n",
              "24        8.3550     ...      230.000000        NaN  60.149918        NaN   \n",
              "25        6.4850     ...      230.000000        NaN  57.503660        NaN   \n",
              "26        4.7725     ...      230.000000        NaN  56.431751        NaN   \n",
              "27        3.0050     ...      230.000000        NaN  56.178631        NaN   \n",
              "28        1.8350     ...      230.000000        NaN  56.935003        NaN   \n",
              "29        2.5625     ...      230.000000        NaN  61.302211        NaN   \n",
              "...          ...     ...             ...        ...        ...        ...   \n",
              "7109      6.9625     ...      144.997943  41.403267  49.462713        2.0   \n",
              "7110      8.4050     ...      144.997943  41.778147  47.676498        2.0   \n",
              "7111      9.5475     ...      144.997943  42.113082  45.928777        2.0   \n",
              "7112     10.4600     ...      144.997943  42.336036  44.089272        2.0   \n",
              "7113     11.9600     ...      144.997943  42.726650  42.168346        2.0   \n",
              "7114     12.1725     ...      144.997943  43.105525  40.108631        2.0   \n",
              "7115     12.4400     ...      144.997943  43.452764  39.967555        2.0   \n",
              "7116     12.2625     ...      144.997943  43.640087  40.155618        2.0   \n",
              "7117     11.9250     ...      144.997943  43.650241  40.254767        2.0   \n",
              "7118     11.1775     ...      144.997943  43.604142  40.261254        2.0   \n",
              "7119     10.6125     ...      144.997943  43.667737  40.247200        2.0   \n",
              "7120      9.7125     ...      144.997943  43.826959  40.126138        2.0   \n",
              "7121      9.8275     ...      144.997943  44.116909  39.706705        2.0   \n",
              "7122     11.4125     ...      144.997943  44.457939  39.166632        2.0   \n",
              "7123     13.6975     ...      147.325286  44.998997  39.327186        2.0   \n",
              "7124     14.0850     ...      147.325286  45.347759  39.532874        2.0   \n",
              "7125      8.2175     ...      147.325286  45.548554  39.936642        2.0   \n",
              "7126      5.0675     ...      147.325286  45.555962  40.400995        2.0   \n",
              "7127      2.5475     ...      147.325286  45.776540  40.438055        2.0   \n",
              "7128      2.3825     ...      147.325286  45.759784  40.330347        2.0   \n",
              "7129      4.3900     ...      147.325286  45.673080  40.328542        2.0   \n",
              "7130      6.4750     ...      147.325286  45.694180  40.453992        2.0   \n",
              "7131      9.6875     ...      147.325286  45.760416  40.575647        2.0   \n",
              "7132      7.2375     ...      147.325286  45.679488  40.615628        2.0   \n",
              "7133      5.0750     ...      147.325286  45.237532  40.405865        2.0   \n",
              "7134      4.5425     ...      147.325286  44.066074  40.028171        2.0   \n",
              "7135      5.6450     ...      147.325286  42.955121  39.702897        2.0   \n",
              "7136      7.7500     ...      147.325286  41.910831  39.593783        2.0   \n",
              "7137     11.5800     ...      147.325286  40.871515  39.530821        2.0   \n",
              "7138     15.5200     ...      147.325286  39.783852  39.452331       31.0   \n",
              "\n",
              "      O3_24_min        O3_2  O3_1_diff        O3_3       O3_12        O3_8  \n",
              "0           NaN         NaN        NaN         NaN         NaN         NaN  \n",
              "1           NaN         NaN        NaN         NaN         NaN         NaN  \n",
              "2           NaN  214.000000 -35.000000         NaN         NaN         NaN  \n",
              "3           NaN  179.000000 -27.000000  214.000000         NaN         NaN  \n",
              "4           NaN  152.000000 -12.000000  179.000000         NaN         NaN  \n",
              "5           NaN  140.000000 -16.000000  152.000000         NaN         NaN  \n",
              "6           NaN  124.000000 -15.000000  140.000000         NaN         NaN  \n",
              "7           NaN  109.000000 -14.000000  124.000000         NaN         NaN  \n",
              "8           NaN   95.000000 -35.220264  109.000000         NaN  214.000000  \n",
              "9           NaN   59.779736 -26.779736   95.000000         NaN  179.000000  \n",
              "10          NaN   33.000000   5.000000   59.779736         NaN  152.000000  \n",
              "11          NaN   38.000000  11.000000   33.000000         NaN  140.000000  \n",
              "12          NaN   49.000000   3.000000   38.000000  214.000000  124.000000  \n",
              "13          NaN   52.000000  10.000000   49.000000  179.000000  109.000000  \n",
              "14          NaN   62.000000  13.000000   52.000000  152.000000   95.000000  \n",
              "15          NaN   75.000000  43.000000   62.000000  140.000000   59.779736  \n",
              "16          NaN  118.000000  33.000000   75.000000  124.000000   33.000000  \n",
              "17          NaN  151.000000  17.000000  118.000000  109.000000   38.000000  \n",
              "18          NaN  168.000000  10.000000  151.000000   95.000000   49.000000  \n",
              "19          NaN  178.000000 -16.000000  168.000000   59.779736   52.000000  \n",
              "20          NaN  162.000000  28.000000  178.000000   33.000000   62.000000  \n",
              "21          NaN  190.000000  40.000000  162.000000   38.000000   75.000000  \n",
              "22          NaN  230.000000 -21.000000  190.000000   49.000000  118.000000  \n",
              "23          NaN  209.000000 -29.000000  230.000000   52.000000  151.000000  \n",
              "24     33.00000  180.000000 -24.000000  209.000000   62.000000  168.000000  \n",
              "25     33.00000  156.000000 -16.000000  180.000000   75.000000  178.000000  \n",
              "26     33.00000  140.000000 -13.000000  156.000000  118.000000  162.000000  \n",
              "27     33.00000  127.000000 -14.000000  140.000000  151.000000  190.000000  \n",
              "28     33.00000  113.000000 -39.000000  127.000000  168.000000  230.000000  \n",
              "29      9.00000   74.000000 -65.000000  113.000000  178.000000  209.000000  \n",
              "...         ...         ...        ...         ...         ...         ...  \n",
              "7109    2.00000   57.820254 -10.340369         NaN         NaN         NaN  \n",
              "7110    2.00000   47.479885  -7.895112         NaN         NaN         NaN  \n",
              "7111    2.00000   39.584773  -4.334315         NaN         NaN         NaN  \n",
              "7112    2.00000   35.250458  -0.495279         NaN         NaN         NaN  \n",
              "7113    2.00000   34.755179  -0.928485         NaN         NaN         NaN  \n",
              "7114   31.00000   33.826693   0.063174         NaN         NaN         NaN  \n",
              "7115   33.70679   33.889867  -0.183077         NaN         NaN         NaN  \n",
              "7116   33.70679   33.706790   7.279440         NaN         NaN         NaN  \n",
              "7117   33.70679   40.986230  16.790214         NaN         NaN         NaN  \n",
              "7118   33.70679   57.776443  17.685339         NaN         NaN         NaN  \n",
              "7119   33.70679   75.461782  17.429788         NaN         NaN         NaN  \n",
              "7120   33.70679   92.891571  16.032205         NaN         NaN         NaN  \n",
              "7121   33.70679  108.923776  14.125449         NaN         NaN         NaN  \n",
              "7122   33.70679  123.049225  12.948154         NaN         NaN         NaN  \n",
              "7123   33.70679  135.997379  11.327907         NaN         NaN         NaN  \n",
              "7124   33.70679  147.325286  -1.166922         NaN         NaN         NaN  \n",
              "7125   33.70679  146.158364  -3.033556         NaN         NaN         NaN  \n",
              "7126   33.70679  143.124808  -8.835485         NaN         NaN         NaN  \n",
              "7127   33.70679  134.289323 -20.182407         NaN         NaN         NaN  \n",
              "7128   33.70679  114.106916 -20.357015         NaN         NaN         NaN  \n",
              "7129   33.70679   93.749902 -18.550328         NaN         NaN         NaN  \n",
              "7130   33.70679   75.199574 -10.039193         NaN         NaN         NaN  \n",
              "7131   33.70679   65.160381  -5.609346         NaN         NaN         NaN  \n",
              "7132   33.70679   59.551034  -3.395691         NaN         NaN         NaN  \n",
              "7133   33.70679   56.155343  -1.777255         NaN         NaN         NaN  \n",
              "7134   33.70679   54.378088  -4.731499         NaN         NaN         NaN  \n",
              "7135   33.70679   49.646589  -7.035217         NaN         NaN         NaN  \n",
              "7136   33.70679   42.611372  -5.575004         NaN         NaN         NaN  \n",
              "7137   33.70679   37.036368  -1.938332         NaN         NaN         NaN  \n",
              "7138   33.70679   35.098036   0.379353         NaN         NaN         NaN  \n",
              "\n",
              "[7139 rows x 68 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "metadata": {
        "id": "WVJAcgMkJTK-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3009
        },
        "outputId": "78f96f47-ef46-4e44-9232-c71025d63e1d"
      },
      "cell_type": "code",
      "source": [
        "final_result = final_prediction.sort_values('utc_time').reset_index()\n",
        "del final_result['index']\n",
        "\n",
        "final_result"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O3</th>\n",
              "      <th>PM10</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>humidity</th>\n",
              "      <th>pressure</th>\n",
              "      <th>stationId</th>\n",
              "      <th>temperature</th>\n",
              "      <th>utc_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "      <th>...</th>\n",
              "      <th>O3_24_max</th>\n",
              "      <th>O3_48_std</th>\n",
              "      <th>O3_24_std</th>\n",
              "      <th>O3_48_min</th>\n",
              "      <th>O3_24_min</th>\n",
              "      <th>O3_2</th>\n",
              "      <th>O3_1_diff</th>\n",
              "      <th>O3_3</th>\n",
              "      <th>O3_12</th>\n",
              "      <th>O3_8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>214.000000</td>\n",
              "      <td>50.898794</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>56.7600</td>\n",
              "      <td>990.815000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>27.2225</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>190.8400</td>\n",
              "      <td>6.5675</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>192.000000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>60.3950</td>\n",
              "      <td>993.827500</td>\n",
              "      <td>tongzhou_aq</td>\n",
              "      <td>27.5975</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>161.5575</td>\n",
              "      <td>11.1550</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>199.000000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>55.1375</td>\n",
              "      <td>992.715000</td>\n",
              "      <td>daxing_aq</td>\n",
              "      <td>28.0275</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>183.7500</td>\n",
              "      <td>8.1825</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>72.000000</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>54.3125</td>\n",
              "      <td>993.305000</td>\n",
              "      <td>yufa_aq</td>\n",
              "      <td>28.1550</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>182.3775</td>\n",
              "      <td>5.5850</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>96.000000</td>\n",
              "      <td>263.092184</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>54.6575</td>\n",
              "      <td>980.360000</td>\n",
              "      <td>donggaocun_aq</td>\n",
              "      <td>28.1275</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>164.1625</td>\n",
              "      <td>10.1825</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>246.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>64.0675</td>\n",
              "      <td>967.460000</td>\n",
              "      <td>mentougou_aq</td>\n",
              "      <td>26.1325</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>233.8900</td>\n",
              "      <td>7.1100</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>189.000000</td>\n",
              "      <td>78.528566</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>56.0750</td>\n",
              "      <td>991.002500</td>\n",
              "      <td>qianmen_aq</td>\n",
              "      <td>27.4875</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>193.4550</td>\n",
              "      <td>6.8850</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>170.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>57.8450</td>\n",
              "      <td>992.807500</td>\n",
              "      <td>shunyi_aq</td>\n",
              "      <td>26.1650</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>160.5775</td>\n",
              "      <td>6.3200</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>63.006685</td>\n",
              "      <td>204.096019</td>\n",
              "      <td>128.444027</td>\n",
              "      <td>54.9025</td>\n",
              "      <td>987.680000</td>\n",
              "      <td>liulihe_aq</td>\n",
              "      <td>27.7000</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>272.9325</td>\n",
              "      <td>5.0450</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>176.000000</td>\n",
              "      <td>158.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>58.8850</td>\n",
              "      <td>979.912500</td>\n",
              "      <td>yungang_aq</td>\n",
              "      <td>27.0150</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>231.5950</td>\n",
              "      <td>6.8075</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>214.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>67.5400</td>\n",
              "      <td>968.240000</td>\n",
              "      <td>beibuxinqu_aq</td>\n",
              "      <td>25.2100</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>217.2900</td>\n",
              "      <td>5.1925</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>56.1400</td>\n",
              "      <td>992.322500</td>\n",
              "      <td>dongsi_aq</td>\n",
              "      <td>27.3525</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>181.9300</td>\n",
              "      <td>7.1900</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>105.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>55.3250</td>\n",
              "      <td>991.397500</td>\n",
              "      <td>nansanhuan_aq</td>\n",
              "      <td>27.6725</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>197.5525</td>\n",
              "      <td>7.0475</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>190.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>56.1400</td>\n",
              "      <td>992.322500</td>\n",
              "      <td>nongzhanguan_aq</td>\n",
              "      <td>27.3525</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>181.9300</td>\n",
              "      <td>7.1900</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>167.000000</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>58.4275</td>\n",
              "      <td>985.910000</td>\n",
              "      <td>wanliu_aq</td>\n",
              "      <td>27.0150</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>207.7575</td>\n",
              "      <td>6.2100</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>208.000000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>56.9000</td>\n",
              "      <td>989.725000</td>\n",
              "      <td>guanyuan_aq</td>\n",
              "      <td>27.2775</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>199.0750</td>\n",
              "      <td>6.3825</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>129.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>74.6850</td>\n",
              "      <td>920.607500</td>\n",
              "      <td>badaling_aq</td>\n",
              "      <td>20.8875</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>199.4600</td>\n",
              "      <td>4.4625</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>211.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>61.4450</td>\n",
              "      <td>979.470000</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>26.5025</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>218.3400</td>\n",
