homework/Day_016_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [EDA] 了解變數分布狀態: Bar & KDE (density plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To do: 變項的分群比較\n",
    "1. 自 20 到 70 歲，切 11 個點，進行分群比較 (KDE plot)\n",
    "2. 以年齡區間為 x, target 為 y 繪製 barplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著調整資料, 並利用提供的程式繪製分布圖"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 如何將資料依照歲數, 將 20 到 70 歲切成11個區間? (In[4], Hint : 使用 numpy.linspace),  \n",
    "  送入繪圖前的除了排序外, 還要注意什麼? (In[5])\n",
    "- 如何調整對應資料, 以繪製長條圖(bar chart)? (In[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入需要的套件\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns # 另一個繪圖-樣式套件\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# 忽略警告訊息\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 設定 data_path\n",
    "dir_data = './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path of read in data: ./data/application_train.csv\n"
     ]
    },
    {
     "data": {
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       "<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",
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       "      <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",
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       "    </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",
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       "      <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",
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       "    </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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 讀取檔案\n",
    "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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 資料整理 ( 'DAYS_BIRTH'全部取絕對值 )\n",
    "app_train['DAYS_BIRTH'] = abs(app_train['DAYS_BIRTH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35.0, 40.0]    42868\n",
      "(40.0, 45.0]    41406\n",
      "(30.0, 35.0]    39440\n",
      "(45.0, 50.0]    35135\n",
      "(50.0, 55.0]    34942\n",
      "(55.0, 60.0]    33120\n",
      "(25.0, 30.0]    32862\n",
      "(60.0, 65.0]    27476\n",
      "(20.0, 25.0]    12159\n",
      "(65.0, 70.0]     8103\n",
      "Name: YEARS_BINNED, dtype: int64\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "      <th>YEARS_BINNED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9461</td>\n",
       "      <td>25.920548</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>16765</td>\n",
       "      <td>45.931507</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19046</td>\n",
       "      <td>52.180822</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>19005</td>\n",
       "      <td>52.068493</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>19932</td>\n",
       "      <td>54.608219</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  DAYS_BIRTH  YEARS_BIRTH  YEARS_BINNED\n",
       "0       1        9461    25.920548  (25.0, 30.0]\n",
       "1       0       16765    45.931507  (45.0, 50.0]\n",
       "2       0       19046    52.180822  (50.0, 55.0]\n",
       "3       0       19005    52.068493  (50.0, 55.0]\n",
       "4       0       19932    54.608219  (50.0, 55.0]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根據年齡分成不同組別 (年齡區間 - 還款與否)\n",
    "age_data = app_train[['TARGET', 'DAYS_BIRTH']] # subset\n",
    "age_data['YEARS_BIRTH'] = age_data['DAYS_BIRTH'] / 365 # day-age to year-age\n",
    "\n",
    "#自 20 到 70 歲，切 11 個點 (得到 10 組)\n",
    "bin_cut = np.linspace(20, 70, 11) \n",
    "age_data['YEARS_BINNED'] = pd.cut(age_data['YEARS_BIRTH'], bins = bin_cut) \n",
    "\n",
    "# 顯示不同組的數量\n",
    "print(age_data['YEARS_BINNED'].value_counts())\n",
    "age_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 繪圖前先排序 / 分組\n",
    "\n",
    "year_group_sorted = age_data['YEARS_BINNED'].unique().sort_values()\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "for i in range(len(year_group_sorted)):\n",
    "    sns.distplot(age_data.loc[(age_data['YEARS_BINNED'] == year_group_sorted[i]) & \\\n",
    "                              (age_data['TARGET'] == 0), 'YEARS_BIRTH'], label = str(year_group_sorted[i]))\n",
    "    \n",
    "    sns.distplot(age_data.loc[(age_data['YEARS_BINNED'] == year_group_sorted[i]) & \\\n",
    "                              (age_data['TARGET'] == 1), 'YEARS_BIRTH'], label = str(year_group_sorted[i]))\n",
    "plt.title('KDE with Age groups')\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>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BINNED</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(20.0, 25.0]</th>\n",
       "      <td>0.123036</td>\n",
       "      <td>8532.795625</td>\n",
       "      <td>23.377522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25.0, 30.0]</th>\n",
       "      <td>0.111436</td>\n",
       "      <td>10155.219250</td>\n",
       "      <td>27.822518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30.0, 35.0]</th>\n",
       "      <td>0.102814</td>\n",
       "      <td>11854.848377</td>\n",
       "      <td>32.479037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35.0, 40.0]</th>\n",
       "      <td>0.089414</td>\n",
       "      <td>13707.908253</td>\n",
       "      <td>37.555913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.0, 45.0]</th>\n",
       "      <td>0.078491</td>\n",
       "      <td>15497.661233</td>\n",
       "      <td>42.459346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45.0, 50.0]</th>\n",
       "      <td>0.074171</td>\n",
       "      <td>17323.900441</td>\n",
       "      <td>47.462741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50.0, 55.0]</th>\n",
       "      <td>0.066968</td>\n",
       "      <td>19196.494791</td>\n",
       "      <td>52.593136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55.0, 60.0]</th>\n",
       "      <td>0.055314</td>\n",
       "      <td>20984.262742</td>\n",
       "      <td>57.491131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60.0, 65.0]</th>\n",
       "      <td>0.052737</td>\n",
       "      <td>22780.547460</td>\n",
       "      <td>62.412459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(65.0, 70.0]</th>\n",
       "      <td>0.037270</td>\n",
       "      <td>24292.614340</td>\n",
       "      <td>66.555108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET    DAYS_BIRTH  YEARS_BIRTH\n",
       "YEARS_BINNED                                     \n",
       "(20.0, 25.0]  0.123036   8532.795625    23.377522\n",
       "(25.0, 30.0]  0.111436  10155.219250    27.822518\n",
       "(30.0, 35.0]  0.102814  11854.848377    32.479037\n",
       "(35.0, 40.0]  0.089414  13707.908253    37.555913\n",
       "(40.0, 45.0]  0.078491  15497.661233    42.459346\n",
       "(45.0, 50.0]  0.074171  17323.900441    47.462741\n",
       "(50.0, 55.0]  0.066968  19196.494791    52.593136\n",
       "(55.0, 60.0]  0.055314  20984.262742    57.491131\n",
       "(60.0, 65.0]  0.052737  22780.547460    62.412459\n",
       "(65.0, 70.0]  0.037270  24292.614340    66.555108"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 計算每個年齡區間的 Target、DAYS_BIRTH與 YEARS_BIRTH 的平均值\n",
    "age_groups  = age_data.groupby('YEARS_BINNED').mean()\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "\n",
    "# 以年齡區間為 x, target 為 y 繪製 barplot\n",
    "px = age_data['YEARS_BINNED']\n",
    "py = app_train['TARGET'] \n",
    "sns.barplot(px, py)\n",
    "\n",
    "# Plot labeling\n",
    "plt.xticks(rotation = 75); plt.xlabel('Age Group (years)'); plt.ylabel('Failure to Repay (%)')\n",
    "plt.title('Failure to Repay by Age Group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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