05. Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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": [
    "## 插播: 在 `matplotlib` 中顯示中文\n",
    "\n",
    "這裡參考 Mosky 在, 比如說[這裡](https://github.com/moskytw/hypothesis-testing-with-python)的做法。\n",
    "\n",
    "如果明明有的字型, `matplotlib` 說找不到的話, 有可能需要讓 `matplotlib` 清掉原本的 cache。\n",
    "\n",
    "    !rm -rf {mpl.get_cachedir()}\n",
    "    matplotlib.font_manager._rebuild()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 Google `Noto Sans CJK TC` 字型, 請到[這裡](https://www.google.com/get/noto/#sans-hant)下載。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rc('font', family='Noto Sans CJK TC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你很好奇有什麼字型可以用...\n",
    "\n",
    "    [f.name for f in mpl.font_manager.fontManager.ttflist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['STIXGeneral',\n",
       " 'DejaVu Sans',\n",
       " 'STIXNonUnicode',\n",
       " 'STIXSizeThreeSym',\n",
       " 'DejaVu Sans Mono',\n",
       " 'DejaVu Serif',\n",
       " 'STIXSizeFiveSym',\n",
       " 'DejaVu Sans Mono',\n",
       " 'STIXGeneral',\n",
       " 'DejaVu Sans',\n",
       " 'STIXSizeTwoSym',\n",
       " 'STIXSizeOneSym',\n",
       " 'cmsy10',\n",
       " 'cmmi10',\n",
       " 'STIXSizeOneSym',\n",
       " 'STIXGeneral',\n",
       " 'STIXSizeFourSym',\n",
       " 'DejaVu Serif Display',\n",
       " 'cmr10',\n",
       " 'cmex10',\n",
       " 'cmtt10',\n",
       " 'DejaVu Sans',\n",
       " 'STIXSizeFourSym',\n",
       " 'DejaVu Serif',\n",
       " 'STIXNonUnicode',\n",
       " 'DejaVu Sans Mono',\n",
       " 'cmb10',\n",
       " 'DejaVu Sans Mono',\n",
       " 'DejaVu Sans Display',\n",
       " 'STIXSizeTwoSym',\n",
       " 'DejaVu Serif',\n",
       " 'cmss10',\n",
       " 'DejaVu Serif',\n",
       " 'STIXNonUnicode',\n",
       " 'STIXSizeThreeSym',\n",
       " 'STIXGeneral',\n",
       " 'DejaVu Sans',\n",
       " 'STIXNonUnicode',\n",
       " 'STIXNonUnicode',\n",
       " 'Noto Serif CJK TC',\n",
       " 'Noto Sans CJK TC',\n",
       " 'System Font',\n",
       " 'Trebuchet MS',\n",
       " '.SF NS Display Condensed',\n",
       " 'STIXSizeThreeSym',\n",
       " '.SF NS Rounded',\n",
       " 'System Font',\n",
       " 'Noto Sans Mono CJK JP',\n",
       " 'System Font',\n",
       " 'Apple Braille',\n",
       " 'Noto Sans CJK TC',\n",
       " 'STIXNonUnicode',\n",
       " 'DIN Alternate',\n",
       " 'Bitstream Vera Sans Mono',\n",
       " 'Verdana',\n",
       " 'Noto Serif CJK JP',\n",
       " 'Noto Sans CJK KR',\n",
       " 'Estrangelo Nisibin Outline',\n",
       " '.SF Compact Text',\n",
       " 'Times New Roman',\n",
       " 'Bitstream Vera Sans',\n",
       " 'Bitstream Vera Sans Mono',\n",
       " 'Luxi Serif',\n",
       " '.Keyboard',\n",
       " '.SF NS Text Condensed',\n",
       " 'Noto Serif CJK KR',\n",
       " '.SF Compact Display',\n",
       " 'Arial Narrow',\n",
       " 'Luxi Mono',\n",
       " '.SF NS Text Condensed',\n",
       " 'Times New Roman',\n",
       " '.SF Compact Display',\n",
       " '.SF NS Symbols',\n",
       " 'jf-jinxuan',\n",
       " 'Noto Serif CJK TC',\n",
       " 'Skia',\n",
       " 'Big Caslon',\n",
       " '.SF NS Rounded',\n",
       " 'Luxi Sans',\n",
       " 'Serto Mardin',\n",
       " '.SF Compact Text',\n",
       " '.LastResort',\n",
       " 'Serto Mardin',\n",
       " 'Bitstream Vera Sans',\n",
       " 'Trebuchet MS',\n",
       " 'Times New Roman',\n",
       " 'Serto Jerusalem Outline',\n",
       " 'Estrangelo Nisibin',\n",
       " 'Estrangelo Midyat',\n",
       " '.SF NS Symbols',\n",
       " 'Noto Serif CJK JP',\n",
       " 'Noto Serif CJK KR',\n",
       " 'Estrangelo TurAbdin',\n",
       " 'Georgia',\n",
       " 'STIXIntegralsSm',\n",
       " '.SF Compact Text',\n",
       " 'jf-jinxuan',\n",
       " 'Noto Serif CJK KR',\n",
       " 'STIXIntegralsUpD',\n",
       " 'Comic Sans MS',\n",
       " 'Noto Serif CJK TC',\n",
       " 'East Syriac Ctesiphon',\n",
       " 'STIXIntegralsUpD',\n",
       " 'Bodoni 72 Smallcaps',\n",
       " 'Serto Urhoy',\n",
       " 'Bradley Hand',\n",
       " '.SF Compact Text',\n",
       " 'STIXNonUnicode',\n",
       " 'Zapf Dingbats',\n",
       " 'Comic Sans MS',\n",
       " '.SF NS Display Condensed',\n",
       " 'System Font',\n",
       " 'Luxi Mono',\n",
       " 'Arial',\n",
       " 'System Font',\n",
       " '.SF Compact Text',\n",
       " 'Serto Jerusalem',\n",
       " 'Noto Serif CJK KR',\n",
       " 'STIXSizeOneSym',\n",
       " 'East Syriac Adiabene',\n",
       " 'Bitstream Vera Sans Mono',\n",
       " 'Ayuthaya',\n",
       " 'System Font',\n",
       " '.SF NS Rounded',\n",
       " 'System Font',\n",
       " '.SF Compact Rounded',\n",
       " 'System Font',\n",
       " 'Serto Malankara',\n",
       " 'Luxi Sans',\n",
       " 'Luxi Mono',\n",
       " 'DIN Condensed',\n",
       " '.SF Compact Rounded',\n",
       " 'Serto Jerusalem',\n",
       " 'Bitstream Vera Sans',\n",
       " 'Bitstream Vera Serif',\n",
       " 'STIXSizeThreeSym',\n",
       " 'Serto Batnan',\n",
       " 'Wingdings',\n",
       " '.SF NS Text Condensed',\n",
       " '.SF Compact Text',\n",
       " 'STIXGeneral',\n",
       " 'Khmer Sangam MN',\n",
       " 'Noto Sans CJK JP',\n",
       " 'STIXVariants',\n",
       " '.SF Compact Rounded',\n",
       " 'Goha-Tibeb Zemen',\n",
       " 'Noto Serif CJK JP',\n",
       " 'Estrangelo Antioch',\n",
       " 'Mishafi',\n",
       " 'System Font',\n",
       " 'Noto Sans CJK JP',\n",
       " 'Bodoni Ornaments',\n",
       " 'STIXGeneral',\n",
       " '.SF Compact Display',\n",
       " 'Noto Serif CJK JP',\n",
       " 'STIXSizeTwoSym',\n",
       " 'Luxi Mono',\n",
       " 'jf-jinxuan',\n",
       " '.SF NS Display Condensed',\n",
       " 'Arial Black',\n",
       " 'Luxi Serif',\n",
       " 'Noto Sans CJK JP',\n",
       " 'jf-jinxuan',\n",
       " 'Kokonor',\n",
       " 'Georgia',\n",
       " 'Arial Rounded MT Bold',\n",
       " '.SF Compact Rounded',\n",
       " 'cwTeX Q Fangsong',\n",
       " 'Noto Sans CJK KR',\n",
       " 'Noto Serif CJK TC',\n",
       " 'Courier New',\n",
       " 'cwTeX Q Hei',\n",
       " '.SF NS Rounded',\n",
       " 'Times New Roman',\n",
       " 'Trebuchet MS',\n",
       " 'Noto Sans CJK JP',\n",
       " '.SF NS Text Condensed',\n",
       " 'Georgia',\n",
       " 'Estrangelo Edessa',\n",
       " 'jf-jinxuan',\n",
       " 'Estrangelo Quenneshrin',\n",
       " 'Verdana',\n",
       " 'STIXSizeFourSym',\n",
       " 'Noto Serif CJK JP',\n",
       " '.SF NS Rounded',\n",
       " 'Diwan Thuluth',\n",
       " 'Noto Serif CJK JP',\n",
       " 'Luxi Serif',\n",
       " 'Serto Kharput',\n",
       " 'jf-jinxuan',\n",
       " 'Trebuchet MS',\n",
       " 'Noto Sans Mono CJK KR',\n",
       " 'Bitstream Vera Serif',\n",
       " '.SF Compact Rounded',\n",
       " '.SF NS Display Condensed',\n",
       " 'Verdana',\n",
       " 'STIXSizeFiveSym',\n",
       " '.SF NS Rounded',\n",
       " '.SF Compact Rounded',\n",
       " 'Gurmukhi MT',\n",
       " 'Noto Sans CJK JP',\n",
       " 'Estrangelo Talada',\n",
       " 'jf-jinxuan',\n",
       " 'Arial Narrow',\n",
       " 'Mishafi Gold',\n",
       " 'Verdana',\n",
       " '.SF NS Symbols',\n",
       " 'Noto Serif CJK TC',\n",
       " 'Bitstream Vera Sans Mono',\n",
       " 'Noto Serif CJK KR',\n",
       " '.SF Compact Text',\n",
       " 'Luxi Sans',\n",
       " 'Apple Chancery',\n",
       " 'Trattatello',\n",
       " 'Noto Sans Mono CJK TC',\n",
       " 'STIXIntegralsUp',\n",
       " 'System Font',\n",
       " 'cwTeX Q Kai',\n",
       " 'Courier New',\n",
       " 'Serto Batnan',\n",
       " '.SF NS Symbols',\n",
       " 'Apple Symbols',\n",
       " 'Noto Sans CJK JP',\n",
       " 'AppleMyungjo',\n",
       " '.SF Compact Text',\n",
       " 'Plantagenet Cherokee',\n",
       " '.SF NS Symbols',\n",
       " '.SF Compact Rounded',\n",
       " 'System Font',\n",
       " '.SF NS Symbols',\n",
       " '.SF Compact Text',\n",
       " 'Impact',\n",
       " 'Krungthep',\n",
       " 'Serto Urhoy',\n",
       " 'Sathu',\n",
       " 'cwTeX Q Yuan',\n",
       " 'Apple Braille',\n",
       " 'Apple Braille',\n",
       " 'STIXIntegralsUp',\n",
       " 'Luxi Sans',\n",
       " 'Courier New',\n",
       " 'Lao Sangam MN',\n",
       " 'System Font',\n",
       " 'Webdings',\n",
       " '.SF NS Display Condensed',\n",
       " '.SF NS Symbols',\n",
       " 'STIXSizeTwoSym',\n",
       " '.SF Compact Display',\n",
       " 'Serto Jerusalem',\n",
       " 'Luxi Serif',\n",
       " 'AppleGothic',\n",
       " 'Goha-Tibeb Zemen',\n",
       " 'STIXGeneral',\n",
       " 'Bitstream Vera Sans',\n",
       " 'Wingdings 2',\n",
       " '.SF Compact Rounded',\n",
       " 'Noto Sans CJK KR',\n",
       " 'Noto Sans CJK KR',\n",
       " 'Arial',\n",
       " '.SF NS Text Condensed',\n",
       " 'Noto Serif CJK TC',\n",
       " '.SF NS Rounded',\n",
       " 'Noto Serif CJK TC',\n",
       " 'jf-jinxuan',\n",
       " 'STIXIntegralsUpSm',\n",
       " 'Noto Serif CJK KR',\n",
       " 'STIXGeneral',\n",
       " 'Symbol',\n",
       " '.SF NS Symbols',\n",
       " 'Apple Braille',\n",
       " 'jf-jinxuan',\n",
       " 'Georgia',\n",
       " 'Apple Braille',\n",
       " 'Noto Sans CJK TC',\n",
       " 'Wingdings 3',\n",
       " 'Noto Mono',\n",
       " 'Noto Sans CJK TC',\n",
       " 'Zapfino',\n",
       " 'Tahoma',\n",
       " 'System Font',\n",
       " '.SF Compact Display',\n",
       " 'STIXNonUnicode',\n",
       " 'Farisi',\n",
       " 'STIXIntegralsD',\n",
       " 'Arial Narrow',\n",
       " 'Courier New',\n",
       " '.SF Compact Display',\n",
       " 'STIXSizeFourSym',\n",
       " '.SF Compact Text',\n",
       " 'STIXSizeOneSym',\n",
       " '.SF NS Symbols',\n",
       " 'Noto Sans Mono CJK KR',\n",
       " 'Arial',\n",
       " 'Noto Sans CJK TC',\n",
       " '.SF NS Text Condensed',\n",
       " '.SF NS Display Condensed',\n",
       " 'Noto Serif CJK JP',\n",
       " 'STIXIntegralsD',\n",
       " 'Noto Sans CJK JP',\n",
       " '.SF NS Rounded',\n",
       " 'System Font',\n",
       " 'Noto Serif CJK KR',\n",
       " '.SF NS Display Condensed',\n",
       " '.SF Compact Display',\n",
       " 'Tahoma',\n",
       " 'Andale Mono',\n",
       " 'Herculanum',\n",
       " 'Brush Script MT',\n",
       " '.SF Compact Text',\n",
       " 'System Font',\n",
       " 'Hoefler Text',\n",
       " '.SF Compact Display',\n",
       " 'Noto Sans CJK TC',\n",
       " 'Noto Sans CJK KR',\n",
       " 'Arial Unicode MS',\n",
       " '.SF NS Display Condensed',\n",
       " 'STIXIntegralsUpSm',\n",
       " '.SF Compact Display',\n",
       " 'Noto Sans Mono CJK TC',\n",
       " 'System Font',\n",
       " 'Arial Narrow',\n",
       " 'Chalkduster',\n",
       " 'Luminari',\n",
       " 'Noto Sans Mono CJK JP',\n",
       " 'cwTeX Q Ming',\n",
       " '.SF Compact Text',\n",
       " 'STIXVariants',\n",
       " 'Noto Sans CJK TC',\n",
       " 'Noto Sans CJK KR',\n",
       " 'Silom',\n",
       " '.SF NS Rounded',\n",
       " 'Arial',\n",
       " '.SF NS Display Condensed',\n",
       " 'STIXIntegralsSm',\n",
       " '.SF Compact Rounded',\n",
       " 'Microsoft Sans Serif',\n",
       " 'Noto Sans CJK KR']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[f.name for f in mpl.font_manager.fontManager.ttflist]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-01 暖身來個比較「正規」的資料\n",
    "\n",
    "### 一些 Open Data\n",
    "\n",
    "* [政府資料開放平台](https://data.gov.tw)\n",
    "* [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)\n",
    "\n",
    "我們這暖身找一個全台大學生人數的數據集, 我們目標是看出各校「女生:男生」比例, 選出最高的前 20 名。\n",
    "\n",
    "這裡要為大家示範的是, 也許就這麼簡單的事, 可能會花你一些工夫去做。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('http://stats.moe.gov.tw/files/detail/107/107_student.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>大專校院校別學生數</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <th>Unnamed: 23</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>107 學年度 SY2018-2019</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>學校代碼</td>\n",
       "      <td>學校名稱</td>\n",
       "      <td>日間∕進修別</td>\n",
       "      <td>等級別</td>\n",
       "      <td>總計</td>\n",
       "      <td>男生計</td>\n",
       "      <td>女生計</td>\n",
       "      <td>一年級男生</td>\n",
       "      <td>一年級女生</td>\n",
