Day_008_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 常用的 DataFrame 操作\n",
    "* merge / transform\n",
    "* subset\n",
    "* groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import 需要的套件\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 設定 data_path\n",
    "dir_data = './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path of read in data: ./data/application_train.csv\n"
     ]
    },
    {
     "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_app = os.path.join(dir_data, 'application_train.csv')\n",
    "print('Path of read in data: %s' % (f_app))\n",
    "app_train = pd.read_csv(f_app)\n",
    "app_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作業\n",
    "1. 請將 app_train 中的 CNT_CHILDREN 依照下列規則分為四組，並將其結果在原本的 dataframe 命名為 CNT_CHILDREN_GROUP\n",
    "    * 0 個小孩\n",
    "    * 有 1 - 2 個小孩\n",
    "    * 有 3 - 5 個小孩\n",
    "    * 有超過 5 個小孩\n",
    "\n",
    "2. 請根據 CNT_CHILDREN_GROUP 以及 TARGET，列出各組的平均 AMT_INCOME_TOTAL，並繪製 baxplot\n",
    "3. 請根據 CNT_CHILDREN_GROUP 以及 TARGET，對 AMT_INCOME_TOTAL 計算 [Z 轉換](https://en.wikipedia.org/wiki/Standard_score) 後的分數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-1, 0], (-1, 0], (0, 2], (0, 2], (2, 5], (2, 5], (2, 5], (5, 100]]\n",
       "Categories (4, interval[int64]): [(-1, 0] < (0, 2] < (2, 5] < (5, 100]]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test pd.cut\n",
    "# 左閉右開\n",
    "nums = [0,0,1,2,3,4,5,6]\n",
    "pd.cut(nums,[-1,0,2,5,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      215371\n",
       "1-2     87868\n",
       "3-5      4230\n",
       "> 5        42\n",
       "Name: CNT_CHILDREN_GROUP, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1\n",
    "cut_rule = [-1,0,2,5,app_train['CNT_CHILDREN'].max()]\n",
    "labels = [\"0\",\"1-2\",\"3-5\",\"> 5\"]\n",
    "qcut\n",
    "app_train['CNT_CHILDREN_GROUP'] = pd.cut(app_train['CNT_CHILDREN'].values, cut_rule, include_lowest=True,labels =labels)\n",
    "app_train['CNT_CHILDREN_GROUP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNT_CHILDREN_GROUP  TARGET\n",
       "0                   0         166747.010980\n",
       "                    1         160804.210548\n",
       "1-2                 0         174590.584401\n",
       "                    1         176115.362720\n",
       "3-5                 0         175053.742913\n",
       "                    1         160677.385714\n",
       "> 5                 0         139159.090909\n",
       "                    1         180000.000000\n",
       "Name: AMT_INCOME_TOTAL, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2-1\n",
    "grp = [\"CNT_CHILDREN_GROUP\",\"TARGET\"]\n",
    "\n",
    "grouped_df = app_train.groupby(grp)['AMT_INCOME_TOTAL']\n",
    "grouped_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2-2\n",
    "\n",
    "plt_column = [\"AMT_INCOME_TOTAL\"]\n",
    "plt_by = [\"CNT_CHILDREN_GROUP\"]\n",
    "\n",
    "app_train.groupby(\"TARGET\").boxplot(column=plt_column, by = plt_by, showfliers = False, figsize=(8,8))\n",
    "plt.suptitle('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_INCOME_TOTAL_Z_BY_CHILDREN_GRP-TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>202500.0</td>\n",
       "      <td>0.491536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>270000.0</td>\n",
       "      <td>0.972437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67500.0</td>\n",
       "      <td>-0.934708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135000.0</td>\n",
       "      <td>-0.298993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121500.0</td>\n",
       "      <td>-0.426136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AMT_INCOME_TOTAL  AMT_INCOME_TOTAL_Z_BY_CHILDREN_GRP-TARGET\n",
       "0          202500.0                                   0.491536\n",
       "1          270000.0                                   0.972437\n",
       "2           67500.0                                  -0.934708\n",
       "3          135000.0                                  -0.298993\n",
       "4          121500.0                                  -0.426136"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3\n",
    "grouped_df = app_train.groupby(['CNT_CHILDREN_GROUP','TARGET'])\n",
    "app_train['AMT_INCOME_TOTAL_Z_BY_CHILDREN_GRP-TARGET'] = grouped_df.apply(lambda x:  (x - np.mean(x)) / (np.std(x)))\n",
    "app_train['AMT_INCOME_TOTAL_Z_BY_CHILDREN_GRP-TARGET'] = app_train[]\n",
    "app_train[['AMT_INCOME_TOTAL','AMT_INCOME_TOTAL_Z_BY_CHILDREN_GRP-TARGET']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}