09 進擊的熊貓.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 09 進擊的熊貓"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先來標準的套件讀入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 很棒的課程和資料\n",
    "\n",
    "上次介紹了 Pandas Q&A 系列影片, 這裡有它的原始檔。最棒的是, 他用的資料都做了很方便的連結。\n",
    "\n",
    "[Pandas Q&A 原始檔及資料連結](https://github.com/justmarkham/pandas-videos)\n",
    "\n",
    "比如說, 我們可以拿 UFO 資料庫來用用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"http://bit.ly/uforeports\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這是不是太方便了!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Groupby\n",
    "\n",
    "我們現在想看一下哪一州容易出現 UFO。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_state = df.groupby(\"State\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_state.sort_values(\"Time\", ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所以加州最容易看到 UFO!! Pandas 其實真的很喜歡把算好的東西放成一個新的 DataFrame。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_rank = df_state.sort_values(\"Time\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UFO 10 大州!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6938decc0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t1VBVfw18oA+AW4C/Ao6rqi1D1TBiNXAecGeS36mqPxhDDTsz1PC4jXT3yro7yRlVdcfI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\nBCSpYYaAJDXs/wOlDlDdgZon9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6938de198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rank[:10].Time.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 3. 自製一個 DataFrame\n",
    "\n",
    "一個 DataFrame 基本上包含:\n",
    "    \n",
    "1. 資料區 (一個 $m\\times n$ 陣列)\n",
    "2. index (列的名字)\n",
    "3. columns (行的名字)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 小插播\n",
    "\n",
    "Python 做一個 ['A', 'B', 'C'] 這類的 list 很簡單。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(\"ABC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 做一個 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(5,3), index=list(range(1,6)),\n",
    "                 columns=list(\"ABC\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.302296</td>\n",
       "      <td>-0.511466</td>\n",
       "      <td>0.284184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.088071</td>\n",
       "      <td>0.768930</td>\n",
       "      <td>-1.073213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.127151</td>\n",
       "      <td>-0.867716</td>\n",
       "      <td>-0.383302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.150264</td>\n",
       "      <td>0.943468</td>\n",
       "      <td>-1.123834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.827608</td>\n",
       "      <td>1.822604</td>\n",
       "      <td>-1.061941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "1 -0.302296 -0.511466  0.284184\n",
       "2 -0.088071  0.768930 -1.073213\n",
       "3  0.127151 -0.867716 -0.383302\n",
       "4 -2.150264  0.943468 -1.123834\n",
       "5 -0.827608  1.822604 -1.061941"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們學到了一些 slicing 的方法..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   -0.302296\n",
       "2   -0.088071\n",
       "3    0.127151\n",
       "4   -2.150264\n",
       "5   -0.827608\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   -0.302296\n",
       "2   -0.088071\n",
       "3    0.127151\n",
       "4   -2.150264\n",
       "5   -0.827608\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們也可以模仿以前 array 的方式找出符合條件的資料。例如我們想找 B 這行大於 0 的資料。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.088071</td>\n",
       "      <td>0.768930</td>\n",
       "      <td>-1.073213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.150264</td>\n",
       "      <td>0.943468</td>\n",
       "      <td>-1.123834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.827608</td>\n",
       "      <td>1.822604</td>\n",
       "      <td>-1.061941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "2 -0.088071  0.768930 -1.073213\n",
       "4 -2.150264  0.943468 -1.123834\n",
       "5 -0.827608  1.822604 -1.061941"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.B > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.088071</td>\n",
       "      <td>0.768930</td>\n",
       "      <td>-1.073213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.150264</td>\n",
       "      <td>0.943468</td>\n",
       "      <td>-1.123834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.827608</td>\n",
       "      <td>1.822604</td>\n",
       "      <td>-1.061941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "2 -0.088071  0.768930 -1.073213\n",
       "4 -2.150264  0.943468 -1.123834\n",
       "5 -0.827608  1.822604 -1.061941"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.B > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. `loc` 定位法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們想讓 B > 0 的那筆資料, C 都變成 0 (不要問我為什麼, 就高興)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yenlung/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df[df.B > 0][\"C\"] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! Pandas 抗議了, 抗議沒關係, 我們看看它幫我們改了沒有。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.302296</td>\n",
       "      <td>-0.511466</td>\n",
       "      <td>0.284184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.088071</td>\n",
       "      <td>0.768930</td>\n",
       "      <td>-1.073213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.127151</td>\n",
       "      <td>-0.867716</td>\n",