       "      <td>二年級男生</td>\n",
       "      <td>...</td>\n",
       "      <td>五年級男生</td>\n",
       "      <td>五年級女生</td>\n",
       "      <td>六年級男生</td>\n",
       "      <td>六年級女生</td>\n",
       "      <td>七年級男生</td>\n",
       "      <td>七年級女生</td>\n",
       "      <td>延修生男生</td>\n",
       "      <td>延修生女生</td>\n",
       "      <td>縣市名稱</td>\n",
       "      <td>體系別</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>D 博士</td>\n",
       "      <td>965</td>\n",
       "      <td>551</td>\n",
       "      <td>414</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>46</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>M 碩士</td>\n",
       "      <td>3,998</td>\n",
       "      <td>1,854</td>\n",
       "      <td>2,144</td>\n",
       "      <td>624</td>\n",
       "      <td>742</td>\n",
       "      <td>632</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>9,630</td>\n",
       "      <td>3,983</td>\n",
       "      <td>5,647</td>\n",
       "      <td>899</td>\n",
       "      <td>1,287</td>\n",
       "      <td>890</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>326</td>\n",
       "      <td>463</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             大專校院校別學生數 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5  \\\n",
       "0  107 學年度 SY2018-2019        NaN        NaN        NaN        NaN        NaN   \n",
       "1                 學校代碼       學校名稱     日間∕進修別        等級別         總計        男生計   \n",
       "2                 0001     國立政治大學        D 日       D 博士        965        551   \n",
       "3                 0001     國立政治大學        D 日       M 碩士      3,998      1,854   \n",
       "4                 0001     國立政治大學        D 日       B 學士      9,630      3,983   \n",
       "\n",
       "  Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9     ...     Unnamed: 15  \\\n",
       "0        NaN        NaN        NaN        NaN     ...             NaN   \n",
       "1        女生計      一年級男生      一年級女生      二年級男生     ...           五年級男生   \n",
       "2        414         96         85         82     ...              71   \n",
       "3      2,144        624        742        632     ...               -   \n",
       "4      5,647        899      1,287        890     ...               -   \n",
       "\n",
       "  Unnamed: 16 Unnamed: 17 Unnamed: 18 Unnamed: 19 Unnamed: 20 Unnamed: 21  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1       五年級女生       六年級男生       六年級女生       七年級男生       七年級女生       延修生男生   \n",
       "2          52          58          46          74          40           -   \n",
       "3           -           -           -           -           -           -   \n",
       "4           -           -           -           -           -         326   \n",
       "\n",
       "  Unnamed: 22 Unnamed: 23 Unnamed: 24  \n",
       "0         NaN         NaN         NaN  \n",
       "1       延修生女生        縣市名稱         體系別  \n",
       "2           -      30 臺北市        1 一般  \n",
       "3           -      30 臺北市        1 一般  \n",
       "4         463      30 臺北市        1 一般  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 把表格弄對\n",
    "\n",
    "欄位名稱被放到第 1 列, 真正要的資料從第二列開始!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames = df.loc[1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>大專校院校別學生數</th>\n",
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       "      <th>Unnamed: 21</th>\n",
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       "      <th>Unnamed: 23</th>\n",
       "      <th>Unnamed: 24</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>D 博士</td>\n",
       "      <td>965</td>\n",
       "      <td>551</td>\n",
       "      <td>414</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>46</td>\n",
       "      <td>74</td>\n",
       "      <td>40</td>\n",
       "      <td>-</td>\n",
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       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>M 碩士</td>\n",
       "      <td>3,998</td>\n",
       "      <td>1,854</td>\n",
       "      <td>2,144</td>\n",
       "      <td>624</td>\n",
       "      <td>742</td>\n",
       "      <td>632</td>\n",
       "      <td>...</td>\n",
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       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>9,630</td>\n",
       "      <td>3,983</td>\n",
       "      <td>5,647</td>\n",
       "      <td>899</td>\n",
       "      <td>1,287</td>\n",
       "      <td>890</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>326</td>\n",
       "      <td>463</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>N 職</td>\n",
       "      <td>M 碩士</td>\n",
       "      <td>1,750</td>\n",
       "      <td>896</td>\n",
       "      <td>854</td>\n",
       "      <td>303</td>\n",
       "      <td>248</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0002</td>\n",
       "      <td>國立清華大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>D 博士</td>\n",
       "      <td>1,664</td>\n",
       "      <td>1,221</td>\n",
       "      <td>443</td>\n",
       "      <td>225</td>\n",
       "      <td>99</td>\n",
       "      <td>201</td>\n",
       "      <td>...</td>\n",
       "      <td>158</td>\n",
       "      <td>50</td>\n",
       "      <td>112</td>\n",
       "      <td>43</td>\n",
       "      <td>168</td>\n",
       "      <td>58</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>18 新竹市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  大專校院校別學生數 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6  \\\n",
       "2      0001     國立政治大學        D 日       D 博士        965        551        414   \n",
       "3      0001     國立政治大學        D 日       M 碩士      3,998      1,854      2,144   \n",
       "4      0001     國立政治大學        D 日       B 學士      9,630      3,983      5,647   \n",
       "5      0001     國立政治大學        N 職       M 碩士      1,750        896        854   \n",
       "6      0002     國立清華大學        D 日       D 博士      1,664      1,221        443   \n",
       "\n",
       "  Unnamed: 7 Unnamed: 8 Unnamed: 9     ...     Unnamed: 15 Unnamed: 16  \\\n",
       "2         96         85         82     ...              71          52   \n",
       "3        624        742        632     ...               -           -   \n",
       "4        899      1,287        890     ...               -           -   \n",
       "5        303        248        253     ...              53          47   \n",
       "6        225         99        201     ...             158          50   \n",
       "\n",
       "  Unnamed: 17 Unnamed: 18 Unnamed: 19 Unnamed: 20 Unnamed: 21 Unnamed: 22  \\\n",
       "2          58          46          74          40           -           -   \n",
       "3           -           -           -           -           -           -   \n",
       "4           -           -           -           -         326         463   \n",
       "5           -           -           -           -           -           -   \n",
       "6         112          43         168          58           -           -   \n",
       "\n",
       "  Unnamed: 23 Unnamed: 24  \n",
       "2      30 臺北市        1 一般  \n",
       "3      30 臺北市        1 一般  \n",
       "4      30 臺北市        1 一般  \n",
       "5      30 臺北市        1 一般  \n",
       "6      18 新竹市        1 一般  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>D 博士</td>\n",
       "      <td>965</td>\n",
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       "      <td>414</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
       "      <td>82</td>\n",
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       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>46</td>\n",
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       "      <td>40</td>\n",
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       "      <td>1 一般</td>\n",
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       "      <td>3,998</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>9,630</td>\n",
       "      <td>3,983</td>\n",
       "      <td>5,647</td>\n",
       "      <td>899</td>\n",
       "      <td>1,287</td>\n",
       "      <td>890</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>326</td>\n",
       "      <td>463</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>N 職</td>\n",
       "      <td>M 碩士</td>\n",
       "      <td>1,750</td>\n",
       "      <td>896</td>\n",
       "      <td>854</td>\n",
       "      <td>303</td>\n",
       "      <td>248</td>\n",
       "      <td>253</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0002</td>\n",
       "      <td>國立清華大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>D 博士</td>\n",
       "      <td>1,664</td>\n",
       "      <td>1,221</td>\n",
       "      <td>443</td>\n",
       "      <td>225</td>\n",
       "      <td>99</td>\n",
       "      <td>201</td>\n",
       "      <td>...</td>\n",
       "      <td>158</td>\n",
       "      <td>50</td>\n",
       "      <td>112</td>\n",
       "      <td>43</td>\n",
       "      <td>168</td>\n",
       "      <td>58</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>18 新竹市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   學校代碼    學校名稱 日間∕進修別   等級別     總計    男生計    女生計 一年級男生  一年級女生 二年級男生  ...   \\\n",
       "2  0001  國立政治大學    D 日  D 博士    965    551    414    96     85    82  ...    \n",
       "3  0001  國立政治大學    D 日  M 碩士  3,998  1,854  2,144   624    742   632  ...    \n",
       "4  0001  國立政治大學    D 日  B 學士  9,630  3,983  5,647   899  1,287   890  ...    \n",
       "5  0001  國立政治大學    N 職  M 碩士  1,750    896    854   303    248   253  ...    \n",
       "6  0002  國立清華大學    D 日  D 博士  1,664  1,221    443   225     99   201  ...    \n",
       "\n",
       "  五年級男生 五年級女生 六年級男生 六年級女生 七年級男生 七年級女生 延修生男生 延修生女生    縣市名稱   體系別  \n",
       "2    71    52    58    46    74    40     -     -  30 臺北市  1 一般  \n",
       "3     -     -     -     -     -     -     -     -  30 臺北市  1 一般  \n",
       "4     -     -     -     -     -     -   326   463  30 臺北市  1 一般  \n",
       "5    53    47     -     -     -     -     -     -  30 臺北市  1 一般  \n",
       "6   158    50   112    43   168    58     -     -  18 新竹市  1 一般  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "undergraduate = df[(df['日間∕進修別'] == 'D 日') & (df['等級別']  == 'B 學士')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
       "      <th>五年級男生</th>\n",
       "      <th>五年級女生</th>\n",
       "      <th>六年級男生</th>\n",
       "      <th>六年級女生</th>\n",
       "      <th>七年級男生</th>\n",
       "      <th>七年級女生</th>\n",
       "      <th>延修生男生</th>\n",
       "      <th>延修生女生</th>\n",
       "      <th>縣市名稱</th>\n",
       "      <th>體系別</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0001</td>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>9,630</td>\n",
       "      <td>3,983</td>\n",
       "      <td>5,647</td>\n",
       "      <td>899</td>\n",
       "      <td>1,287</td>\n",
       "      <td>890</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>326</td>\n",
       "      <td>463</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0002</td>\n",
       "      <td>國立清華大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>8,689</td>\n",
       "      <td>4,696</td>\n",
       "      <td>3,993</td>\n",
       "      <td>1,089</td>\n",
       "      <td>865</td>\n",
       "      <td>1,087</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>286</td>\n",
       "      <td>166</td>\n",
       "      <td>18 新竹市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0003</td>\n",
       "      <td>國立臺灣大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>16,604</td>\n",
       "      <td>9,447</td>\n",
       "      <td>7,157</td>\n",
       "      <td>2,126</td>\n",
       "      <td>1,525</td>\n",
       "      <td>2,110</td>\n",
       "      <td>...</td>\n",
       "      <td>184</td>\n",
       "      <td>98</td>\n",
       "      <td>126</td>\n",
       "      <td>61</td>\n",
       "      <td>102</td>\n",
       "      <td>35</td>\n",
       "      <td>671</td>\n",
       "      <td>537</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0004</td>\n",
       "      <td>國立臺灣師範大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>8,079</td>\n",
       "      <td>3,583</td>\n",
       "      <td>4,496</td>\n",
       "      <td>846</td>\n",