              "      <td>5.9225</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>199.000000</td>\n",
              "      <td>119.094552</td>\n",
              "      <td>108.457247</td>\n",
              "      <td>56.1000</td>\n",
              "      <td>987.815000</td>\n",
              "      <td>fengtaihuayuan_aq</td>\n",
              "      <td>27.4825</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>216.1475</td>\n",
              "      <td>6.6125</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>176.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>55.3250</td>\n",
              "      <td>991.397500</td>\n",
              "      <td>wanshouxigong_aq</td>\n",
              "      <td>27.6725</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>197.5525</td>\n",
              "      <td>7.0475</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>144.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>53.1700</td>\n",
              "      <td>979.132500</td>\n",
              "      <td>pinggu_aq</td>\n",
              "      <td>28.0775</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>164.3350</td>\n",
              "      <td>9.5550</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>237.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>53.2750</td>\n",
              "      <td>976.292500</td>\n",
              "      <td>miyun_aq</td>\n",
              "      <td>25.8925</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>174.7275</td>\n",
              "      <td>5.9350</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>75.1675</td>\n",
              "      <td>955.592500</td>\n",
              "      <td>dingling_aq</td>\n",
              "      <td>22.7750</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>183.6925</td>\n",
              "      <td>3.1050</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>164.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>67.5100</td>\n",
              "      <td>994.860000</td>\n",
              "      <td>yongledian_aq</td>\n",
              "      <td>27.8150</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>155.9875</td>\n",
              "      <td>14.6475</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>78.681545</td>\n",
              "      <td>97.663440</td>\n",
              "      <td>81.140083</td>\n",
              "      <td>74.5100</td>\n",
              "      <td>916.055000</td>\n",
              "      <td>yanqin_aq</td>\n",
              "      <td>19.9725</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>183.2250</td>\n",
              "      <td>3.1550</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>44.653570</td>\n",
              "      <td>206.489610</td>\n",
              "      <td>136.062072</td>\n",
              "      <td>56.1400</td>\n",
              "      <td>992.322500</td>\n",
              "      <td>dongsihuan_aq</td>\n",
              "      <td>27.3525</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>181.9300</td>\n",
              "      <td>7.1900</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>93.731998</td>\n",
              "      <td>127.704761</td>\n",
              "      <td>78.094151</td>\n",
              "      <td>65.0050</td>\n",
              "      <td>967.717500</td>\n",
              "      <td>huairou_aq</td>\n",
              "      <td>23.6650</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>171.7500</td>\n",
              "      <td>4.7525</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>161.000000</td>\n",
              "      <td>106.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>55.7975</td>\n",
              "      <td>991.650000</td>\n",
              "      <td>tiantan_aq</td>\n",
              "      <td>27.5925</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>189.2475</td>\n",
              "      <td>7.4225</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>172.000000</td>\n",
              "      <td>198.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>59.8200</td>\n",
              "      <td>978.952500</td>\n",
              "      <td>gucheng_aq</td>\n",
              "      <td>26.8500</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>227.2475</td>\n",
              "      <td>6.6550</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>153.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>55.7975</td>\n",
              "      <td>991.650000</td>\n",
              "      <td>yongdingmennei_aq</td>\n",
              "      <td>27.5925</td>\n",
              "      <td>2017-07-09 13:00:00</td>\n",
              "      <td>189.2475</td>\n",
              "      <td>7.4225</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249835</th>\n",
              "      <td>73.964334</td>\n",
              "      <td>68.124561</td>\n",
              "      <td>26.489201</td>\n",
              "      <td>30.5000</td>\n",
              "      <td>993.926875</td>\n",
              "      <td>huairou_aq</td>\n",
              "      <td>12.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>355.8025</td>\n",
              "      <td>14.1750</td>\n",
              "      <td>...</td>\n",
              "      <td>159.461348</td>\n",
              "      <td>33.015790</td>\n",
              "      <td>31.638311</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>68.443027</td>\n",
              "      <td>73.118068</td>\n",
              "      <td>0.009068</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249836</th>\n",
              "      <td>27.755870</td>\n",
              "      <td>80.168670</td>\n",
              "      <td>29.226463</td>\n",
              "      <td>24.5000</td>\n",
              "      <td>1015.757325</td>\n",
              "      <td>yizhuang_aq</td>\n",
              "      <td>14.5000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>269.0250</td>\n",
              "      <td>18.7425</td>\n",
              "      <td>...</td>\n",
              "      <td>137.722657</td>\n",
              "      <td>43.681527</td>\n",
              "      <td>41.142985</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>18.523489</td>\n",
              "      <td>18.523489</td>\n",
              "      <td>2.739143</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249837</th>\n",
              "      <td>27.962923</td>\n",
              "      <td>76.794328</td>\n",
              "      <td>26.555019</td>\n",
              "      <td>20.0000</td>\n",
              "      <td>1012.555875</td>\n",
              "      <td>xizhimenbei_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>355.0900</td>\n",
              "      <td>15.9700</td>\n",
              "      <td>...</td>\n",
              "      <td>120.257734</td>\n",
              "      <td>39.749442</td>\n",
              "      <td>38.788318</td>\n",
              "      <td>13.459410</td>\n",
              "      <td>13.459410</td>\n",
              "      <td>14.064011</td>\n",
              "      <td>4.553288</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249838</th>\n",
              "      <td>45.983174</td>\n",
              "      <td>66.809803</td>\n",
              "      <td>25.826839</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>1014.183325</td>\n",
              "      <td>shunyi_aq</td>\n",
              "      <td>13.5000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>356.6525</td>\n",
              "      <td>15.2950</td>\n",
              "      <td>...</td>\n",
              "      <td>146.547172</td>\n",
              "      <td>40.298514</td>\n",
              "      <td>38.172653</td>\n",
              "      <td>33.037649</td>\n",
              "      <td>40.777048</td>\n",
              "      <td>42.089551</td>\n",
              "      <td>0.529628</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249839</th>\n",
              "      <td>37.988906</td>\n",
              "      <td>82.476818</td>\n",
              "      <td>26.716769</td>\n",
              "      <td>19.5000</td>\n",
              "      <td>1003.072500</td>\n",
              "      <td>gucheng_aq</td>\n",
              "      <td>14.2500</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>352.4325</td>\n",
              "      <td>16.9400</td>\n",
              "      <td>...</td>\n",
              "      <td>163.144415</td>\n",
              "      <td>49.643952</td>\n",
              "      <td>48.083932</td>\n",
              "      <td>18.467404</td>\n",
              "      <td>26.129587</td>\n",
              "      <td>32.750925</td>\n",
              "      <td>2.034849</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249840</th>\n",
              "      <td>35.350021</td>\n",
              "      <td>75.113974</td>\n",
              "      <td>26.793407</td>\n",
              "      <td>19.5000</td>\n",
              "      <td>1010.370525</td>\n",
              "      <td>wanliu_aq</td>\n",
              "      <td>14.5000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>353.0750</td>\n",
              "      <td>15.7300</td>\n",
              "      <td>...</td>\n",
              "      <td>148.084430</td>\n",
              "      <td>46.881445</td>\n",
              "      <td>44.043155</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>26.239384</td>\n",
              "      <td>27.342082</td>\n",
              "      <td>2.762898</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249841</th>\n",
              "      <td>32.500400</td>\n",
              "      <td>84.942863</td>\n",
              "      <td>29.443991</td>\n",
              "      <td>19.7500</td>\n",
              "      <td>1007.421650</td>\n",
              "      <td>fangshan_aq</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>267.8500</td>\n",
              "      <td>17.8875</td>\n",
              "      <td>...</td>\n",
              "      <td>147.218636</td>\n",
              "      <td>47.427405</td>\n",
              "      <td>44.333966</td>\n",
              "      <td>21.826116</td>\n",
              "      <td>25.182381</td>\n",
              "      <td>26.502762</td>\n",
              "      <td>1.733353</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249842</th>\n",
              "      <td>69.429435</td>\n",
              "      <td>69.448908</td>\n",
              "      <td>21.406479</td>\n",
              "      <td>22.5000</td>\n",
              "      <td>992.053100</td>\n",
              "      <td>pingchang_aq</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>341.6050</td>\n",
              "      <td>15.1600</td>\n",
              "      <td>...</td>\n",
              "      <td>162.441755</td>\n",
              "      <td>38.855715</td>\n",
              "      <td>36.581387</td>\n",
              "      <td>44.344299</td>\n",
              "      <td>44.580891</td>\n",
              "      <td>68.118883</td>\n",
              "      <td>0.440978</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249843</th>\n",
              "      <td>60.459663</td>\n",
              "      <td>76.978394</td>\n",
              "      <td>24.468140</td>\n",
              "      <td>20.7500</td>\n",
              "      <td>989.598250</td>\n",
              "      <td>mentougou_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>349.1025</td>\n",
              "      <td>17.7300</td>\n",
              "      <td>...</td>\n",
              "      <td>159.262640</td>\n",
              "      <td>40.339864</td>\n",
              "      <td>38.682702</td>\n",
              "      <td>40.009032</td>\n",
              "      <td>41.297589</td>\n",
              "      <td>57.043451</td>\n",
              "      <td>1.341238</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249844</th>\n",
              "      <td>77.916926</td>\n",
              "      <td>69.898835</td>\n",
              "      <td>27.476521</td>\n",
              "      <td>35.2500</td>\n",
              "      <td>985.152450</td>\n",
              "      <td>miyunshuiku_aq</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>357.6850</td>\n",
              "      <td>12.8600</td>\n",
              "      <td>...</td>\n",
              "      <td>165.572024</td>\n",
              "      <td>31.314656</td>\n",
              "      <td>31.423813</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>75.332854</td>\n",
              "      <td>77.765249</td>\n",
              "      <td>-0.094181</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249845</th>\n",
              "      <td>29.850502</td>\n",
              "      <td>72.859283</td>\n",
              "      <td>26.899990</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>1013.901050</td>\n",
              "      <td>nansanhuan_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>357.8000</td>\n",
              "      <td>17.4125</td>\n",
              "      <td>...</td>\n",
              "      <td>121.450644</td>\n",
              "      <td>38.370614</td>\n",
              "      <td>37.872680</td>\n",
              "      <td>13.233959</td>\n",
              "      <td>14.724258</td>\n",
              "      <td>15.866933</td>\n",
              "      <td>4.783436</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249846</th>\n",
              "      <td>27.158190</td>\n",
              "      <td>97.484879</td>\n",
              "      <td>32.049339</td>\n",
              "      <td>23.0000</td>\n",
              "      <td>1008.391350</td>\n",
              "      <td>liulihe_aq</td>\n",
              "      <td>15.2500</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>2.5825</td>\n",
              "      <td>18.0800</td>\n",
              "      <td>...</td>\n",
              "      <td>140.169661</td>\n",
              "      <td>47.638598</td>\n",
              "      <td>43.939658</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>17.340784</td>\n",
              "      <td>22.168661</td>\n",
              "      <td>0.600465</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249847</th>\n",
              "      <td>70.995866</td>\n",
              "      <td>68.853429</td>\n",
              "      <td>27.213857</td>\n",
              "      <td>39.2500</td>\n",
              "      <td>1001.082075</td>\n",
              "      <td>pinggu_aq</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>352.9125</td>\n",
              "      <td>14.4750</td>\n",
              "      <td>...</td>\n",
              "      <td>145.907711</td>\n",
              "      <td>27.419003</td>\n",
              "      <td>26.769513</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>67.145608</td>\n",
              "      <td>70.233780</td>\n",
              "      <td>-0.105809</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249848</th>\n",
              "      <td>71.505360</td>\n",
              "      <td>67.946899</td>\n",
              "      <td>26.257464</td>\n",
              "      <td>39.2500</td>\n",
              "      <td>1002.132475</td>\n",
              "      <td>donggaocun_aq</td>\n",
              "      <td>12.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>352.7600</td>\n",
              "      <td>14.5525</td>\n",
              "      <td>...</td>\n",
              "      <td>154.596273</td>\n",
              "      <td>29.536398</td>\n",
              "      <td>28.914087</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>70.004868</td>\n",
              "      <td>71.818000</td>\n",
              "      <td>-0.611975</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249849</th>\n",
              "      <td>33.153397</td>\n",
              "      <td>71.929181</td>\n",
              "      <td>21.288061</td>\n",
              "      <td>27.2500</td>\n",
              "      <td>940.820350</td>\n",
              "      <td>yanqin_aq</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>332.7975</td>\n",
              "      <td>15.6325</td>\n",
              "      <td>...</td>\n",
              "      <td>116.388255</td>\n",
              "      <td>40.278433</td>\n",
              "      <td>32.008899</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>13.997432</td>\n",
              "      <td>30.542885</td>\n",
              "      <td>-0.329631</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249850</th>\n",
              "      <td>35.385144</td>\n",
              "      <td>75.295722</td>\n",
              "      <td>27.269991</td>\n",
              "      <td>20.0000</td>\n",
              "      <td>1012.555875</td>\n",
              "      <td>guanyuan_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>355.0900</td>\n",
              "      <td>15.9700</td>\n",
              "      <td>...</td>\n",
              "      <td>152.107809</td>\n",
              "      <td>46.058312</td>\n",
              "      <td>44.160367</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>27.467878</td>\n",
              "      <td>27.467878</td>\n",
              "      <td>3.131944</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249851</th>\n",
              "      <td>29.873742</td>\n",
              "      <td>75.206923</td>\n",
              "      <td>26.686531</td>\n",
              "      <td>21.5000</td>\n",
              "      <td>1014.388675</td>\n",
