6.4 Store Currency Data Into Database.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 爬取汇率信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "payload = {\n",
    "'projectBean.startDate':'2017-02-01',\n",
    "'projectBean.endDate':'2017-05-01',\n",
    "'queryYN':'true'\n",
    "}\n",
    "res = requests.post('http://www.safe.gov.cn/AppStructured/view/project_RMBQuery.action', data = payload)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#res.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 剖析汇率信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import pandas\n",
    "soup = BeautifulSoup(res.text, 'html.parser')\n",
    "type(soup.select('#InfoTable')[0])\n",
    "type(soup.select('#InfoTable')[0].prettify('utf-8'))\n",
    "#pandas.read_html(soup.select('#InfoTable')[0])\n",
    "dfs = pandas.read_html(soup.select('#InfoTable')[0].prettify('utf-8'), header=0)\n",
    "#len(dfs)\n",
    "#dfs[0]\n",
    "df_rates = dfs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>美元</th>\n",
       "      <th>欧元</th>\n",
       "      <th>日元</th>\n",
       "      <th>港元</th>\n",
       "      <th>英镑</th>\n",
       "      <th>林吉特</th>\n",
       "      <th>卢布</th>\n",
       "      <th>兰特</th>\n",
       "      <th>韩元</th>\n",
       "      <th>...</th>\n",
       "      <th>丹麦克朗</th>\n",
       "      <th>瑞典克朗</th>\n",
       "      <th>挪威克朗</th>\n",
       "      <th>里拉</th>\n",
       "      <th>比索</th>\n",
       "      <th>澳元</th>\n",
       "      <th>加元</th>\n",
       "      <th>新西兰元</th>\n",
       "      <th>新加坡元</th>\n",
       "      <th>瑞士法郎</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>689.31</td>\n",
       "      <td>749.45</td>\n",
       "      <td>6.2023</td>\n",
       "      <td>88.584</td>\n",
       "      <td>889.61</td>\n",
       "      <td>62.822</td>\n",
       "      <td>826.93</td>\n",
       "      <td>193.24</td>\n",
       "      <td>16387.0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.20</td>\n",
       "      <td>128.04</td>\n",
       "      <td>124.23</td>\n",
       "      <td>51.631</td>\n",
       "      <td>275.98</td>\n",
       "      <td>515.34</td>\n",
       "      <td>506.28</td>\n",
       "      <td>474.81</td>\n",
       "      <td>493.90</td>\n",
       "      <td>693.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>688.96</td>\n",
       "      <td>751.87</td>\n",
       "      <td>6.1977</td>\n",
       "      <td>88.546</td>\n",
       "      <td>885.74</td>\n",
       "      <td>62.829</td>\n",
       "      <td>827.94</td>\n",
       "      <td>192.26</td>\n",
       "      <td>16383.0</td>\n",
       "      <td>...</td>\n",
       "      <td>98.94</td>\n",
       "      <td>127.03</td>\n",
       "      <td>124.47</td>\n",
       "      <td>51.810</td>\n",
       "      <td>278.13</td>\n",
       "      <td>515.42</td>\n",
       "      <td>506.37</td>\n",
       "      <td>475.50</td>\n",
       "      <td>494.06</td>\n",
       "      <td>694.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>688.45</td>\n",
       "      <td>752.50</td>\n",
       "      <td>6.1964</td>\n",
       "      <td>88.477</td>\n",
       "      <td>883.71</td>\n",
       "      <td>63.213</td>\n",
       "      <td>814.87</td>\n",
       "      <td>189.81</td>\n",
       "      <td>16358.0</td>\n",
       "      <td>...</td>\n",
       "      <td>98.86</td>\n",
       "      <td>127.36</td>\n",
       "      <td>124.41</td>\n",
       "      <td>51.996</td>\n",
       "      <td>274.05</td>\n",
       "      <td>519.05</td>\n",
       "      <td>507.26</td>\n",
       "      <td>478.34</td>\n",
       "      <td>493.98</td>\n",
       "      <td>693.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>688.33</td>\n",
       "      <td>747.63</td>\n",
       "      <td>6.2746</td>\n",
       "      <td>88.478</td>\n",
       "      <td>880.27</td>\n",
       "      <td>63.662</td>\n",
       "      <td>810.73</td>\n",
       "      <td>189.17</td>\n",
       "      <td>16452.0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.43</td>\n",
       "      <td>128.67</td>\n",
       "      <td>124.27</td>\n",
       "      <td>51.934</td>\n",
       "      <td>273.42</td>\n",
       "      <td>520.55</td>\n",
       "      <td>508.40</td>\n",
       "      <td>482.56</td>\n",
       "      <td>494.36</td>\n",
       "      <td>691.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>686.73</td>\n",
       "      <td>746.24</td>\n",
       "      <td>6.2433</td>\n",
       "      <td>88.316</td>\n",
       "      <td>880.67</td>\n",
       "      <td>63.827</td>\n",
       "      <td>818.72</td>\n",
       "      <td>189.23</td>\n",
       "      <td>16457.0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.68</td>\n",
       "      <td>128.77</td>\n",
       "      <td>124.19</td>\n",
       "      <td>52.596</td>\n",
       "      <td>271.20</td>\n",
       "      <td>519.10</td>\n",
       "      <td>509.39</td>\n",
       "      <td>483.42</td>\n",
       "      <td>493.15</td>\n",
       "      <td>691.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           日期      美元      欧元      日元      港元      英镑     林吉特      卢布      兰特  \\\n",
