pandas-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas教程\n",
    "### 2018七月在线 机器学习集训营 julyedu.com \n",
    "by 褚则伟 zeweichu@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "- Groupby和Aggregate\n",
    "- transform\n",
    "- 表格的匹配与拼接\n",
    "- bikes项目"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Groupby and Aggregate\n",
    "### 2018七月在线 机器学习集训营 julyedu.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "举个栗子，假设我们现在有一张公司每个员工的收入流水。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Bonus   Name  Salary  Year\n",
      "0   3000   July   10000  2016\n",
      "1   1000  Zewei    2000  2016\n",
      "2   1000  Zewei    4000  2016\n",
      "3   1200    Han    5000  2016\n",
      "4   4000   July   18000  2017\n",
      "5   2300   July   25000  2017\n",
      "6    500  Zewei    3000  2017\n",
      "7   1000   July    4000  2017\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "salaries = pd.DataFrame({\n",
    "    'Name': ['July', 'Zewei', 'Zewei', 'Han', 'July', 'July', 'Zewei', 'July'],\n",
    "    'Year': [2016,2016,2016,2016,2017,2017,2017,2017],\n",
    "    'Salary': [10000,2000,4000,5000,18000,25000,3000,4000],\n",
    "    'Bonus': [3000,1000,1000,1200,4000,2300,500,1000]\n",
    "})\n",
    "print(salaries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我给大家演示一下什么叫做Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x7fcc986b6e48>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name = salaries.groupby('Name')\n",
    "group_by_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "groupby构造了一个GroupBy object，我们可以对这个object做各种操作，比如求个和，当然我们后面还会详细的介绍各种GroupBy之后的操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <td>1200</td>\n",
       "      <td>5000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>10300</td>\n",
       "      <td>57000</td>\n",
       "      <td>8067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>2500</td>\n",
       "      <td>9000</td>\n",
       "      <td>6049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bonus  Salary  Year\n",
       "Name                      \n",
       "Han     1200    5000  2016\n",
       "July   10300   57000  8067\n",
       "Zewei   2500    9000  6049"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "groupby还有一些高级的用法，我们来看看[documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.660528</td>\n",
       "      <td>-1.763113</td>\n",
       "      <td>-0.298845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.854158</td>\n",
       "      <td>0.827719</td>\n",
       "      <td>2.076892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.026881</td>\n",
       "      <td>-1.254434</td>\n",
       "      <td>0.821393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>0.242696</td>\n",
       "      <td>1.483995</td>\n",
       "      <td>-1.493355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>1.033790</td>\n",
       "      <td>1.567744</td>\n",
       "      <td>-0.281585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.908586</td>\n",
       "      <td>1.104421</td>\n",
       "      <td>-1.018270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.618399</td>\n",
       "      <td>0.790107</td>\n",
       "      <td>1.157953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>0.912363</td>\n",
       "      <td>-1.722328</td>\n",
       "      <td>-0.161141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D         E\n",
       "0  foo    one -0.660528 -1.763113 -0.298845\n",
       "1  bar    one -0.854158  0.827719  2.076892\n",
       "2  foo    two -0.026881 -1.254434  0.821393\n",
       "3  bar  three  0.242696  1.483995 -1.493355\n",
       "4  foo    two  1.033790  1.567744 -0.281585\n",
       "5  bar    two -0.908586  1.104421 -1.018270\n",
       "6  foo    one  0.618399  0.790107  1.157953\n",
       "7  foo  three  0.912363 -1.722328 -0.161141"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar','foo', 'bar', 'foo', 'foo'],\n",
    "                   'B' : ['one', 'one', 'two', 'three','two', 'two', 'one', 'three'],\n",
    "                   'C' : np.random.randn(8),'D' : np.random.randn(8), \n",
    "                   'E' : np.random.randn(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_letter_type(letter):\n",
    "    if letter.lower() in \"aeiou\":\n",
    "        return \"vowel\"\n",
    "    else:\n",
    "        return \"consonant\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grouped = df.groupby(get_letter_type, axis=1)"
   ]
  },
  {
   "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>consonant</th>\n",
       "      <th>vowel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.423641</td>\n",
       "      <td>-0.298845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.026439</td>\n",
       "      <td>2.076892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.281315</td>\n",
       "      <td>0.821393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.726691</td>\n",
       "      <td>-1.493355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.601534</td>\n",
       "      <td>-0.281585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.195834</td>\n",
       "      <td>-1.018270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.408506</td>\n",
       "      <td>1.157953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.809964</td>\n",
       "      <td>-0.161141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   consonant     vowel\n",
       "0  -2.423641 -0.298845\n",
       "1  -0.026439  2.076892\n",
       "2  -1.281315  0.821393\n",
       "3   1.726691 -1.493355\n",
       "4   2.601534 -0.281585\n",
       "5   0.195834 -1.018270\n",
       "6   1.408506  1.157953\n",
       "7  -0.809964 -0.161141"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### groupby之后一般要做一些aggregate操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <td>1200</td>\n",
       "      <td>5000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>10300</td>\n",
       "      <td>57000</td>\n",
       "      <td>8067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>2500</td>\n",
       "      <td>9000</td>\n",
       "      <td>6049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bonus  Salary  Year\n",
       "Name                      \n",
       "Han     1200    5000  2016\n",
       "July   10300   57000  8067\n",
       "Zewei   2500    9000  6049"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认会排序，也可以选择不排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>10300</td>\n",
       "      <td>57000</td>\n",
       "      <td>8067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>2500</td>\n",
       "      <td>9000</td>\n",
       "      <td>6049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <td>1200</td>\n",
       "      <td>5000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bonus  Salary  Year\n",
       "Name                      \n",
       "July   10300   57000  8067\n",
       "Zewei   2500    9000  6049\n",
       "Han     1200    5000  2016"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salaries.groupby('Name', sort=False).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以这么写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <td>1200</td>\n",
       "      <td>5000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>10300</td>\n",
