Day_014_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作業\n",
    "### 請根據不同的 HOUSETYPE_MODE 對 AMT_CREDIT 繪製 Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import 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",
    "%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": 17,
   "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  \\\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "block of flats      150503\n",
       "specific housing      1499\n",
       "terraced house        1212\n",
       "Name: HOUSETYPE_MODE, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train['HOUSETYPE_MODE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x2160 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\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",
    "    plt.title(str(unique_house_type[i]))\n",
    "plt.show()    "
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}