       "      <td>1,066</td>\n",
       "      <td>850</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>240</td>\n",
       "      <td>271</td>\n",
       "      <td>30 臺北市</td>\n",
       "      <td>3 師範</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0005</td>\n",
       "      <td>國立成功大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>11,390</td>\n",
       "      <td>7,123</td>\n",
       "      <td>4,267</td>\n",
       "      <td>1,672</td>\n",
       "      <td>1,010</td>\n",
       "      <td>1,651</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>27</td>\n",
       "      <td>53</td>\n",
       "      <td>21</td>\n",
       "      <td>355</td>\n",
       "      <td>149</td>\n",
       "      <td>21 臺南市</td>\n",
       "      <td>1 一般</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    學校代碼      學校名稱 日間∕進修別   等級別      總計    男生計    女生計  一年級男生  一年級女生  二年級男生  \\\n",
       "4   0001    國立政治大學    D 日  B 學士   9,630  3,983  5,647    899  1,287    890   \n",
       "8   0002    國立清華大學    D 日  B 學士   8,689  4,696  3,993  1,089    865  1,087   \n",
       "13  0003    國立臺灣大學    D 日  B 學士  16,604  9,447  7,157  2,126  1,525  2,110   \n",
       "17  0004  國立臺灣師範大學    D 日  B 學士   8,079  3,583  4,496    846  1,066    850   \n",
       "21  0005    國立成功大學    D 日  B 學士  11,390  7,123  4,267  1,672  1,010  1,651   \n",
       "\n",
       "    ...  五年級男生 五年級女生 六年級男生 六年級女生 七年級男生 七年級女生 延修生男生 延修生女生    縣市名稱   體系別  \n",
       "4   ...      -     -     -     -     -     -   326   463  30 臺北市  1 一般  \n",
       "8   ...      -     -     -     -     -     -   286   166  18 新竹市  1 一般  \n",
       "13  ...    184    98   126    61   102    35   671   537  30 臺北市  1 一般  \n",
       "17  ...      -     -     -     -     -     -   240   271  30 臺北市  3 師範  \n",
       "21  ...     82    68    49    27    53    21   355   149  21 臺南市  1 一般  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undergraduate.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![view 和 copy](images/pandas_view_copy.png)\n",
    "\n",
    "注意接下來我們用了 `copy`, 你可能感覺好像不需要用, 事實上沒有用也可以執行, 只是 `pandas` 會警告你...\n",
    "\n",
    "這牽涉大概每個用 `pandas` 的人, 都會碰到的 SettingWithCopyWarning!\n",
    "\n",
    "請參考[這個影片](https://youtu.be/4R4WsDJ-KVc), 理解要怎麼解決。事實上這是一個非常棒的 `pandas` 系列教學!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = undergraduate.loc[:, '學校名稱':'女生計'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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>學校名稱</th>\n",
       "      <th>日間∕進修別</th>\n",
       "      <th>等級別</th>\n",
       "      <th>總計</th>\n",
       "      <th>男生計</th>\n",
       "      <th>女生計</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>9,630</td>\n",
       "      <td>3,983</td>\n",
       "      <td>5,647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>國立清華大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>8,689</td>\n",
       "      <td>4,696</td>\n",
       "      <td>3,993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>國立臺灣大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>16,604</td>\n",
       "      <td>9,447</td>\n",
       "      <td>7,157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>國立臺灣師範大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>8,079</td>\n",
       "      <td>3,583</td>\n",
       "      <td>4,496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>國立成功大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>11,390</td>\n",
       "      <td>7,123</td>\n",
       "      <td>4,267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        學校名稱 日間∕進修別   等級別      總計    男生計    女生計\n",
       "4     國立政治大學    D 日  B 學士   9,630  3,983  5,647\n",
       "8     國立清華大學    D 日  B 學士   8,689  4,696  3,993\n",
       "13    國立臺灣大學    D 日  B 學士  16,604  9,447  7,157\n",
       "17  國立臺灣師範大學    D 日  B 學士   8,079  3,583  4,496\n",
       "21    國立成功大學    D 日  B 學士  11,390  7,123  4,267"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 有逗點數字的修正\n",
    "\n",
    "我們來修正一下有逗點的數字..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import locale\n",
    "\n",
    "# atof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'zh_TW.UTF-8'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locale.setlocale(locale.LC_NUMERIC, '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['男生計'] = df2['男生計'].apply(locale.atof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['女生計'] = df2['女生計'].apply(locale.atof)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 終於可以算了!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['女男比例'] = df2.女生計 / df2.男生計"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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>學校名稱</th>\n",
       "      <th>日間∕進修別</th>\n",
       "      <th>等級別</th>\n",
       "      <th>總計</th>\n",
       "      <th>男生計</th>\n",
       "      <th>女生計</th>\n",
       "      <th>女男比例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>國立政治大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>9,630</td>\n",
       "      <td>3983.0</td>\n",
       "      <td>5647.0</td>\n",
       "      <td>1.417776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>國立清華大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>8,689</td>\n",
       "      <td>4696.0</td>\n",
       "      <td>3993.0</td>\n",
       "      <td>0.850298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>國立臺灣大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>16,604</td>\n",
       "      <td>9447.0</td>\n",
       "      <td>7157.0</td>\n",
       "      <td>0.757595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>國立臺灣師範大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>8,079</td>\n",
       "      <td>3583.0</td>\n",
       "      <td>4496.0</td>\n",
       "      <td>1.254814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>國立成功大學</td>\n",
       "      <td>D 日</td>\n",
       "      <td>B 學士</td>\n",
       "      <td>11,390</td>\n",
       "      <td>7123.0</td>\n",
       "      <td>4267.0</td>\n",
       "      <td>0.599045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        學校名稱 日間∕進修別   等級別      總計     男生計     女生計      女男比例\n",
       "4     國立政治大學    D 日  B 學士   9,630  3983.0  5647.0  1.417776\n",
       "8     國立清華大學    D 日  B 學士   8,689  4696.0  3993.0  0.850298\n",
       "13    國立臺灣大學    D 日  B 學士  16,604  9447.0  7157.0  0.757595\n",
       "17  國立臺灣師範大學    D 日  B 學士   8,079  3583.0  4496.0  1.254814\n",
       "21    國立成功大學    D 日  B 學士  11,390  7123.0  4267.0  0.599045"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 找出前 20 名\n",
    "\n",
    "找出女男比例最高前 20 所大學, 並且畫出圖來!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.sort_values(by = ['女男比例'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1249f29b0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3[:20].plot.bar(x='學校名稱', y=['女男比例'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-02 例子、例子、例子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 我的 Mac 有沒有好棒棒"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_html('https://browser.geekbench.com/mac-benchmarks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "mac = df[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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>Mac</th>\n",
       "      <th>Score</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iMac (27-inch Retina Mid 2017) Intel Core i7-7...</td>\n",
       "      <td>5684</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mac mini (Late 2018) Intel Core i7-8700B @ 3.2...</td>\n",
       "      <td>5659</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MacBook Pro (15-inch Mid 2018) Intel Core i9-8...</td>\n",
       "      <td>5347</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iMac Pro (Late 2017) Intel Xeon W-2150B @ 3.0 ...</td>\n",
       "      <td>5320</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iMac (27-inch Retina Late 2015) Intel Core i7-...</td>\n",
       "      <td>5282</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Mac  Score  Unnamed: 2\n",
       "0  iMac (27-inch Retina Mid 2017) Intel Core i7-7...   5684         NaN\n",
       "1  Mac mini (Late 2018) Intel Core i7-8700B @ 3.2...   5659         NaN\n",
       "2  MacBook Pro (15-inch Mid 2018) Intel Core i9-8...   5347         NaN\n",
       "3  iMac Pro (Late 2017) Intel Xeon W-2150B @ 3.0 ...   5320         NaN\n",
       "4  iMac (27-inch Retina Late 2015) Intel Core i7-...   5282         NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "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>Mac</th>\n",
       "      <th>Score</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MacBook Pro (15-inch Mid 2018) Intel Core i9-8...</td>\n",
       "      <td>5347</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MacBook Pro (13-inch Mid 2018) Intel Core i7-8...</td>\n",
       "      <td>5138</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MacBook Pro (15-inch Mid 2018) Intel Core i7-8...</td>\n",
       "      <td>5054</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MacBook Pro (15-inch Mid 2018) Intel Core i7-8...</td>\n",
       "      <td>4926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MacBook Pro (13-inch Mid 2018) Intel Core i5-8...</td>\n",
       "      <td>4523</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Mac  Score  Unnamed: 2\n",
       "2   MacBook Pro (15-inch Mid 2018) Intel Core i9-8...   5347         NaN\n",
       "10  MacBook Pro (13-inch Mid 2018) Intel Core i7-8...   5138         NaN\n",
       "13  MacBook Pro (15-inch Mid 2018) Intel Core i7-8...   5054         NaN\n",
       "14  MacBook Pro (15-inch Mid 2018) Intel Core i7-8...   4926         NaN\n",
       "22  MacBook Pro (13-inch Mid 2018) Intel Core i5-8...   4523         NaN"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mac[mac.Mac.str.contains('Mid 2018')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "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>Mac</th>\n",
       "      <th>Score</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iMac (27-inch Retina Mid 2017) Intel Core i7-7...</td>\n",
       "      <td>5684</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mac mini (Late 2018) Intel Core i7-8700B @ 3.2...</td>\n",
       "      <td>5659</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Mac  Score  Unnamed: 2\n",
       "0  iMac (27-inch Retina Mid 2017) Intel Core i7-7...   5684         NaN\n",
       "1  Mac mini (Late 2018) Intel Core i7-8700B @ 3.2...   5659         NaN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mac[mac.Score > 5347]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 大樂透"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_html('http://www.9800.com.tw/statistics.asp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lot = df[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames = df_lot.loc[1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lot = df_lot.loc[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lot.columns = colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "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>期次</th>\n",
       "      <th>開獎日期</th>\n",
       "      <th>球號</th>\n",
       "      <th>特號</th>\n",
       "      <th>單雙</th>\n",
       "      <th>和值</th>\n",
       "      <th>均值</th>\n",
       "      <th>同尾</th>\n",
       "      <th>連號</th>\n",
       "      <th>首尾差</th>\n",
       "      <th>尾數和</th>\n",
       "      <th>首數和</th>\n",
       "      <th>nan</th>\n",
       "      <th>nan</th>\n",
       "      <th>nan</th>\n",
       "      <th>nan</th>\n",
       "      <th>nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108011</td>\n",
       "      <td>2019-02-02</td>\n",
       "      <td>02</td>\n",
       "      <td>07</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>49</td>\n",
       "      <td>41</td>\n",
       "      <td>3:3</td>\n",
       "      <td>161</td>\n",
       "      <td>26</td>\n",
       "      <td>2尾(2)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108012</td>\n",
       "      <td>2019-02-03</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>1:5</td>\n",
       "      <td>177</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3637</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108013</td>\n",
       "      <td>2019-02-04</td>\n",
       "      <td>06</td>\n",
       "      <td>08</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "      <td>49</td>\n",
