Day_005_HW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import 需要的套件\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 設定 data_path\n",
    "dir_data = './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_app_train = os.path.join(dir_data, 'application_train.csv')\n",
    "app_train = pd.read_csv(f_app_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 練習時間"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "觀察有興趣的欄位的資料分佈，並嘗試找出有趣的訊息\n",
    "#### Eg\n",
    "- 計算任意欄位的平均數及標準差\n",
    "- 畫出任意欄位的[直方圖](https://zh.wikipedia.org/zh-tw/%E7%9B%B4%E6%96%B9%E5%9B%BE)\n",
    "\n",
    "### Hints:\n",
    "- [Descriptive Statistics For pandas Dataframe](https://chrisalbon.com/python/data_wrangling/pandas_dataframe_descriptive_stats/)\n",
    "- [pandas 中的繪圖函數](https://amaozhao.gitbooks.io/pandas-notebook/content/pandas%E4%B8%AD%E7%9A%84%E7%BB%98%E5%9B%BE%E5%87%BD%E6%95%B0.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "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>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</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>count</th>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>307499.000000</td>\n",
       "      <td>3.072330e+05</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>278180.518577</td>\n",
       "      <td>0.080729</td>\n",
       "      <td>0.417052</td>\n",
       "      <td>1.687979e+05</td>\n",
       "      <td>5.990260e+05</td>\n",
       "      <td>27108.573909</td>\n",
       "      <td>5.383962e+05</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>-16036.995067</td>\n",
       "      <td>63815.045904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008130</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.006402</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.034362</td>\n",
       "      <td>0.267395</td>\n",
       "      <td>0.265474</td>\n",
       "      <td>1.899974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102790.175348</td>\n",
       "      <td>0.272419</td>\n",
       "      <td>0.722121</td>\n",
       "      <td>2.371231e+05</td>\n",
       "      <td>4.024908e+05</td>\n",
       "      <td>14493.737315</td>\n",
       "      <td>3.694465e+05</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>4363.988632</td>\n",
       "      <td>141275.766519</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089798</td>\n",
       "      <td>0.024387</td>\n",
       "      <td>0.022518</td>\n",
       "      <td>0.018299</td>\n",
       "      <td>0.083849</td>\n",
       "      <td>0.110757</td>\n",
       "      <td>0.204685</td>\n",
       "      <td>0.916002</td>\n",
       "      <td>0.794056</td>\n",
       "      <td>1.869295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100002.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.565000e+04</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>1615.500000</td>\n",
       "      <td>4.050000e+04</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>-25229.000000</td>\n",
       "      <td>-17912.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>189145.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.125000e+05</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>16524.000000</td>\n",
       "      <td>2.385000e+05</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>-19682.000000</td>\n",
       "      <td>-2760.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>278202.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.471500e+05</td>\n",
       "      <td>5.135310e+05</td>\n",
       "      <td>24903.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>-15750.000000</td>\n",
       "      <td>-1213.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>367142.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.025000e+05</td>\n",
       "      <td>8.086500e+05</td>\n",
       "      <td>34596.000000</td>\n",
       "      <td>6.795000e+05</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-12413.000000</td>\n",
       "      <td>-289.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>456255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.170000e+08</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>258025.500000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>0.072508</td>\n",
       "      <td>-7489.000000</td>\n",
       "      <td>365243.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SK_ID_CURR         TARGET   CNT_CHILDREN  AMT_INCOME_TOTAL  \\\n",
       "count  307511.000000  307511.000000  307511.000000      3.075110e+05   \n",
       "mean   278180.518577       0.080729       0.417052      1.687979e+05   \n",
       "std    102790.175348       0.272419       0.722121      2.371231e+05   \n",
       "min    100002.000000       0.000000       0.000000      2.565000e+04   \n",
       "25%    189145.500000       0.000000       0.000000      1.125000e+05   \n",
       "50%    278202.000000       0.000000       0.000000      1.471500e+05   \n",