              "      <td>tiantan_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>357.6075</td>\n",
              "      <td>17.0325</td>\n",
              "      <td>...</td>\n",
              "      <td>134.147296</td>\n",
              "      <td>42.728196</td>\n",
              "      <td>41.381298</td>\n",
              "      <td>16.758649</td>\n",
              "      <td>16.758649</td>\n",
              "      <td>16.926415</td>\n",
              "      <td>4.142925</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249852</th>\n",
              "      <td>26.312138</td>\n",
              "      <td>75.975121</td>\n",
              "      <td>26.358577</td>\n",
              "      <td>21.2500</td>\n",
              "      <td>1014.948875</td>\n",
              "      <td>dongsihuan_aq</td>\n",
              "      <td>15.5000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>356.8850</td>\n",
              "      <td>15.7800</td>\n",
              "      <td>...</td>\n",
              "      <td>120.723636</td>\n",
              "      <td>39.639426</td>\n",
              "      <td>39.990105</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7.898687</td>\n",
              "      <td>8.809635</td>\n",
              "      <td>4.322466</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249853</th>\n",
              "      <td>36.740592</td>\n",
              "      <td>75.184830</td>\n",
              "      <td>26.688334</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>1013.901050</td>\n",
              "      <td>wanshouxigong_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>357.8000</td>\n",
              "      <td>17.4125</td>\n",
              "      <td>...</td>\n",
              "      <td>143.749507</td>\n",
              "      <td>40.126738</td>\n",
              "      <td>40.346965</td>\n",
              "      <td>29.525124</td>\n",
              "      <td>29.525124</td>\n",
              "      <td>29.525124</td>\n",
              "      <td>2.489174</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249854</th>\n",
              "      <td>71.860973</td>\n",
              "      <td>70.407269</td>\n",
              "      <td>21.415570</td>\n",
              "      <td>23.7500</td>\n",
              "      <td>981.156025</td>\n",
              "      <td>dingling_aq</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>340.1775</td>\n",
              "      <td>15.2775</td>\n",
              "      <td>...</td>\n",
              "      <td>159.347619</td>\n",
              "      <td>33.950306</td>\n",
              "      <td>33.676655</td>\n",
              "      <td>50.993614</td>\n",
              "      <td>55.283383</td>\n",
              "      <td>70.351978</td>\n",
              "      <td>0.398348</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249855</th>\n",
              "      <td>49.807490</td>\n",
              "      <td>74.991062</td>\n",
              "      <td>26.999236</td>\n",
              "      <td>19.7500</td>\n",
              "      <td>1001.783300</td>\n",
              "      <td>yungang_aq</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>353.9075</td>\n",
              "      <td>17.4675</td>\n",
              "      <td>...</td>\n",
              "      <td>151.157376</td>\n",
              "      <td>40.578193</td>\n",
              "      <td>38.397492</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>36.451192</td>\n",
              "      <td>44.541235</td>\n",
              "      <td>1.584290</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249856</th>\n",
              "      <td>32.262099</td>\n",
              "      <td>72.649996</td>\n",
              "      <td>25.911421</td>\n",
              "      <td>20.2500</td>\n",
              "      <td>1013.490875</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>15.2500</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>355.4450</td>\n",
              "      <td>15.5200</td>\n",
              "      <td>...</td>\n",
              "      <td>147.442821</td>\n",
              "      <td>43.933463</td>\n",
              "      <td>43.934745</td>\n",
              "      <td>23.276741</td>\n",
              "      <td>23.276741</td>\n",
              "      <td>23.276741</td>\n",
              "      <td>3.076776</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249857</th>\n",
              "      <td>35.636193</td>\n",
              "      <td>71.680864</td>\n",
              "      <td>19.871305</td>\n",
              "      <td>25.7500</td>\n",
              "      <td>944.891450</td>\n",
              "      <td>badaling_aq</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>330.0700</td>\n",
              "      <td>15.9275</td>\n",
              "      <td>...</td>\n",
              "      <td>120.754697</td>\n",
              "      <td>42.610463</td>\n",
              "      <td>33.383519</td>\n",
              "      <td>16.912394</td>\n",
              "      <td>17.317705</td>\n",
              "      <td>33.289467</td>\n",
              "      <td>0.225696</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249858</th>\n",
              "      <td>29.658486</td>\n",
              "      <td>81.046499</td>\n",
              "      <td>32.250555</td>\n",
              "      <td>24.5000</td>\n",
              "      <td>1015.912325</td>\n",
              "      <td>yufa_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>2.6375</td>\n",
              "      <td>19.5775</td>\n",
              "      <td>...</td>\n",
              "      <td>136.241839</td>\n",
              "      <td>43.295631</td>\n",
              "      <td>39.660868</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>24.625141</td>\n",
              "      <td>24.625141</td>\n",
              "      <td>0.609899</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249859</th>\n",
              "      <td>56.008546</td>\n",
              "      <td>71.924615</td>\n",
              "      <td>22.709951</td>\n",
              "      <td>20.5000</td>\n",
              "      <td>994.760600</td>\n",
              "      <td>beibuxinqu_aq</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>344.0150</td>\n",
              "      <td>15.8250</td>\n",
              "      <td>...</td>\n",
              "      <td>161.381667</td>\n",
              "      <td>46.267793</td>\n",
              "      <td>42.296767</td>\n",
              "      <td>27.460224</td>\n",
              "      <td>30.513818</td>\n",
              "      <td>53.553590</td>\n",
              "      <td>0.413166</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249860</th>\n",
              "      <td>27.854559</td>\n",
              "      <td>78.228507</td>\n",
              "      <td>29.189819</td>\n",
              "      <td>28.2500</td>\n",
              "      <td>1017.105350</td>\n",
              "      <td>yongledian_aq</td>\n",
              "      <td>14.7500</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>357.0050</td>\n",
              "      <td>20.1800</td>\n",
              "      <td>...</td>\n",
              "      <td>133.564376</td>\n",
              "      <td>44.906201</td>\n",
              "      <td>42.120370</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>12.877153</td>\n",
              "      <td>12.877153</td>\n",
              "      <td>4.271468</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249861</th>\n",
              "      <td>27.641816</td>\n",
              "      <td>75.179503</td>\n",
              "      <td>26.685644</td>\n",
              "      <td>21.5000</td>\n",
              "      <td>1014.388675</td>\n",
              "      <td>yongdingmennei_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>357.6075</td>\n",
              "      <td>17.0325</td>\n",
              "      <td>...</td>\n",
              "      <td>143.654856</td>\n",
              "      <td>47.123974</td>\n",
              "      <td>44.934560</td>\n",
              "      <td>17.132813</td>\n",
              "      <td>17.132813</td>\n",
              "      <td>17.132813</td>\n",
              "      <td>3.371352</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249862</th>\n",
              "      <td>28.070573</td>\n",
              "      <td>87.776219</td>\n",
              "      <td>30.642411</td>\n",
              "      <td>22.7500</td>\n",
              "      <td>1015.021325</td>\n",
              "      <td>daxing_aq</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>89.8575</td>\n",
              "      <td>19.0800</td>\n",
              "      <td>...</td>\n",
              "      <td>158.174933</td>\n",
              "      <td>50.135816</td>\n",
              "      <td>49.602632</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>18.411707</td>\n",
              "      <td>18.411707</td>\n",
              "      <td>2.728445</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249863</th>\n",
              "      <td>32.316764</td>\n",
              "      <td>79.519368</td>\n",
              "      <td>26.899631</td>\n",
              "      <td>21.2500</td>\n",
              "      <td>1014.948875</td>\n",
              "      <td>dongsi_aq</td>\n",
              "      <td>15.5000</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>356.8850</td>\n",
              "      <td>15.7800</td>\n",
              "      <td>...</td>\n",
              "      <td>148.896396</td>\n",
              "      <td>43.710618</td>\n",
              "      <td>43.997341</td>\n",
              "      <td>24.904695</td>\n",
              "      <td>24.904695</td>\n",
              "      <td>24.904695</td>\n",
              "      <td>1.766225</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249864</th>\n",
              "      <td>59.955018</td>\n",
              "      <td>70.890384</td>\n",
              "      <td>26.766142</td>\n",
              "      <td>19.2500</td>\n",
              "      <td>1004.976725</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>14.2500</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>349.9075</td>\n",
              "      <td>15.8975</td>\n",
              "      <td>...</td>\n",
              "      <td>162.401507</td>\n",
              "      <td>37.512920</td>\n",
              "      <td>37.667889</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>52.389139</td>\n",
              "      <td>58.834869</td>\n",
              "      <td>-0.125419</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>249865 rows × 68 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                O3        PM10       PM2.5  humidity     pressure  \\\n",
              "0       214.000000   50.898794   63.000000   56.7600   990.815000   \n",
              "1       192.000000  143.000000   72.000000   60.3950   993.827500   \n",
              "2       199.000000  113.000000   59.000000   55.1375   992.715000   \n",
              "3        72.000000   83.000000   56.000000   54.3125   993.305000   \n",
              "4        96.000000  263.092184  153.000000   54.6575   980.360000   \n",
              "5       246.000000  120.000000   81.000000   64.0675   967.460000   \n",
              "6       189.000000   78.528566   71.000000   56.0750   991.002500   \n",
              "7       170.000000  177.000000   99.000000   57.8450   992.807500   \n",
              "8        63.006685  204.096019  128.444027   54.9025   987.680000   \n",
              "9       176.000000  158.000000   93.000000   58.8850   979.912500   \n",
              "10      214.000000  138.000000   80.000000   67.5400   968.240000   \n",
              "11      200.000000  113.000000   64.000000   56.1400   992.322500   \n",
              "12      105.000000   94.000000   79.000000   55.3250   991.397500   \n",
              "13      190.000000  101.000000   64.000000   56.1400   992.322500   \n",
              "14      167.000000  197.000000   81.000000   58.4275   985.910000   \n",
              "15      208.000000  114.000000   68.000000   56.9000   989.725000   \n",
              "16      129.000000   91.000000   31.000000   74.6850   920.607500   \n",
              "17      211.000000  100.000000   58.000000   61.4450   979.470000   \n",
              "18      199.000000  119.094552  108.457247   56.1000   987.815000   \n",
              "19      176.000000  111.000000   67.000000   55.3250   991.397500   \n",
              "20      144.000000  199.000000  156.000000   53.1700   979.132500   \n",
              "21      237.000000  117.000000   60.000000   53.2750   976.292500   \n",
              "22       94.000000   94.000000   34.000000   75.1675   955.592500   \n",
              "23      164.000000   67.000000   53.000000   67.5100   994.860000   \n",
              "24       78.681545   97.663440   81.140083   74.5100   916.055000   \n",
              "25       44.653570  206.489610  136.062072   56.1400   992.322500   \n",
              "26       93.731998  127.704761   78.094151   65.0050   967.717500   \n",
              "27      161.000000  106.000000   55.000000   55.7975   991.650000   \n",
              "28      172.000000  198.000000   78.000000   59.8200   978.952500   \n",
              "29      153.000000   96.000000   69.000000   55.7975   991.650000   \n",
              "...            ...         ...         ...       ...          ...   \n",
              "249835   73.964334   68.124561   26.489201   30.5000   993.926875   \n",
              "249836   27.755870   80.168670   29.226463   24.5000  1015.757325   \n",
              "249837   27.962923   76.794328   26.555019   20.0000  1012.555875   \n",
              "249838   45.983174   66.809803   25.826839   28.0000  1014.183325   \n",
              "249839   37.988906   82.476818   26.716769   19.5000  1003.072500   \n",
              "249840   35.350021   75.113974   26.793407   19.5000  1010.370525   \n",
              "249841   32.500400   84.942863   29.443991   19.7500  1007.421650   \n",
              "249842   69.429435   69.448908   21.406479   22.5000   992.053100   \n",
              "249843   60.459663   76.978394   24.468140   20.7500   989.598250   \n",
              "249844   77.916926   69.898835   27.476521   35.2500   985.152450   \n",
              "249845   29.850502   72.859283   26.899990   21.0000  1013.901050   \n",
              "249846   27.158190   97.484879   32.049339   23.0000  1008.391350   \n",
              "249847   70.995866   68.853429   27.213857   39.2500  1001.082075   \n",
              "249848   71.505360   67.946899   26.257464   39.2500  1002.132475   \n",
              "249849   33.153397   71.929181   21.288061   27.2500   940.820350   \n",
              "249850   35.385144   75.295722   27.269991   20.0000  1012.555875   \n",
              "249851   29.873742   75.206923   26.686531   21.5000  1014.388675   \n",
              "249852   26.312138   75.975121   26.358577   21.2500  1014.948875   \n",
              "249853   36.740592   75.184830   26.688334   21.0000  1013.901050   \n",
              "249854   71.860973   70.407269   21.415570   23.7500   981.156025   \n",
              "249855   49.807490   74.991062   26.999236   19.7500  1001.783300   \n",
              "249856   32.262099   72.649996   25.911421   20.2500  1013.490875   \n",
              "249857   35.636193   71.680864   19.871305   25.7500   944.891450   \n",
              "249858   29.658486   81.046499   32.250555   24.5000  1015.912325   \n",
              "249859   56.008546   71.924615   22.709951   20.5000   994.760600   \n",
              "249860   27.854559   78.228507   29.189819   28.2500  1017.105350   \n",
              "249861   27.641816   75.179503   26.685644   21.5000  1014.388675   \n",
              "249862   28.070573   87.776219   30.642411   22.7500  1015.021325   \n",
              "249863   32.316764   79.519368   26.899631   21.2500  1014.948875   \n",
              "249864   59.955018   70.890384   26.766142   19.2500  1004.976725   \n",
              "\n",
              "                stationId  temperature             utc_time  wind_direction  \\\n",
              "0         aotizhongxin_aq      27.2225  2017-07-09 13:00:00        190.8400   \n",
              "1             tongzhou_aq      27.5975  2017-07-09 13:00:00        161.5575   \n",
              "2               daxing_aq      28.0275  2017-07-09 13:00:00        183.7500   \n",
              "3                 yufa_aq      28.1550  2017-07-09 13:00:00        182.3775   \n",
              "4           donggaocun_aq      28.1275  2017-07-09 13:00:00        164.1625   \n",
              "5            mentougou_aq      26.1325  2017-07-09 13:00:00        233.8900   \n",
              "6              qianmen_aq      27.4875  2017-07-09 13:00:00        193.4550   \n",
              "7               shunyi_aq      26.1650  2017-07-09 13:00:00        160.5775   \n",
              "8              liulihe_aq      27.7000  2017-07-09 13:00:00        272.9325   \n",
              "9              yungang_aq      27.0150  2017-07-09 13:00:00        231.5950   \n",
              "10          beibuxinqu_aq      25.2100  2017-07-09 13:00:00        217.2900   \n",
              "11              dongsi_aq      27.3525  2017-07-09 13:00:00        181.9300   \n",
