Day_008_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 對資料做更多處理 : 顯示特定欄位的統計值與直方圖"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 試著顯示特定欄位的基礎統計數值 (In[4], Out[4], Hint : describe())\n",
    "- 試著顯示特定欄位的直方圖 (In[5], Out[5], Hint : .hist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./application_train.csv\n"
     ]
    }
   ],
   "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",
    "data_path = './application_train.csv'\n",
    "# print(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f_app_train = os.path.join(dir_data, 'application_train.csv')\n",
    "raw_data = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMT_INCOME_TOTAL mean:168797.9192969845, AMT_INCOME_TOTAL std:237123.14627885626\n"
     ]
    },
    {
     "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": [
    "# raw_data.head()\n",
    "total = raw_data['AMT_INCOME_TOTAL']\n",
    "# total.std()\n",
    "t_mean = total.mean()\n",
    "t_std = total.std()\n",
    "print(f'AMT_INCOME_TOTAL mean:{t_mean}, AMT_INCOME_TOTAL std:{t_std}')\n",
    "col = raw_data['NAME_CONTRACT_TYPE']\n",
    "dic = {}\n",
    "for index, key in enumerate(list(col.unique())):\n",
    "    dic[key] = index\n",
    "col = pd.DataFrame(col.map(lambda x : dic[x]))\n",
    "count = {index:len(col[col['NAME_CONTRACT_TYPE'] == index]) for index in dic.values()}\n",
    "plt.title(\"NAME_CONTRACT_TYPE\")\n",
    "plt.bar(x = count.keys(), height = count.values(), color = ['b','r'])\n",
    "plt.show()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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