Weather.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Weather.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNHtS709cPL4HtU5naZNU8R",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nikunjmistry22/Weather_Data_Analysis/blob/main/Weather.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "5F3i2x_PIhxZ",
        "outputId": "fa8cb876-3cc1-48d9-8e91-12a46deeb213"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-aea2aaf8-2bd0-48ff-958f-26e4f77f593b\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-aea2aaf8-2bd0-48ff-958f-26e4f77f593b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving 1. Weather Data.csv to 1. Weather Data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('1. Weather Data.csv')\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5NaiOFBqFsYB",
        "outputId": "1ba4f9ce-a3ba-4bd6-b225-d923de28e382"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e43f94aa-ee2e-4a5d-9f85-569ae455ffeb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Date/Time</th>\n",
              "      <th>Temp_C</th>\n",
              "      <th>Dew Point Temp_C</th>\n",
              "      <th>Rel Hum_%</th>\n",
              "      <th>Wind Speed_km/h</th>\n",
              "      <th>Visibility_km</th>\n",
              "      <th>Press_kPa</th>\n",
              "      <th>Weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1/1/2012 0:00</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>-3.9</td>\n",
              "      <td>86</td>\n",
              "      <td>4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>101.24</td>\n",
              "      <td>Fog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/1/2012 1:00</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>-3.7</td>\n",
              "      <td>87</td>\n",
              "      <td>4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>101.24</td>\n",
              "      <td>Fog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1/1/2012 2:00</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>-3.4</td>\n",
              "      <td>89</td>\n",
              "      <td>7</td>\n",
              "      <td>4.0</td>\n",
              "      <td>101.26</td>\n",
              "      <td>Freezing Drizzle,Fog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/1/2012 3:00</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-3.2</td>\n",
              "      <td>88</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>101.27</td>\n",
              "      <td>Freezing Drizzle,Fog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1/1/2012 4:00</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-3.3</td>\n",
              "      <td>88</td>\n",
              "      <td>7</td>\n",
              "      <td>4.8</td>\n",
              "      <td>101.23</td>\n",
              "      <td>Fog</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8779</th>\n",
              "      <td>12/31/2012 19:00</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-2.7</td>\n",
              "      <td>81</td>\n",
              "      <td>30</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.13</td>\n",
              "      <td>Snow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8780</th>\n",
              "      <td>12/31/2012 20:00</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-2.4</td>\n",
              "      <td>83</td>\n",
              "      <td>24</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.03</td>\n",
              "      <td>Snow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8781</th>\n",
              "      <td>12/31/2012 21:00</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>93</td>\n",
              "      <td>28</td>\n",
              "      <td>4.8</td>\n",
              "      <td>99.95</td>\n",
              "      <td>Snow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8782</th>\n",
              "      <td>12/31/2012 22:00</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>89</td>\n",
              "      <td>28</td>\n",
              "      <td>9.7</td>\n",
              "      <td>99.91</td>\n",
              "      <td>Snow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8783</th>\n",
              "      <td>12/31/2012 23:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.1</td>\n",
              "      <td>86</td>\n",
              "      <td>30</td>\n",
              "      <td>11.3</td>\n",
              "      <td>99.89</td>\n",
              "      <td>Snow</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8784 rows × 8 columns</p>\n",
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            "text/plain": [
              "             Date/Time  Temp_C  Dew Point Temp_C  Rel Hum_%  Wind Speed_km/h  \\\n",
              "0        1/1/2012 0:00    -1.8              -3.9         86                4   \n",
              "1        1/1/2012 1:00    -1.8              -3.7         87                4   \n",
              "2        1/1/2012 2:00    -1.8              -3.4         89                7   \n",
              "3        1/1/2012 3:00    -1.5              -3.2         88                6   \n",
              "4        1/1/2012 4:00    -1.5              -3.3         88                7   \n",
              "...                ...     ...               ...        ...              ...   \n",
              "8779  12/31/2012 19:00     0.1              -2.7         81               30   \n",
              "8780  12/31/2012 20:00     0.2              -2.4         83               24   \n",
              "8781  12/31/2012 21:00    -0.5              -1.5         93               28   \n",