              "12          nansanhuan_aq      27.6725  2017-07-09 13:00:00        197.5525   \n",
              "13        nongzhanguan_aq      27.3525  2017-07-09 13:00:00        181.9300   \n",
              "14              wanliu_aq      27.0150  2017-07-09 13:00:00        207.7575   \n",
              "15            guanyuan_aq      27.2775  2017-07-09 13:00:00        199.0750   \n",
              "16            badaling_aq      20.8875  2017-07-09 13:00:00        199.4600   \n",
              "17           zhiwuyuan_aq      26.5025  2017-07-09 13:00:00        218.3400   \n",
              "18      fengtaihuayuan_aq      27.4825  2017-07-09 13:00:00        216.1475   \n",
              "19       wanshouxigong_aq      27.6725  2017-07-09 13:00:00        197.5525   \n",
              "20              pinggu_aq      28.0775  2017-07-09 13:00:00        164.3350   \n",
              "21               miyun_aq      25.8925  2017-07-09 13:00:00        174.7275   \n",
              "22            dingling_aq      22.7750  2017-07-09 13:00:00        183.6925   \n",
              "23          yongledian_aq      27.8150  2017-07-09 13:00:00        155.9875   \n",
              "24              yanqin_aq      19.9725  2017-07-09 13:00:00        183.2250   \n",
              "25          dongsihuan_aq      27.3525  2017-07-09 13:00:00        181.9300   \n",
              "26             huairou_aq      23.6650  2017-07-09 13:00:00        171.7500   \n",
              "27             tiantan_aq      27.5925  2017-07-09 13:00:00        189.2475   \n",
              "28             gucheng_aq      26.8500  2017-07-09 13:00:00        227.2475   \n",
              "29      yongdingmennei_aq      27.5925  2017-07-09 13:00:00        189.2475   \n",
              "...                   ...          ...                  ...             ...   \n",
              "249835         huairou_aq      12.0000  2018-05-02 23:00:00        355.8025   \n",
              "249836        yizhuang_aq      14.5000  2018-05-02 23:00:00        269.0250   \n",
              "249837     xizhimenbei_aq      15.0000  2018-05-02 23:00:00        355.0900   \n",
              "249838          shunyi_aq      13.5000  2018-05-02 23:00:00        356.6525   \n",
              "249839         gucheng_aq      14.2500  2018-05-02 23:00:00        352.4325   \n",
              "249840          wanliu_aq      14.5000  2018-05-02 23:00:00        353.0750   \n",
              "249841        fangshan_aq      13.0000  2018-05-02 23:00:00        267.8500   \n",
              "249842       pingchang_aq      14.0000  2018-05-02 23:00:00        341.6050   \n",
              "249843       mentougou_aq      15.0000  2018-05-02 23:00:00        349.1025   \n",
              "249844     miyunshuiku_aq      11.0000  2018-05-02 23:00:00        357.6850   \n",
              "249845      nansanhuan_aq      15.0000  2018-05-02 23:00:00        357.8000   \n",
              "249846         liulihe_aq      15.2500  2018-05-02 23:00:00          2.5825   \n",
              "249847          pinggu_aq      11.0000  2018-05-02 23:00:00        352.9125   \n",
              "249848      donggaocun_aq      12.0000  2018-05-02 23:00:00        352.7600   \n",
              "249849          yanqin_aq       9.0000  2018-05-02 23:00:00        332.7975   \n",
              "249850        guanyuan_aq      15.0000  2018-05-02 23:00:00        355.0900   \n",
              "249851         tiantan_aq      15.0000  2018-05-02 23:00:00        357.6075   \n",
              "249852      dongsihuan_aq      15.5000  2018-05-02 23:00:00        356.8850   \n",
              "249853   wanshouxigong_aq      15.0000  2018-05-02 23:00:00        357.8000   \n",
              "249854        dingling_aq      14.0000  2018-05-02 23:00:00        340.1775   \n",
              "249855         yungang_aq      14.0000  2018-05-02 23:00:00        353.9075   \n",
              "249856    aotizhongxin_aq      15.2500  2018-05-02 23:00:00        355.4450   \n",
              "249857        badaling_aq       9.0000  2018-05-02 23:00:00        330.0700   \n",
              "249858            yufa_aq      15.0000  2018-05-02 23:00:00          2.6375   \n",
              "249859      beibuxinqu_aq      14.0000  2018-05-02 23:00:00        344.0150   \n",
              "249860      yongledian_aq      14.7500  2018-05-02 23:00:00        357.0050   \n",
              "249861  yongdingmennei_aq      15.0000  2018-05-02 23:00:00        357.6075   \n",
              "249862          daxing_aq      15.0000  2018-05-02 23:00:00         89.8575   \n",
              "249863          dongsi_aq      15.5000  2018-05-02 23:00:00        356.8850   \n",
              "249864       zhiwuyuan_aq      14.2500  2018-05-02 23:00:00        349.9075   \n",
              "\n",
              "        wind_speed  ...    O3_24_max  O3_48_std  O3_24_std  O3_48_min  \\\n",
              "0           6.5675  ...          NaN        NaN        NaN        NaN   \n",
              "1          11.1550  ...          NaN        NaN        NaN        NaN   \n",
              "2           8.1825  ...          NaN        NaN        NaN        NaN   \n",
              "3           5.5850  ...          NaN        NaN        NaN        NaN   \n",
              "4          10.1825  ...          NaN        NaN        NaN        NaN   \n",
              "5           7.1100  ...          NaN        NaN        NaN        NaN   \n",
              "6           6.8850  ...          NaN        NaN        NaN        NaN   \n",
              "7           6.3200  ...          NaN        NaN        NaN        NaN   \n",
              "8           5.0450  ...          NaN        NaN        NaN        NaN   \n",
              "9           6.8075  ...          NaN        NaN        NaN        NaN   \n",
              "10          5.1925  ...          NaN        NaN        NaN        NaN   \n",
              "11          7.1900  ...          NaN        NaN        NaN        NaN   \n",
              "12          7.0475  ...          NaN        NaN        NaN        NaN   \n",
              "13          7.1900  ...          NaN        NaN        NaN        NaN   \n",
              "14          6.2100  ...          NaN        NaN        NaN        NaN   \n",
              "15          6.3825  ...          NaN        NaN        NaN        NaN   \n",
              "16          4.4625  ...          NaN        NaN        NaN        NaN   \n",
              "17          5.9225  ...          NaN        NaN        NaN        NaN   \n",
              "18          6.6125  ...          NaN        NaN        NaN        NaN   \n",
              "19          7.0475  ...          NaN        NaN        NaN        NaN   \n",
              "20          9.5550  ...          NaN        NaN        NaN        NaN   \n",
              "21          5.9350  ...          NaN        NaN        NaN        NaN   \n",
              "22          3.1050  ...          NaN        NaN        NaN        NaN   \n",
              "23         14.6475  ...          NaN        NaN        NaN        NaN   \n",
              "24          3.1550  ...          NaN        NaN        NaN        NaN   \n",
              "25          7.1900  ...          NaN        NaN        NaN        NaN   \n",
              "26          4.7525  ...          NaN        NaN        NaN        NaN   \n",
              "27          7.4225  ...          NaN        NaN        NaN        NaN   \n",
              "28          6.6550  ...          NaN        NaN        NaN        NaN   \n",
              "29          7.4225  ...          NaN        NaN        NaN        NaN   \n",
              "...            ...  ...          ...        ...        ...        ...   \n",
              "249835     14.1750  ...   159.461348  33.015790  31.638311  45.000000   \n",
              "249836     18.7425  ...   137.722657  43.681527  41.142985   4.000000   \n",
              "249837     15.9700  ...   120.257734  39.749442  38.788318  13.459410   \n",
              "249838     15.2950  ...   146.547172  40.298514  38.172653  33.037649   \n",
              "249839     16.9400  ...   163.144415  49.643952  48.083932  18.467404   \n",
              "249840     15.7300  ...   148.084430  46.881445  44.043155  11.000000   \n",
              "249841     17.8875  ...   147.218636  47.427405  44.333966  21.826116   \n",
              "249842     15.1600  ...   162.441755  38.855715  36.581387  44.344299   \n",
              "249843     17.7300  ...   159.262640  40.339864  38.682702  40.009032   \n",
              "249844     12.8600  ...   165.572024  31.314656  31.423813  48.000000   \n",
              "249845     17.4125  ...   121.450644  38.370614  37.872680  13.233959   \n",
              "249846     18.0800  ...   140.169661  47.638598  43.939658   7.000000   \n",
              "249847     14.4750  ...   145.907711  27.419003  26.769513  53.000000   \n",
              "249848     14.5525  ...   154.596273  29.536398  28.914087  52.000000   \n",
              "249849     15.6325  ...   116.388255  40.278433  32.008899   2.000000   \n",
              "249850     15.9700  ...   152.107809  46.058312  44.160367  16.000000   \n",
              "249851     17.0325  ...   134.147296  42.728196  41.381298  16.758649   \n",
              "249852     15.7800  ...   120.723636  39.639426  39.990105   2.000000   \n",
              "249853     17.4125  ...   143.749507  40.126738  40.346965  29.525124   \n",
              "249854     15.2775  ...   159.347619  33.950306  33.676655  50.993614   \n",
              "249855     17.4675  ...   151.157376  40.578193  38.397492  33.000000   \n",
              "249856     15.5200  ...   147.442821  43.933463  43.934745  23.276741   \n",
              "249857     15.9275  ...   120.754697  42.610463  33.383519  16.912394   \n",
              "249858     19.5775  ...   136.241839  43.295631  39.660868  11.000000   \n",
              "249859     15.8250  ...   161.381667  46.267793  42.296767  27.460224   \n",
              "249860     20.1800  ...   133.564376  44.906201  42.120370   4.000000   \n",
              "249861     17.0325  ...   143.654856  47.123974  44.934560  17.132813   \n",
              "249862     19.0800  ...   158.174933  50.135816  49.602632  15.000000   \n",
              "249863     15.7800  ...   148.896396  43.710618  43.997341  24.904695   \n",
              "249864     15.8975  ...   162.401507  37.512920  37.667889  50.440662   \n",
              "\n",
              "        O3_24_min       O3_2  O3_1_diff  O3_3  O3_12  O3_8  \n",
              "0             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "1             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "2             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "3             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "4             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "5             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "6             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "7             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "8             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "9             NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "10            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "11            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "12            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "13            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "14            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "15            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "16            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "17            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "18            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "19            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "20            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "21            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "22            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "23            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "24            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "25            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "26            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "27            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "28            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "29            NaN        NaN        NaN   NaN    NaN   NaN  \n",
              "...           ...        ...        ...   ...    ...   ...  \n",
              "249835  68.443027  73.118068   0.009068   NaN    NaN   NaN  \n",
              "249836  18.523489  18.523489   2.739143   NaN    NaN   NaN  \n",
              "249837  13.459410  14.064011   4.553288   NaN    NaN   NaN  \n",
              "249838  40.777048  42.089551   0.529628   NaN    NaN   NaN  \n",
              "249839  26.129587  32.750925   2.034849   NaN    NaN   NaN  \n",
              "249840  26.239384  27.342082   2.762898   NaN    NaN   NaN  \n",
              "249841  25.182381  26.502762   1.733353   NaN    NaN   NaN  \n",
              "249842  44.580891  68.118883   0.440978   NaN    NaN   NaN  \n",
              "249843  41.297589  57.043451   1.341238   NaN    NaN   NaN  \n",
              "249844  75.332854  77.765249  -0.094181   NaN    NaN   NaN  \n",
              "249845  14.724258  15.866933   4.783436   NaN    NaN   NaN  \n",
              "249846  17.340784  22.168661   0.600465   NaN    NaN   NaN  \n",
              "249847  67.145608  70.233780  -0.105809   NaN    NaN   NaN  \n",
              "249848  70.004868  71.818000  -0.611975   NaN    NaN   NaN  \n",
              "249849  13.997432  30.542885  -0.329631   NaN    NaN   NaN  \n",
              "249850  27.467878  27.467878   3.131944   NaN    NaN   NaN  \n",
              "249851  16.758649  16.926415   4.142925   NaN    NaN   NaN  \n",
              "249852   7.898687   8.809635   4.322466   NaN    NaN   NaN  \n",
              "249853  29.525124  29.525124   2.489174   NaN    NaN   NaN  \n",
              "249854  55.283383  70.351978   0.398348   NaN    NaN   NaN  \n",
              "249855  36.451192  44.541235   1.584290   NaN    NaN   NaN  \n",
              "249856  23.276741  23.276741   3.076776   NaN    NaN   NaN  \n",
              "249857  17.317705  33.289467   0.225696   NaN    NaN   NaN  \n",
              "249858  24.625141  24.625141   0.609899   NaN    NaN   NaN  \n",
              "249859  30.513818  53.553590   0.413166   NaN    NaN   NaN  \n",
              "249860  12.877153  12.877153   4.271468   NaN    NaN   NaN  \n",
              "249861  17.132813  17.132813   3.371352   NaN    NaN   NaN  \n",
              "249862  18.411707  18.411707   2.728445   NaN    NaN   NaN  \n",
              "249863  24.904695  24.904695   1.766225   NaN    NaN   NaN  \n",
              "249864  52.389139  58.834869  -0.125419   NaN    NaN   NaN  \n",
              "\n",
              "[249865 rows x 68 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        }
      ]
    },
    {
      "metadata": {
        "id": "tQzS8MBHE0k8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "final_result[248185:].to_csv('drive/final_result_1.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2RTc0Qn4F5KE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "result = final_result[248185:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2Xp_-SUcGh8M",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "final_result.to_csv('drive/final_result_all.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nCjIcFExGpaa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "360c54bd-a463-4592-e0fd-50a3a70ca5c4"
      },
      "cell_type": "code",
      "source": [
        "# result_PM25 = pd.Series(result['PM2.5'],name='PM25_c')\n",
        "# res_test = prediction_data.copy().reset_index()\n",
        "# result = pd.concat([res_test,result],axis=1)\n",
        "\n",
        "fix_station = result[result['stationId'] == 'aotizhongxin_aq']\n",
        "fix_station['local_time'] = pd.to_datetime(fix_station['local_time'])\n",
        "fix_station = fix_station.set_index(['local_time'])\n",
        "plt.title('aotizhongxin_O3',fontsize = 15)\n",
        "plt.xticks(rotation = 45)\n",
        "plt.xlabel('time',fontsize = 10)\n",
        "plt.ylabel('concentration',fontsize = 10)\n",
        "# plt.plot(fix_station['O3'], 'r-')\n",
        "plt.plot(fix_station['O3'], 'b-')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-ead5cb63908e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfix_station\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'stationId'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'aotizhongxin_aq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mfix_station\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_time'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfix_station\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfix_station\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix_station\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'aotizhongxin_O3'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'result' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "oC0MJUyqHVQO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "result_format = pd.read_csv('drive/final_result_1.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xtKcL_MTwRE0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3026
