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"
     ]
    },
    {
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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",
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       "    <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>0</td>\n",
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       "      <td>0</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",
       "      <td>0</td>\n",
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       "<p>5 rows × 122 columns</p>\n",
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      ],
      "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        TARGET  DAYS_BIRTH\n",
      "0            1        9461\n",
      "1            0       16765\n",
      "2            0       19046\n",
      "3            0       19005\n",
      "4            0       19932\n",
      "5            0       16941\n",
      "6            0       13778\n",
      "7            0       18850\n",
      "8            0       20099\n",
      "9            0       14469\n",
      "10           0       10197\n",
      "11           0       20417\n",
      "12           0       13439\n",
      "13           0       14086\n",
      "14           0       14583\n",
      "15           0        8728\n",
      "16           0       12931\n",
      "17           0        9776\n",
      "18           0       17718\n",
      "19           0       11348\n",
      "20           0       18252\n",
      "21           0       14815\n",
      "22           0       11146\n",
      "23           0       24827\n",
      "24           0       11286\n",
      "25           0       19334\n",
      "26           1       18724\n",
      "27           0       15948\n",
      "28           0        9994\n",
      "29           0       10341\n",
      "...        ...         ...\n",
      "307481       1       20644\n",
      "307482       0       14106\n",
      "307483       0       24911\n",
      "307484       0       12847\n",
      "307485       0       11973\n",
      "307486       0       16010\n",
      "307487       0       23125\n",
      "307488       0       10092\n",
      "307489       1       16471\n",
      "307490       0        9874\n",
      "307491       0       10976\n",
      "307492       0       20965\n",
      "307493       0       17533\n",
      "307494       0       14958\n",
      "307495       0       20922\n",
      "307496       0       17345\n",
      "307497       0       16679\n",
      "307498       0       19102\n",
      "307499       0       16988\n",
      "307500       0       20390\n",
      "307501       0       12961\n",
      "307502       0       16063\n",
      "307503       0       11870\n",
      "307504       0       16705\n",
      "307505       0       24384\n",
      "307506       0        9327\n",
      "307507       0       20775\n",
      "307508       0       14966\n",
      "307509       1       11961\n",
      "307510       0       16856\n",
      "\n",
      "[307511 rows x 2 columns]\n",
      "(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"
     ]
    },
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       "      <th></th>\n",
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       "      <th>DAYS_BIRTH</th>\n",
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      "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": 19,
     "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 召喚子集(就是只有兩欄的資料出來) [307511 rows x 2 columns]\n",
    "print(age_data)\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",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "bin_cut =  np.linspace(20,70, num = 11) #[20. 25. 30. 35. 40. 45. 50. 55. 60. 65. 70.]\n",
    "\n",
    "age_data['YEARS_BINNED'] = pd.cut(age_data['YEARS_BIRTH'], bins =bin_cut ) \n",
    "\n",
    "# 顯示不同組的數量use ---> value_counts()\n",
    "print(age_data['YEARS_BINNED'].value_counts())\n",
    "age_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>DAYS_BIRTH</th>\n",
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       "      <td>46.413699</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>13778</td>\n",
       "      <td>37.747945</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>18850</td>\n",
       "      <td>51.643836</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>20099</td>\n",
       "      <td>55.065753</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>14469</td>\n",
       "      <td>39.641096</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>10197</td>\n",
       "      <td>27.936986</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>20417</td>\n",
       "      <td>55.936986</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>13439</td>\n",
       "      <td>36.819178</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>14086</td>\n",
       "      <td>38.591781</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>14583</td>\n",
       "      <td>39.953425</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>8728</td>\n",
       "      <td>23.912329</td>\n",
       "      <td>(20.0, 25.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>12931</td>\n",
       "      <td>35.427397</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>9776</td>\n",
       "      <td>26.783562</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>17718</td>\n",
       "      <td>48.542466</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>11348</td>\n",
       "      <td>31.090411</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>18252</td>\n",