       "      <td>0:6</td>\n",
       "      <td>140</td>\n",
       "      <td>23</td>\n",
       "      <td>4尾(2), 6尾(2)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>108014</td>\n",
       "      <td>2019-02-05</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>33</td>\n",
       "      <td>3:3</td>\n",
       "      <td>189</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>108015</td>\n",
       "      <td>2019-02-06</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>39</td>\n",
       "      <td>12</td>\n",
       "      <td>4:2</td>\n",
       "      <td>168</td>\n",
       "      <td>28</td>\n",
       "      <td>9尾(2)</td>\n",
       "      <td>2829</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       期次        開獎日期  球號  特號  單雙  和值  均值  同尾  連號  首尾差  尾數和 首數和           NaN  \\\n",
       "2  108011  2019-02-02  02  07  22  35  46  49  41  3:3  161  26         2尾(2)   \n",
       "3  108012  2019-02-03  12  24  28  36  37  40   6  1:5  177  29           NaN   \n",
       "4  108013  2019-02-04  06  08  24  32  34  36  49  0:6  140  23  4尾(2), 6尾(2)   \n",
       "5  108014  2019-02-05  13  18  30  34  45  49  33  3:3  189  31           NaN   \n",
       "6  108015  2019-02-06  17  23  28  29  32  39  12  4:2  168  28         9尾(2)   \n",
       "\n",
       "    NaN   NaN   NaN   NaN  \n",
       "2   NaN  47.0  31.0  13.0  \n",
       "3  3637  28.0  27.0  15.0  \n",
       "4   NaN  30.0  30.0  11.0  \n",
       "5   NaN  36.0  29.0  16.0  \n",
       "6  2829  22.0  38.0  13.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-03 正式開始\n",
    "\n",
    "首先我們來讀入一個 CSV 檔, 這裡有個「假的」學測成績, 叫 `grades.csv` 我們來練習一下。\n",
    "\n",
    "我們也有把檔案放在網路上, 所以可以用\n",
    "\n",
    "    pd.read_csv('https://bitly.com/gradescsv')\n",
    "    \n",
    "讀入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset/grades.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用 `df` 是標準的叫法 (雖然這名稱我們隨便取也可以), 意思是 Data Frame, 這是 `pandas` 兩大資料結構之一。我們可以把 Data Frame 想成一張表格 (雖然其實可以是很多張表格)。\n",
    "\n",
    "我們來看看我們 `df` 的前五筆資料。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>劉俊安</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>胡玉華</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黃淑婷</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>陳上紫</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>崔靜成</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會\n",
       "0  劉俊安   9  10  15  10  13\n",
       "1  胡玉華  10  10  10   8   9\n",
       "2  黃淑婷  13  15   8  11  14\n",
       "3  陳上紫  10  10   8   9  14\n",
       "4  崔靜成  13  12  14  12  13"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你曾經手動讀入 CSV 檔, 就知道這省了多少事 (雖然我個人還挺喜歡純手動帶進 CSV)。\n",
    "\n",
    "#### Excel 檔也可以快速讀入\n",
    "\n",
    "不只 CSV 檔, 很多資料檔案, 像 Excel 檔都很容易在 `pandas` 完成。使用法是這樣:\n",
    "\n",
    "    df2 = pd.read_excel('filename.xls', 'sheetname')\n",
    "    \n",
    "其中 sheetname 那裡要放工作表的名稱, 如果是中文的最好改成英文。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-04 Pandas 基本資料結構\n",
    "\n",
    "Pandas 有兩個基本資料結構:\n",
    "\n",
    "* <b style=\"color:red;\">DataFrame</b>: 可以想成一個表格。\n",
    "* <b style=\"color:red;\">Series</b>: 表格的某一列、某一行, 基本上就是我們以前的 list 或 array\n",
    "\n",
    "一個 DataFrame, 我們有 `index` (列的名稱), `columns` (行的名稱)。\n",
    "\n",
    "#### DataFrame\n",
    "\n",
    "![DataFrame 的結構](images/indexcol.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Series\n",
    "\n",
    "剛剛說 series 大概就是一個 list, 一個 array。其實更精準的說, 其實是一個有 \"index\" 的 array。\n",
    "\n",
    "DataFrame 的每一行或每一列其實也都是一個 series。我們來看個例子, 例如所有同學的國文成績, 就是一個 series。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      9\n",
       "1     10\n",
       "2     13\n",
       "3     10\n",
       "4     13\n",
       "5     13\n",
       "6     11\n",
       "7      8\n",
       "8      9\n",
       "9     10\n",
       "10    14\n",
       "11    13\n",
       "12    11\n",
       "13    15\n",
       "14     8\n",
       "15    11\n",
       "16    14\n",
       "17    12\n",
       "18     9\n",
       "19    14\n",
       "20    13\n",
       "21     9\n",
       "22    12\n",
       "23    10\n",
       "24    10\n",
       "25    13\n",
       "26    13\n",
       "27     8\n",
       "28    15\n",
       "29    14\n",
       "      ..\n",
       "70     9\n",
       "71     9\n",
       "72    10\n",
       "73    13\n",
       "74    14\n",
       "75    13\n",
       "76     9\n",
       "77    13\n",
       "78    12\n",
       "79     9\n",
       "80    13\n",
       "81    11\n",
       "82    11\n",
       "83    10\n",
       "84    10\n",
       "85    10\n",
       "86    11\n",
       "87    10\n",
       "88    11\n",
       "89    13\n",
       "90     8\n",
       "91    14\n",
       "92     8\n",
       "93    11\n",
       "94    10\n",
       "95     9\n",
       "96     8\n",
       "97    14\n",
       "98    15\n",
       "99     9\n",
       "Name: 國文, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['國文']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 Python 3 中, 我們終於可以和英文同步, 用這種很炫的方式叫出所有國文成績。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      9\n",
       "1     10\n",
       "2     13\n",
       "3     10\n",
       "4     13\n",
       "5     13\n",
       "6     11\n",
       "7      8\n",
       "8      9\n",
       "9     10\n",
       "10    14\n",
       "11    13\n",
       "12    11\n",
       "13    15\n",
       "14     8\n",
       "15    11\n",
       "16    14\n",
       "17    12\n",
       "18     9\n",
       "19    14\n",
       "20    13\n",
       "21     9\n",
       "22    12\n",
       "23    10\n",
       "24    10\n",
       "25    13\n",
       "26    13\n",
       "27     8\n",
       "28    15\n",
       "29    14\n",
       "      ..\n",
       "70     9\n",
       "71     9\n",
       "72    10\n",
       "73    13\n",
       "74    14\n",
       "75    13\n",
       "76     9\n",
       "77    13\n",
       "78    12\n",
       "79     9\n",
       "80    13\n",
       "81    11\n",
       "82    11\n",
       "83    10\n",
       "84    10\n",
       "85    10\n",
       "86    11\n",
       "87    10\n",
       "88    11\n",
       "89    13\n",
       "90     8\n",
       "91    14\n",
       "92     8\n",
       "93    11\n",
       "94    10\n",
       "95     9\n",
       "96     8\n",
       "97    14\n",
       "98    15\n",
       "99     9\n",
       "Name: 國文, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 資料畫出來\n",
    "\n",
    "要畫個圖很容易。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1151687f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.國文.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "當然, 在這個例子中, 其實畫 histogram 圖更有意義一點。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1171e4e10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.國文.hist(bins=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-05 一些基本的資料分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算平均。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.39"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算標準差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1968526614594834"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不如就該算的都幫我們算算..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.390000</td>\n",
       "      <td>11.380000</td>\n",
       "      <td>11.570000</td>\n",
       "      <td>11.03000</td>\n",
       "      <td>11.83000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.196853</td>\n",
       "      <td>2.273164</td>\n",
       "      <td>2.310516</td>\n",
       "      <td>2.21772</td>\n",
       "      <td>2.48655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>12.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>14.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>15.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               國文          英文          數學         自然         社會\n",
       "count  100.000000  100.000000  100.000000  100.00000  100.00000\n",
       "mean    11.390000   11.380000   11.570000   11.03000   11.83000\n",
       "std      2.196853    2.273164    2.310516    2.21772    2.48655\n",
       "min      8.000000    8.000000    8.000000    8.00000    8.00000\n",
       "25%      9.000000    9.000000   10.000000    9.00000    9.00000\n",
       "50%     11.000000   11.000000   11.000000   11.00000   12.00000\n",
       "75%     13.000000   13.000000   14.000000   13.00000   14.00000\n",
       "max     15.000000   15.000000   15.000000   15.00000   15.00000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有時我們很愛看的相關係數矩陣。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>國文</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160158</td>\n",
       "      <td>-0.310899</td>\n",
       "      <td>-0.110236</td>\n",
       "      <td>-0.028421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英文</th>\n",
       "      <td>0.160158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025656</td>\n",
       "      <td>0.113929</td>\n",
       "      <td>-0.063512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>數學</th>\n",
       "      <td>-0.310899</td>\n",
       "      <td>0.025656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014371</td>\n",
       "      <td>0.041651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>自然</th>\n",
       "      <td>-0.110236</td>\n",
       "      <td>0.113929</td>\n",
       "      <td>0.014371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.156594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>社會</th>\n",
       "      <td>-0.028421</td>\n",
       "      <td>-0.063512</td>\n",
       "      <td>0.041651</td>\n",
       "      <td>-0.156594</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          國文        英文        數學        自然        社會\n",
       "國文  1.000000  0.160158 -0.310899 -0.110236 -0.028421\n",
       "英文  0.160158  1.000000  0.025656  0.113929 -0.063512\n",
       "數學 -0.310899  0.025656  1.000000  0.014371  0.041651\n",
       "自然 -0.110236  0.113929  0.014371  1.000000 -0.156594\n",
       "社會 -0.028421 -0.063512  0.041651 -0.156594  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只算兩科間的相關係數當然也可以。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3108989822179331"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.國文.corr(df.數學)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-06 增加一行\n",
    "\n",
    "### 【技巧】\n",
    "\n",
    "我們增加一行, 加入總級分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['總級分'] = df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "      <th>總級分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>劉俊安</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>胡玉華</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黃淑婷</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>陳上紫</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>崔靜成</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會  總級分\n",
       "0  劉俊安   9  10  15  10  13   57\n",
       "1  胡玉華  10  10  10   8   9   47\n",
       "2  黃淑婷  13  15   8  11  14   61\n",
       "3  陳上紫  10  10   8   9  14   51\n",
       "4  崔靜成  13  12  14  12  13   64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【技巧】\n",
    "\n",
    "有計算的當然也可以的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['加權'] = df.數學 * 2 + df.英文 * 1.5 + df.社會"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "      <th>總級分</th>\n",
       "      <th>加權</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>57</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>胡玉華</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>47</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>黃淑婷</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>61</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>陳上紫</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>51</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>崔靜成</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>64</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會  總級分    加權\n",
       "0  劉俊安   9  10  15  10  13   57  58.0\n",
       "1  胡玉華  10  10  10   8   9   47  44.0\n",
       "2  黃淑婷  13  15   8  11  14   61  52.5\n",
       "3  陳上紫  10  10   8   9  14   51  45.0\n",
       "4  崔靜成  13  12  14  12  13   64  59.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-07 排序和 index 重設\n",
    "\n",
    "### 【重點】排序的方法\n",
    "\n",