       "0  2017-04-28  689.31  749.45  6.2023  88.584  889.61  62.822  826.93  193.24   \n",
       "1  2017-04-27  688.96  751.87  6.1977  88.546  885.74  62.829  827.94  192.26   \n",
       "2  2017-04-26  688.45  752.50  6.1964  88.477  883.71  63.213  814.87  189.81   \n",
       "3  2017-04-25  688.33  747.63  6.2746  88.478  880.27  63.662  810.73  189.17   \n",
       "4  2017-04-24  686.73  746.24  6.2433  88.316  880.67  63.827  818.72  189.23   \n",
       "\n",
       "        韩元   ...     丹麦克朗    瑞典克朗    挪威克朗      里拉      比索      澳元      加元  \\\n",
       "0  16387.0   ...    99.20  128.04  124.23  51.631  275.98  515.34  506.28   \n",
       "1  16383.0   ...    98.94  127.03  124.47  51.810  278.13  515.42  506.37   \n",
       "2  16358.0   ...    98.86  127.36  124.41  51.996  274.05  519.05  507.26   \n",
       "3  16452.0   ...    99.43  128.67  124.27  51.934  273.42  520.55  508.40   \n",
       "4  16457.0   ...    99.68  128.77  124.19  52.596  271.20  519.10  509.39   \n",
       "\n",
       "     新西兰元    新加坡元    瑞士法郎  \n",
       "0  474.81  493.90  693.82  \n",
       "1  475.50  494.06  694.12  \n",
       "2  478.34  493.98  693.05  \n",
       "3  482.56  494.36  691.44  \n",
       "4  483.42  493.15  691.59  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#pandas.melt(df_rates, col_level=0, id_vars=['日期'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_rates = pandas.melt(df_rates, col_level=0, id_vars=['日期'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df_rates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_rates.columns = ['date', 'currency', 'exchange']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>currency</th>\n",
       "      <th>exchange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>美元</td>\n",
       "      <td>689.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>美元</td>\n",
       "      <td>686.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date currency  exchange\n",
       "0  2017-04-28       美元    689.31\n",
       "1  2017-04-27       美元    688.96\n",
       "2  2017-04-26       美元    688.45\n",
       "3  2017-04-25       美元    688.33\n",
       "4  2017-04-24       美元    686.73"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将数据储存到数据库中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sqlite3 as lite\n",
    "with lite.connect('currency.sqlite') as db:\n",
    "    df_rates.to_sql('currency_data', con=db, if_exists='replace', index = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 产生多天日期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-04-02 2017-05-01\n",
      "2017-03-03 2017-04-01\n",
      "2017-02-01 2017-03-02\n",
      "2017-01-02 2017-01-31\n",
      "2016-12-03 2017-01-01\n",
      "2016-11-03 2016-12-02\n",
      "2016-10-04 2016-11-02\n",
      "2016-09-04 2016-10-03\n",
      "2016-08-05 2016-09-03\n",
      "2016-07-06 2016-08-04\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "current_time = datetime.now()\n",
    "for i in range(1,300,30):\n",
    "    #print(i)\n",
    "    start_date = (current_time - timedelta(days = i + 30)).strftime('%Y-%m-%d')\n",
    "    end_date   = (current_time - timedelta(days = i + 1)).strftime('%Y-%m-%d')\n",
    "    print(start_date, end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立取得汇率函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getCurrency(start, end):\n",
    "    payload = {\n",
    "    'projectBean.startDate':start,\n",
    "    'projectBean.endDate':end,\n",
    "    'queryYN':'true'\n",
    "    }\n",
    "    res = requests.post('http://www.safe.gov.cn/AppStructured/view/project_RMBQuery.action', data = payload)\n",
    "    soup = BeautifulSoup(res.text, 'html.parser')\n",
    "    dfs = pandas.read_html(soup.select('#InfoTable')[0].prettify('utf-8'), header=0)\n",
    "    df_rates = dfs[0]\n",
    "    \n",
    "    df_rates = pandas.melt(df_rates, col_level=0, id_vars=['日期'])\n",
    "    df_rates.columns = ['date', 'currency', 'exchange']\n",
    "    \n",
    "    with lite.connect('currency.sqlite') as db:\n",
    "        df_rates.to_sql('currency_data', con=db, if_exists='append', index = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 抓取多天汇率数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "current_time = datetime.now()\n",
    "for i in range(1,300,30):\n",
    "    start_date = (current_time - timedelta(days = i + 30)).strftime('%Y-%m-%d')\n",
    "    end_date   = (current_time - timedelta(days = i + 1)).strftime('%Y-%m-%d')\n",
    "    print(start_date, end_date)\n",
    "    getCurrency(start_date, end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 取得美元数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with lite.connect('currency.sqlite') as db:\n",
    "    df = pandas.read_sql(\"SELECT * FROM currency_data WHERE currency = '美元'\", con=db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        257\n",
       "currency    257\n",
       "exchange    257\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>currency</th>\n",
       "      <th>exchange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>美元</td>\n",