       "      <td>50.005479</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>14815</td>\n",
       "      <td>40.589041</td>\n",
       "      <td>(40.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>11146</td>\n",
       "      <td>30.536986</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>24827</td>\n",
       "      <td>68.019178</td>\n",
       "      <td>(65.0, 70.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>11286</td>\n",
       "      <td>30.920548</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>19334</td>\n",
       "      <td>52.969863</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>18724</td>\n",
       "      <td>51.298630</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>15948</td>\n",
       "      <td>43.693151</td>\n",
       "      <td>(40.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>9994</td>\n",
       "      <td>27.380822</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>10341</td>\n",
       "      <td>28.331507</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307481</th>\n",
       "      <td>1</td>\n",
       "      <td>20644</td>\n",
       "      <td>56.558904</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307482</th>\n",
       "      <td>0</td>\n",
       "      <td>14106</td>\n",
       "      <td>38.646575</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307483</th>\n",
       "      <td>0</td>\n",
       "      <td>24911</td>\n",
       "      <td>68.249315</td>\n",
       "      <td>(65.0, 70.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307484</th>\n",
       "      <td>0</td>\n",
       "      <td>12847</td>\n",
       "      <td>35.197260</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307485</th>\n",
       "      <td>0</td>\n",
       "      <td>11973</td>\n",
       "      <td>32.802740</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307486</th>\n",
       "      <td>0</td>\n",
       "      <td>16010</td>\n",
       "      <td>43.863014</td>\n",
       "      <td>(40.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307487</th>\n",
       "      <td>0</td>\n",
       "      <td>23125</td>\n",
       "      <td>63.356164</td>\n",
       "      <td>(60.0, 65.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307488</th>\n",
       "      <td>0</td>\n",
       "      <td>10092</td>\n",
       "      <td>27.649315</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307489</th>\n",
       "      <td>1</td>\n",
       "      <td>16471</td>\n",
       "      <td>45.126027</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307490</th>\n",
       "      <td>0</td>\n",
       "      <td>9874</td>\n",
       "      <td>27.052055</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307491</th>\n",
       "      <td>0</td>\n",
       "      <td>10976</td>\n",
       "      <td>30.071233</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307492</th>\n",
       "      <td>0</td>\n",
       "      <td>20965</td>\n",
       "      <td>57.438356</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307493</th>\n",
       "      <td>0</td>\n",
       "      <td>17533</td>\n",
       "      <td>48.035616</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307494</th>\n",
       "      <td>0</td>\n",
       "      <td>14958</td>\n",
       "      <td>40.980822</td>\n",
       "      <td>(40.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307495</th>\n",
       "      <td>0</td>\n",
       "      <td>20922</td>\n",
       "      <td>57.320548</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307496</th>\n",
       "      <td>0</td>\n",
       "      <td>17345</td>\n",
       "      <td>47.520548</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307497</th>\n",
       "      <td>0</td>\n",
       "      <td>16679</td>\n",
       "      <td>45.695890</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307498</th>\n",
       "      <td>0</td>\n",
       "      <td>19102</td>\n",
       "      <td>52.334247</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307499</th>\n",
       "      <td>0</td>\n",
       "      <td>16988</td>\n",
       "      <td>46.542466</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307500</th>\n",
       "      <td>0</td>\n",
       "      <td>20390</td>\n",
       "      <td>55.863014</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307501</th>\n",
       "      <td>0</td>\n",
       "      <td>12961</td>\n",
       "      <td>35.509589</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307502</th>\n",
       "      <td>0</td>\n",
       "      <td>16063</td>\n",
       "      <td>44.008219</td>\n",
       "      <td>(40.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307503</th>\n",
       "      <td>0</td>\n",
       "      <td>11870</td>\n",
       "      <td>32.520548</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307504</th>\n",
       "      <td>0</td>\n",
       "      <td>16705</td>\n",
       "      <td>45.767123</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307505</th>\n",
       "      <td>0</td>\n",
       "      <td>24384</td>\n",
       "      <td>66.805479</td>\n",
       "      <td>(65.0, 70.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307506</th>\n",
       "      <td>0</td>\n",
       "      <td>9327</td>\n",
       "      <td>25.553425</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307507</th>\n",
       "      <td>0</td>\n",
       "      <td>20775</td>\n",
       "      <td>56.917808</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307508</th>\n",
       "      <td>0</td>\n",
       "      <td>14966</td>\n",
       "      <td>41.002740</td>\n",
