Day_013_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 常用的 DataFrame 操作\n",
    "* merge / transform\n",
    "* subset\n",
    "* groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 練習填入對應的欄位資料或公式, 完成題目的要求 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 填入適當的輸入資料, 讓後面的程式顯示題目要求的結果 (Hint: 填入對應區間或欄位即可, In[4]~In[6], Out[4]~In[6])\n",
    "- 填入z轉換的計算方式, 完成轉換後的數值 (Hint: 參照標準化公式, In[7])"
   ]
  },
  {
   "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\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "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": [
    {
     "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": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-inf, 0.0]    215371\n",
       "(0.0, 2.0]      87868\n",
       "(2.0, 5.0]       4230\n",
       "(5.0, inf]         42\n",
       "Name: CNT_CHILDREN_GROUP, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1\n",
    "\"\"\"\n",
    "Your code here\n",
    "\"\"\"\n",
    "cut_rule = [-np.inf, 0, 2, 5, np.inf]\n",
    "\n",
    "app_train['CNT_CHILDREN_GROUP'] = pd.cut(app_train['CNT_CHILDREN'].values, cut_rule, include_lowest=True)\n",
    "app_train['CNT_CHILDREN_GROUP'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNT_CHILDREN_GROUP  TARGET\n",
       "(-inf, 0.0]         0         166747.010980\n",
       "                    1         160804.210548\n",
       "(0.0, 2.0]          0         174590.584401\n",
       "                    1         176115.362720\n",
       "(2.0, 5.0]          0         175053.742913\n",
       "                    1         160677.385714\n",
       "(5.0, inf]          0         139159.090909\n",
       "                    1         180000.000000\n",
       "Name: AMT_INCOME_TOTAL, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2-1\n",
    "\"\"\"\n",
    "Your code here\n",
    "\"\"\"\n",
    "#grp = ['CNT_CHILDREN_GROUP', 'TARGET']\n",
    "\n",
    "grouped_df = app_train.groupby(['CNT_CHILDREN_GROUP', 'TARGET'])['AMT_INCOME_TOTAL']\n",
    "grouped_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2-2\n",
    "\"\"\"\n",
    "Your code here\n",
    "\"\"\"\n",
    "plt_column = 'AMT_INCOME_TOTAL'\n",
    "plt_by = ['CNT_CHILDREN_GROUP', 'TARGET']\n",
    "\n",
    "app_train.boxplot(column=plt_column, by = plt_by, showfliers = False, figsize=(12,12))\n",
    "plt.suptitle('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3\n",
    "\"\"\"\n",
    "Your code here\n",
    "\"\"\"\n",
    "app_train['AMT_INCOME_TOTAL_Z_BY_CHILDREN_GRP-TARGET'] = grouped_df.apply(lambda x:(x-np.mean(x))/np.std(x))\n",
    "\n",
    "app_train[['AMT_INCOME_TOTAL','AMT_INCOME_TOTAL_Z_BY_CHILDREN_GRP-TARGET']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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