10 [補充] 泰迪熊 Tidy Data with Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 泰迪熊 Tidy Data with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先來個標準設定。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mac/anaconda/lib/python3.6/site-packages/matplotlib/font_manager.py:280: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  'Matplotlib is building the font cache using fc-list. '\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 `seaborn` 讓圖漂亮一點。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "畫圖要使用中文的設定, 我用了很普級的 `SimHei`, 還是沒有請自行換一個。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 選個普通的黑體字\n",
    "plt.rcParams['axes.unicode_minus']=False # 負號不出問題"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這裡使用 Tidy Data 創始人的書 \"R for Data Analysis\" 中介紹 Tidy Data 的部份。可以在網路上看到書中的內容:\n",
    "\n",
    "[Tidy Data](http://r4ds.had.co.nz/tidy-data.html)\n",
    "\n",
    "例子就是書上的例子, 只是翻成了中文。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 四個我們使用的 DataFrame\n",
    "\n",
    "這裡有四張表格, 其實內容完全一樣, 只是整理呈現的方式不同。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### table1\n",
    "\n",
    "只有這個是 tidy data!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col1 = ['國家', '年份', '案例', '人口']\n",
    "data1 = [['阿富汗', 1999, 745, 19987071],\n",
    "['阿富汗', 2000, 2666, 20595360],\n",
    "['巴西', 1999, 37737, 172006362],\n",
    "['巴西', 2000, 80488, 174504898],\n",
    "['中國', 1999, 212258, 1272915272],\n",
    "['中國', 2000, 213766, 1280428583]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table1 = pd.DataFrame(data=data1, columns=col1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>國家</th>\n",
       "      <th>年份</th>\n",
       "      <th>案例</th>\n",
       "      <th>人口</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>1999</td>\n",
       "      <td>745</td>\n",
       "      <td>19987071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>2000</td>\n",
       "      <td>2666</td>\n",
       "      <td>20595360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>巴西</td>\n",
       "      <td>1999</td>\n",
       "      <td>37737</td>\n",
       "      <td>172006362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>巴西</td>\n",
       "      <td>2000</td>\n",
       "      <td>80488</td>\n",
       "      <td>174504898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中國</td>\n",
       "      <td>1999</td>\n",
       "      <td>212258</td>\n",
       "      <td>1272915272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>中國</td>\n",
       "      <td>2000</td>\n",
       "      <td>213766</td>\n",
       "      <td>1280428583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    國家    年份      案例          人口\n",
       "0  阿富汗  1999     745    19987071\n",
       "1  阿富汗  2000    2666    20595360\n",
       "2   巴西  1999   37737   172006362\n",
       "3   巴西  2000   80488   174504898\n",
       "4   中國  1999  212258  1272915272\n",
       "5   中國  2000  213766  1280428583"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col2 = ['國家', '年份', '類別', '數量']\n",
    "data2 = [['阿富汗', 1999, '案例', 745],\n",
    "       ['阿富汗', 1999, '人口', 19987071],\n",
    "       ['阿富汗', 2000, '案例', 2666],\n",
    "       ['阿富汗', 2000, '人口', 20595360],\n",
    "       ['巴西', 1999, '案例', 37737],\n",
    "       ['巴西', 1999, '人口', 172006362],\n",
    "       ['巴西', 2000, '案例', 80488],\n",
    "       ['巴西', 2000, '人口', 174504898],\n",
    "       ['中國', 1999, '案例', 212258],\n",
    "       ['中國', 1999, '人口', 1272915272],\n",
    "       ['中國', 2000, '案例', 213766],\n",
    "       ['中國', 2000, '人口', 1280428583]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table2 = pd.DataFrame(data=data2, columns=col2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>國家</th>\n",
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       "      <th>類別</th>\n",
       "      <th>數量</th>\n",
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       "      <th>0</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>1999</td>\n",
       "      <td>案例</td>\n",
       "      <td>745</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>1999</td>\n",
       "      <td>人口</td>\n",
       "      <td>19987071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>2000</td>\n",
       "      <td>案例</td>\n",
       "      <td>2666</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>2000</td>\n",
       "      <td>人口</td>\n",
       "      <td>20595360</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>巴西</td>\n",
       "      <td>1999</td>\n",
       "      <td>案例</td>\n",
       "      <td>37737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>巴西</td>\n",
       "      <td>1999</td>\n",
       "      <td>人口</td>\n",
       "      <td>172006362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>巴西</td>\n",