              "8782  12/31/2012 22:00    -0.2              -1.8         89               28   \n",
              "8783  12/31/2012 23:00     0.0              -2.1         86               30   \n",
              "\n",
              "      Visibility_km  Press_kPa               Weather  \n",
              "0               8.0     101.24                   Fog  \n",
              "1               8.0     101.24                   Fog  \n",
              "2               4.0     101.26  Freezing Drizzle,Fog  \n",
              "3               4.0     101.27  Freezing Drizzle,Fog  \n",
              "4               4.8     101.23                   Fog  \n",
              "...             ...        ...                   ...  \n",
              "8779            9.7     100.13                  Snow  \n",
              "8780            9.7     100.03                  Snow  \n",
              "8781            4.8      99.95                  Snow  \n",
              "8782            9.7      99.91                  Snow  \n",
              "8783           11.3      99.89                  Snow  \n",
              "\n",
              "[8784 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
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      "cell_type": "code",
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        "data.shape"
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        "outputId": "bcee220b-0988-4006-a350-6b31ebbc9032"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8784, 8)"
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          "metadata": {},
          "execution_count": 9
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        "outputId": "097dd61a-5a4b-4002-d724-16a23c9759dc"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=8784, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
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      "source": [
        "data.dtypes"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date/Time            object\n",
              "Temp_C              float64\n",
              "Dew Point Temp_C    float64\n",
              "Rel Hum_%             int64\n",
              "Wind Speed_km/h       int64\n",
              "Visibility_km       float64\n",
              "Press_kPa           float64\n",
              "Weather              object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
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    {
      "cell_type": "code",
      "source": [
        "data.count"
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        "outputId": "d1d5363c-27f7-4dfd-e5d5-bba0a3002245"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.count of              Date/Time  Temp_C  Dew Point Temp_C  Rel Hum_% Wind Speed_km/h  \\\n",
              "0        1/1/2012 0:00    -1.8              -3.9         86           float   \n",
              "1        1/1/2012 1:00    -1.8              -3.7         87           float   \n",
              "2        1/1/2012 2:00    -1.8              -3.4         89           float   \n",
              "3        1/1/2012 3:00    -1.5              -3.2         88           float   \n",
              "4        1/1/2012 4:00    -1.5              -3.3         88           float   \n",
              "...                ...     ...               ...        ...             ...   \n",
              "8779  12/31/2012 19:00     0.1              -2.7         81           float   \n",
              "8780  12/31/2012 20:00     0.2              -2.4         83           float   \n",
              "8781  12/31/2012 21:00    -0.5              -1.5         93           float   \n",
              "8782  12/31/2012 22:00    -0.2              -1.8         89           float   \n",
              "8783  12/31/2012 23:00     0.0              -2.1         86           float   \n",
              "\n",
              "      Visibility_km  Press_kPa               Weather  \n",
              "0               8.0     101.24                   Fog  \n",
              "1               8.0     101.24                   Fog  \n",
              "2               4.0     101.26  Freezing Drizzle,Fog  \n",
              "3               4.0     101.27  Freezing Drizzle,Fog  \n",
              "4               4.8     101.23                   Fog  \n",
              "...             ...        ...                   ...  \n",
              "8779            9.7     100.13                  Snow  \n",
              "8780            9.7     100.03                  Snow  \n",
              "8781            4.8      99.95                  Snow  \n",
              "8782            9.7      99.91                  Snow  \n",
              "8783           11.3      99.89                  Snow  \n",
              "\n",
              "[8784 rows x 8 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
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        "data.nunique()"
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        "id": "kujSUYj3IQlm",
        "outputId": "ec88c5df-c469-46b0-9dd3-ed9b86846e55"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date/Time           8784\n",
              "Temp_C               533\n",
              "Dew Point Temp_C     489\n",
              "Rel Hum_%             83\n",
              "Wind Speed_km/h        1\n",
              "Visibility_km         24\n",
              "Press_kPa            518\n",
              "Weather               50\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
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    {
      "cell_type": "code",
      "source": [
        "data.info()"