       "      <td>-0.383302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.150264</td>\n",
       "      <td>0.943468</td>\n",
       "      <td>-1.123834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.827608</td>\n",
       "      <td>1.822604</td>\n",
       "      <td>-1.061941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "1 -0.302296 -0.511466  0.284184\n",
       "2 -0.088071  0.768930 -1.073213\n",
       "3  0.127151 -0.867716 -0.383302\n",
       "4 -2.150264  0.943468 -1.123834\n",
       "5 -0.827608  1.822604 -1.061941"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "沒。有。改!!\n",
    "\n",
    "這也太可怕了! 這原因是\n",
    "\n",
    "    df[df.B > 0][\"C\"]\n",
    "    \n",
    "的時候 `pandas` 在\n",
    "\n",
    "    df[df.B > 0]\n",
    "    \n",
    "就回傳一個新的 DataFrame 給你, 然後改了這個 DataFrame 的 C 欄... 因此 `df` 根本沒有碰!!\n",
    "\n",
    "正確改變要用 `loc`, `loc`, `loc`!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 基本的 `loc` 使用方式\n",
    "\n",
    "基本上 `loc` 這樣使用\n",
    "\n",
    "    df[列的範圍, 行的範圍]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.768930</td>\n",
       "      <td>-1.073213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.867716</td>\n",
       "      <td>-0.383302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          B         C\n",
       "2  0.768930 -1.073213\n",
       "3 -0.867716 -0.383302"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2:3, \"B\":\"C\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只有一列或一行就用括號!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7689303797741851"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(2), (\"B\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們來改改看..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[(2), (\"B\")] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
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       "</style>\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",
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       "      <th>1</th>\n",
       "      <td>-0.302296</td>\n",
       "      <td>-0.511466</td>\n",
       "      <td>0.284184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.088071</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.073213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.127151</td>\n",
       "      <td>-0.867716</td>\n",
       "      <td>-0.383302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.150264</td>\n",
       "      <td>0.943468</td>\n",
       "      <td>-1.123834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.827608</td>\n",
       "      <td>1.822604</td>\n",
       "      <td>-1.061941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "1 -0.302296 -0.511466  0.284184\n",
       "2 -0.088071 -1.000000 -1.073213\n",
       "3  0.127151 -0.867716 -0.383302\n",
       "4 -2.150264  0.943468 -1.123834\n",
       "5 -0.827608  1.822604 -1.061941"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "改成了!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[df.B > 0, \"C\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.088071</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.073213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.127151</td>\n",
       "      <td>-0.867716</td>\n",
       "      <td>-0.383302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.150264</td>\n",
       "      <td>0.943468</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.827608</td>\n",
       "      <td>1.822604</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "1 -0.302296 -0.511466  0.284184\n",
       "2 -0.088071 -1.000000 -1.073213\n",
       "3  0.127151 -0.867716 -0.383302\n",
       "4 -2.150264  0.943468  0.000000\n",
       "5 -0.827608  1.822604  0.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "就是這樣!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 讀入真正的股票資料"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自 `pandas` 0.19.0 版開始, `pandas.io.data` 和 `pandas.io.wb` 已經從 `pandas` 系統移出, 獨立在\n",
    "\n",
    "    pandas_datareader\n",
    "    \n",
    "套件中。\n",
    "\n",
    "使用 Anaconda 的話, 請用標準安裝方式安裝 `pandas-datareader`:\n",
    "    \n",
    "    conda install pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas_datareader.data as web\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這裡除了我們說的 `pandas_datareader`, 還有 `datetime` 是標準的日期套件。\n",
    "\n",
    "假設我們要 Apple 的股票, 日期從 2010 年 1 月 1 日, 2016 年 10 月 31 日。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "symbol = \"AAPL\"\n",
    "day_start = datetime.datetime(2010,1,1)\n",
    "day_end = datetime.datetime(2017,6,16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = web.DataReader(symbol, 'google', day_start, day_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-12</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-13</th>\n",
       "      <td>147.16</td>\n",
       "      <td>147.45</td>\n",
       "      <td>145.15</td>\n",
       "      <td>146.59</td>\n",
       "      <td>34165445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-14</th>\n",
       "      <td>147.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>143.84</td>\n",
       "      <td>145.16</td>\n",
       "      <td>31531232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-15</th>\n",
       "      <td>143.32</td>\n",
       "      <td>144.48</td>\n",
       "      <td>142.21</td>\n",
       "      <td>144.29</td>\n",
       "      <td>32165373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-16</th>\n",
