Day_018_HW-groupby.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# [作業目標]\n",
    "- 使用 Day 17 剛學到的方法, 對較完整的資料生成離散化特徵\n",
    "- 觀察上述離散化特徵, 對於目標值的預測有沒有幫助"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# [作業重點]\n",
    "- 仿照 Day 17 的語法, 將年齡資料 ('DAYS_BIRTH' 除以 365) 離散化\n",
    "- 繪製上述的 \"離散化標籤\" 與目標值 ('TARGET') 的長條圖"
   ]
  },
  {
   "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",
    "%matplotlib inline\n",
    "\n",
    "# 設定 data_path\n",
    "# dir_data = './data/'\n",
    "# f_app_train = os.path.join(dir_data, 'application_train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 之前做過的處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 122)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 讀取資料檔\n",
    "app_train = pd.read_csv(r\"C:\\Users\\user\\100 days\\data\\application_train.csv\")\n",
    "app_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 122)\n"
     ]
    },
    {
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       "  <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",
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       "      <td>100002</td>\n",
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       "      <td>100004</td>\n",
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       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
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       "      <td>M</td>\n",
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       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
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      "text/plain": [
       "   SK_ID_CURR  TARGET  NAME_CONTRACT_TYPE CODE_GENDER  FLAG_OWN_CAR  \\\n",
       "0      100002       1                   0           M             0   \n",
       "1      100003       0                   0           F             0   \n",
       "2      100004       0                   1           M             1   \n",
       "3      100006       0                   0           F             0   \n",
       "4      100007       0                   0           M             0   \n",
       "\n",
       "   FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0                1             0          202500.0    406597.5      24700.5   \n",
       "1                0             0          270000.0   1293502.5      35698.5   \n",
       "2                1             0           67500.0    135000.0       6750.0   \n",
       "3                1             0          135000.0    312682.5      29686.5   \n",
       "4                1             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0                0   \n",
       "1  ...                 0                0                0                0   \n",
       "2  ...                 0                0                0                0   \n",
       "3  ...                 0                0                0                0   \n",
       "4  ...                 0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\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_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\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_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將只有兩種值的類別型欄位, 做 Label Encoder, 計算相關係數時讓這些欄位可以被包含在內\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# 檢查每一個 column\n",
    "for col in app_train:\n",
    "    if app_train[col].dtype == 'object':\n",
    "        # 如果只有兩種值的類別型欄位\n",
    "        if len(list(app_train[col].unique())) <= 2:\n",
    "            # 就做 Label Encoder, 以加入相關係數檢查\n",
    "            app_train[col] = le.fit_transform(app_train[col])            \n",
    "print(app_train.shape)\n",
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 受雇日數為異常值的資料, 另外設一個欄位記錄, 並將異常的日數轉成空值 (np.nan)\n",
    "app_train['DAYS_EMPLOYED_ANOM'] = app_train[\"DAYS_EMPLOYED\"] == 365243\n",
    "app_train['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True)\n",
    "\n",
    "# 出生日數 (DAYS_BIRTH) 取絕對值 \n",
    "app_train['DAYS_BIRTH'] = abs(app_train['DAYS_BIRTH'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 練習時間\n",
    "參考 Day 17 範例程式，離散化你覺得有興趣的欄位，並嘗試找出有趣的訊息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "連續型特徵離散化\n",
    "雖然由之前的分析發現 DAYS_BIRTH 與 TARGET 的相關係數只有 -0.07，\n",
    "但在往後的課程裡，為了實際解決問題，我們將需透過一些處理方法，改善相關係數。\n",
    "目前我們先以對資料的常識理解，去觀察這兩個欄位的交叉狀況。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between TARGET and DAYS_BIRTH -0.07823930830982712\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    307511.000000\n",
       "mean         43.936973\n",
       "std          11.956133\n",
       "min          20.517808\n",
       "25%          34.008219\n",
       "50%          43.150685\n",
       "75%          53.923288\n",
       "max          69.120548\n",
       "Name: DAYS_BIRTH, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 先來看看 'DAYS_BIRTH' 的統計描述吧 (pandas.describe())\n",
    "print(\"Correlation between TARGET and DAYS_BIRTH\", app_train['DAYS_BIRTH'].corr(app_train['TARGET']))\n",
    "(app_train['DAYS_BIRTH']/365).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# 年齡最小是 20，最大是 69，那我們可以把年齡每 5 歲為一組後再來觀察其與預測目標的關係\n",
    "# 將年齡相關資料, 另外存成一個 DataFrame 來處理\n",
    "age_data = app_train[['TARGET', 'DAYS_BIRTH']]\n",
    "age_data['YEARS_BIRTH'] = age_data['DAYS_BIRTH'] / 365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "      <th>YEARS_BINNED</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>20099</td>\n",
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       "      <td>(55.0, 60.0]</td>\n",
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       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>14469</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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]\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]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將年齡資料離散化 / 分組\n",
    "age_data['YEARS_BINNED'] = pd.cut(age_data['YEARS_BIRTH'], bins = np.linspace(20, 70, num = 11))\n",
    "age_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "        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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 依照組別彙整年齡資料 \"每一欄的平均值\"\n",
    "age_groups  = age_data.groupby('YEARS_BINNED').mean()\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
    "# 繪製目標值平均與分組組別的長條圖\n",
    "plt.bar(range(len(age_groups.index)), age_groups['TARGET'])\n",
    "\n",
    "# 加上 X, y 座標說明, 以及圖表的標題\n",
    "plt.xticks(range(len(age_groups.index)), age_groups.index, rotation = 75); plt.xlabel('Age Group (years)'); plt.ylabel('Average Failure to Repay')\n",
    "plt.title('Failure to Repay by Age Group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可以非常明顯地看出趨勢，越年輕的客戶族群的違約遲繳的可能越大！\n",
    "# 2019/09/22"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}