       "75%    367142.500000       0.000000       1.000000      2.025000e+05   \n",
       "max    456255.000000       1.000000      19.000000      1.170000e+08   \n",
       "\n",
       "         AMT_CREDIT    AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "count  3.075110e+05  307499.000000     3.072330e+05   \n",
       "mean   5.990260e+05   27108.573909     5.383962e+05   \n",
       "std    4.024908e+05   14493.737315     3.694465e+05   \n",
       "min    4.500000e+04    1615.500000     4.050000e+04   \n",
       "25%    2.700000e+05   16524.000000     2.385000e+05   \n",
       "50%    5.135310e+05   24903.000000     4.500000e+05   \n",
       "75%    8.086500e+05   34596.000000     6.795000e+05   \n",
       "max    4.050000e+06  258025.500000     4.050000e+06   \n",
       "\n",
       "       REGION_POPULATION_RELATIVE     DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
       "count               307511.000000  307511.000000  307511.000000   \n",
       "mean                     0.020868  -16036.995067   63815.045904   \n",
       "std                      0.013831    4363.988632  141275.766519   \n",
       "min                      0.000290  -25229.000000  -17912.000000   \n",
       "25%                      0.010006  -19682.000000   -2760.000000   \n",
       "50%                      0.018850  -15750.000000   -1213.000000   \n",
       "75%                      0.028663  -12413.000000    -289.000000   \n",
       "max                      0.072508   -7489.000000  365243.000000   \n",
       "\n",
       "                  ...              FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  \\\n",
       "count             ...                 307511.000000     307511.000000   \n",
       "mean              ...                      0.008130          0.000595   \n",
       "std               ...                      0.089798          0.024387   \n",
       "min               ...                      0.000000          0.000000   \n",
       "25%               ...                      0.000000          0.000000   \n",
       "50%               ...                      0.000000          0.000000   \n",
       "75%               ...                      0.000000          0.000000   \n",
       "max               ...                      1.000000          1.000000   \n",
       "\n",
       "       FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "count     307511.000000     307511.000000               265992.000000   \n",
       "mean           0.000507          0.000335                    0.006402   \n",
       "std            0.022518          0.018299                    0.083849   \n",
       "min            0.000000          0.000000                    0.000000   \n",
       "25%            0.000000          0.000000                    0.000000   \n",
       "50%            0.000000          0.000000                    0.000000   \n",
       "75%            0.000000          0.000000                    0.000000   \n",
       "max            1.000000          1.000000                    4.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "count              265992.000000               265992.000000   \n",
       "mean                    0.007000                    0.034362   \n",
       "std                     0.110757                    0.204685   \n",
       "min                     0.000000                    0.000000   \n",
       "25%                     0.000000                    0.000000   \n",
       "50%                     0.000000                    0.000000   \n",
       "75%                     0.000000                    0.000000   \n",
       "max                     9.000000                    8.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "count              265992.000000              265992.000000   \n",
       "mean                    0.267395                   0.265474   \n",
       "std                     0.916002                   0.794056   \n",
       "min                     0.000000                   0.000000   \n",
       "25%                     0.000000                   0.000000   \n",
       "50%                     0.000000                   0.000000   \n",
       "75%                     0.000000                   0.000000   \n",
       "max                    27.000000                 261.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "count               265992.000000  \n",
       "mean                     1.899974  \n",
       "std                      1.869295  \n",
       "min                      0.000000  \n",
       "25%                      0.000000  \n",
       "50%                      1.000000  \n",
       "75%                      3.000000  \n",
       "max                     25.000000  \n",
       "\n",
       "[8 rows x 106 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "app_train['AMT_ANNUITY'].hist(figsize=(20,10), bins=20) # bins為長條組數, total 20 groups\n",
    "plt.show() # 1e7 means 1*10^7"
   ]
  },
  {
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}