homework/Day_005_HW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import 需要的套件\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 設定 data_path\n",
    "dir_data = './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_app_train = os.path.join(dir_data, 'application_train.csv')\n",
    "app_train = pd.read_csv(f_app_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "3             ...                             0                0   \n",
       "4             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                        NaN   \n",
       "4                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                         0.0   \n",
       "1                       0.0                         0.0   \n",
       "2                       0.0                         0.0   \n",
       "3                       NaN                         NaN   \n",
       "4                       0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        NaN                        NaN   \n",
       "4                        0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "3                         NaN  \n",
       "4                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 練習時間"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "觀察有興趣的欄位的資料分佈，並嘗試找出有趣的訊息\n",
    "#### Eg\n",
    "- 計算任意欄位的平均數及標準差\n",
    "- 畫出任意欄位的[直方圖](https://zh.wikipedia.org/zh-tw/%E7%9B%B4%E6%96%B9%E5%9B%BE)\n",
    "\n",
    "### Hints:\n",
    "- [Descriptive Statistics For pandas Dataframe](https://chrisalbon.com/python/data_wrangling/pandas_dataframe_descriptive_stats/)\n",
    "- [pandas 中的繪圖函數](https://amaozhao.gitbooks.io/pandas-notebook/content/pandas%E4%B8%AD%E7%9A%84%E7%BB%98%E5%9B%BE%E5%87%BD%E6%95%B0.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均數： 168797.9192969845\n"
     ]
    }
   ],
   "source": [
    "print(\"平均數：\", app_train[\"AMT_INCOME_TOTAL\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "標準差： 237123.14627885626\n"
     ]
    }
   ],
   "source": [
    "print(\"標準差：\", app_train[\"AMT_INCOME_TOTAL\"].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.075110e+05\n",
       "mean     1.687979e+05\n",
       "std      2.371231e+05\n",
       "min      2.565000e+04\n",
       "25%      1.125000e+05\n",
       "50%      1.471500e+05\n",
       "75%      2.025000e+05\n",
       "max      1.170000e+08\n",
       "Name: AMT_INCOME_TOTAL, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train[\"AMT_INCOME_TOTAL\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1350b57b8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "app_train[\"AMT_INCOME_TOTAL\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}