        },
        "outputId": "b0e52d99-d0e8-4cc8-eacc-a6c4f6e35ff0"
      },
      "cell_type": "code",
      "source": [
        "result_format = pd.read_csv('drive/final_result_1.csv')\n",
        "result_format.rename(columns={'stationId': 'test_id'}, inplace=True)\n",
        "\n",
        "\n",
        "result_format = result_format.sort_values(by=['test_id', 'local_time']).reset_index()\n",
        "del result_format['index']\n",
        "# result_format = result_format[['test_id', 'PM2.5', 'PM10', 'O3']]\n",
        "\n",
        "frame = result_format[['test_id', 'hour']].apply(\n",
        "    lambda x: x.test_id +'#'+str(x.hour), axis=1)\n",
        "t = pd.concat([result_format, frame], axis=1)\n",
        "\n",
        "t"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>O3</th>\n",
              "      <th>PM10</th>\n",
              "      <th>PM2.5</th>\n",
              "      <th>humidity</th>\n",
              "      <th>pressure</th>\n",
              "      <th>test_id</th>\n",
              "      <th>temperature</th>\n",
              "      <th>utc_time</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>...</th>\n",
              "      <th>O3_48_std</th>\n",
              "      <th>O3_24_std</th>\n",
              "      <th>O3_48_min</th>\n",
              "      <th>O3_24_min</th>\n",
              "      <th>O3_2</th>\n",
              "      <th>O3_1_diff</th>\n",
              "      <th>O3_3</th>\n",
              "      <th>O3_12</th>\n",
              "      <th>O3_8</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>248185</td>\n",
              "      <td>53.750796</td>\n",
              "      <td>230.915390</td>\n",
              "      <td>72.478330</td>\n",
              "      <td>36.25</td>\n",
              "      <td>1008.698525</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>19.250</td>\n",
              "      <td>2018-05-01 00:00:00</td>\n",
              "      <td>16.7850</td>\n",
              "      <td>...</td>\n",
              "      <td>53.345352</td>\n",
              "      <td>45.789070</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>aotizhongxin_aq#0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>248248</td>\n",
              "      <td>72.775901</td>\n",
              "      <td>200.288391</td>\n",
              "      <td>65.580854</td>\n",
              "      <td>31.25</td>\n",
              "      <td>1008.631125</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>20.750</td>\n",
              "      <td>2018-05-01 01:00:00</td>\n",
              "      <td>24.7325</td>\n",
              "      <td>...</td>\n",
              "      <td>52.702992</td>\n",
              "      <td>45.806419</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>22.750796</td>\n",
              "      <td>2.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>aotizhongxin_aq#1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>248266</td>\n",
              "      <td>88.088125</td>\n",
              "      <td>180.452955</td>\n",
              "      <td>59.065879</td>\n",
              "      <td>26.75</td>\n",
              "      <td>1008.819050</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>21.250</td>\n",
              "      <td>2018-05-01 02:00:00</td>\n",
              "      <td>30.6150</td>\n",
              "      <td>...</td>\n",
              "      <td>51.865409</td>\n",
              "      <td>45.756096</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>53.750796</td>\n",
              "      <td>19.025105</td>\n",
              "      <td>31.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>aotizhongxin_aq#2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>248307</td>\n",
              "      <td>103.816672</td>\n",
              "      <td>164.304360</td>\n",
              "      <td>50.805108</td>\n",
              "      <td>24.00</td>\n",
              "      <td>1009.149025</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>21.000</td>\n",
              "      <td>2018-05-01 03:00:00</td>\n",
              "      <td>33.2825</td>\n",
              "      <td>...</td>\n",
              "      <td>51.395290</td>\n",
              "      <td>45.858419</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>72.775901</td>\n",
              "      <td>15.312224</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>aotizhongxin_aq#3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>248338</td>\n",
              "      <td>117.051853</td>\n",
              "      <td>150.176732</td>\n",
              "      <td>44.683942</td>\n",
              "      <td>20.25</td>\n",
              "      <td>1008.308600</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>22.500</td>\n",
              "      <td>2018-05-01 04:00:00</td>\n",
              "      <td>38.3700</td>\n",
              "      <td>...</td>\n",
              "      <td>51.004777</td>\n",
              "      <td>46.317854</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>88.088125</td>\n",
              "      <td>15.728547</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>aotizhongxin_aq#4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>248365</td>\n",
              "      <td>128.046533</td>\n",
              "      <td>139.920208</td>\n",
              "      <td>40.174454</td>\n",
              "      <td>20.75</td>\n",
              "      <td>1007.379200</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>22.750</td>\n",
              "      <td>2018-05-01 05:00:00</td>\n",
              "      <td>40.9300</td>\n",
              "      <td>...</td>\n",
              "      <td>51.044725</td>\n",
              "      <td>46.948503</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>103.816672</td>\n",
              "      <td>13.235181</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>aotizhongxin_aq#5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>248427</td>\n",
              "      <td>134.543473</td>\n",
              "      <td>129.652972</td>\n",
              "      <td>36.771153</td>\n",
              "      <td>21.50</td>\n",
              "      <td>1007.492475</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>22.500</td>\n",
              "      <td>2018-05-01 06:00:00</td>\n",
              "      <td>40.8850</td>\n",
              "      <td>...</td>\n",
              "      <td>51.305685</td>\n",
              "      <td>47.756834</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>117.051853</td>\n",
              "      <td>10.994680</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>aotizhongxin_aq#6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>248458</td>\n",
              "      <td>141.307920</td>\n",
              "      <td>122.415838</td>\n",
              "      <td>35.325219</td>\n",
              "      <td>22.25</td>\n",
              "      <td>1007.728950</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>21.250</td>\n",
              "      <td>2018-05-01 07:00:00</td>\n",
              "      <td>38.6650</td>\n",
              "      <td>...</td>\n",
              "      <td>51.073920</td>\n",
              "      <td>48.340912</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>128.046533</td>\n",
              "      <td>6.496939</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>aotizhongxin_aq#7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>248466</td>\n",
              "      <td>144.454763</td>\n",
              "      <td>113.789682</td>\n",
              "      <td>35.940571</td>\n",
              "      <td>24.00</td>\n",
              "      <td>1007.536325</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>20.750</td>\n",
              "      <td>2018-05-01 08:00:00</td>\n",
              "      <td>35.9075</td>\n",
              "      <td>...</td>\n",
              "      <td>50.667983</td>\n",
              "      <td>49.019115</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>134.543473</td>\n",
              "      <td>6.764447</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>248501</td>\n",
              "      <td>141.024331</td>\n",
              "      <td>109.738826</td>\n",
              "      <td>36.272321</td>\n",
              "      <td>25.50</td>\n",
              "      <td>1007.974000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>18.750</td>\n",
              "      <td>2018-05-01 09:00:00</td>\n",
              "      <td>39.7925</td>\n",
              "      <td>...</td>\n",
              "      <td>49.558751</td>\n",
              "      <td>49.581248</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>141.307920</td>\n",
              "      <td>3.146843</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>248565</td>\n",
              "      <td>129.921983</td>\n",
              "      <td>109.565664</td>\n",
              "      <td>39.075513</td>\n",
              "      <td>40.00</td>\n",
              "      <td>1008.978525</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>19.000</td>\n",
              "      <td>2018-05-01 10:00:00</td>\n",
              "      <td>94.6775</td>\n",
              "      <td>...</td>\n",
              "      <td>48.332067</td>\n",
              "      <td>49.844783</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>144.454763</td>\n",
              "      <td>-3.430432</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>248593</td>\n",
              "      <td>110.192724</td>\n",
              "      <td>113.015705</td>\n",
              "      <td>42.308680</td>\n",
              "      <td>45.00</td>\n",
              "      <td>1009.887550</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>17.250</td>\n",
              "      <td>2018-05-01 11:00:00</td>\n",
              "      <td>122.8775</td>\n",
              "      <td>...</td>\n",
              "      <td>46.190993</td>\n",
              "      <td>49.653482</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>141.024331</td>\n",
              "      <td>-11.102347</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>248629</td>\n",
              "      <td>92.056876</td>\n",
              "      <td>116.214409</td>\n",
              "      <td>43.420200</td>\n",
              "      <td>48.75</td>\n",
              "      <td>1010.302350</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>16.250</td>\n",
              "      <td>2018-05-01 12:00:00</td>\n",
              "      <td>129.6600</td>\n",
              "      <td>...</td>\n",
              "      <td>43.527286</td>\n",
              "      <td>49.973240</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>129.921983</td>\n",
              "      <td>-19.729259</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>248661</td>\n",
              "      <td>75.308867</td>\n",
              "      <td>116.034804</td>\n",
              "      <td>44.603342</td>\n",
              "      <td>50.75</td>\n",
              "      <td>1010.769225</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>15.000</td>\n",
              "      <td>2018-05-01 13:00:00</td>\n",
              "      <td>143.5850</td>\n",
              "      <td>...</td>\n",
              "      <td>42.410263</td>\n",
              "      <td>49.915167</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>110.192724</td>\n",
              "      <td>-18.135848</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>248688</td>\n",
              "      <td>61.331908</td>\n",
              "      <td>114.898320</td>\n",
              "      <td>44.701219</td>\n",
              "      <td>51.75</td>\n",
              "      <td>1011.260425</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>15.000</td>\n",
              "      <td>2018-05-01 14:00:00</td>\n",
              "      <td>191.5425</td>\n",
              "      <td>...</td>\n",
              "      <td>41.603969</td>\n",
              "      <td>49.709529</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>92.056876</td>\n",
              "      <td>-16.748008</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>248722</td>\n",
              "      <td>48.109772</td>\n",
              "      <td>113.447175</td>\n",
              "      <td>42.254816</td>\n",
              "      <td>52.00</td>\n",
              "      <td>1011.431950</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>15.000</td>\n",
              "      <td>2018-05-01 15:00:00</td>\n",
              "      <td>265.1900</td>\n",
              "      <td>...</td>\n",
              "      <td>41.226029</td>\n",
              "      <td>49.733667</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>75.308867</td>\n",
              "      <td>-13.976959</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>248777</td>\n",
              "      <td>37.164691</td>\n",
              "      <td>109.583046</td>\n",
              "      <td>39.982237</td>\n",
              "      <td>59.75</td>\n",
              "      <td>1010.971050</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.400</td>\n",
              "      <td>2018-05-01 16:00:00</td>\n",
              "      <td>263.1050</td>\n",
              "      <td>...</td>\n",
              "      <td>41.101251</td>\n",
              "      <td>50.008458</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>61.331908</td>\n",
              "      <td>-13.222136</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>248797</td>\n",
              "      <td>28.512077</td>\n",
              "      <td>107.432835</td>\n",
              "      <td>37.766158</td>\n",
              "      <td>58.00</td>\n",
              "      <td>1010.793325</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.100</td>\n",
              "      <td>2018-05-01 17:00:00</td>\n",
              "      <td>298.9950</td>\n",
              "      <td>...</td>\n",
              "      <td>41.439161</td>\n",
              "      <td>50.303376</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>48.109772</td>\n",
              "      <td>-10.945081</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>248815</td>\n",
              "      <td>24.206878</td>\n",
              "      <td>103.179188</td>\n",
              "      <td>35.981870</td>\n",
              "      <td>55.25</td>\n",
              "      <td>1010.790725</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.000</td>\n",
              "      <td>2018-05-01 18:00:00</td>\n",
              "      <td>321.1225</td>\n",
              "      <td>...</td>\n",
              "      <td>42.025241</td>\n",
              "      <td>50.284461</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>37.164691</td>\n",
              "      <td>-8.652614</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>248868</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>98.009471</td>\n",
              "      <td>34.150027</td>\n",
              "      <td>53.25</td>\n",
              "      <td>1011.141875</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.000</td>\n",
              "      <td>2018-05-01 19:00:00</td>\n",
              "      <td>330.7975</td>\n",
              "      <td>...</td>\n",
              "      <td>42.712567</td>\n",
              "      <td>49.164020</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>28.512077</td>\n",
              "      <td>-4.305199</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>248890</td>\n",
              "      <td>24.156339</td>\n",
              "      <td>94.216178</td>\n",
              "      <td>32.209020</td>\n",
              "      <td>51.00</td>\n",
              "      <td>1011.277550</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>13.000</td>\n",
              "      <td>2018-05-01 20:00:00</td>\n",
              "      <td>336.7350</td>\n",
              "      <td>...</td>\n",
              "      <td>43.292351</td>\n",
              "      <td>48.035413</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>24.206878</td>\n",
              "      <td>-0.810881</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>248946</td>\n",
              "      <td>25.737573</td>\n",
              "      <td>88.444339</td>\n",
              "      <td>30.621508</td>\n",
              "      <td>47.25</td>\n",
              "      <td>1011.210625</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>14.200</td>\n",
              "      <td>2018-05-01 21:00:00</td>\n",
              "      <td>340.9900</td>\n",
              "      <td>...</td>\n",
              "      <td>43.732553</td>\n",
              "      <td>46.827655</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>0.760341</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>248983</td>\n",
              "      <td>29.325595</td>\n",
              "      <td>85.006979</td>\n",
              "      <td>28.706127</td>\n",
              "      <td>34.00</td>\n",
              "      <td>1011.902075</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>12.750</td>\n",
              "      <td>2018-05-01 22:00:00</td>\n",
              "      <td>350.1750</td>\n",
              "      <td>...</td>\n",