       "      <td>2000</td>\n",
       "      <td>案例</td>\n",
       "      <td>80488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>巴西</td>\n",
       "      <td>2000</td>\n",
       "      <td>人口</td>\n",
       "      <td>174504898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>中國</td>\n",
       "      <td>1999</td>\n",
       "      <td>案例</td>\n",
       "      <td>212258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>中國</td>\n",
       "      <td>1999</td>\n",
       "      <td>人口</td>\n",
       "      <td>1272915272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>中國</td>\n",
       "      <td>2000</td>\n",
       "      <td>案例</td>\n",
       "      <td>213766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>中國</td>\n",
       "      <td>2000</td>\n",
       "      <td>人口</td>\n",
       "      <td>1280428583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     國家    年份  類別          數量\n",
       "0   阿富汗  1999  案例         745\n",
       "1   阿富汗  1999  人口    19987071\n",
       "2   阿富汗  2000  案例        2666\n",
       "3   阿富汗  2000  人口    20595360\n",
       "4    巴西  1999  案例       37737\n",
       "5    巴西  1999  人口   172006362\n",
       "6    巴西  2000  案例       80488\n",
       "7    巴西  2000  人口   174504898\n",
       "8    中國  1999  案例      212258\n",
       "9    中國  1999  人口  1272915272\n",
       "10   中國  2000  案例      213766\n",
       "11   中國  2000  人口  1280428583"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### table3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col3 = [\"國家\", \"年份\", \"比例\"]\n",
    "data3 = [['阿富汗', 1999, '745/19987071'],\n",
    "       ['阿富汗', 2000, '2666/20595360'],\n",
    "       ['巴西', 1999, '37737/172006362'],\n",
    "       ['巴西', 2000, '80488/174504898'],\n",
    "       ['中國', 1999, '212258/1272915272'],\n",
    "       ['中國', 2000, '213766/1280428583']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table3 = pd.DataFrame(data=data3, columns=col3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>國家</th>\n",
       "      <th>年份</th>\n",
       "      <th>比例</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>1999</td>\n",
       "      <td>745/19987071</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>2000</td>\n",
       "      <td>2666/20595360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>巴西</td>\n",
       "      <td>1999</td>\n",
       "      <td>37737/172006362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>巴西</td>\n",
       "      <td>2000</td>\n",
       "      <td>80488/174504898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中國</td>\n",
       "      <td>1999</td>\n",
       "      <td>212258/1272915272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>中國</td>\n",
       "      <td>2000</td>\n",
       "      <td>213766/1280428583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    國家    年份                 比例\n",
       "0  阿富汗  1999       745/19987071\n",
       "1  阿富汗  2000      2666/20595360\n",
       "2   巴西  1999    37737/172006362\n",
       "3   巴西  2000    80488/174504898\n",
       "4   中國  1999  212258/1272915272\n",
       "5   中國  2000  213766/1280428583"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### table4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分成兩個表格, `table4a` 是案例, `table4b` 是人口。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col4a = ['國家', 1999, 2000]\n",
    "data4a = [['阿富汗', 745, 2666],\n",
    "       ['巴西', 37737, 80488],\n",
    "       ['中國', 212258, 213766]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table4a = pd.DataFrame(data=data4a, columns=col4a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col4b = ['國家', 1999, 2000]\n",
    "data4b = [['阿富汗', 19987071, 20595360],\n",
    "       ['巴西', 172006362, 174504898],\n",
    "       ['中國', 1272915272, 1280428583]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table4b = pd.DataFrame(data=data4b, columns=col4b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>中國</td>\n",
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       "      <td>213766</td>\n",
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       "    國家    1999    2000\n",
       "0  阿富汗     745    2666\n",
       "1   巴西   37737   80488\n",
       "2   中國  212258  213766"
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    "table4a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>國家</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>19987071</td>\n",
       "      <td>20595360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>巴西</td>\n",
       "      <td>172006362</td>\n",
       "      <td>174504898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中國</td>\n",
       "      <td>1272915272</td>\n",
       "      <td>1280428583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    國家        1999        2000\n",
