Day_019_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作業\n",
    "### 請使用 application_train.csv, 根據不同的 HOUSETYPE_MODE 對 AMT_CREDIT 繪製 Histogram"
   ]
  },
  {
   "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",
    "- 如何將列出相異的 HOUSETYPE_MODE 類別 (In[3])\n",
    "- 如何依照不同的 HOUSETYPE_MODE 類別指定資料, 並繪製長條圖(.hist())? (In[3])"
   ]
  },
  {
   "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",
    "\n",
    "# 忽略警告訊息\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\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": {
      "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>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",
       "      <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>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",
       "      <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>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",
       "      <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>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",
       "      <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         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 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": 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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "app_train['HOUSETYPE_MODE'].fillna('none type', inplace = True)\n",
    "app_train['HOUSETYPE_MODE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x2160 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用不同的 HOUSETYPE_MODE 類別繪製圖形, 並使用 subplot 排版\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "unique_house_type = app_train['HOUSETYPE_MODE'].unique()\n",
    "\n",
    "nrows = len(unique_house_type)\n",
    "ncols = nrows // 2\n",
    "\n",
    "plt.figure(figsize=(10,30))\n",
    "for i in range(len(unique_house_type)):\n",
    "    plt.subplot(nrows, ncols, i+1)\n",
    "    \"\"\"\n",
    "    Your Code Here\n",
    "    \"\"\"\n",
    "    app_train.loc[(app_train['HOUSETYPE_MODE'] == unique_house_type[i]) , 'AMT_CREDIT'].hist()\n",
    "    \n",
    "    plt.title(str(unique_house_type[i]))\n",
    "plt.show()    "
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}