Day_015_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# [作業目標]\n",
    "- 請同學試著使用 pandas.corr() 這個函數來顯示相關係數並加以觀察結果 \n",
    "- 思考1 : 使用 pandas 有沒有什麼寫法, 可以顯示欄位中最大的幾筆, 以及最小幾筆呢? (Hint: 排序後列出前幾筆/後幾筆)\n",
    "- 思考2 : 試著使用散佈圖, 顯示相關度最大/最小的特徵與目標值的關係, 如果圖形不明顯, 是否有調整的方法?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# [作業重點]\n",
    "- 綜合前幾單元的作法, 試試看是否能夠用繪圖顯示出特徵與目標的相關性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 122)"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "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",
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       "      <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",
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       "      <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",
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       "      <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 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": 28,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'CODE_GENDER',\n",
      "       'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',\n",
      "       'AMT_CREDIT', 'AMT_ANNUITY',\n",
      "       ...\n",
      "       'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21',\n",
      "       'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
      "       'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',\n",
      "       'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR',\n",
      "       'DAYS_EMPLOYED_ANOM'],\n",
      "      dtype='object', length=123)\n"
     ]
    }
   ],
   "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",
    "print(app_train.columns)\n",
    "app_train['DAYS_EMPLOYED_ANOM'] = app_train[\"DAYS_EMPLOYED\"] == 365243\n",
    "\n",
    "app_train['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True)\n",
    "# print(app_train[\"DAYS_BIRTH\"])\n",
    "# 出生日數 (DAYS_BIRTH) 取絕對值 \n",
    "app_train['DAYS_BIRTH'] = abs(app_train['DAYS_BIRTH'])\n",
    "# print(app_train[\"DAYS_BIRTH\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 相關係數\n",
    "一樣，pandas 很貼心地讓我們可以非常容易計算相關係數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 觀察相關係數\n",
    "target_corr = pd.DataFrame(app_train.corr()['TARGET'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 練習時間\n",
    "列出目標 (TARGET) 與所有欄位之間相關係數，數值最大以及最小各 15 個\n",
    "\n",
    "通過相關係數的結果觀察有興趣的欄位與 TARGET 或其他欄位的相關係數，並嘗試找出有趣的訊息\n",
    "- 最好的方式當然是畫圖，舉例來說，我們知道  EXT_SOURCE_3 這個欄位和 TARGET 之間的相關係數是 -0.178919 (在已經這個資料集已經是最負的了！)，那我們可以 EXT_SOURCE_3  為 x 軸， TARGET 為 y 軸，把資料給畫出來"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "target_corr = target_corr[np.isnan(target_corr) == False]\n",
    "print(type(target_corr))\n",
    "target_corr = target_corr.sort_values('TARGET')\n",
    "target_corr.head(15)\n",
    "head_15 = target_corr.head(15)\n",
    "tail_15 = target_corr.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 15 artists>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# width 不能接受二維的陣列\n",
    "plt.barh(y = head_15.index, width = head_15.values.reshape(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 15 artists>"
      ]
     },
     "execution_count": 85,
     "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": [
    "plt.barh(y = tail_15.index, width = tail_15.values.reshape(15))"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}