              "      <td>44.257615</td>\n",
              "      <td>45.494544</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>24.156339</td>\n",
              "      <td>1.581234</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>249001</td>\n",
              "      <td>32.577911</td>\n",
              "      <td>85.309896</td>\n",
              "      <td>28.696736</td>\n",
              "      <td>27.25</td>\n",
              "      <td>1012.411750</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>14.250</td>\n",
              "      <td>2018-05-01 23:00:00</td>\n",
              "      <td>265.8200</td>\n",
              "      <td>...</td>\n",
              "      <td>44.687147</td>\n",
              "      <td>43.928774</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>25.737573</td>\n",
              "      <td>3.588022</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>249042</td>\n",
              "      <td>42.231044</td>\n",
              "      <td>85.729193</td>\n",
              "      <td>29.678425</td>\n",
              "      <td>22.00</td>\n",
              "      <td>1013.258400</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>17.500</td>\n",
              "      <td>2018-05-02 00:00:00</td>\n",
              "      <td>269.4225</td>\n",
              "      <td>...</td>\n",
              "      <td>45.018288</td>\n",
              "      <td>43.859928</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>29.325595</td>\n",
              "      <td>3.252317</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>249073</td>\n",
              "      <td>59.450841</td>\n",
              "      <td>85.684076</td>\n",
              "      <td>32.360874</td>\n",
              "      <td>19.00</td>\n",
              "      <td>1013.056975</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>19.500</td>\n",
              "      <td>2018-05-02 01:00:00</td>\n",
              "      <td>180.0825</td>\n",
              "      <td>...</td>\n",
              "      <td>45.165644</td>\n",
              "      <td>44.182184</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>32.577911</td>\n",
              "      <td>9.653133</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>249106</td>\n",
              "      <td>78.628749</td>\n",
              "      <td>86.611752</td>\n",
              "      <td>32.992267</td>\n",
              "      <td>16.00</td>\n",
              "      <td>1012.529175</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>21.250</td>\n",
              "      <td>2018-05-02 02:00:00</td>\n",
              "      <td>269.5025</td>\n",
              "      <td>...</td>\n",
              "      <td>45.162682</td>\n",
              "      <td>44.319132</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>42.231044</td>\n",
              "      <td>17.219797</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>249132</td>\n",
              "      <td>96.105619</td>\n",
              "      <td>83.174176</td>\n",
              "      <td>30.211409</td>\n",
              "      <td>14.00</td>\n",
              "      <td>1012.551350</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>21.750</td>\n",
              "      <td>2018-05-02 03:00:00</td>\n",
              "      <td>355.3725</td>\n",
              "      <td>...</td>\n",
              "      <td>45.137143</td>\n",
              "      <td>44.258573</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>59.450841</td>\n",
              "      <td>19.177908</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>249190</td>\n",
              "      <td>111.609490</td>\n",
              "      <td>79.274921</td>\n",
              "      <td>28.222433</td>\n",
              "      <td>12.75</td>\n",
              "      <td>1010.968000</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>22.250</td>\n",
              "      <td>2018-05-02 04:00:00</td>\n",
              "      <td>349.7275</td>\n",
              "      <td>...</td>\n",
              "      <td>45.248941</td>\n",
              "      <td>44.085712</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>78.628749</td>\n",
              "      <td>17.476870</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>249217</td>\n",
              "      <td>124.833065</td>\n",
              "      <td>76.060307</td>\n",
              "      <td>27.156685</td>\n",
              "      <td>11.75</td>\n",
              "      <td>1010.517450</td>\n",
              "      <td>aotizhongxin_aq</td>\n",
              "      <td>22.250</td>\n",
              "      <td>2018-05-02 05:00:00</td>\n",
              "      <td>344.0325</td>\n",
              "      <td>...</td>\n",
              "      <td>45.459348</td>\n",
              "      <td>43.887702</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>23.395997</td>\n",
              "      <td>96.105619</td>\n",
              "      <td>15.503871</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aotizhongxin_aq#5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1650</th>\n",
              "      <td>248817</td>\n",
              "      <td>51.315323</td>\n",
              "      <td>87.073929</td>\n",
              "      <td>38.495762</td>\n",
              "      <td>53.00</td>\n",
              "      <td>1003.093750</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.150</td>\n",
              "      <td>2018-05-01 18:00:00</td>\n",
              "      <td>319.5100</td>\n",
              "      <td>...</td>\n",
              "      <td>28.752072</td>\n",
              "      <td>34.700467</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>53.690975</td>\n",
              "      <td>60.783619</td>\n",
              "      <td>-5.927222</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1651</th>\n",
              "      <td>248852</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>83.420931</td>\n",
              "      <td>37.135095</td>\n",
              "      <td>50.25</td>\n",
              "      <td>1003.479700</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.100</td>\n",
              "      <td>2018-05-01 19:00:00</td>\n",
              "      <td>326.2450</td>\n",
              "      <td>...</td>\n",
              "      <td>29.427323</td>\n",
              "      <td>34.836851</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>51.315323</td>\n",
              "      <td>54.856397</td>\n",
              "      <td>-3.541074</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1652</th>\n",
              "      <td>248907</td>\n",
              "      <td>50.720821</td>\n",
              "      <td>79.862915</td>\n",
              "      <td>35.617655</td>\n",
              "      <td>48.50</td>\n",
              "      <td>1003.589325</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.325</td>\n",
              "      <td>2018-05-01 20:00:00</td>\n",
              "      <td>329.3525</td>\n",
              "      <td>...</td>\n",
              "      <td>30.009287</td>\n",
              "      <td>35.388507</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>51.315323</td>\n",
              "      <td>-0.874661</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1653</th>\n",
              "      <td>248920</td>\n",
              "      <td>50.641485</td>\n",
              "      <td>76.056706</td>\n",
              "      <td>33.141202</td>\n",
              "      <td>44.75</td>\n",
              "      <td>1003.099400</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.900</td>\n",
              "      <td>2018-05-01 21:00:00</td>\n",
              "      <td>333.5150</td>\n",
              "      <td>...</td>\n",
              "      <td>30.481954</td>\n",
              "      <td>36.039856</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>0.280158</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1654</th>\n",
              "      <td>248955</td>\n",
              "      <td>51.427176</td>\n",
              "      <td>75.458249</td>\n",
              "      <td>31.707881</td>\n",
              "      <td>34.00</td>\n",
              "      <td>1003.444750</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>12.750</td>\n",
              "      <td>2018-05-01 22:00:00</td>\n",
              "      <td>344.1925</td>\n",
              "      <td>...</td>\n",
              "      <td>30.895670</td>\n",
              "      <td>36.406044</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.720821</td>\n",
              "      <td>-0.079336</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1655</th>\n",
              "      <td>249021</td>\n",
              "      <td>52.389139</td>\n",
              "      <td>73.429555</td>\n",
              "      <td>31.243540</td>\n",
              "      <td>27.75</td>\n",
              "      <td>1003.236275</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>14.250</td>\n",
              "      <td>2018-05-01 23:00:00</td>\n",
              "      <td>351.9400</td>\n",
              "      <td>...</td>\n",
              "      <td>31.447507</td>\n",
              "      <td>37.326104</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.641485</td>\n",
              "      <td>0.785691</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1656</th>\n",
              "      <td>249053</td>\n",
              "      <td>63.794346</td>\n",
              "      <td>75.000531</td>\n",
              "      <td>32.666992</td>\n",
              "      <td>22.25</td>\n",
              "      <td>1003.338225</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>17.750</td>\n",
              "      <td>2018-05-02 00:00:00</td>\n",
              "      <td>267.3000</td>\n",
              "      <td>...</td>\n",
              "      <td>31.972719</td>\n",
              "      <td>38.347511</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>51.427176</td>\n",
              "      <td>0.961963</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1657</th>\n",
              "      <td>249060</td>\n",
              "      <td>80.343241</td>\n",
              "      <td>75.721948</td>\n",
              "      <td>35.133720</td>\n",
              "      <td>18.50</td>\n",
              "      <td>1003.729325</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>20.250</td>\n",
              "      <td>2018-05-02 01:00:00</td>\n",
              "      <td>89.0075</td>\n",
              "      <td>...</td>\n",
              "      <td>32.239490</td>\n",
              "      <td>38.902375</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>52.389139</td>\n",
              "      <td>11.405207</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1658</th>\n",
              "      <td>249116</td>\n",
              "      <td>97.637252</td>\n",
              "      <td>76.191116</td>\n",
              "      <td>35.294571</td>\n",
              "      <td>15.75</td>\n",
              "      <td>1004.041025</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>21.500</td>\n",
              "      <td>2018-05-02 02:00:00</td>\n",
              "      <td>266.9400</td>\n",
              "      <td>...</td>\n",
              "      <td>32.283765</td>\n",
              "      <td>39.013989</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>63.794346</td>\n",
              "      <td>16.548895</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1659</th>\n",
              "      <td>249163</td>\n",
              "      <td>113.848296</td>\n",
              "      <td>74.526365</td>\n",
              "      <td>32.494184</td>\n",
              "      <td>14.25</td>\n",
              "      <td>1004.724150</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>22.000</td>\n",
              "      <td>2018-05-02 03:00:00</td>\n",
              "      <td>351.9775</td>\n",
              "      <td>...</td>\n",
              "      <td>32.268308</td>\n",
              "      <td>38.907964</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>80.343241</td>\n",
              "      <td>17.294011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1660</th>\n",
              "      <td>249169</td>\n",
              "      <td>128.389323</td>\n",
              "      <td>72.528836</td>\n",
              "      <td>30.031112</td>\n",
              "      <td>13.25</td>\n",
              "      <td>1002.659175</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>22.500</td>\n",
              "      <td>2018-05-02 04:00:00</td>\n",
              "      <td>348.5125</td>\n",
              "      <td>...</td>\n",
              "      <td>32.335044</td>\n",
              "      <td>38.755852</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>97.637252</td>\n",
              "      <td>16.211043</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1661</th>\n",
              "      <td>249228</td>\n",
              "      <td>140.949430</td>\n",
              "      <td>71.086227</td>\n",
              "      <td>29.003153</td>\n",
              "      <td>12.25</td>\n",
              "      <td>1002.419750</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.250</td>\n",
              "      <td>2018-05-02 05:00:00</td>\n",
              "      <td>342.7250</td>\n",
              "      <td>...</td>\n",
              "      <td>32.634893</td>\n",
              "      <td>38.711577</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>113.848296</td>\n",
              "      <td>14.541027</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1662</th>\n",
              "      <td>249247</td>\n",
              "      <td>150.215382</td>\n",
              "      <td>71.304508</td>\n",
              "      <td>28.026299</td>\n",
              "      <td>12.00</td>\n",
              "      <td>1001.924875</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>24.750</td>\n",
              "      <td>2018-05-02 06:00:00</td>\n",
              "      <td>343.3800</td>\n",
              "      <td>...</td>\n",
              "      <td>33.237092</td>\n",
              "      <td>38.866994</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>128.389323</td>\n",
              "      <td>12.560107</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1663</th>\n",
              "      <td>249298</td>\n",
              "      <td>157.210338</td>\n",
              "      <td>71.712074</td>\n",
              "      <td>28.760360</td>\n",
              "      <td>11.50</td>\n",
              "      <td>998.495500</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.750</td>\n",
              "      <td>2018-05-02 07:00:00</td>\n",
              "      <td>352.4150</td>\n",
              "      <td>...</td>\n",
              "      <td>34.065399</td>\n",
              "      <td>39.230653</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>140.949430</td>\n",
              "      <td>9.265952</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1664</th>\n",
              "      <td>249327</td>\n",
              "      <td>162.401507</td>\n",
              "      <td>71.850442</td>\n",
              "      <td>30.712675</td>\n",
              "      <td>12.00</td>\n",
              "      <td>999.067800</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>22.750</td>\n",
              "      <td>2018-05-02 08:00:00</td>\n",
              "      <td>6.2600</td>\n",
              "      <td>...</td>\n",
              "      <td>35.048335</td>\n",
              "      <td>39.693101</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>150.215382</td>\n",
              "      <td>6.994956</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1665</th>\n",
              "      <td>249343</td>\n",
              "      <td>159.070394</td>\n",
              "      <td>72.697723</td>\n",
              "      <td>32.751208</td>\n",
              "      <td>13.75</td>\n",
              "      <td>999.322800</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.750</td>\n",
              "      <td>2018-05-02 09:00:00</td>\n",
              "      <td>18.1250</td>\n",
              "      <td>...</td>\n",
              "      <td>36.158233</td>\n",
              "      <td>40.123320</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>157.210338</td>\n",
              "      <td>5.191169</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1666</th>\n",
              "      <td>249383</td>\n",
              "      <td>149.816805</td>\n",
              "      <td>74.869204</td>\n",
              "      <td>35.742976</td>\n",
              "      <td>15.50</td>\n",
              "      <td>999.261800</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>23.250</td>\n",
              "      <td>2018-05-02 10:00:00</td>\n",
              "      <td>41.2000</td>\n",
              "      <td>...</td>\n",
              "      <td>37.071276</td>\n",
              "      <td>40.392334</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>162.401507</td>\n",
              "      <td>-3.331113</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1667</th>\n",
              "      <td>249442</td>\n",
              "      <td>133.704259</td>\n",
              "      <td>79.329689</td>\n",
              "      <td>37.468317</td>\n",
              "      <td>19.25</td>\n",
              "      <td>999.160175</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>21.750</td>\n",
