homework/Day_018_HW.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/'"
   ]
  },
  {
   "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",
    "f_app_train = os.path.join(dir_data, 'application_train.csv')\n",
    "app_train = pd.read_csv(f_app_train)\n",
    "app_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 122)\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>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <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",
       "      <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>0</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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                   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  \\\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": [
    "# 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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "app_train['YEARS_BIRTH'] = app_train['DAYS_BIRTH']/365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35.099, 39.959]    41758\n",
       "(39.959, 44.819]    40350\n",
       "(30.238, 35.099]    38430\n",
       "(44.819, 49.679]    34311\n",
       "(49.679, 54.54]     33544\n",
       "(25.378, 30.238]    33127\n",
       "(54.54, 59.4]       32650\n",
       "(59.4, 64.26]       27685\n",
       "(20.469, 25.378]    13679\n",
       "(64.26, 69.121]     11977\n",
       "Name: equal_width_age, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#等寬劃分\n",
    "app_train[\"equal_width_age\"] = pd.cut(app_train[\"YEARS_BIRTH\"], 10)\n",
    "app_train[\"equal_width_age\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116e86940>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.barplot(app_train['equal_width_age'], app_train['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43.151, 47.178]    30764\n",
       "(28.175, 32.038]    30760\n",
       "(36.0, 39.496]      30759\n",
       "(51.74, 56.093]     30758\n",
       "(56.093, 60.77]     30757\n",
       "(20.517, 28.175]    30752\n",
       "(39.496, 43.151]    30745\n",
       "(32.038, 36.0]      30745\n",
       "(60.77, 69.121]     30738\n",
       "(47.178, 51.74]     30733\n",
       "Name: equal_freq_age, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#等頻劃分\n",
    "app_train[\"equal_freq_age\"] = pd.qcut(app_train[\"YEARS_BIRTH\"], 10)\n",
    "app_train[\"equal_freq_age\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116ef1438>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(app_train['equal_freq_age'], app_train['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}