       "0  阿富汗    19987071    20595360\n",
       "1   巴西   172006362   174504898\n",
       "2   中國  1272915272  1280428583"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 畫個圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DQUJEJM/efvttVqxYwYc//GG2bdvG/fffz7PPPsukSZP61/nrX//KxRdfzMyZM9mwYQMH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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11393fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 為了畫在同一張圖\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for country, group in table1.groupby('國家'):\n",
    "    group.plot(x='年份', y='案例', \n",
    "               ax=ax, label=country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tidy Data 是什麼?\n",
    "\n",
    "1. 每個變數 (variable) 形成一行 (column)。\n",
    "2. 每個觀察 (observation) 形成一列 (row)。\n",
    "3. 每類觀察單位 (observational unit) 形成一個表格。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 手法一: melt\n",
    "\n",
    "我們來看看 table4a。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>國家</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>745</td>\n",
       "      <td>2666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>巴西</td>\n",
       "      <td>37737</td>\n",
       "      <td>80488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中國</td>\n",
       "      <td>212258</td>\n",
       "      <td>213766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    國家    1999    2000\n",
       "0  阿富汗     745    2666\n",
       "1   巴西   37737   80488\n",
       "2   中國  212258  213766"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這個有個問題是 1999, 2000 是「值」(values) 而不是變數名稱, 我們想增加一行叫「年份」的變數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "table4a_tidy = pd.melt(table4a, id_vars='國家', var_name='年份', value_name='案例')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>國家</th>\n",
       "      <th>年份</th>\n",
       "      <th>案例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>1999</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>巴西</td>\n",
       "      <td>1999</td>\n",
       "      <td>37737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>中國</td>\n",
       "      <td>1999</td>\n",
       "      <td>212258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>阿富汗</td>\n",
       "      <td>2000</td>\n",
       "      <td>2666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>巴西</td>\n",
       "      <td>2000</td>\n",
       "      <td>80488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>中國</td>\n",
       "      <td>2000</td>\n",
       "      <td>213766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    國家    年份      案例\n",
       "0  阿富汗  1999     745\n",
       "1   巴西  1999   37737\n",
       "2   中國  1999  212258\n",
       "3  阿富汗  2000    2666\n",
       "4   巴西  2000   80488\n",
       "5   中國  2000  213766"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4a_tidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 工作中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table4a  # cases\n",
    "#> # A tibble: 3 × 3\n",
    "#>       country `1999` `2000`\n",
    "#> *       <chr>  <int>  <int>\n",
    "#> 1 Afghanistan    745   2666\n",
    "#> 2      Brazil  37737  80488\n",
    "#> 3       China 212258 213766\n",
    "table4b  # population\n",
    "#> # A tibble: 3 × 3\n",
    "#>       country     `1999`     `2000`\n",
    "#> *       <chr>      <int>      <int>\n",
    "#> 1 Afghanistan   19987071   20595360\n",
    "#> 2      Brazil  172006362  174504898\n",
    "#> 3       China 1272915272 1280428583"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tb4a = table1[['國家', '年份', '案例']].copy()\n",
    "tb4b = table1[['國家', '年份', '人口']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table4a = tb4a.pivot_table(index=\"國家\", columns=\"年份\", values=\"案例\")\n",
    "table4a.reset_index(drop=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table4b = tb4b.pivot_table(index=\"國家\", columns=\"年份\", values=\"人口\")\n",
    "table4b.reset_index(drop=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['阿富汗', 19987071, 20595360],\n",
       "       ['巴西', 172006362, 174504898],\n",
       "       ['中國', 1272915272, 1280428583]], dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4b.sort_values(by='國家', ascending=False).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['國家', 1999, 2000], dtype='object', name='年份')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4b.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['中國', 1272915272, 1280428583],\n",
       "       ['巴西', 172006362, 174504898],\n",
       "       ['阿富汗', 19987071, 20595360]], dtype=object)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4b.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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