       "      <td>689.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>美元</td>\n",
       "      <td>686.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date currency  exchange\n",
       "0  2017-04-28       美元    689.31\n",
       "1  2017-04-27       美元    688.96\n",
       "2  2017-04-26       美元    688.45\n",
       "3  2017-04-25       美元    688.33\n",
       "4  2017-04-24       美元    686.73"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['date'] = pandas.to_datetime(df['date'], format= '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 257 entries, 0 to 256\n",
      "Data columns (total 3 columns):\n",
      "date        257 non-null datetime64[ns]\n",
      "currency    257 non-null object\n",
      "exchange    257 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 6.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.index = df.date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制美元汇率线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb98d400>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gtfeGdGpseJ+zUESEiUmR/hYEPluP1DJ/Qrx/gdfVyyaREmNnf3kTV+ZN7NU6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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb98cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% pylab inline\n",
    "df.plot(kind = 'line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mvg30'] = df['exchange'].rolling(window = 30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb5b8f60>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W+6pTkUfWlqX0OPY/BrttRejdTr2Hbe9wbd9pJFzci0+2p7E/o5RvH75Eu6aU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb5b06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['exchange', 'mvg30']].plot(kind = 'line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 取得美元、英镑数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with lite.connect('currency.sqlite') as db:\n",
    "    df = pandas.read_sql(\"SELECT * FROM currency_data WHERE currency IN ('美元', '英镑') \", con=db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['美元', '英镑'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.currency.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['date'] = pandas.to_datetime(df['date'], format= '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 514 entries, 0 to 513\n",
      "Data columns (total 3 columns):\n",
      "date        514 non-null datetime64[ns]\n",
      "currency    514 non-null object\n",
      "exchange    514 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 12.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>currency</th>\n",
       "      <th>exchange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>美元</td>\n",
       "      <td>689.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>美元</td>\n",
       "      <td>688.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>美元</td>\n",
       "      <td>686.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date currency  exchange\n",
       "0 2017-04-28       美元    689.31\n",
       "1 2017-04-27       美元    688.96\n",
       "2 2017-04-26       美元    688.45\n",
       "3 2017-04-25       美元    688.33\n",
       "4 2017-04-24       美元    686.73"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立Pivot Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df.pivot_table(index= 'date', columns= 'currency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">exchange</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>currency</th>\n",
       "      <th>美元</th>\n",
       "      <th>英镑</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-06</th>\n",
       "      <td>668.57</td>\n",
       "      <td>866.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-07</th>\n",
       "      <td>668.20</td>\n",
       "      <td>862.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-08</th>\n",
       "      <td>668.53</td>\n",
       "      <td>863.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-11</th>\n",
       "      <td>668.43</td>\n",
       "      <td>865.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-12</th>\n",
       "      <td>669.50</td>\n",
       "      <td>869.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           exchange        \n",
       "currency         美元      英镑\n",
       "date                       \n",
       "2016-07-06   668.57  866.47\n",
       "2016-07-07   668.20  862.48\n",
       "2016-07-08   668.53  863.40\n",
       "2016-07-11   668.43  865.25\n",
       "2016-07-12   669.50  869.77"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制美元、英镑汇率线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb821390>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ydXFELWmtSUtLIy4ujo4dz2/xemmuEcKO5eXl4evrKwG+iVJK4evre0FXYhLk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\nNcIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa55e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(kind= 'line', rot= 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242.7259259259259"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 * 1024 *1024 / 30 / 24 / 60"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}