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        "outputId": "c4c927b5-3d9e-41fc-e7f9-1b3891ad351d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8784 entries, 0 to 8783\n",
            "Data columns (total 8 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Date/Time         8784 non-null   object \n",
            " 1   Temp_C            8784 non-null   float64\n",
            " 2   Dew Point Temp_C  8784 non-null   float64\n",
            " 3   Rel Hum_%         8784 non-null   int64  \n",
            " 4   Wind Speed_km/h   8784 non-null   object \n",
            " 5   Visibility_km     8784 non-null   float64\n",
            " 6   Press_kPa         8784 non-null   float64\n",
            " 7   Weather           8784 non-null   object \n",
            "dtypes: float64(4), int64(1), object(3)\n",
            "memory usage: 549.1+ KB\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "data.head(2)\n",
        "data[data.Weather == 'Clear']"
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          "height": 423
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              "                                                     [key], {});\n",
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              "  "
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              "             Date/Time  Temp_C  Dew Point Temp_C  Rel Hum_% Wind Speed_km/h  \\\n",
              "67      1/3/2012 19:00   -16.9             -24.8         50           float   \n",
              "114     1/5/2012 18:00    -7.1             -14.4         56           float   \n",
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              "117     1/5/2012 21:00    -9.0             -14.8         63           float   \n",
              "...                ...     ...               ...        ...             ...   \n",
              "8646   12/26/2012 6:00   -13.4             -14.8         89           float   \n",
              "8698  12/28/2012 10:00    -6.1              -8.6         82           float   \n",
              "8713   12/29/2012 1:00   -11.9             -13.6         87           float   \n",
              "8714   12/29/2012 2:00   -11.8             -13.1         90           float   \n",
              "8756  12/30/2012 20:00   -13.8             -16.5         80           float   \n",
              "\n",
              "      Visibility_km  Press_kPa Weather  \n",
              "67             25.0     101.74   Clear  \n",
              "114            25.0     100.71   Clear  \n",
              "115            25.0     100.80   Clear  \n",
              "116            25.0     100.83   Clear  \n",
              "117            25.0     100.83   Clear  \n",
              "...             ...        ...     ...  \n",
              "8646           25.0     102.47   Clear  \n",
              "8698           24.1     101.27   Clear  \n",
              "8713           25.0     101.31   Clear  \n",
              "8714           25.0     101.33   Clear  \n",
              "8756           25.0     101.52   Clear  \n",
              "\n",
              "[1326 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
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    {
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        "data.head(3)\n",
        "data.groupby('Weather').get_group('Clear')"
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              "<p>1326 rows × 8 columns</p>\n",
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              "             Date/Time  Temp_C  Dew Point Temp_C  Rel Hum_% Wind Speed_km/h  \\\n",
              "67      1/3/2012 19:00   -16.9             -24.8         50           float   \n",
              "114     1/5/2012 18:00    -7.1             -14.4         56           float   \n",
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              "...                ...     ...               ...        ...             ...   \n",
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              "8756  12/30/2012 20:00   -13.8             -16.5         80           float   \n",
              "\n",
              "      Visibility_km  Press_kPa Weather  \n",
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              "115            25.0     100.80   Clear  \n",
              "116            25.0     100.83   Clear  \n",
              "117            25.0     100.83   Clear  \n",
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              "8646           25.0     102.47   Clear  \n",
              "8698           24.1     101.27   Clear  \n",
              "8713           25.0     101.31   Clear  \n",
              "8714           25.0     101.33   Clear  \n",
              "8756           25.0     101.52   Clear  \n",
              "\n",
              "[1326 rows x 8 columns]"
            ]
          },
          "metadata": {},
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    {
      "cell_type": "code",
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        "data.head(3)\n",
        "data[(data['Weather'] == 'Fog') & (data['Temp_C'] > -1)]"
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        "id": "tXGiyORHLC2T",
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              "      <th>8232</th>\n",
              "      <td>12/9/2012 0:00</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>94</td>\n",
              "      <td>float</td>\n",
              "      <td>9.7</td>\n",
              "      <td>101.07</td>\n",
              "      <td>Fog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8483</th>\n",
              "      <td>12/19/2012 11:00</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>96</td>\n",
              "      <td>float</td>\n",