    "我們依總級分來排序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "      <th>總級分</th>\n",
       "      <th>加權</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>施雅鈴</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>66</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>李正偉</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>66</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>陳怡潔</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>65</td>\n",
       "      <td>51.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>蔡亦瑄</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>65</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>胡淳茜</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>65</td>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>曾怡君</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "      <td>62.0</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>陳怡婷</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>俞志峰</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>李士賢</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>65</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     姓名  國文  英文  數學  自然  社會  總級分    加權\n",
       "80  施雅鈴  13  15  12  13  13   66  59.5\n",
       "12  李正偉  11  15  11  14  15   66  59.5\n",
       "54  陳怡潔  15  15   9  15  11   65  51.5\n",
       "25  蔡亦瑄  13  13  14  13  12   65  59.5\n",
       "57  胡淳茜  12  15  14  13  11   65  61.5\n",
       "37  曾怡君  11  12  15  13  14   65  62.0\n",
       "48  陳怡婷  15  14  12   9  15   65  60.0\n",
       "64  俞志峰   9  14  13  14  15   65  62.0\n",
       "83  李士賢  10  14  15  13  13   65  64.0\n",
       "87  趙偉希  10  13  14  13  15   65  62.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='總級分', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】排序的方法\n",
    "\n",
    "加權分最高, 同分才看總級分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.sort_values(by = ['加權', '總級分'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>36</th>\n",
       "      <td>詹威德</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>葉儀依</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>劉麗芬</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     姓名  國文  英文  數學  自然  社會  總級分    加權\n",
       "83  李士賢  10  14  15  13  13   65  64.0\n",
       "36  詹威德  12  13  15  10  14   64  63.5\n",
       "70  葉儀依   9  13  15   8  14   59  63.5\n",
       "68  劉麗芬   8  14  14  14  14   64  63.0\n",
       "87  趙偉希  10  13  14  13  15   65  62.5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】重設 index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.index = range(1, 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
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       "      <td>李士賢</td>\n",
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       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>65</td>\n",
       "      <td>64.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>詹威德</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
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       "    <tr>\n",
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       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>63.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>劉麗芬</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會  總級分    加權\n",
       "1  李士賢  10  14  15  13  13   65  64.0\n",
       "2  詹威德  12  13  15  10  14   64  63.5\n",
       "3  葉儀依   9  13  15   8  14   59  63.5\n",
       "4  劉麗芬   8  14  14  14  14   64  63.0\n",
       "5  趙偉希  10  13  14  13  15   65  62.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-08 篩出我們要的資料\n",
    "\n",
    "基本上和 NumPy 的 array 篩法很像。\n",
    "\n",
    "### 【重點】\n",
    "\n",
    "找出數學滿級分同學。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4      False\n",
       "5      False\n",
       "6       True\n",
       "7      False\n",
       "8       True\n",
       "9      False\n",
       "10      True\n",
       "11     False\n",
       "12      True\n",
       "13     False\n",
       "14      True\n",
       "15     False\n",
       "16     False\n",
       "17     False\n",
       "18     False\n",
       "19     False\n",
       "20      True\n",
       "21     False\n",
       "22      True\n",
       "23     False\n",
       "24      True\n",
       "25     False\n",
       "26     False\n",
       "27     False\n",
       "28     False\n",
       "29     False\n",
       "30     False\n",
       "       ...  \n",
       "71     False\n",
       "72     False\n",
       "73     False\n",
       "74     False\n",
       "75     False\n",
       "76     False\n",
       "77     False\n",
       "78     False\n",
       "79     False\n",
       "80     False\n",
       "81     False\n",
       "82     False\n",
       "83     False\n",
       "84     False\n",
       "85     False\n",
       "86     False\n",
       "87     False\n",
       "88     False\n",
       "89     False\n",
       "90     False\n",
       "91     False\n",
       "92     False\n",
       "93     False\n",
       "94     False\n",
       "95     False\n",
       "96     False\n",
       "97     False\n",
       "98     False\n",
       "99     False\n",
       "100    False\n",
       "Name: 數學, Length: 100, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.數學 == 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>數學</th>\n",
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       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
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       "      <td>63.5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>曾怡君</td>\n",
       "      <td>11</td>\n",
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       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "      <td>62.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>陳竹伯</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>61</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>胡勝傑</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>61.5</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>吳志遠</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>周育霖</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>61</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>劉俊安</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>57</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>林哲法</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>62</td>\n",
       "      <td>57.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>段冠廷</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>59</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>張雅彬</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>56</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>芮秋辛</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>54</td>\n",
       "      <td>53.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     姓名  國文  英文  數學  自然  社會  總級分    加權\n",
       "1   李士賢  10  14  15  13  13   65  64.0\n",
       "2   詹威德  12  13  15  10  14   64  63.5\n",
       "3   葉儀依   9  13  15   8  14   59  63.5\n",
       "6   曾怡君  11  12  15  13  14   65  62.0\n",
       "8   陳竹伯  10  12  15  10  14   61  62.0\n",
       "10  胡勝傑   8  11  15  10  15   59  61.5\n",
       "12  吳志遠  13  15  15   8   8   59  60.5\n",
       "14  周育霖   9  12  15  13  12   61  60.0\n",
       "20  劉俊安   9  10  15  10  13   57  58.0\n",
       "22  林哲法  14   9  15  10  14   62  57.5\n",
       "24  段冠廷   9   8  15  12  15   59  57.0\n",
       "36  張雅彬  10  11  15  12   8   56  54.5\n",
       "46  芮秋辛  11  10  15  10   8   54  53.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.數學 == 15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】\n",
    "\n",
    "找出數學和英文都滿級分的同學。要注意 `and` 要用 `&`, `or` 要用 `|`。每個條件一定要加弧號。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李士賢</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
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       "      <td>13</td>\n",
       "      <td>65</td>\n",
       "      <td>64.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>吳志遠</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "     姓名  國文  英文  數學  自然  社會  總級分    加權\n",
       "1   李士賢  10  14  15  13  13   65  64.0\n",
       "12  吳志遠  13  15  15   8   8   59  60.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[(df2.數學 == 15) & (df2.英文 > 13)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-09 刪除一行或一列\n",
    "\n",
    "### 【重點】刪掉一行\n",
    "\n",
    "我們來刪掉總級分的那行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>葉儀依</td>\n",
       "      <td>9</td>\n",
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       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>63.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>劉麗芬</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>63.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會  總級分    加權\n",
       "1  李士賢  10  14  15  13  13   65  64.0\n",
       "2  詹威德  12  13  15  10  14   64  63.5\n",
       "3  葉儀依   9  13  15   8  14   59  63.5\n",
       "4  劉麗芬   8  14  14  14  14   64  63.0\n",
       "5  趙偉希  10  13  14  13  15   65  62.5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>劉麗芬</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>63.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會    加權\n",
       "1  李士賢  10  14  15  13  13  64.0\n",
       "2  詹威德  12  13  15  10  14  63.5\n",
       "3  葉儀依   9  13  15   8  14  63.5\n",
       "4  劉麗芬   8  14  14  14  14  63.0\n",
       "5  趙偉希  10  13  14  13  15  62.5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop('總級分', axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <th>加權</th>\n",
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       "  <tbody>\n",
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       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
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       "      <td>64.0</td>\n",
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       "      <td>10</td>\n",
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       "      <td>63.5</td>\n",
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       "      <th>3</th>\n",
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       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>59</td>\n",
       "      <td>63.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>劉麗芬</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會  總級分    加權\n",
       "1  李士賢  10  14  15  13  13   65  64.0\n",
       "2  詹威德  12  13  15  10  14   64  63.5\n",
       "3  葉儀依   9  13  15   8  14   59  63.5\n",
       "4  劉麗芬   8  14  14  14  14   64  63.0\n",
       "5  趙偉希  10  13  14  13  15   65  62.5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】改變原有的 DataFrame\n",
    "\n",
    "我們會發現 `pandas` 很多動作都沒有更改原有的 DataFrame, 真的要改要加入\n",
    "\n",
    "    inplace=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.drop('總級分', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>14</td>\n",
       "      <td>15</td>\n",
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       "      <td>13</td>\n",
       "      <td>64.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>詹威德</td>\n",
       "      <td>12</td>\n",
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       "      <td>63.5</td>\n",
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       "      <td>9</td>\n",
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       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>劉麗芬</td>\n",
       "      <td>8</td>\n",
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       "      <td>14</td>\n",
       "      <td>63.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會    加權\n",
       "1  李士賢  10  14  15  13  13  64.0\n",
       "2  詹威德  12  13  15  10  14  63.5\n",
       "3  葉儀依   9  13  15   8  14  63.5\n",
       "4  劉麗芬   8  14  14  14  14  63.0\n",
       "5  趙偉希  10  13  14  13  15  62.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】刪掉一列\n",
    "\n",
    "刪掉列就是指定要刪去的 index。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>李士賢</td>\n",
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       "      <td>14</td>\n",
       "      <td>15</td>\n",
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       "      <td>64.0</td>\n",
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       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