              "      <td>2018-05-02 11:00:00</td>\n",
              "      <td>50.0025</td>\n",
              "      <td>...</td>\n",
              "      <td>37.820411</td>\n",
              "      <td>40.722144</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>159.070394</td>\n",
              "      <td>-9.253589</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1668</th>\n",
              "      <td>249451</td>\n",
              "      <td>114.706417</td>\n",
              "      <td>84.323301</td>\n",
              "      <td>39.185715</td>\n",
              "      <td>22.25</td>\n",
              "      <td>1001.030000</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>20.000</td>\n",
              "      <td>2018-05-02 12:00:00</td>\n",
              "      <td>51.9750</td>\n",
              "      <td>...</td>\n",
              "      <td>38.181364</td>\n",
              "      <td>41.024454</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>149.816805</td>\n",
              "      <td>-16.112545</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1669</th>\n",
              "      <td>249510</td>\n",
              "      <td>98.016550</td>\n",
              "      <td>90.355135</td>\n",
              "      <td>41.405284</td>\n",
              "      <td>25.25</td>\n",
              "      <td>1000.753175</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>19.750</td>\n",
              "      <td>2018-05-02 13:00:00</td>\n",
              "      <td>251.5850</td>\n",
              "      <td>...</td>\n",
              "      <td>38.252568</td>\n",
              "      <td>41.140544</td>\n",
              "      <td>49.747964</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>133.704259</td>\n",
              "      <td>-18.997842</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1670</th>\n",
              "      <td>249523</td>\n",
              "      <td>85.444540</td>\n",
              "      <td>95.119606</td>\n",
              "      <td>39.226628</td>\n",
              "      <td>26.50</td>\n",
              "      <td>999.425400</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>18.500</td>\n",
              "      <td>2018-05-02 14:00:00</td>\n",
              "      <td>311.4175</td>\n",
              "      <td>...</td>\n",
              "      <td>37.598000</td>\n",
              "      <td>41.118468</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>114.706417</td>\n",
              "      <td>-16.689868</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1671</th>\n",
              "      <td>249563</td>\n",
              "      <td>76.592036</td>\n",
              "      <td>94.693404</td>\n",
              "      <td>36.996745</td>\n",
              "      <td>26.75</td>\n",
              "      <td>1000.455750</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>18.250</td>\n",
              "      <td>2018-05-02 15:00:00</td>\n",
              "      <td>315.3700</td>\n",
              "      <td>...</td>\n",
              "      <td>37.532040</td>\n",
              "      <td>40.985303</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>98.016550</td>\n",
              "      <td>-12.572010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1672</th>\n",
              "      <td>249609</td>\n",
              "      <td>71.481714</td>\n",
              "      <td>89.382344</td>\n",
              "      <td>35.194759</td>\n",
              "      <td>23.75</td>\n",
              "      <td>1000.637775</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.850</td>\n",
              "      <td>2018-05-02 16:00:00</td>\n",
              "      <td>267.4975</td>\n",
              "      <td>...</td>\n",
              "      <td>37.490625</td>\n",
              "      <td>40.757611</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>85.444540</td>\n",
              "      <td>-8.852504</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1673</th>\n",
              "      <td>249628</td>\n",
              "      <td>68.426922</td>\n",
              "      <td>86.954479</td>\n",
              "      <td>35.203153</td>\n",
              "      <td>25.25</td>\n",
              "      <td>1002.680875</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.650</td>\n",
              "      <td>2018-05-02 17:00:00</td>\n",
              "      <td>264.1225</td>\n",
              "      <td>...</td>\n",
              "      <td>37.491775</td>\n",
              "      <td>40.413070</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>76.592036</td>\n",
              "      <td>-5.110322</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1674</th>\n",
              "      <td>249661</td>\n",
              "      <td>64.843440</td>\n",
              "      <td>82.599817</td>\n",
              "      <td>33.953513</td>\n",
              "      <td>27.25</td>\n",
              "      <td>1001.021050</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.300</td>\n",
              "      <td>2018-05-02 18:00:00</td>\n",
              "      <td>341.9925</td>\n",
              "      <td>...</td>\n",
              "      <td>37.406950</td>\n",
              "      <td>39.901004</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>71.481714</td>\n",
              "      <td>-3.054792</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1675</th>\n",
              "      <td>249691</td>\n",
              "      <td>61.136622</td>\n",
              "      <td>79.387957</td>\n",
              "      <td>32.652726</td>\n",
              "      <td>27.75</td>\n",
              "      <td>1002.308425</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.125</td>\n",
              "      <td>2018-05-02 19:00:00</td>\n",
              "      <td>327.5225</td>\n",
              "      <td>...</td>\n",
              "      <td>37.156274</td>\n",
              "      <td>39.324803</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>68.426922</td>\n",
              "      <td>-3.583482</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1676</th>\n",
              "      <td>249754</td>\n",
              "      <td>59.022329</td>\n",
              "      <td>75.991217</td>\n",
              "      <td>31.529607</td>\n",
              "      <td>26.75</td>\n",
              "      <td>1002.989550</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.025</td>\n",
              "      <td>2018-05-02 20:00:00</td>\n",
              "      <td>321.4175</td>\n",
              "      <td>...</td>\n",
              "      <td>37.151015</td>\n",
              "      <td>38.831115</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.641485</td>\n",
              "      <td>64.843440</td>\n",
              "      <td>-3.706818</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1677</th>\n",
              "      <td>249790</td>\n",
              "      <td>58.834869</td>\n",
              "      <td>71.830438</td>\n",
              "      <td>30.184657</td>\n",
              "      <td>25.50</td>\n",
              "      <td>1002.600300</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>13.000</td>\n",
              "      <td>2018-05-02 21:00:00</td>\n",
              "      <td>327.4700</td>\n",
              "      <td>...</td>\n",
              "      <td>37.255260</td>\n",
              "      <td>38.431763</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>50.641485</td>\n",
              "      <td>61.136622</td>\n",
              "      <td>-2.114293</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1678</th>\n",
              "      <td>249809</td>\n",
              "      <td>58.709449</td>\n",
              "      <td>71.006134</td>\n",
              "      <td>28.020952</td>\n",
              "      <td>22.25</td>\n",
              "      <td>1002.987400</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>11.000</td>\n",
              "      <td>2018-05-02 22:00:00</td>\n",
              "      <td>339.6325</td>\n",
              "      <td>...</td>\n",
              "      <td>37.229250</td>\n",
              "      <td>38.029218</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>51.427176</td>\n",
              "      <td>59.022329</td>\n",
              "      <td>-0.187460</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1679</th>\n",
              "      <td>249864</td>\n",
              "      <td>59.955018</td>\n",
              "      <td>70.890384</td>\n",
              "      <td>26.766142</td>\n",
              "      <td>19.25</td>\n",
              "      <td>1004.976725</td>\n",
              "      <td>zhiwuyuan_aq</td>\n",
              "      <td>14.250</td>\n",
              "      <td>2018-05-02 23:00:00</td>\n",
              "      <td>349.9075</td>\n",
              "      <td>...</td>\n",
              "      <td>37.512920</td>\n",
              "      <td>37.667889</td>\n",
              "      <td>50.440662</td>\n",
              "      <td>52.389139</td>\n",
              "      <td>58.834869</td>\n",
              "      <td>-0.125419</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>zhiwuyuan_aq#23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1680 rows × 70 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0          O3        PM10      PM2.5  humidity     pressure  \\\n",
              "0         248185   53.750796  230.915390  72.478330     36.25  1008.698525   \n",
              "1         248248   72.775901  200.288391  65.580854     31.25  1008.631125   \n",
              "2         248266   88.088125  180.452955  59.065879     26.75  1008.819050   \n",
              "3         248307  103.816672  164.304360  50.805108     24.00  1009.149025   \n",
              "4         248338  117.051853  150.176732  44.683942     20.25  1008.308600   \n",
              "5         248365  128.046533  139.920208  40.174454     20.75  1007.379200   \n",
              "6         248427  134.543473  129.652972  36.771153     21.50  1007.492475   \n",
              "7         248458  141.307920  122.415838  35.325219     22.25  1007.728950   \n",
              "8         248466  144.454763  113.789682  35.940571     24.00  1007.536325   \n",
              "9         248501  141.024331  109.738826  36.272321     25.50  1007.974000   \n",
              "10        248565  129.921983  109.565664  39.075513     40.00  1008.978525   \n",
              "11        248593  110.192724  113.015705  42.308680     45.00  1009.887550   \n",
              "12        248629   92.056876  116.214409  43.420200     48.75  1010.302350   \n",
              "13        248661   75.308867  116.034804  44.603342     50.75  1010.769225   \n",
              "14        248688   61.331908  114.898320  44.701219     51.75  1011.260425   \n",
              "15        248722   48.109772  113.447175  42.254816     52.00  1011.431950   \n",
              "16        248777   37.164691  109.583046  39.982237     59.75  1010.971050   \n",
              "17        248797   28.512077  107.432835  37.766158     58.00  1010.793325   \n",
              "18        248815   24.206878  103.179188  35.981870     55.25  1010.790725   \n",
              "19        248868   23.395997   98.009471  34.150027     53.25  1011.141875   \n",
              "20        248890   24.156339   94.216178  32.209020     51.00  1011.277550   \n",
              "21        248946   25.737573   88.444339  30.621508     47.25  1011.210625   \n",
              "22        248983   29.325595   85.006979  28.706127     34.00  1011.902075   \n",
              "23        249001   32.577911   85.309896  28.696736     27.25  1012.411750   \n",
              "24        249042   42.231044   85.729193  29.678425     22.00  1013.258400   \n",
              "25        249073   59.450841   85.684076  32.360874     19.00  1013.056975   \n",
              "26        249106   78.628749   86.611752  32.992267     16.00  1012.529175   \n",
              "27        249132   96.105619   83.174176  30.211409     14.00  1012.551350   \n",
              "28        249190  111.609490   79.274921  28.222433     12.75  1010.968000   \n",
              "29        249217  124.833065   76.060307  27.156685     11.75  1010.517450   \n",
              "...          ...         ...         ...        ...       ...          ...   \n",
              "1650      248817   51.315323   87.073929  38.495762     53.00  1003.093750   \n",
              "1651      248852   50.440662   83.420931  37.135095     50.25  1003.479700   \n",
              "1652      248907   50.720821   79.862915  35.617655     48.50  1003.589325   \n",
              "1653      248920   50.641485   76.056706  33.141202     44.75  1003.099400   \n",
              "1654      248955   51.427176   75.458249  31.707881     34.00  1003.444750   \n",
              "1655      249021   52.389139   73.429555  31.243540     27.75  1003.236275   \n",
              "1656      249053   63.794346   75.000531  32.666992     22.25  1003.338225   \n",
              "1657      249060   80.343241   75.721948  35.133720     18.50  1003.729325   \n",
              "1658      249116   97.637252   76.191116  35.294571     15.75  1004.041025   \n",
              "1659      249163  113.848296   74.526365  32.494184     14.25  1004.724150   \n",
              "1660      249169  128.389323   72.528836  30.031112     13.25  1002.659175   \n",
              "1661      249228  140.949430   71.086227  29.003153     12.25  1002.419750   \n",
              "1662      249247  150.215382   71.304508  28.026299     12.00  1001.924875   \n",
              "1663      249298  157.210338   71.712074  28.760360     11.50   998.495500   \n",
              "1664      249327  162.401507   71.850442  30.712675     12.00   999.067800   \n",
              "1665      249343  159.070394   72.697723  32.751208     13.75   999.322800   \n",
              "1666      249383  149.816805   74.869204  35.742976     15.50   999.261800   \n",
              "1667      249442  133.704259   79.329689  37.468317     19.25   999.160175   \n",
              "1668      249451  114.706417   84.323301  39.185715     22.25  1001.030000   \n",
              "1669      249510   98.016550   90.355135  41.405284     25.25  1000.753175   \n",
              "1670      249523   85.444540   95.119606  39.226628     26.50   999.425400   \n",
              "1671      249563   76.592036   94.693404  36.996745     26.75  1000.455750   \n",
              "1672      249609   71.481714   89.382344  35.194759     23.75  1000.637775   \n",
              "1673      249628   68.426922   86.954479  35.203153     25.25  1002.680875   \n",
              "1674      249661   64.843440   82.599817  33.953513     27.25  1001.021050   \n",
              "1675      249691   61.136622   79.387957  32.652726     27.75  1002.308425   \n",
              "1676      249754   59.022329   75.991217  31.529607     26.75  1002.989550   \n",
              "1677      249790   58.834869   71.830438  30.184657     25.50  1002.600300   \n",
              "1678      249809   58.709449   71.006134  28.020952     22.25  1002.987400   \n",
              "1679      249864   59.955018   70.890384  26.766142     19.25  1004.976725   \n",
              "\n",
              "              test_id  temperature             utc_time  wind_direction  \\\n",
              "0     aotizhongxin_aq       19.250  2018-05-01 00:00:00         16.7850   \n",
              "1     aotizhongxin_aq       20.750  2018-05-01 01:00:00         24.7325   \n",
              "2     aotizhongxin_aq       21.250  2018-05-01 02:00:00         30.6150   \n",
              "3     aotizhongxin_aq       21.000  2018-05-01 03:00:00         33.2825   \n",
              "4     aotizhongxin_aq       22.500  2018-05-01 04:00:00         38.3700   \n",
              "5     aotizhongxin_aq       22.750  2018-05-01 05:00:00         40.9300   \n",
              "6     aotizhongxin_aq       22.500  2018-05-01 06:00:00         40.8850   \n",
              "7     aotizhongxin_aq       21.250  2018-05-01 07:00:00         38.6650   \n",
              "8     aotizhongxin_aq       20.750  2018-05-01 08:00:00         35.9075   \n",
              "9     aotizhongxin_aq       18.750  2018-05-01 09:00:00         39.7925   \n",
              "10    aotizhongxin_aq       19.000  2018-05-01 10:00:00         94.6775   \n",
              "11    aotizhongxin_aq       17.250  2018-05-01 11:00:00        122.8775   \n",
              "12    aotizhongxin_aq       16.250  2018-05-01 12:00:00        129.6600   \n",
              "13    aotizhongxin_aq       15.000  2018-05-01 13:00:00        143.5850   \n",
              "14    aotizhongxin_aq       15.000  2018-05-01 14:00:00        191.5425   \n",
              "15    aotizhongxin_aq       15.000  2018-05-01 15:00:00        265.1900   \n",
              "16    aotizhongxin_aq       13.400  2018-05-01 16:00:00        263.1050   \n",
              "17    aotizhongxin_aq       13.100  2018-05-01 17:00:00        298.9950   \n",
              "18    aotizhongxin_aq       13.000  2018-05-01 18:00:00        321.1225   \n",
              "19    aotizhongxin_aq       13.000  2018-05-01 19:00:00        330.7975   \n",
              "20    aotizhongxin_aq       13.000  2018-05-01 20:00:00        336.7350   \n",
              "21    aotizhongxin_aq       14.200  2018-05-01 21:00:00        340.9900   \n",