       "      <td>(40.0, 45.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307509</th>\n",
       "      <td>1</td>\n",
       "      <td>11961</td>\n",
       "      <td>32.769863</td>\n",
       "      <td>(30.0, 35.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307510</th>\n",
       "      <td>0</td>\n",
       "      <td>16856</td>\n",
       "      <td>46.180822</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>307511 rows × 4 columns</p>\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]\n",
       "5            0       16941    46.413699  (45.0, 50.0]\n",
       "6            0       13778    37.747945  (35.0, 40.0]\n",
       "7            0       18850    51.643836  (50.0, 55.0]\n",
       "8            0       20099    55.065753  (55.0, 60.0]\n",
       "9            0       14469    39.641096  (35.0, 40.0]\n",
       "10           0       10197    27.936986  (25.0, 30.0]\n",
       "11           0       20417    55.936986  (55.0, 60.0]\n",
       "12           0       13439    36.819178  (35.0, 40.0]\n",
       "13           0       14086    38.591781  (35.0, 40.0]\n",
       "14           0       14583    39.953425  (35.0, 40.0]\n",
       "15           0        8728    23.912329  (20.0, 25.0]\n",
       "16           0       12931    35.427397  (35.0, 40.0]\n",
       "17           0        9776    26.783562  (25.0, 30.0]\n",
       "18           0       17718    48.542466  (45.0, 50.0]\n",
       "19           0       11348    31.090411  (30.0, 35.0]\n",
       "20           0       18252    50.005479  (50.0, 55.0]\n",
       "21           0       14815    40.589041  (40.0, 45.0]\n",
       "22           0       11146    30.536986  (30.0, 35.0]\n",
       "23           0       24827    68.019178  (65.0, 70.0]\n",
       "24           0       11286    30.920548  (30.0, 35.0]\n",
       "25           0       19334    52.969863  (50.0, 55.0]\n",
       "26           1       18724    51.298630  (50.0, 55.0]\n",
       "27           0       15948    43.693151  (40.0, 45.0]\n",
       "28           0        9994    27.380822  (25.0, 30.0]\n",
       "29           0       10341    28.331507  (25.0, 30.0]\n",
       "...        ...         ...          ...           ...\n",
       "307481       1       20644    56.558904  (55.0, 60.0]\n",
       "307482       0       14106    38.646575  (35.0, 40.0]\n",
       "307483       0       24911    68.249315  (65.0, 70.0]\n",
       "307484       0       12847    35.197260  (35.0, 40.0]\n",
       "307485       0       11973    32.802740  (30.0, 35.0]\n",
       "307486       0       16010    43.863014  (40.0, 45.0]\n",
       "307487       0       23125    63.356164  (60.0, 65.0]\n",
       "307488       0       10092    27.649315  (25.0, 30.0]\n",
       "307489       1       16471    45.126027  (45.0, 50.0]\n",
       "307490       0        9874    27.052055  (25.0, 30.0]\n",
       "307491       0       10976    30.071233  (30.0, 35.0]\n",
       "307492       0       20965    57.438356  (55.0, 60.0]\n",
       "307493       0       17533    48.035616  (45.0, 50.0]\n",
       "307494       0       14958    40.980822  (40.0, 45.0]\n",
       "307495       0       20922    57.320548  (55.0, 60.0]\n",
       "307496       0       17345    47.520548  (45.0, 50.0]\n",
       "307497       0       16679    45.695890  (45.0, 50.0]\n",
       "307498       0       19102    52.334247  (50.0, 55.0]\n",
       "307499       0       16988    46.542466  (45.0, 50.0]\n",
       "307500       0       20390    55.863014  (55.0, 60.0]\n",
       "307501       0       12961    35.509589  (35.0, 40.0]\n",
       "307502       0       16063    44.008219  (40.0, 45.0]\n",
       "307503       0       11870    32.520548  (30.0, 35.0]\n",
       "307504       0       16705    45.767123  (45.0, 50.0]\n",
       "307505       0       24384    66.805479  (65.0, 70.0]\n",
       "307506       0        9327    25.553425  (25.0, 30.0]\n",
       "307507       0       20775    56.917808  (55.0, 60.0]\n",
       "307508       0       14966    41.002740  (40.0, 45.0]\n",
       "307509       1       11961    32.769863  (30.0, 35.0]\n",
       "307510       0       16856    46.180822  (45.0, 50.0]\n",
       "\n",
       "[307511 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_group_sorted = np.sort(age_data[\"YEARS_BINNED\"].unique())\n",
    "year_group_sorted\n",
    "age_data['YEARS_BINNED']\n",
    "age_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 繪圖前先排序 / 分組\n",
    "\n",
    "year_group_sorted = np.sort(age_data[\"YEARS_BINNED\"].unique())\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "for i in range(len(year_group_sorted)):\n",
    "\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": 29,
   "metadata": {
    "scrolled": true
   },
   "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": 29,
     "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()  #根據YEARS_BINNED分群,算出每一群的平均值\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalIndex([(20.0, 25.0], (25.0, 30.0], (30.0, 35.0], (35.0, 40.0],\n",
       "                  (40.0, 45.0], (45.0, 50.0], (50.0, 55.0], (55.0, 60.0],\n",
       "                  (60.0, 65.0], (65.0, 70.0]],\n",
       "                 categories=[(20.0, 25.0], (25.0, 30.0], (30.0, 35.0], (35.0, 40.0], (40.0, 45.0], (45.0, 50.0], (50.0, 55.0], (55.0, 60.0], ...], ordered=True, name='YEARS_BINNED', dtype='category')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_groups.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "px = age_groups.index\n",
    "py = age_groups['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": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}