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       "      <th>3</th>\n",
       "      <td>葉儀依</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>劉麗芬</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會    加權\n",
       "1  李士賢  10  14  15  13  13  64.0\n",
       "2  詹威德  12  13  15  10  14  63.5\n",
       "3  葉儀依   9  13  15   8  14  63.5\n",
       "4  劉麗芬   8  14  14  14  14  63.0\n",
       "5  趙偉希  10  13  14  13  15  62.5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
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       "  <tbody>\n",
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       "      <td>李士賢</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>詹威德</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>葉儀依</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>劉麗芬</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>曾怡君</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會    加權\n",
       "1  李士賢  10  14  15  13  13  64.0\n",
       "2  詹威德  12  13  15  10  14  63.5\n",
       "3  葉儀依   9  13  15   8  14  63.5\n",
       "4  劉麗芬   8  14  14  14  14  63.0\n",
       "6  曾怡君  11  12  15  13  14  62.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop(5).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】刪掉一列\n",
    "\n",
    "通常刪掉符合條件的比較合理 (注意是找到要刪掉的部份, 再找出相對的 index)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "      <th>加權</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李士賢</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>詹威德</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>葉儀依</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>曾怡君</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會    加權\n",
       "1  李士賢  10  14  15  13  13  64.0\n",
       "2  詹威德  12  13  15  10  14  63.5\n",
       "3  葉儀依   9  13  15   8  14  63.5\n",
       "5  趙偉希  10  13  14  13  15  62.5\n",
       "6  曾怡君  11  12  15  13  14  62.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop(df2[df2.姓名=='劉麗芬'].index).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】刪掉一列\n",
    "\n",
    "聰明的你是不是想到, 那直接篩出我們要留的就好了啊!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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>姓名</th>\n",
       "      <th>國文</th>\n",
       "      <th>英文</th>\n",
       "      <th>數學</th>\n",
       "      <th>自然</th>\n",
       "      <th>社會</th>\n",
       "      <th>加權</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李士賢</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>詹威德</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>葉儀依</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>趙偉希</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>曾怡君</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  國文  英文  數學  自然  社會    加權\n",
       "1  李士賢  10  14  15  13  13  64.0\n",
       "2  詹威德  12  13  15  10  14  63.5\n",
       "3  葉儀依   9  13  15   8  14  63.5\n",
       "5  趙偉希  10  13  14  13  15  62.5\n",
       "6  曾怡君  11  12  15  13  14  62.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.姓名 != '劉麗芬'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-10 真實股價資料\n",
    "\n",
    "有個從 `Pandas` 獨立出來的套件叫 `pandas-datareader`, 幾經波折, 先是 Yahoo! 的財務資料不能用, 後來又是 Google 的資料不能用, 不過至少現在看來 Yahoo! 還可以使用。\n",
    "\n",
    "安裝 `pandas-datareader` 就標準 `conda` 安裝:\n",
    "\n",
    "    conda install pandas-datareader\n",
    "    \n",
    "如果裝過, 但很久沒更新就用:\n",
    "\n",
    "    conda update pandas-datareader\n",
    "    \n",
    "要注意這些指令是要在終端機下的, 如果堅持不要離開 Jupyter Notebook 的安裝, 可以用:\n",
    "\n",
    "    !conda install pandas-datareader -y\n",
    "    \n",
    "你要記得下達 `-y` 這個動作, 不然你的電腦會永遠在那等你按 `y`, 但你其實按不了..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pdr.get_data_yahoo('AAPL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('dataset/aapl.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們把這個檔案以 `aapl.csv` 存起來, 之後可以這樣讀入。\n",
    "\n",
    "    df = pd.read_csv('dataset/aapl.csv', index_col=\"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>30.478571</td>\n",
       "      <td>30.080000</td>\n",
       "      <td>30.447144</td>\n",
       "      <td>30.104286</td>\n",
       "      <td>88102700.0</td>\n",
       "      <td>20.073631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>30.642857</td>\n",
       "      <td>30.340000</td>\n",
       "      <td>30.490000</td>\n",
       "      <td>30.572857</td>\n",
       "      <td>123432400.0</td>\n",
       "      <td>20.386072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.798571</td>\n",
       "      <td>30.464285</td>\n",
       "      <td>30.657143</td>\n",
       "      <td>30.625713</td>\n",
       "      <td>150476200.0</td>\n",
       "      <td>20.421322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.747143</td>\n",
       "      <td>30.107143</td>\n",
       "      <td>30.625713</td>\n",
       "      <td>30.138571</td>\n",
       "      <td>138040000.0</td>\n",
       "      <td>20.096491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.285715</td>\n",
       "      <td>29.864286</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>30.082857</td>\n",
       "      <td>119282800.0</td>\n",
       "      <td>20.059338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close       Volume  Adj Close\n",
       "Date                                                                          \n",
       "2009-12-31  30.478571  30.080000  30.447144  30.104286   88102700.0  20.073631\n",
       "2010-01-04  30.642857  30.340000  30.490000  30.572857  123432400.0  20.386072\n",
       "2010-01-05  30.798571  30.464285  30.657143  30.625713  150476200.0  20.421322\n",
       "2010-01-06  30.747143  30.107143  30.625713  30.138571  138040000.0  20.096491\n",
       "2010-01-07  30.285715  29.864286  30.250000  30.082857  119282800.0  20.059338"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 只要最後 240 個交易日!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[-240:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 20 日的移動平均"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117f51828>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZmqsmJ7kXPS1IjeG1tUcoq7FSVmslzN+EyaBr3otGCK052dsXw+sL4JplEJzQha9COR2pEbziVTaHk5KaevfUBIBRryM22Lfl5A6w8TlwWOGGz2Dcz3o0uQMIIVj/u7N497ZpHb7vwtRY7E7J/3bk4nBKwvxNmA26E3PwjQ2cDdcsh4oceHUe5O/pguiV05lK8IpXFVbVIyVNEnybLGWw5TWtzW74IO8G1wFxIb4Ee9iBsrHU+GDiQ3xZuvEogDvBW1trFzzoLLj5a0DAx6o2Xjk1KsErXnW0pAaAxNCWe7w3s/lVsFbDrPu8GFX3EUJwfmoMmcU1CKHN55uN+rb7wUePguk/h4I9UJ7dfcEq/Y5K8IpXZRVrdeDJER4keIcNtrwCg8+BmNFejqz7LEiNBeDs4VEkhvm5LrK2Uw451FWZc+ibts9TlDaoBK94VVZJDSaDjrjg1nu8ux1YAdUFMOV27wfWjcYlhHD33EH85tzhAJiNbUzRNAgfDKED4fD33RCh0l+pKhrFq7KKaxgQ5tf6BdXGdn0AQQkw+GzvB9aNdDrhTu6A6yJrOwleCEg5U1sH4LCrNgZKp6gRvOJVWSU1ba9SbWCzQOYPMPz8Hq+a8TaTJwkeYOAcsFZB3k7vB6X0SyrBK15TXF1PVkktAz2Zfz+8EuyWE3PP/ZjZoG82RXMgvxKn86T9cZJna39nrO6myJT+RiV4xSucTsntS7aiE3DRuPi2Ty46BJ/fC8FJkDyrewLsQSfXweeWWzjvv2t5dMW+picGRELCFEj7uJsjVPoLleCVTnM6Jcu25pBZVA3gbqoF8O2+fLZnl/PohaMZHR/c+oOUZWkbXwgdXPcxGMxejrrnnTwHX+zawPvN9VnNT069AgrToCCtm6JT+hOV4JVOqayzcduSrfz2w9385/t01qUXM/kv37PnWAU5pbU8tmI/AyP8uXRCG6N3a42W3G21cP0nENF8M5D+yHTSQqdyi83976o6W9OTR10MQq9dbFWUDlKX5pUOO1pSw81vbuFoSS2JYb5sP1pGqJ8Rp4SX12ayI7uMqjob7942DYO+jTHE3v9pI/jrPtYW95wmzIamC53Ka080MduRXc6coY02ow+IOlFNc9bDoFNjMsVz6rdF6ZBaq52b39xCSY2Vt2+Zyk0zBpJbbmHZ1hwAPt91nEqLjaW3Tmt7agZg2xsQORxS5nZD5L2H2aCj3nZiDr689sSoPa/C0vwOqVdARTakf9sd4Sn9iErwSocs33qMjKIanls8gemDwt290utsTs4YGsmAcD/evmUqqQntJPe83ZC7DSbe2O07NPW0IF8jtTYHNfXalsVNE3zznvOMulh7I/z051CR211hKv2AmqJROiSvog6TXseMQeEAjIwL4pyR0UQEmHho4Uj8W9j1qEXb3gSDj7ZL02lmTEIwUsKuY+XMGBRBWa2VQLMBs1FPfksJ3ugLVy6Bl+fChzfBjV+AvuONz5TTjxrBKx1SXmslxM/o3o/UqNfxyvWT+OulYzxP7tYa2L0MRl4MfmFejLZ3Gp+oferZfrQMgAqLjRB/I3EhPi2P4AEih8GFT0POJvj+T90UqdLXqQSvdEhZrZVQP9OpPcjej7QVmpNu6pqg+phgPyNDogLY5krw5bVWQnxNxAT5tDwH3yD1chhzNWx+GezNd5dSlJOpBK90SFmNjRC/U5we2PamNqecOLVLYuqLJg4IZXt2OU6npKxW+57GBrcxgm8w5BxtM5SiA90TqNKnqQSvdEjDtnOd5r64etNpd3G1sQkDQqmw2MgsrtamaPxMxIb4UlVnp9p18bVFseO0v/N3d0+gSp+mErzSIWW1VkI6O0VjrYHvHtYuro49/S6uNtZQfbTtaJn2PfXVRvAAeeVtTNOEpWibkOft6o4wlT5OJXjFY1JKymtthHZmiqamBN66EI78CAv+Dr6hXR9gH5IS4U+In5FNmaVUWLTvaYJr16ucstrW76jTQcwYOK46TCrtUwle8VhVvR27U3b8ImttKbw+Hwr2wpVvw8QbvBNgHyKEYGJSKF/uzUNKGBQVQFKYK8GXtjGCB4ifoI3g7fVtn6ec9lSCVzxWVqNVboR2dA5+13tQchiu+RBGLPJCZH3ThAGh1Nm0lgVjE0KICDDha9STXdrGCB4gaRo46tUoXmmXSvCKx8pcKy47PEWz93/atMLA2V6Iqu+akKRNUwX6GBgQ7ocQgoRQX3LaS/AN1UfZG7wcodLXqQSveKzM1RSrQxdZSzMhdyuMvtRLUfVdYxOD0esEYxKC3QvHEsP8yClrZ4omIErbs/XImm6IUunL2k3wQogAIcQSIcQOIcR6IUSKEMJHCLFcCLFXCPGsEELnOneSEGK7EGKXEGKB98NXulOlq61tsG8HOlxsfUNrdzvmai9F1Xf5mQz8Yu5grp+e7D6WGOrLsdJapJSt3xG0VcCZP0BlnldjVPo2T0bwo4GlUsrxwFLgMeBOIEtKORqIBBomVl8FrgPmA88IIVSvm36kztUB0dfkaUuCWtjxtjbvHhTrxcj6rl+dM5RzR8W4v04M86Oq3k6FxdbGvYCxPwPphN0feDlCpS9rN8FLKTdKKb9xfbkOSAIWAqtcx5YBC4UQ8UCslDJNSlkA7AcmeyFmpYdYrK4Eb/RwU+wd74ClDKbe5cWo+hd3qWR7lTQRg7W5+F3vQXujfeW01dE5+KnAdiAWOOg6lgvEnXSs8fEmhBC3CyG2CiG2FhUVdTxipcfUukbwfiYPErzDDhue0ZLQgOlejqz/SAzzBdqphW8wbrHWsiB3u5ejUvoqjxO8ECIQuA946qT7BgLypGMnH3eTUr4spZwkpZwUGRl58s1KL1bnGsGbDR782qR9DOXZMPOXXo6qf0l018J7kOBHXQKmQFj1mBrFKy3yKMELIUzAR8ATUspM4DgwzHXzMNfXjY81Pq70ExabA1+j3l3x0aq0j+Gr32oNxYae1z3B9RNBPkaCfY2ejeB9gmHeI5C5Gna97/3glD7HkyoaAbwJbJVSLnEdXgE07LM2F/hCSnkcyBFCjBZCRAPhwNauD1npKRabA9+2pmfqKmD5TbD8RghNhquWqj1EOyExzLf9OfgGk27RpsG++T1UqylPpSlP/vfdBVwNzHWVSu4AvgEShRB7gTy0hA9wK7AE+A64S0rZRls8pa+xWJ1tX2D99mHY9ymc9RDc8p12IVDpsMRQP8+maEB7A73gaaiv1pK8ojTSbr2blPJ54PkWbmrWDlBKuR2Y0AVxKb1QXVsj+KKDWknklNthzm+6N7B+JjHMj5X7C3E6JTqdBy2Vo4bDzHth7T/hrIchdID3g1T6BPX5WfFYwxx8i9b+W2sDrJL7KUsM9cXqcFJY1YFmYqMu0f7O3uidoJQ+SSV4xWO1VnvLCb70COxZDpNuBv+I7g+sn3FX0nhyobVB1AgwB6v+NEoTKsErHrPYnPi0NEWz/mnQ6WH63d0fVD/UoVLJBjo9JE5RCV5pQiV4pZnqejtpxyuaHa+zOvA1nvQrU5ELO5ZqS+eDmq1rUzohPsS12MnTSpoGA2ZoC5+qC70QldJRBZV1XPTcT21vpO5lKsErzfzj6wMsfHod6zOKmxy32Bz4Ne5DIyWs/gsgYfavuzfIfszHqCc6yNyxKRqAQa7K5cwfujwmpeOWbclhV045SzYc7bEYVILvJuszinlxTUb7XQJ7gY2ZpQD88dO0JsctNgc+DXPw1UXw/mLYuRSm3qEqN7pYUpgfR0tqOnanmLHgFw6HV3onKKVD/MzaYKi2rU3UvUwl+G5QWFXHXe9s58mvDvDCmoyeDqdNtVY7GUXVAGQUVVNvd7hv06Zo9FqlxvPTtERy7hMw79GeCrffGhUXzN7cSmwOJxUWG794dzsFlXVt30mng4FnQNba7glSaZPeVeFaY3W0faIXqQTfDV5fl0VNvZ1xiSG88VNWrx7Fbztaht0puXhcHFLCsUabT2hlkgK+egCMvnDHGu3Cqlqt2uUmDgjFYnNwIK+KtelFrNidx6oDHsytJ06BylzVJ74XqKzTRu61VjWC79e+31/AtJRwrp6cSFFVPRlF2kdvKWWLFzN70qoDhZgMOi6dkABAdok2D2y1O7E7Jcl1ByBvp7awJmpET4bar01K1rbz23q0lF055QAczK9q/47xk7S/c1WXkJ7WsEFOcZW1x2JQCd7LjpbUcLiwmrOGRzEtJRyAjZklADz/QwYLn17H1qzSHotvR3YZ246WAdobzrdpBcwZEsGI2CAAslzzwBZXq+Bx+cu1DoZj1Q5N3hQb7EtMkA+7j1Ww65g2CDhU4EGCj0kFnRGOqQTf0xo2bcmr7LkqGrXjkhc5nJI/f74PvU5wzshoEkJ9SQj15a9f7mf70TJ+OKQ1h/rxUBGTksO8GovTKVl9sJDIQDOp8cHsyCln+9EyHv9iPwCzBkeQW24ht9zCL+cNISLAhL9Jz1HXCL7O5iCCClIKvoXJN4M50KvxKhAb4kN+RR17XAneoxG80UdL8rnbvByd0p7KOi3BF1TUe952ooupBO9Ff/1yP6sOFPLYxaPdi1deu2Eyb/x0hC/35Lnn6F5Yk8E5I2NITQj2Wiy/eG87X+7JByAu2IfjFU0v2FXW2RgZG8TC1FgWjYlDCEFSuD97cyuwO5xYrA5uNnyFTtphym1ei1M5IdzfxKYjpVhsDoZFB3KwoIri6noiAsxt3zFhEux8F5wObQGU0iMqLdr/b6vDSV5lnXt9Q3dSUzRe8v7mbF5dd4QbZyRz3bQTJYTDYgJ58rIxbHloHp/ePZNfnzMUm0Ny+YvrsdqdXRrDkg1ZLHpmLasPFvLV3nyunpzIPy4fw8i44Ca/bJv/cDaf/WIWz10zgfvPHeZuKHbh2Dj2HC3gzy8txbHlNW7Qf0N+4gKIGNKlcSotC/M3UeUaBMwZqrWA8GwefiJYq7UGcEqPqbDYSIn0x6ATPLf6cI/EoEbwLukFVfzp8zSeXzyRYD/jKT2WzeHkT5+nMXNwOA8tbPlCpNmgZ2xiCEOjA6kqPkberu8pWbGeWF0F1JZASBKEDIAh50D4oA7HUFFr48mvDlBrdXDn29uQEqamhHHJ+ASumJQIwLOr0jlcWE1UkE/zByjL4q6c33CHz2p0hU4ohAwZS/2036G2z+4eYf4nRuqzhkTyytojHMyvYubgdvr9NL7QGj3SixEqbamsszElOYwzhkby1vosrp06gJFxQd0ag0rwLi/9mMlPh0v4aPsxbp418JQeK6+8jjqbk4vGxWPQt/AhyemAmmIoTMN3/bP8IWMlmMC5Uwf+4eAbCoe/B3sdfP2ANqeaPFtbij5wjraTTzteWZtJrdXBojGxrNitlcwNCPdvcs4vzmplJF5dCG9eAHXl6GbdS6ZpKE9uNzB76hSuG5Xc0W+H0knh/ib3v0fEBhLmb/JsBB8+SFvwtPFFGDxPtZDoIRUWG0G+Rn559lA+2ZHLoyvSeO+2ae3viNaFVIJ3cbpq0zcdKTnlBJ/tahKVGOp34qDNAqufgP2fQXkOSNfiB/9IOPMP3LI+lNKg4fxs2kAiAkzMSAnHp/Y4pH0C6d/C1tdh4/Ng8IURF0DyLBh9GZgDmj1/aY2VN346wsIxsVw8Lt6d4JNPSvAtslm0Fao1RXDTlxA/gRTg5Tmn9C1ROiHMleB1AsL9ze55+HYJAZe8DMtvgFfOhsXvQ+xYL0erNOZ0Sqrr7QT5Ggn2M3Lf/GE8/Mlevt6bz4LUWNalFzMuKYQAs3dTsErwLhmF2urNVQcK+fWyXSwcE8PsIZFU19kJ8TM2e9f9em8++RUWXv4xkzEJIVw5OYE5QyIx6HXuBJ8U7krwNcWw9Ao4vh2Gna8l5sBYCIrX+ocYfUmsTGPl+ix2fLgbgAfPH8Ftc1Jg5j3aH3u9Vhmx50PY+yHsWQbf/0m7oBY1EkZcCAkTAXjpxwxqbQ5+NW+Iu7VAoI+B0PamnixlsOJXWondVW9DvNq7pSeFBWgJPjzAjF4nGBYTyPKtOZ5VZAyZBzd/A+9eBa8vgDvXdmqqT+mcqno7UkKQj5ZifzY5kSXrs3hxTQbfpOXzyc7jPHDecO4607s/E5Xg0eq/0wurWTQmFrMa7hSTAAAgAElEQVRBz7f78vlo+zHOT41h9YEiHjhvGDfOHOg+t7Cqnjvf0crQ4kN82ZJVytdp+UQFmrlp5kAqLDaMekGMqR72fqU15Ko4Ble/C8MXthjDIxeM5Nfzh1Jea+OS59c3r3k2mLUpmgEz4Px/wrHNsOU1KNwHGath4wtw2auUJp3LkvVHuWhsHIOjApFSEmg2kBzu3/JHQ1ud1u53/+eQvweQcM6j2qcEpUc1TNFEBWpz8cNiAqmxOsgtt7irstoUMxpu/hqeGqvttjXvT94LVgHA7nBy37JdxIZo17VC/LSfoUGvY/6oaJ7/IYNdxyq4dloSN89K9no8KsEDueUWaq0Opg8K55qpA7DaU7nnvR3ussJ3N2dzw4xkquvt3PLWVjYf0RYm/W7BcK6alIi/2cDqg4Us3ZTN374+gAE7rwS8hP6f14HTDgHRcP2nkDSt1RiEEAT6GAn0MZIS6c+R4jYaTel02mO5Hq+2vBDzu5egX3YdImISqfYFXD9hBDidCJ2OyyYmEBl4UmmdlHDoG/j2QSg5DANmwpm/1z5RJE45tW+o0iXCTkrwQ6O1tQcH86s8S/AAIYkw6Cztk99Zf1RtJbzshR8y+GzXcffX4xJD3P+eNCAMKbVeVDfOGIjZ4P0SVpXgOdE9MTVeu3hpMuiYNzKar9O0BH+ooJo1h4p4amW6e9FJcrgfd55x4uPVuaNimD8ymse/2E/Ahn8y1/4TTL0TRl6sJcwO1CMPDPdn5YECj861OZxc+fZBInz/xpvn78f83V9ZZn4M3n0MEGAO5E/RoyFwDnxTDZZyrUqnJF1L7OFD4NqPtItxSq8S7qqiiXQneO16y8GCKuaNjPb8gcYthg9vgt3va/9WvGJXTjlPrUwn1M9IWa2NuGAfBkWeuO41PklL9jFBTY97k0rwwBe7jxMf4utO8AAzBmltBSYnh5JVUstNb25BLwTPLp7AqLggTIbmIyEhBL8flIVu68dkxC5k0IK/dSqe5Ah/iqutLHpmLa9cP4nY4NYXSLz8YyZ7cyuJCDDBrbfxcPooQrK+5uG5kVBXCXUVcPArWPMkGP20Ch2/MAgbBDP+D8ZdA/pTKwtVvMPXpGdodABjErTEEOhjJD7E17NKmsZGXgwJL8C3D8HQ87Sfv9Klaq12fvXBTiIDzTx/zQQueX49s4dENpkWDfEzMTk5lNHxwd1WSXPaJ/iiqnrWphdzy6yBTb7pcSG+3DJrILOHROBwSn774W7+emkq80fFtP5gtjoMX94HMaMZdNOrnY5pYIT28XtvbiVv/pTF789vuZY+o6iap1am42PUUVxtpbrezt5COwnxF8CMySdOXPA3kE61qrEP+vZXZzT5enhMYMcTvE4Hi/4DL82BlY/CBf/twggVgDd+yiKzuIZ3b53K+KRQ/nzhKGYPab5eYdkd07s1rtN+Qu7lHzNwSslVkxOb3fbwopGcOSyKs0dEs/WheW0ndylh/TNQlQfzHweTh3OkLWiYawV4b3M2pTVNu9EdL7ew+JWN3L10Oz4GHb87bzgAhwurySiqZmjMSX1ihFDJvZ9ICvfjWEd3egLtguu4xbD7A3D0XPva/mr70TKGRgcww7UI7YYZyaRENi9hFkJ0ax38aZ3gfzxUxFvrj3LxuPgWfxiNtflDKcuCdy6F1Y/DkHO1xUinICUygJW/PoMv7plFnd3JL97d3qSH/IfbjrE+o4QD+VU8tHCku1HZ4yv2YXdKZrW30lHpswLNBmptDpxOz/YUsDmcvLspG7vDqW0GYquFov1ejvL0sy+vkpGx3btK1ROnbYL/8VARty7ZyqCoAB5e1Mnl3FLChufh+emQsxkW/AN+9p42Yj5FgyIDGBUXzMMLR7A+o4QfDhYhpeTxFfv493eHGB4TyBs3TeaKSQnuevutR8uYNyK6/aXsSp/lbzYg5Yn2ze35bl8Bf/h4D5uzSt3rJFQr4a5VVmMlr6LO3WK7NzktE/zGzBItuUcG8O6tUwlttCS8Q7I3wDe/11aV3r0Jpt7e5VMhV09JIj7El6dXpZNbbuHVdUcAWDw1ibnDohBCEORjJNTPiNmg45ELVO+R/szftfKxxsN9Pndka73+Ky12CB0IvmFqM5Autj+vEqDb+8x44rRM8O9vzibAbDi15A7afKbRH654E4ITuiy+xox6HXeeOYgd2eW8ulZL7n+5ZDTXTm26yfWts1OatCVW+id/szaAqPYwwe/KqThxvhAwcDakfQplR70W4+lmr2tXtg6N4LvpOshpmeDLLTbiQ3xPLbnb6yHtYxixCEzerWm9YmICUYFm3lyfhV4nuGxCQrOl6nfPHcyVk5pfKFb6F3+TNoKv9WAjZ7vDyZ5cV4J3bT7BOY9pf3/yc3B2bXvq09WO7HISw3zb79PfoK4Cnp2oLT7zstMywVdabAT7nmLtd/q32g9qzJVdE1QbfIx6bp+TAsDgyAB3fxnl9NPQnMqTEfzBgir3XL37/NABcN5f4eg62PSi1+I8nezILmdCUqjnd/jxH9onqPDB3gvK5bRM8Fobz1NcArD7A/CPgoFndklM7Vk8NYnIQDMTBnTgF0npdzoyB98wPQNa8yu38dfC0AVas7ri9K4O8bSSV2Ehv7KO8Y1aErR9h91aG+dx10DcOO8Gx2ma4Cvr7Kc2greUa31cRl8G+u5ZK+ZnMvDlPbN5eFHLi56U04N/B0bwO3PKCPUzEuZvorqu0flCwAVPaX2Sdr3vrVBPC5/u1PrOTB7owepgez18fKe2knj+Y16OTONRghdCPCCEyBNC3Ov6OkYIsVYIcUQI8ZYQQuc6vlAIsVsIsV0IMdGbgZ+KSouNIJ9TSPD7PgWHtVumZxqLDDTjZzrtFx+f1gLcI/iW5+CXb83hiS+1OvedOeWMTQwh0MfQ/A0hMFobQWat82q8/VmFxcZzqw9z1vAoRsW1swmPpQw+vxcK0+DCZ7qtXYSnI/jPXH8a3At8AwwCjMDZQggj8CwwD7gOeLkL4+wydTYH9XYnQacygt+9TGvSFTe+6wJTFA/4uapoaq0tj+B/8+FuXv4xk+ySWtILqxmXqG0q0WQE32DATG2PAWsnVsYqbD5SSlWd3X19rEUOu7bC/alx2qelWffB0HO7LUaPEryUcj9wvNGhSiBLSukEtgFWYApQKKUslFKmASYhRK/bvrPSVU3Q6QRfnqNdoBpzZZcsaFKUjmioomltisbPtWH6v787iJQw1pXgq1o6P3k2OG2Qvd5r8fZn27PLMOhEk5bAzax/WmvyljAJ7lwH8x7pvgDp/Bz8C8AdQoi7gGHAOiAWaLyNey7QbDNIIcTtQoitQoitRUVFnXz6zqu0aL/oDTutdNjOd7W/U6/ooogUxXN6ncDXqG/1Imt8iNZ59BPX3PC4BNcUTUsj+IGzwRQIez/2Wrz92fajZYyKC2q9qq2+Whu9Dz5Ha8kdM7p7A6TzCX4B8C1gBiYBDQXYjR8vEGjWMENK+bKUcpKUclJkZGQnn77zKizaCL7DF1mlhJ+egh/+qv3Awk5t31ZF6Sx/s4HqVubgG36/G4T6mwgwG6hpaUrH6Nrfd/9n2s5eisfsDie7j1Uwvq3yyLT/gaUU5tzffYGdpLMJ/jfAk1LK/wJPA3eiTeEMa3TOMJpO63Sp3HILqzzcFKOxTk3ROGzw2S/guz/CqIvhyiUdfl5F6SoB5tZH8BUWG1e7OqM2bDDh39ocPMDYq6C+EvZ94pVY+6sD+doagzbLlne+BxFDIXFq9wV2ks4m+CCgoUpmCOALbAbChRDRQohU4KiUMr8LYmzRnz5L4+Y3t/Lsqo7V8Va6RjgdqqLZ/hbseAfm/BYue/2UWgEryqnyNxtavMjaUECQGObHd7+aw6vXTwIgwKeVOXiA5Dnagpstnd+/4HTU0OOn1fr3gjTt2sbYq3v0Wl27CV4IESuE2IE2Sv+NEOJ74EbgZSHEYbRE/xcppR24G/gOeAu4zWtRA0dLtD1L/7cjt0P3q+zoFI3TCZte0ipm5v5B7Wmp9Dh/UwtljzT9dDokOpBw19L5QLMBq91Jvb2FaR2dDibfCse2wPGdXo27P9meXU5koJmE0FZ2W/v2YfAJgYk3dW9gJ2k3W0kp86SU46WUsVLKBCnlPCnlOinlGCnlYCnleVLKQte5X7mOT5BSbvdW0PV2B5lFWoIvrqr3+H5Op3T35vB4Jevej6D4EEz7uaqaUXqFQB8D+45Xsj6juMnx1gYv7dXOM/Zn2naOW17p+mD7qe3ZZUxICml5n4j07yFjJZzx2x7fHrFPDkcPF1Zjd0qGRQdSWWfHavesadJ3+wtYtvUYl46P92xH84pc+Pp3ED9RW7WqKL3AXWcOwt9s4J/fHGxyvLUCgphgHwD34KYZ3xBtt6ed78Le/3V9wP1McXU9R0tqW+4/k70JVvxSa8082auTGB7pkwn+QJ62J+Us156HJTWejeJzSrUFHY9cOKr9k6uLYOkV2vLii55TW94pvcak5DDOGx3D/rwqHI12dmotwc8dHkVkoJlX12a2/qDnPAqJ0+CjW2H/Cq/E3RdtO1rGXe9sY0NGifvYjuxygKYXWG0W+PoP8Pq5gIDLXgXDKXSr7SJ9MsE7pWRIVACTXN/gkmprO/fQVLoqCRo+srbIUgZr/wUvTIfSTLjqbYhS/V+U3mV0XDAWm4OMomr3sdYSvNmg58YZyaxNL+ZAfmXLD2jyh2uWQfwEWH4jZK7xVuh9gsXq4PEV+7j8xfV8nZbPta9tcm/sscO1wCk1vlF7ghX3wcbnYPIt8PP12sKmXqBPJvgrJiXy3X1nEBWkffQsqvZsBF9VZyPAbECva2Uufdub8J/R2s7zMWPg5q9g0NwuilpRuk5qgpZc9rqmXWwOJ+sPa6PMlgoIrpmahK9R7940pkXmQLjmQwiM1QY5p6F3Nh7lT5+lcduSrby67giLpySx9rdzMRt07u/d9uwyRjZe4JS9CXa9C7N+BQv/pX0fe4k+3bkqIkD7COTpCL6qzt72CtaQJBi2AGbeCzGpXRGionjFoMgAfIw69uRWcOmEBJ74cj/Ltx0DWl6lHeJn4rzRMaw51M7qcd8QrTZ+7b+0acqA7l+M2FGrDhSwI7uca6YOcF9v6Ki8CgvPrjrM0k3ZgFZP8fMzB/Hb84YDcNmEBN7fks0fF41kV04FV7nWGmAph//dCsGJMPvXXfJ6ulKfHME3aNhBpbgDI/jAturfB52lzZ2p5K70cnqdYGRsEGm5lRwurObN9VkATEsJw6Bv+b91dJAP5bVWpGy2wLypUZeAdML+T7s46q5nczi55a2tPLPqMB9tP9apx8gqruHyFzbw0fZjxLreIKSE2UNOvLnNGxmNzSFZtjUHi82hLSKTEj77P6g8Dpe/0atG7g36dIL3M+nxMeoo8TjB2wnsbA8aRellUuODSTtewWMr9hFgMrDtoXm8f/v0Vs8P8TNic8j2t/uLGgkRw/pEj5qc0loa3q8aiig64mB+FVe8tAGLzcGHd87gx9/Oxc+kx6TXuVcCg9bTB+DVddqF6glJodrisP2fwdmPQOLkU38xXtCnE7wQgogAM8UeX2S1qQSv9Buj44OpsTpYc6iIe84e4l7Y1JpQP+3Ta/lJ/WqaEQJGXwpHf4LKvK4K1ysa1sMAHC3pWIK3OZxc+9omdAKW3TGN0fHBGPU65o2IZvaQiBNz7PZ6ggs3cWFwBjFVaTzg9xkJy+bDl/fDkPkw/Rdd+ZK6VJ/PdmH+JspqPZ+DHxQZ4OWIFKV7NFxoTQ7344YZye2eH+yrXbMqq7G6u062avRlWmO9nUt7tFlWezKLtSqiM4dFkl5Q3c7ZJ9TU29l1rJyiqnqeXTyewVEnplf+c5VrK7283VqLkj3LwFLG06C1V3QCxqkw/3GYeGOvXt3e5xN8kI/RvYKvPWqKRulPBkcGsGhMLNdMHYDJ0H6SCXGN4E/uONmiiCGQMlebhphxT6+o6W5JZlENYf4mxsQH8+OhIqx2p0ffi+te28R2Vz375OSmq0319RWw7Ho4sgb0ZhixCEZfTpVdT0lZKcnjztJ2xOoD+ny2C/I1kF/ZfqtTKWX7F1kVpQ8x6HU8u3iCx+c3JPjyWs8GREy7C969Eg5/B8MXdiZEr8ssriElwp+kcH+cUusyOzDCv8377D5W7k7uoF18dqsqgHcu1dqTzP+LtsLX1W4g0PWnL+n7Cd7DEXy93YnNIdUIXjlthfppo/ByS9MpzVfXZvL4F/s59PiCpqPfQWeBb5jWvqAXJvh6u4O9uRVcNiGBAeFah9f1GcXuBP/7/+3BqBdY7U7mDI3E16TnhwOFfLuvAF+jHovNwZnDTioD/fxebYHj4g+019/H9flsF+RrdHfRa0vDOWoEr5yuGhZAnTyCf/wLbZPukpp6YoMbzc3rjTDyItj9AZRkQPigbovVE9uyyqi1OjhjaCTjE0OYkhzGnz/fh9mg55yR0by3Odt97vtbcgDwMeqYMSiC66YPYFRckHsLRAAK9sGhr+DM3/eL5A79IcH7GKizaa1Q22ogdspb9SlKH+dj1MqKG2rhN2aWNikFLK2xNk3wANPvhn2fwhvnww2fQ+TQbo66dWsOFWHUC6YPCseg1/HSdRO5a+k27l++i1FxQQDoBNxxxiASQn1JDPVjysCw1rfYW/+01lVzyu3d+Cq8q89nu4admarq7JgDWk/wVXWd2OhDUfqZEF8TBZX13LdsFx/vyOXcUScuFpbWtFCNFjEEbvoS3roQ3jwfrv8Uoj1o1tcN1hwqYnJyGP6u3lKh/iaW3jqN51Yf5r/fHyLc38TmB+e13pqkseJ02LNc643fwy1+u1LfT/A+JyoDItqoA26o/e3QVn2K0s+E+Bn5bJe2k2ZimC/fpJ3Y9rLFBA9as72bvoK3LoA3F8J1H2sb4PSg/Io6DuRX8fsFw5sc1+sE95w9hDOHReJwyvaTu8MGG56DNX/TRu+9uKa9M3pvAaeHGjbuaO9Ca165VmkTF9K5XhWK0h9EBfnga9TzwjUTePHaiYT5m/jD+VqSbLOnU8RgbSRvCoS3LoK8Xd0UccvWHCoE4IyTL5K6jEkIaXtDbAC7FV6bD98/os2537UeQhK7OtQe1W9G8JWtbSrscrzcgl4niApUCV45fT1xyWicTkhyVZ1sfXAeAE9+daD9BYNhA7Uk/8pZ8M2DcGPP9Y1fc6iImCAfhkWfQuHinuVwfDtc+AxMuL7rgutF+sEI3pXg2xnBHy+3EBPk49l8nKL0Uwmhfu7kDqDTCXQ6QaifiZLWpmgaC0nU2uJmrdXa5PYAu8PJ2vRizhga2fKWeZ5wOmDdfyA6FcZf17UB9iJ9PsE3lH61Vyp5vMKipmcUpRVh/iZKPezpxMQbwBQAO972blCtWJteTFWdvdXpGY8cWAEl6TD7vn6913KfT/DuKRpLe1M0dc1LwBRFAVwJvtEIftvRMmrqW/k/ZfKHERdq5ZM2SzdFqPk2LZ+b39qCyaBj5qCIzj2IlFq/+7BBWp1/P9bnE7yPUYdRL9h8pKTVdqFOpySvwkJcew2WFOU0FR5gcu9tfKyslsteWM+fP09r/Q7jr4X6SnjnMqgp7qYoYcmGo8QF+/LlPbMJ9utkRVz6t9pF4lm/7Pd7Lff5BC+EYMHoWFYfLGL231dz3Wubmo08CqvqsTkk8WqKRlFaFBPkS06Zhf15laQd1/YePVJc0/odkmfCpa9A7jZ4eS7k7/F6jPkVdfyUUcxlExMYHNXBrrB2K6R9Am9fAu9eBcFJMOZq7wTai/T5BA/w9M/Gs+6BudxxRgpr04v56XDTEcWnO3MB2i+bUpTT1O1zUgjzM3Hzm1v4bp9WGx8V1M6AaMyVWn280w5vLISig16NMaOoGilhekp4x+5Yng1Pj4PlN0DRITjzd3Drd722Q2ZX6hcJHrTqgHvPHoJOQNrxSj7ZkcurazOx2p288VMW01PCGd14F3RFUdxign14/cbJVFpsfOja27XVOfjG4ifALd+AwQxLr9D2cfUSi2snKn9zB6dVvnoALGXwsw/gl7u1BB8Y44UIe59+k+AB/EwGUiIDeGtDFr/8YCePf7GfT3fmkl9Zx+1zUno6PEXp1UbGBfHcNRPcpcSebmZPSBIsfh+qC2Dln70WX61NS/C+rfWSaUnmD3DwS5jzGxh2Xr+fcz9Zv0rwoG2C0Lhb3hNf7mdIVABnDO39u8MrSk87c1gUn/1iJmcPj2q9dUFL4idqq0GzN3otNotV+0Tha/IwSTud8N0jEJQA037utbh6s36X4Bu6yP398jEAlNXauG1OCjq1wElRPDIqLpjBUQEUV9cjG3a09kT8BK223FLe/rmd0LBZuJ/JwwX4+z6BvJ1w1oNgPD0LLPpdgr9tTgor/m8WV05KJD7El8hAMxeNi+vpsBSlTwkPMFFvd1LjSqoeiXPtLpW30ysxWWwNCd6DEbyU8MOTEDUSxlzllXj6gj7fi+ZkPka9+2Lqk5elYtTr2uwTryhKc+H+WmfWkup6AswepomGDpPHtkLKmV0ek8XqQAgwe7DnKpmrofggXPLSaTfv3phHI3ghxANCiDwhxL2ur01CiBeFELuFECuFEPGu47cKIfYIIdYLIZK9F7ZnZg+JZFpHS6oURSE8QCshLPb0QitofdRjx2oLibyg1urA16hvv/9Mxmpt6z3/SBh1iVdi6Ss8naL5zPWnwc8Bm5RyDHAHUCiEiAR+B0wFHgP+3ZWBKorSfRr2Viiuru/YHYcvgpzNUF3Y5THVWh1tT89Ya+GTu+Hti0FnhKvf1co3T2MeJXgp5X7geKND1wL/ct12WEppA+YD26SUtcA3wEzR6VZviqL0pGjXIqeCyrqO3XH4QkDCqse1efAuVGdztF1Bs+4/sPMdrdvlXT9B4pQuff6+qLMXWROAhUKIbUKIV4QQeiAWOAggpXQCZUCzva+EELcLIbYKIbYWFXlvUYSiKJ0X7m/CqBfkVXQwwUeP0hLs9rfgy/u7NMnXWu34GVu5HlBTDBuf15qHzfsTGFXfKej8RVZ/IB+YBHwKNLRka/yGEQg0++lKKV8GXgaYNGlS177FK4rSJXQ6QXSQD/kdTfAAZz+i9Vtf/zQIPZz/9y6JqdbqwKe1Efy6/4CtFuY+2CXP1V90NsHnAZuklFIIsRoYDBxDm39HCBEIhKKN4hVF6YNig33Iq+hEO2Ah4JxHtf1ON70AIxbBwDmnHI/F6sCvpVWstaWw5VWtHDJy2Ck/T3/S2SmaT4HLXFMzc4G9wLfAeCGEH3Am8JXs0CoJRVF6k5hgX49H8HaHk8OF1dS6VpsihDZVEhgHKx/rkqkai62Vi6xpH4O9DqbddcrP0d+0m+CFELFCiB3AncBvhBDfA48DZwH7gGNSyi+llMXAE8Am4PfAr70XtqIo3qaN4OvaXc36n+8OMfpP3zDv32t48qsDJ24w+sAZv4Vjm+HQNx1+/jd/OsKNb2x2Nz2zWFu5yLpnOUQOh5gxHX6O/q7dBC+lzJNSjpdSxkopE6SU86SUFVLKi6SUw6SUP2907utSylQp5QwpZZZXI1cUxatignyotzub9HaSUvLHT/eyPVubfX193RGeWpnOnCGRRASYmjcoG38thA6ElY9q8/ItkFK2+Cay7nAxPxws4s53tmG1O9118E2UZUH2Bq11sSraa6bftSpQFKVrxARrpZL5jUolKyw2lmw4yqXPr+erPXk89sU+5o+M5oVrJxIZqL0hNKE3wtl/hMI02Lm02XNIKbnm1U384r0dzW4rqrYS4mdkbXox9y/fRU29vfkUzZ7l2t+pV5zai+2nVIJXFKVFYf7aatayRl0lG69svWvpdsYnhvD0z8aj1wlMBh319hZG6aMugYQpWm18fXWTmzZklrA+o4QvduexK6dpk7KS6nrmDovigfOG89mu41TV2/Ft3GgsbzdsegmSZmgti5VmVIJXFKVFoX6uBN9oiqbEtbLV36TnZ1OSePWGyfi4pk3Meh3Wk0fwoE2dnPuE1i/+07u1FacuL/yQQUSAmRA/I//9/lCTu5VUW4kIMHHnGSkMjda26HOP4A99C28sAL0JFqlF861RCV5RlBaFuja1Lqs9MWovcY3mP7xrBn+9NNU9ygcwG3VYHS0keIDEyVpVzb5P4bX5UJrJnmMVrE0v5pZZA7ltdgqrDxa55/Zr6u1YbA7CA8wIIZiUrK2ZlBLtgu17V0FYCty6EqJGdPlr7y9UglcUpUXBrgRfYWk+gm9oRtaYqbURfINZv4JrlkNFDrx8JivXrcXPpOfaaUncMCOZMH8T//0+3fU82htJQ0+cqQO1BJ9XYYH1z0BworYfbFDsqb/QfkwleEVRWmQ26PEz6Vucgw/zayHBG1pP8FJKnE4JQ86B238AnYFFhx5iZpIvgT5GAswGbp+Two+HitiYWcIPh7RmZQ1vJOenxnLHnBR+OdkXstbBuMVgDujaF9wPqQSvKEqrQnyNTefga+oJ9TNi0DdPHdpF1uYJvqS6nnn/XsOjK/ZpB8IGUrXgGQY6snio5kmwa28a108fQLi/iatf3sgfP00DINI1gjfqdfx+VjAxK27QKnPGXt3VL7Vf6ncbfiiK0nVC/ExUWBrNwVdbCQ9ouQWvuZUR/KMr9pFRVENGUQ0PLRyBQa9jg24i39tv5e9lr2jz6RNuwC8wll9P8eGZ1XncafiMS/Q/EfiaFaTrMfUm0Blg8TIITfbGy+13VIJXFKVVof4njeCrrYT7N5+eAdcUTQsXWXfmlKMT4JSwPbucKQPD2JdXyXLnXB4/dximHx6FjFUALAYW+4BTCj5xzuSimZPR6/WA1HrOTLpJ21RE8YhK8IqitCrE18T+ikr318U19YyICWrxXJNe32wEX2dzkF1ayy0zB/LWhiw+2ZnLlIFhZBXXEBfsi2n6bTBxMZRmQv1dSWEAAA62SURBVFU+VOVBfTXZodOpKQtFPz3Zi6+u/1MJXlGUVoX4Gak4eQTfQgUN0OJCp4yiaqSE8Umh1NkdvLc5hxumJ3OkpJbkCD/XHf0hJlX745Ls+qOcGnWRVVGUVoX6mSi32JBSYnM4qbDY3Btyn8xk0GFzuKplXNILtJWrQ6MD+PU5wwgwG3h0RRpZxTUkh/t3y2s4nakEryhKq0L8jDicktIaq7tcsrURvNmgpZPG8/DphVUYdIIB4f6E+pu475yh/HS4hAqLjYERKsF7m0rwiqK0qmEF6aoDhe4a+IiOJPiCapIj/DG5brtmahLDogMB1Ai+G6gEryhKq8YmBJMY5suK3XmU1DSsYm19igZocqE1vbDa3UcGwKDX8djFo0mJ8GdMYrAXI1dAJXhFUdoghGDRmDjWHS52z6e3WibpWvzUsNipzubgaEkNg6MCm5w3ZWAYq+4/k6hAHy9GroBK8IqitGPRmFgcTsnSTUeB1kfwZqOWTmrq7UgpOVJcg1PCkCjVUqCnqDJJRVHaNDI2iJQIfzKKajDqBUE+LacNk15r5Tv/Pz8SEWAmPkQboQ+JVgm+p6gRvKIobRJCsGhsHADh/lr73pY0zMEDJIb5UlhVT1SgWVXL9CA1glcUpV0XjInl6ZXprZZIQtMEf//8YcwYFI6UoNOpvVJ7ikrwiqK0a0h0IKnxWkVNa8yNEnyInxEhhNoHu4epBK8oikfevmVKm6PxxiP40Bb6xSvdTyV4RVE8EtJO0jbpm47glZ6nLrIqitIlGk/R+Lo24lZ6lkrwiqJ0CbPhRFJvrdJG6V4qwSuK0iUaz8ErvYP6iSiK0iVUgu991E9EUZQuoRJ876N+IoqidInGVTRK76B+IoqidAmjXruwOlg1F+s1VB28oihdQgjB8junMyhSJfjewqMRvBDiASFEnhDi3pOO/1sIsbrR17cKIfYIIdYLIZK7NlRFUXq7yclhhLXSL17pfp6O4D8DUhofEEIMB2YBNa6vI4HfAWOAM4B/A5d2WaSKoihKh3g0gpdS7geOn3T4X8CTjb6eD2yTUtYC3wAzhVrtoCiK0mM6dZFVCLEIyAH+v70zj7WrqsL476OlFLCFFspcKkIhCEKLRUaxGpRBQQETohUQqdAA2lCGRjSYMiOjJqjMs0RBiVJBBgGRMJSWGUEGEZCxhTIUBGn7+cfeL1wefe3tG+45d7/1S27y7jnn3rO+t89Zd5+11157VsPmNYF/AtheCMwFhi/iswdKmilp5uzZs7tz+iAIgqAJltrBSxoE/Di/Fvd9QwB3PsD2ubbH2R43YsSIpT19EARB0CTdyaLZHlgFmA4sB6wv6UzgXmArAElDgGGkXnwQBEFQAUvt4G3fAmwIIGkUcLHtwyStChwraQVgPHC97Y/14IMgCILWsEQHL2lN4DpgDWCBpN1s79j5ONtzJJ0I3AO8DXy7t40NgiAImkdVdrIlzQaebeEpVwXmtPB8raJUXVCmthI1QXm66qxnlO0lDmJW6uBbjaSZtsdVbUdvU6ouKFNbiZqgPF0l6IlaNEEQBIUSDj4IgqBQ+puDP7dqA/qIUnVBmdpK1ATl6Wp7Pf0qBh8EQdCf6G89+CAIgn5DOPg2JIq4BUHQDMU5eElrSVqpajt6G0kjJe0GUNIMYUnFXYMdlPhDXJImSctWbUNfU8zNJWlZSccDfwXWr9qe3iLrOgF4kFQCoginmHWdCBwtaduG7W3tQCQNlHSYpDVtu5C2GihpmqR1s6YBVdvUE/K1dyZwtqQv521tfd11RdtffACS1gD+AGwBjLN9X8Um9Qq5LPOjwHzgEHJpiVyOud05CViPVGJ6qqR9JA1o56cTSSsCJ5AWvpkG7d9WuZc7Jb/OAbC9oFKjeoCkkcDFwPKkAokXQ1lPxY20tYOXtEr+87+k2vSn235H0qcltW0vvkHX08B3bP8UeBJ4Ju9vyx5Uhy5JywMbAEfZvgq4Ftgb+GKF5nUbSR01nd4HzgN2BDaRND7vb7v26mgr2x8AlwEjgY06woQNmtuChntqHnCN7Um2zwPuzNVvi+zFt6WDlzRU0hXAVZIOAVYghWZ2lfRb0gV5qaTJkj626Ehd6aTrYGCB7Rl591Bgd2i/HtQi2msg8B/gJ/mQfwCvAZ+VNLQiM5caSatKOgfYLT99zAf+Zfth4BLgR9Be7dX5GpS0ge2XbL9BWgPidADb89vBIS7i2htq+2pJK0q6GdgcOFnSxnSvfHqtaUsHT3J0b5IqVq4GnG/774BIIY0tgdOATwLbVWRjd2jUtSrwq44duUzzHEm7Q9v1Nhp1rU5yfocDW0m6FDgVeCjvW9hG2vYCdiA9eXwKPhKSuZxUffWgimzrLo1tNYIclgGwfSXpGjw6v2+HsEbne+pCANvvkMIzmwDPAQeS1pMuirZy8A03voFhtl/O4Yu1JO1vewpwqu2Ftq8hOf+FnT5bO7rQdSywkqRD8zGDgRuBsZIGtsPN1YWuY0jOcC/bY0iOcA/bpwM75eNqra1B14XA14AVgW3yWghIUl6b+BfAhLxtHaXV0GpJF201jYZrMPMDoOOaHC1p5Rab2hRL0DMZwPbltj+wfQqwLVBLLT2hLRx8R2M13PhvkXoSY/L7w4Hjc6xzvqRBkjYg9eDf7vTZ2tCEriNJWSYDbb9HWgd3FWoeq25C1xTgtKzrRtuvShpLGkd5vfUWN8cidM23/TRwPclBbNy43/ZfgHclvUkaeK1dCGBprsF83CzgcUnvA1NJT821oUk9UyUtI2mIpGGS1iMlMrzVeov7lto6eEmrSJoqaUNgcN42OO++n7Rc4KaSVsjhi1nA/qTHyp+ReoaX27699dZ3zVLquhWYCXw/759FcvIvt9jsJdKN9poJ7C9pgKQjSL3hW/Ojc23oQtcg+Igjvxr4ABivPAcj6zqGtFDOZNuH5l595WSntkfH4GLe1tQ1KGkSsBIwyfZE25Uvy9lNPRNJWVxnAFcBl9m+t8Wm9zm161EASNoauAB4hHSDPA+ckXuxkNZ7fQr4HClz4SrgDuA12y9KugA4vG6DWz3Q9RKkVbNI8epa0QNdc2wvkPRn4Kw8SFkbFqPrf3n/dsDjtl8DfkfKBNpM0rrA1cBtwEk5E6UWSNqV5NRmAF8C7gMuavYaBB4AtqpLW/VAz6u2H5J0OvBY3XxFb1HXHvxqwMO29yYNNM5VmmwxRtIzpBvpEtLknylKGSf78mE45uGaNlh3ddX90bGn7fVYXRxGJxan6ylgT1KKLnmQv8Ox7wwMtn17nZx7ZiRwge19gd8DEyRtL2ltSc/RdVvNA7B9d83aqqd6Hqmpr+gVatGDlzSa1Cu4PG+aTYql7wUMB0YBQ4C5wH4NYZcLJD1BmuF5hO2bW2v54ulFXbe01vLFE+3FXOB7HbpyfHoPYG1gQp10ZU1b274sb1of+Hf++37gHWASKaVzQv6hgpq2VWl6+prKygXnTANL2pw0OWQ50j/+JqVshNGkgalHge1JedP32z5baXbdwjr+8oaufqVrIEnXQknD6hCPhi41HWX7BklfIc2Kvo2Ulvoq8BngCts3K5VWUJ3aqjQ9raTKEE3HuV8Efgj8GtgvN+a7th8EHgNm2d4ZOB+YJGmwU2pTXRssdPUfXfOd897r4twzi9K0j6RlbN9IilkPBQYBVwBv8GG4rI4/xKXpaRktd/CSxku6BjhO0ka2Z9u+G7gBeA84IB/3CVK9iI7Mg42A2xoGT2pF6ApdVdOEpokAtv9me5rtKbZfIQ0g1y5HvzQ9VdBSB59vlqOBS0mxssmSvpl3vwhMB74qaR3b84A5wERJt5PyV6e30t5mCV2hq2qa1LSLpLXz8UPzYPG1pAk+T1VgdpeUpqcybPfpCxhAmoj0BWAsKUd9FCmneA/gj8DwfOxI4BRgSn4/jJRzu0tf2xm6Qle76eqmpsn5/crAONJgceVaStRTh1ef9uAlbUYqArYdqW7HWcCGwCinR9w7SINWBwDYfh64EjhI0kvA522/afv6vrRzaQldoatqeqDp4KxpW9szbV9Yhf2dKU1PXejrEM0g0mzSPYGTSQWlNiDX5yANhswARkoarlTX/SxSWdzdbf+pj+3rLqErdFVNTzR93fZ1Fdi8OErTUwv62sG/zIfxyndIecVHAltI+q7TJJDHSSU8XydNGjnN9s6u97Th0BW6qqYnmmYs8hurpTQ9taClefCSppPqSS8P7EOqrTKY9Ch2oGuYmdAMoau9KFFXaZpK01MVLZnJmieEmPTE8JDt1yQZGEOq+Di1HRssdLUXJeoqTVNpeqqmVaUKFpJmn70KbKe0ssqDwNGuV12LpSV0tRcl6ipNU2l6KqUlDt5pKvc4UpGftYArbV/UinP3JaGrvShRV2maStNTNa0sNjaAtOL8cc7lVgshdLUXJeoqTVNpeiqjZYOsubZH7VZV6imhq70oUVdpmkrTUyWVVZMMgiAI+pa6LvgRBEEQ9JBw8EEQBIUSDj4IgqBQwsEHQRAUSjj4oN8hab6kByQ9JOmXufb44o5fV9JOrbIvCHqLcPBBf+Rt22OAzYFXgCVNpNkU2LnPrQqCXibSJIN+h6S5toflv5cBngB2IFUx/DmwCal87beA+cBNpKJXLwBbAeuQfhRWA+4BDnA/XvczqC/Rgw/6NU6LZj8MjAbmAWfZ3hI4EzjC9hPAMcAVtsfmmZXnAofY3hh4DvhGNdYHweJpZamCIKgrAhbYXiDpXUnHAluyiIWbJa0IbANcJglSYaw5rTQ2CJolHHzQr5E0ANgMeFLSlqRVgo4krf952qI+Asy1PbZ1VgZB94gQTdBvUeqCTwPutv0KKQ5/l+07SYs+d/ACsDaA7XnAs5L2zt+xuqRlW2t5EDRHOPigPzJE0gOk2PswYGLefg2wraQ7geENx98FjJY0S9LqpBWGDpL0IPAb0mBrENSOyKIJgiAolOjBB0EQFEo4+CAIgkIJBx8EQVAo4eCDIAgKJRx8EARBoYSDD4IgKJRw8EEQBIXyf7QZmG44Gx3/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.plot()\n",
    "df.Close.rolling(20).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 20 日和 60 日的移動平均"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117f01710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.plot(legend=True)\n",
    "df.Close.rolling(20).mean().plot(label=\"$MA_{20}$\", legend=True)\n",
    "df.Close.rolling(60).mean().plot(label=\"$MA_{60}$\", legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 05-11 手工打造一個 DataFrame*\n",
    "\n",
    "有時我們用手工打造一個簡單的 DataFrame, 可以更理解整個結構。其實很容易, 一個 DataFrame 基本上就包含兩個主要部份:\n",
    "\n",
    "* 資料本身: 通常一個二維陣列 (矩陣)\n",
    "* 行、列的名稱\n",
    "\n",
    "我們來個簡單的小例子。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata = np.random.randn(4,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.21822157, -0.25159939, -0.19534263],\n",
       "       [ 0.10606576, -0.39832127, -1.42026132],\n",
       "       [-1.95442479, -1.717565  ,  0.92151077],\n",
       "       [ 1.14457225,  0.71271714, -0.09496264]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把行列的名字放進去, 就成一個 DataFrame。我們列的部份先讓 Python 自己產生。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(mydata, columns=list('ABC'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.218222</td>\n",
       "      <td>-0.251599</td>\n",
       "      <td>-0.195343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.106066</td>\n",
       "      <td>-0.398321</td>\n",
       "      <td>-1.420261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.954425</td>\n",
       "      <td>-1.717565</td>\n",
       "      <td>0.921511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.144572</td>\n",
       "      <td>0.712717</td>\n",
       "      <td>-0.094963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  1.218222 -0.251599 -0.195343\n",
       "1  0.106066 -0.398321 -1.420261\n",
       "2 -1.954425 -1.717565  0.921511\n",
       "3  1.144572  0.712717 -0.094963"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 兩個表格上下貼起來\n",
    "\n",
    "我們再來生一個 DataFrame, 再「貼」起來。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(np.random.randn(3,3), columns=list('ABC'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.033421</td>\n",
       "      <td>-0.281653</td>\n",
       "      <td>1.243136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.776597</td>\n",
       "      <td>0.665904</td>\n",
       "      <td>1.747222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.079714</td>\n",
       "      <td>-2.033644</td>\n",
       "      <td>-1.908974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.033421 -0.281653  1.243136\n",
       "1  1.776597  0.665904  1.747222\n",
       "2  0.079714 -2.033644 -1.908974"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.218222</td>\n",
       "      <td>-0.251599</td>\n",
       "      <td>-0.195343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.106066</td>\n",
       "      <td>-0.398321</td>\n",
       "      <td>-1.420261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.954425</td>\n",
       "      <td>-1.717565</td>\n",
       "      <td>0.921511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.144572</td>\n",
       "      <td>0.712717</td>\n",
       "      <td>-0.094963</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "          A         B         C\n",
       "0  1.218222 -0.251599 -0.195343\n",
       "1  0.106066 -0.398321 -1.420261\n",
       "2 -1.954425 -1.717565  0.921511\n",
       "3  1.144572  0.712717 -0.094963"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.concat([df, df2], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.106066</td>\n",
       "      <td>-0.398321</td>\n",
       "      <td>-1.420261</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.954425</td>\n",
       "      <td>-1.717565</td>\n",
       "      <td>0.921511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.144572</td>\n",
       "      <td>0.712717</td>\n",
       "      <td>-0.094963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.033421</td>\n",
       "      <td>-0.281653</td>\n",
       "      <td>1.243136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.776597</td>\n",
       "      <td>0.665904</td>\n",
       "      <td>1.747222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.079714</td>\n",
       "      <td>-2.033644</td>\n",
       "      <td>-1.908974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  1.218222 -0.251599 -0.195343\n",
       "1  0.106066 -0.398321 -1.420261\n",
       "2 -1.954425 -1.717565  0.921511\n",
       "3  1.144572  0.712717 -0.094963\n",
       "0 -0.033421 -0.281653  1.243136\n",
       "1  1.776597  0.665904  1.747222\n",
       "2  0.079714 -2.033644 -1.908974"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.index = range(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1.218222</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.106066</td>\n",
       "      <td>-0.398321</td>\n",
       "      <td>-1.420261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.954425</td>\n",
       "      <td>-1.717565</td>\n",
       "      <td>0.921511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.144572</td>\n",
       "      <td>0.712717</td>\n",
       "      <td>-0.094963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.033421</td>\n",
       "      <td>-0.281653</td>\n",
       "      <td>1.243136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.776597</td>\n",
       "      <td>0.665904</td>\n",
       "      <td>1.747222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.079714</td>\n",
       "      <td>-2.033644</td>\n",
       "      <td>-1.908974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  1.218222 -0.251599 -0.195343\n",
       "1  0.106066 -0.398321 -1.420261\n",
       "2 -1.954425 -1.717565  0.921511\n",
       "3  1.144572  0.712717 -0.094963\n",
       "4 -0.033421 -0.281653  1.243136\n",
       "5  1.776597  0.665904  1.747222\n",
       "6  0.079714 -2.033644 -1.908974"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前面我們弄得亂七八糟的 index 重設一下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 横向的貼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>-0.398321</td>\n",
       "      <td>-1.420261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.954425</td>\n",
       "      <td>-1.717565</td>\n",
       "      <td>0.921511</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.144572</td>\n",
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       "      <td>-0.094963</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0  1.218222 -0.251599 -0.195343\n",
       "1  0.106066 -0.398321 -1.420261\n",
       "2 -1.954425 -1.717565  0.921511\n",
       "3  1.144572  0.712717 -0.094963"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.033421</td>\n",
       "      <td>-0.281653</td>\n",
       "      <td>1.243136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.776597</td>\n",
       "      <td>0.665904</td>\n",
       "      <td>1.747222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.079714</td>\n",
       "      <td>-2.033644</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.033421 -0.281653  1.243136\n",
       "1  1.776597  0.665904  1.747222\n",
       "2  0.079714 -2.033644 -1.908974"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "等等, 這大小好像不太對也可以嗎? 答案是可以的!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfx = pd.concat([df, df2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.776597</td>\n",
       "      <td>0.665904</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.921511</td>\n",
       "      <td>0.079714</td>\n",
       "      <td>-2.033644</td>\n",
       "      <td>-1.908974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.144572</td>\n",
       "      <td>0.712717</td>\n",
       "      <td>-0.094963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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       "          A         B         C         A         B         C\n",
       "0  1.218222 -0.251599 -0.195343 -0.033421 -0.281653  1.243136\n",
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       "3  1.144572  0.712717 -0.094963       NaN       NaN       NaN"
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     "execution_count": 61,
     "metadata": {},
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   "source": [
    "dfx"
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  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
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   "source": [
    "dfx.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 63,
     "metadata": {},
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    }
   ],
   "source": [
    "dfx.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 大一點的例子\n",
    "\n",
    "我們來做前面「假的」學測資料。首先要有「假的」同學名單, 如果有興趣產生很多名字, 可以用這個服務。\n",
    "\n",
    "[中文姓名產生器](http://www.richyli.com/name/index.asp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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