              "22    aotizhongxin_aq       12.750  2018-05-01 22:00:00        350.1750   \n",
              "23    aotizhongxin_aq       14.250  2018-05-01 23:00:00        265.8200   \n",
              "24    aotizhongxin_aq       17.500  2018-05-02 00:00:00        269.4225   \n",
              "25    aotizhongxin_aq       19.500  2018-05-02 01:00:00        180.0825   \n",
              "26    aotizhongxin_aq       21.250  2018-05-02 02:00:00        269.5025   \n",
              "27    aotizhongxin_aq       21.750  2018-05-02 03:00:00        355.3725   \n",
              "28    aotizhongxin_aq       22.250  2018-05-02 04:00:00        349.7275   \n",
              "29    aotizhongxin_aq       22.250  2018-05-02 05:00:00        344.0325   \n",
              "...               ...          ...                  ...             ...   \n",
              "1650     zhiwuyuan_aq       13.150  2018-05-01 18:00:00        319.5100   \n",
              "1651     zhiwuyuan_aq       13.100  2018-05-01 19:00:00        326.2450   \n",
              "1652     zhiwuyuan_aq       13.325  2018-05-01 20:00:00        329.3525   \n",
              "1653     zhiwuyuan_aq       13.900  2018-05-01 21:00:00        333.5150   \n",
              "1654     zhiwuyuan_aq       12.750  2018-05-01 22:00:00        344.1925   \n",
              "1655     zhiwuyuan_aq       14.250  2018-05-01 23:00:00        351.9400   \n",
              "1656     zhiwuyuan_aq       17.750  2018-05-02 00:00:00        267.3000   \n",
              "1657     zhiwuyuan_aq       20.250  2018-05-02 01:00:00         89.0075   \n",
              "1658     zhiwuyuan_aq       21.500  2018-05-02 02:00:00        266.9400   \n",
              "1659     zhiwuyuan_aq       22.000  2018-05-02 03:00:00        351.9775   \n",
              "1660     zhiwuyuan_aq       22.500  2018-05-02 04:00:00        348.5125   \n",
              "1661     zhiwuyuan_aq       23.250  2018-05-02 05:00:00        342.7250   \n",
              "1662     zhiwuyuan_aq       24.750  2018-05-02 06:00:00        343.3800   \n",
              "1663     zhiwuyuan_aq       23.750  2018-05-02 07:00:00        352.4150   \n",
              "1664     zhiwuyuan_aq       22.750  2018-05-02 08:00:00          6.2600   \n",
              "1665     zhiwuyuan_aq       23.750  2018-05-02 09:00:00         18.1250   \n",
              "1666     zhiwuyuan_aq       23.250  2018-05-02 10:00:00         41.2000   \n",
              "1667     zhiwuyuan_aq       21.750  2018-05-02 11:00:00         50.0025   \n",
              "1668     zhiwuyuan_aq       20.000  2018-05-02 12:00:00         51.9750   \n",
              "1669     zhiwuyuan_aq       19.750  2018-05-02 13:00:00        251.5850   \n",
              "1670     zhiwuyuan_aq       18.500  2018-05-02 14:00:00        311.4175   \n",
              "1671     zhiwuyuan_aq       18.250  2018-05-02 15:00:00        315.3700   \n",
              "1672     zhiwuyuan_aq       13.850  2018-05-02 16:00:00        267.4975   \n",
              "1673     zhiwuyuan_aq       13.650  2018-05-02 17:00:00        264.1225   \n",
              "1674     zhiwuyuan_aq       13.300  2018-05-02 18:00:00        341.9925   \n",
              "1675     zhiwuyuan_aq       13.125  2018-05-02 19:00:00        327.5225   \n",
              "1676     zhiwuyuan_aq       13.025  2018-05-02 20:00:00        321.4175   \n",
              "1677     zhiwuyuan_aq       13.000  2018-05-02 21:00:00        327.4700   \n",
              "1678     zhiwuyuan_aq       11.000  2018-05-02 22:00:00        339.6325   \n",
              "1679     zhiwuyuan_aq       14.250  2018-05-02 23:00:00        349.9075   \n",
              "\n",
              "             ...          O3_48_std  O3_24_std  O3_48_min  O3_24_min  \\\n",
              "0            ...          53.345352  45.789070   2.000000   2.000000   \n",
              "1            ...          52.702992  45.806419   2.000000   2.000000   \n",
              "2            ...          51.865409  45.756096   2.000000   2.000000   \n",
              "3            ...          51.395290  45.858419   2.000000   2.000000   \n",
              "4            ...          51.004777  46.317854   2.000000   2.000000   \n",
              "5            ...          51.044725  46.948503   2.000000   2.000000   \n",
              "6            ...          51.305685  47.756834   2.000000   2.000000   \n",
              "7            ...          51.073920  48.340912   2.000000   2.000000   \n",
              "8            ...          50.667983  49.019115   2.000000   2.000000   \n",
              "9            ...          49.558751  49.581248   2.000000   2.000000   \n",
              "10           ...          48.332067  49.844783   2.000000   2.000000   \n",
              "11           ...          46.190993  49.653482   2.000000   2.000000   \n",
              "12           ...          43.527286  49.973240   2.000000   2.000000   \n",
              "13           ...          42.410263  49.915167   2.000000   2.000000   \n",
              "14           ...          41.603969  49.709529   2.000000   2.000000   \n",
              "15           ...          41.226029  49.733667   2.000000   2.000000   \n",
              "16           ...          41.101251  50.008458   2.000000   2.000000   \n",
              "17           ...          41.439161  50.303376   2.000000   2.000000   \n",
              "18           ...          42.025241  50.284461   2.000000   2.000000   \n",
              "19           ...          42.712567  49.164020   2.000000   2.000000   \n",
              "20           ...          43.292351  48.035413   2.000000   2.000000   \n",
              "21           ...          43.732553  46.827655   2.000000   2.000000   \n",
              "22           ...          44.257615  45.494544   2.000000   2.000000   \n",
              "23           ...          44.687147  43.928774   2.000000  23.395997   \n",
              "24           ...          45.018288  43.859928   2.000000  23.395997   \n",
              "25           ...          45.165644  44.182184   2.000000  23.395997   \n",
              "26           ...          45.162682  44.319132   2.000000  23.395997   \n",
              "27           ...          45.137143  44.258573   2.000000  23.395997   \n",
              "28           ...          45.248941  44.085712   2.000000  23.395997   \n",
              "29           ...          45.459348  43.887702   2.000000  23.395997   \n",
              "...          ...                ...        ...        ...        ...   \n",
              "1650         ...          28.752072  34.700467  49.747964  53.690975   \n",
              "1651         ...          29.427323  34.836851  49.747964  51.315323   \n",
              "1652         ...          30.009287  35.388507  49.747964  50.440662   \n",
              "1653         ...          30.481954  36.039856  49.747964  50.440662   \n",
              "1654         ...          30.895670  36.406044  49.747964  50.440662   \n",
              "1655         ...          31.447507  37.326104  49.747964  50.440662   \n",
              "1656         ...          31.972719  38.347511  49.747964  50.440662   \n",
              "1657         ...          32.239490  38.902375  49.747964  50.440662   \n",
              "1658         ...          32.283765  39.013989  49.747964  50.440662   \n",
              "1659         ...          32.268308  38.907964  49.747964  50.440662   \n",
              "1660         ...          32.335044  38.755852  49.747964  50.440662   \n",
              "1661         ...          32.634893  38.711577  49.747964  50.440662   \n",
              "1662         ...          33.237092  38.866994  49.747964  50.440662   \n",
              "1663         ...          34.065399  39.230653  49.747964  50.440662   \n",
              "1664         ...          35.048335  39.693101  49.747964  50.440662   \n",
              "1665         ...          36.158233  40.123320  49.747964  50.440662   \n",
              "1666         ...          37.071276  40.392334  49.747964  50.440662   \n",
              "1667         ...          37.820411  40.722144  49.747964  50.440662   \n",
              "1668         ...          38.181364  41.024454  49.747964  50.440662   \n",
              "1669         ...          38.252568  41.140544  49.747964  50.440662   \n",
              "1670         ...          37.598000  41.118468  50.440662  50.440662   \n",
              "1671         ...          37.532040  40.985303  50.440662  50.440662   \n",
              "1672         ...          37.490625  40.757611  50.440662  50.440662   \n",
              "1673         ...          37.491775  40.413070  50.440662  50.440662   \n",
              "1674         ...          37.406950  39.901004  50.440662  50.440662   \n",
              "1675         ...          37.156274  39.324803  50.440662  50.440662   \n",
              "1676         ...          37.151015  38.831115  50.440662  50.641485   \n",
              "1677         ...          37.255260  38.431763  50.440662  50.641485   \n",
              "1678         ...          37.229250  38.029218  50.440662  51.427176   \n",
              "1679         ...          37.512920  37.667889  50.440662  52.389139   \n",
              "\n",
              "            O3_2  O3_1_diff  O3_3  O3_12  O3_8                   0  \n",
              "0       2.000000  29.000000   2.0   96.0  49.0   aotizhongxin_aq#0  \n",
              "1      31.000000  22.750796   2.0   98.0  28.0   aotizhongxin_aq#1  \n",
              "2      53.750796  19.025105  31.0   86.0   2.0   aotizhongxin_aq#2  \n",
              "3      72.775901  15.312224   NaN   72.0   2.0   aotizhongxin_aq#3  \n",
              "4      88.088125  15.728547   NaN   49.0   2.0   aotizhongxin_aq#4  \n",
              "5     103.816672  13.235181   NaN   28.0   2.0   aotizhongxin_aq#5  \n",
              "6     117.051853  10.994680   NaN    2.0   2.0   aotizhongxin_aq#6  \n",
              "7     128.046533   6.496939   NaN    2.0  31.0   aotizhongxin_aq#7  \n",
              "8     134.543473   6.764447   NaN    2.0   NaN   aotizhongxin_aq#8  \n",
              "9     141.307920   3.146843   NaN    2.0   NaN   aotizhongxin_aq#9  \n",
              "10    144.454763  -3.430432   NaN    2.0   NaN  aotizhongxin_aq#10  \n",
              "11    141.024331 -11.102347   NaN   31.0   NaN  aotizhongxin_aq#11  \n",
              "12    129.921983 -19.729259   NaN    NaN   NaN  aotizhongxin_aq#12  \n",
              "13    110.192724 -18.135848   NaN    NaN   NaN  aotizhongxin_aq#13  \n",
              "14     92.056876 -16.748008   NaN    NaN   NaN  aotizhongxin_aq#14  \n",
              "15     75.308867 -13.976959   NaN    NaN   NaN  aotizhongxin_aq#15  \n",
              "16     61.331908 -13.222136   NaN    NaN   NaN  aotizhongxin_aq#16  \n",
              "17     48.109772 -10.945081   NaN    NaN   NaN  aotizhongxin_aq#17  \n",
              "18     37.164691  -8.652614   NaN    NaN   NaN  aotizhongxin_aq#18  \n",
              "19     28.512077  -4.305199   NaN    NaN   NaN  aotizhongxin_aq#19  \n",
              "20     24.206878  -0.810881   NaN    NaN   NaN  aotizhongxin_aq#20  \n",
              "21     23.395997   0.760341   NaN    NaN   NaN  aotizhongxin_aq#21  \n",
              "22     24.156339   1.581234   NaN    NaN   NaN  aotizhongxin_aq#22  \n",
              "23     25.737573   3.588022   NaN    NaN   NaN  aotizhongxin_aq#23  \n",
              "24     29.325595   3.252317   NaN    NaN   NaN   aotizhongxin_aq#0  \n",
              "25     32.577911   9.653133   NaN    NaN   NaN   aotizhongxin_aq#1  \n",
              "26     42.231044  17.219797   NaN    NaN   NaN   aotizhongxin_aq#2  \n",
              "27     59.450841  19.177908   NaN    NaN   NaN   aotizhongxin_aq#3  \n",
              "28     78.628749  17.476870   NaN    NaN   NaN   aotizhongxin_aq#4  \n",
              "29     96.105619  15.503871   NaN    NaN   NaN   aotizhongxin_aq#5  \n",
              "...          ...        ...   ...    ...   ...                 ...  \n",
              "1650   60.783619  -5.927222   NaN    NaN   NaN     zhiwuyuan_aq#18  \n",
              "1651   54.856397  -3.541074   NaN    NaN   NaN     zhiwuyuan_aq#19  \n",
              "1652   51.315323  -0.874661   NaN    NaN   NaN     zhiwuyuan_aq#20  \n",
              "1653   50.440662   0.280158   NaN    NaN   NaN     zhiwuyuan_aq#21  \n",
              "1654   50.720821  -0.079336   NaN    NaN   NaN     zhiwuyuan_aq#22  \n",
              "1655   50.641485   0.785691   NaN    NaN   NaN     zhiwuyuan_aq#23  \n",
              "1656   51.427176   0.961963   NaN    NaN   NaN      zhiwuyuan_aq#0  \n",
              "1657   52.389139  11.405207   NaN    NaN   NaN      zhiwuyuan_aq#1  \n",
              "1658   63.794346  16.548895   NaN    NaN   NaN      zhiwuyuan_aq#2  \n",
              "1659   80.343241  17.294011   NaN    NaN   NaN      zhiwuyuan_aq#3  \n",
              "1660   97.637252  16.211043   NaN    NaN   NaN      zhiwuyuan_aq#4  \n",
              "1661  113.848296  14.541027   NaN    NaN   NaN      zhiwuyuan_aq#5  \n",
              "1662  128.389323  12.560107   NaN    NaN   NaN      zhiwuyuan_aq#6  \n",
              "1663  140.949430   9.265952   NaN    NaN   NaN      zhiwuyuan_aq#7  \n",
              "1664  150.215382   6.994956   NaN    NaN   NaN      zhiwuyuan_aq#8  \n",
              "1665  157.210338   5.191169   NaN    NaN   NaN      zhiwuyuan_aq#9  \n",
              "1666  162.401507  -3.331113   NaN    NaN   NaN     zhiwuyuan_aq#10  \n",
              "1667  159.070394  -9.253589   NaN    NaN   NaN     zhiwuyuan_aq#11  \n",
              "1668  149.816805 -16.112545   NaN    NaN   NaN     zhiwuyuan_aq#12  \n",
              "1669  133.704259 -18.997842   NaN    NaN   NaN     zhiwuyuan_aq#13  \n",
              "1670  114.706417 -16.689868   NaN    NaN   NaN     zhiwuyuan_aq#14  \n",
              "1671   98.016550 -12.572010   NaN    NaN   NaN     zhiwuyuan_aq#15  \n",
              "1672   85.444540  -8.852504   NaN    NaN   NaN     zhiwuyuan_aq#16  \n",
              "1673   76.592036  -5.110322   NaN    NaN   NaN     zhiwuyuan_aq#17  \n",
              "1674   71.481714  -3.054792   NaN    NaN   NaN     zhiwuyuan_aq#18  \n",
              "1675   68.426922  -3.583482   NaN    NaN   NaN     zhiwuyuan_aq#19  \n",
              "1676   64.843440  -3.706818   NaN    NaN   NaN     zhiwuyuan_aq#20  \n",
              "1677   61.136622  -2.114293   NaN    NaN   NaN     zhiwuyuan_aq#21  \n",
              "1678   59.022329  -0.187460   NaN    NaN   NaN     zhiwuyuan_aq#22  \n",
              "1679   58.834869  -0.125419   NaN    NaN   NaN     zhiwuyuan_aq#23  \n",
              "\n",
              "[1680 rows x 70 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "aHikUWN13ady",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "ba27c9a5-e8c5-4aaa-bc22-cb71c47ee706"
      },
      "cell_type": "code",
      "source": [
        "result_submission = result_format[['stationId','day','hour','PM2.5','PM10','O3']]\n",
        "station_dic = {}\n",
        "station_dic_reverse = {}\n",
        "station_sbumission_list = ['dongsi_aq','tiantan_aq','guanyuan_aq','wanshouxigong_aq','aotizhongxin_aq','nongzhanguan_aq','wanliu_aq','beibuxinqu_aq',\n",
        "               'zhiwuyuan_aq','fengtaihuayuan_aq','yungang_aq','gucheng_aq','fangshan_aq','daxing_aq','yizhuang_aq','tongzhou_aq',\n",
        "                'shunyi_aq','pingchang_aq','mentougou_aq','pinggu_aq','huairou_aq','miyun_aq','yanqin_aq','dingling_aq','badaling_aq',\n",
        "               'miyunshuiku_aq','donggaocun_aq','yongledian_aq','yufa_aq','liulihe_aq','qianmen_aq','yongdingmennei_aq','xizhimenbei_aq',\n",
        "               'nansanhuan_aq','dongsihuan_aq']\n",
        "for i in range(len(station_sbumission_list)):\n",
        "    station_dic[station_sbumission_list[i]] = i\n",
        "    station_dic_reverse[i] = station_sbumission_list[i]\n",
        "result_submission['stationId'] = result_submission['stationId'].apply(lambda x: station_dic[x])\n",
        "\n",
        "def rep_hour(day,hour):\n",
        "    if day > 1:\n",
        "        hour += 24\n",
        "    return hour\n",
        "\n",
        "result_submission['hour'] = result_submission.apply(lambda row: rep_hour(row['day'],row['hour']),axis = 1)\n",
        "result_submission['hour'] = result_submission['hour'].astype(int)\n",
        "final_res_sub = result_submission.sort_values(by = ['stationId','hour'])\n",
        "final_res_sub['stationId'] = final_res_sub['stationId'].apply(lambda x: station_dic_reverse[x])\n",
        "\n",
        "def rep_id(station_id,hour):\n",
        "    hour = str(int(hour))\n",
        "    res = station_id + '#' + hour\n",
        "    return res\n",
        "\n",
        "final_res_sub['stationId'] = final_res_sub.apply(lambda row: rep_id(row['stationId'],row['hour']),axis = 1)\n",
        "final_res_sub = final_res_sub.reset_index()\n",
        "final_res_sub.drop(['index','day','hour'],axis = 1,inplace=True)\n",
        "final_res_sub.columns = ['test_id','PM2.5','PM10','O3']\n",
        "final_res_sub.to_csv('drive/submission.csv',index = None)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "SYJR3spyNXCU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}