       "      <td>143.78</td>\n",
       "      <td>144.50</td>\n",
       "      <td>142.20</td>\n",
       "      <td>142.27</td>\n",
       "      <td>50361093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-06-12  145.74  146.09  142.51  145.42  72307330\n",
       "2017-06-13  147.16  147.45  145.15  146.59  34165445\n",
       "2017-06-14  147.50  147.50  143.84  145.16  31531232\n",
       "2017-06-15  143.32  144.48  142.21  144.29  32165373\n",
       "2017-06-16  143.78  144.50  142.20  142.27  50361093"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe692a97908>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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56ZqRTFqS+zj9Vf9wT0RHK2q4+umlPLV4K71zUklPindOCxxkt+yb4tg04t9L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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe692ad76d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 一些基本的計算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 差分\n",
    "\n",
    "我們來考慮股票的計算。其實用點數有時不太容易分析, 我們更重要是看漲還是跌。\n",
    "\n",
    "換句話說, 我們重視的是\n",
    "\n",
    "$$S_{j} - S_{j-1}$$\n",
    "\n",
    "也就是當期和上一期的差別。這樣怎麼算呢?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"Diff\"] = df.Close.diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Diff</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>2010-01-04</th>\n",
       "      <td>30.49</td>\n",
       "      <td>30.64</td>\n",
       "      <td>30.34</td>\n",
       "      <td>30.57</td>\n",
       "      <td>123432050</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.66</td>\n",
       "      <td>30.80</td>\n",
       "      <td>30.46</td>\n",
       "      <td>30.63</td>\n",
       "      <td>150476004</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.63</td>\n",
       "      <td>30.75</td>\n",
       "      <td>30.11</td>\n",
       "      <td>30.14</td>\n",
       "      <td>138039594</td>\n",
       "      <td>-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.25</td>\n",
       "      <td>30.29</td>\n",
       "      <td>29.86</td>\n",
       "      <td>30.08</td>\n",
       "      <td>119282324</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.04</td>\n",
       "      <td>30.29</td>\n",
       "      <td>29.87</td>\n",
       "      <td>30.28</td>\n",
       "      <td>111969081</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume  Diff\n",
       "Date                                                   \n",
       "2010-01-04  30.49  30.64  30.34  30.57  123432050   NaN\n",
       "2010-01-05  30.66  30.80  30.46  30.63  150476004  0.06\n",
       "2010-01-06  30.63  30.75  30.11  30.14  138039594 -0.49\n",
       "2010-01-07  30.25  30.29  29.86  30.08  119282324 -0.06\n",
       "2010-01-08  30.04  30.29  29.87  30.28  111969081  0.20"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe691a43c88>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GZ311XSK5pyZFX92ZKT/jMeNqRf+20ebOCIWyjlu0Pfw9GyYgq7c0RWPmG/GCyN3PurZd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\nRpK3wb4JYxsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe692bde2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Diff.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 事實上\n",
    "\n",
    "更有道理的是取這個值:\n",
    "\n",
    "$$\\frac{S_j - S_{j-1}}{S_{j-1}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"Rel_Diff\"] = df.Diff / df.Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Rel_Diff</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-12</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "      <td>-3.56</td>\n",
       "      <td>-0.024481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-13</th>\n",
       "      <td>147.16</td>\n",
       "      <td>147.45</td>\n",
       "      <td>145.15</td>\n",
       "      <td>146.59</td>\n",
       "      <td>34165445</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.007981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-14</th>\n",
       "      <td>147.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>143.84</td>\n",
       "      <td>145.16</td>\n",
       "      <td>31531232</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-0.009851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-15</th>\n",
       "      <td>143.32</td>\n",
       "      <td>144.48</td>\n",
       "      <td>142.21</td>\n",
       "      <td>144.29</td>\n",
       "      <td>32165373</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.006030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-16</th>\n",
       "      <td>143.78</td>\n",
       "      <td>144.50</td>\n",
       "      <td>142.20</td>\n",
       "      <td>142.27</td>\n",
       "      <td>50361093</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-0.014198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume  Diff  Rel_Diff\n",
       "Date                                                                \n",
       "2017-06-12  145.74  146.09  142.51  145.42  72307330 -3.56 -0.024481\n",
       "2017-06-13  147.16  147.45  145.15  146.59  34165445  1.17  0.007981\n",
       "2017-06-14  147.50  147.50  143.84  145.16  31531232 -1.43 -0.009851\n",
       "2017-06-15  143.32  144.48  142.21  144.29  32165373 -0.87 -0.006030\n",
       "2017-06-16  143.78  144.50  142.20  142.27  50361093 -2.02 -0.014198"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  }
 ],
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