              "      <td>9.7</td>\n",
              "      <td>100.44</td>\n",
              "      <td>Fog</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>113 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdce3e31-5344-4b7a-902c-d64cd3606807')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cdce3e31-5344-4b7a-902c-d64cd3606807 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cdce3e31-5344-4b7a-902c-d64cd3606807');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             Date/Time  Temp_C  Dew Point Temp_C  Rel Hum_% Wind Speed_km/h  \\\n",
              "11      1/1/2012 11:00    -0.5              -2.1         89           float   \n",
              "12      1/1/2012 12:00    -0.2              -2.0         88           float   \n",
              "13      1/1/2012 13:00     0.2              -1.7         87           float   \n",
              "14      1/1/2012 14:00     0.8              -1.1         87           float   \n",
              "15      1/1/2012 15:00     1.8              -0.4         85           float   \n",
              "...                ...     ...               ...        ...             ...   \n",
              "8085   12/2/2012 21:00     6.7               6.6         99           float   \n",
              "8215    12/8/2012 7:00     2.3               1.0         91           float   \n",
              "8230   12/8/2012 22:00     1.2               0.6         96           float   \n",
              "8232    12/9/2012 0:00     1.5               0.7         94           float   \n",
              "8483  12/19/2012 11:00     0.8               0.2         96           float   \n",
              "\n",
              "      Visibility_km  Press_kPa Weather  \n",
              "11              4.0     100.98     Fog  \n",
              "12              4.8     100.79     Fog  \n",
              "13              4.8     100.58     Fog  \n",
              "14              4.8     100.31     Fog  \n",
              "15              6.4     100.07     Fog  \n",
              "...             ...        ...     ...  \n",
              "8085            8.0     100.70     Fog  \n",
              "8215            8.0     101.09     Fog  \n",
              "8230            6.4     100.84     Fog  \n",
              "8232            9.7     101.07     Fog  \n",
              "8483            9.7     100.44     Fog  \n",
              "\n",
              "[113 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(data['Temp_C'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "OzDtlWzkdM-w",
        "outputId": "a9d2344a-df40-4b20-f9f2-d1dc0f00bd0f"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(data['Visibility_km'],bins=10,facecolor=\"red\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "eUqtM63ygMx7",
        "outputId": "16906263-0897-4bb7-8e9b-e6d94bb43667"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.stem(data['Rel Hum_%'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "sCGcOX9zgwtH",
        "outputId": "9aab0d8b-4fd5-4a12-bfdc-bd14fe0ad85a"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_html('Weather-Report')\n",
        "pd.read_html('Weather-Report')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z888UnHUhJgT",
        "outputId": "435771c3-644b-49a5-efed-3254f1c9e25e"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[      Unnamed: 0         Date/Time  Temp_C  Dew Point Temp_C  Rel Hum_%  \\\n",
              " 0              0     1/1/2012 0:00    -1.8              -3.9         86   \n",
              " 1              1     1/1/2012 1:00    -1.8              -3.7         87   \n",
              " 2              2     1/1/2012 2:00    -1.8              -3.4         89   \n",
              " 3              3     1/1/2012 3:00    -1.5              -3.2         88   \n",
              " 4              4     1/1/2012 4:00    -1.5              -3.3         88   \n",
              " ...          ...               ...     ...               ...        ...   \n",
              " 8779        8779  12/31/2012 19:00     0.1              -2.7         81   \n",
              " 8780        8780  12/31/2012 20:00     0.2              -2.4         83   \n",
              " 8781        8781  12/31/2012 21:00    -0.5              -1.5         93   \n",
              " 8782        8782  12/31/2012 22:00    -0.2              -1.8         89   \n",
              " 8783        8783  12/31/2012 23:00     0.0              -2.1         86   \n",
              " \n",
              "      Wind Speed_km/h  Visibility_km  Press_kPa               Weather  \n",
              " 0              float            8.0     101.24                   Fog  \n",
              " 1              float            8.0     101.24                   Fog  \n",
              " 2              float            4.0     101.26  Freezing Drizzle,Fog  \n",
              " 3              float            4.0     101.27  Freezing Drizzle,Fog  \n",
              " 4              float            4.8     101.23                   Fog  \n",
              " ...              ...            ...        ...                   ...  \n",
              " 8779           float            9.7     100.13                  Snow  \n",
              " 8780           float            9.7     100.03                  Snow  \n",
              " 8781           float            4.8      99.95                  Snow  \n",
              " 8782           float            9.7      99.91                  Snow  \n",
              " 8783           float           11.3      99.89                  Snow  \n",
              " \n",
              " [8784 rows x 9 columns]]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    }
  ]
}