       "      <td>57000</td>\n",
       "      <td>8067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>2500</td>\n",
       "      <td>9000</td>\n",
       "      <td>6049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bonus  Salary  Year\n",
       "Name                      \n",
       "Han     1200    5000  2016\n",
       "July   10300   57000  8067\n",
       "Zewei   2500    9000  6049"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.aggregate(sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "group by的attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'July': Int64Index([0, 4, 5, 7], dtype='int64'), 'Han': Int64Index([3], dtype='int64'), 'Zewei': Int64Index([1, 2, 6], dtype='int64')}\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(group_by_name.groups)\n",
    "print(len(group_by_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以用多个columns做group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <th>2016</th>\n",
       "      <td>1200</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">July</th>\n",
       "      <th>2016</th>\n",
       "      <td>3000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>7300</td>\n",
       "      <td>47000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Zewei</th>\n",
       "      <th>2016</th>\n",
       "      <td>2000</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>500</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Bonus  Salary\n",
       "Name  Year               \n",
       "Han   2016   1200    5000\n",
       "July  2016   3000   10000\n",
       "      2017   7300   47000\n",
       "Zewei 2016   2000    6000\n",
       "      2017    500    3000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name_year = salaries.groupby(['Name', 'Year'])\n",
    "group_by_name_year.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除了sum之外，我们再来尝试一下size,mean,median "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name   Year\n",
       "Han    2016    1\n",
       "July   2016    1\n",
       "       2017    3\n",
       "Zewei  2016    2\n",
       "       2017    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name_year.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <td>1200.000000</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>2575.000000</td>\n",
       "      <td>14250.0</td>\n",
       "      <td>2016.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>833.333333</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2016.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Bonus   Salary         Year\n",
       "Name                                    \n",
       "Han    1200.000000   5000.0  2016.000000\n",
       "July   2575.000000  14250.0  2016.750000\n",
       "Zewei   833.333333   3000.0  2016.333333"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <td>1200</td>\n",
       "      <td>5000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>2650</td>\n",
       "      <td>14000</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>1000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bonus  Salary  Year\n",
       "Name                      \n",
       "Han     1200    5000  2016\n",
       "July    2650   14000  2017\n",
       "Zewei   1000    3000  2016"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.median()"
   ]
  },
  {
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Bonus</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Salary</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>Han</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2575.000000</td>\n",
       "      <td>1260.621540</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14250.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19750.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016.750000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.75</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>3.0</td>\n",
       "      <td>833.333333</td>\n",
       "      <td>288.675135</td>\n",
       "      <td>500.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016.333333</td>\n",
       "      <td>0.57735</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2016.5</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Bonus                                                                    \\\n",
       "      count         mean          std     min     25%     50%     75%     max   \n",
       "Name                                                                            \n",
       "Han     1.0  1200.000000          NaN  1200.0  1200.0  1200.0  1200.0  1200.0   \n",
       "July    4.0  2575.000000  1260.621540  1000.0  1975.0  2650.0  3250.0  4000.0   \n",
       "Zewei   3.0   833.333333   288.675135   500.0   750.0  1000.0  1000.0  1000.0   \n",
       "\n",
       "      Salary            ...                      Year                        \\\n",
       "       count     mean   ...        75%      max count         mean      std   \n",
       "Name                    ...                                                   \n",
       "Han      1.0   5000.0   ...     5000.0   5000.0   1.0  2016.000000      NaN   \n",
       "July     4.0  14250.0   ...    19750.0  25000.0   4.0  2016.750000  0.50000   \n",
       "Zewei    3.0   3000.0   ...     3500.0   4000.0   3.0  2016.333333  0.57735   \n",
       "\n",
       "                                                \n",
       "          min      25%     50%     75%     max  \n",
       "Name                                            \n",
       "Han    2016.0  2016.00  2016.0  2016.0  2016.0  \n",
       "July   2016.0  2016.75  2017.0  2017.0  2017.0  \n",
       "Zewei  2016.0  2016.00  2016.0  2016.5  2017.0  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iterate GroupBy object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Han\n",
      "   Bonus Name  Salary  Year\n",
      "3   1200  Han    5000  2016\n",
      "July\n",
      "   Bonus  Name  Salary  Year\n",
      "0   3000  July   10000  2016\n",
      "4   4000  July   18000  2017\n",
      "5   2300  July   25000  2017\n",
      "7   1000  July    4000  2017\n",
      "Zewei\n",
      "   Bonus   Name  Salary  Year\n",
      "1   1000  Zewei    2000  2016\n",
      "2   1000  Zewei    4000  2016\n",
      "6    500  Zewei    3000  2017\n"
     ]
    }
   ],
   "source": [
    "for name, group in group_by_name:\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择一个group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Bonus  Salary  Year\n",
      "1   1000    2000  2016\n",
      "2   1000    4000  2016\n",
      "6    500    3000  2017\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(group_by_name.get_group(\"Zewei\"))\n",
    "type(group_by_name.get_group(\"Zewei\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "agg是aggregate的alias，可以替代使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Bonus</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Salary</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></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>Han</th>\n",
       "      <td>1200</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>10300</td>\n",
       "      <td>2575.000000</td>\n",
       "      <td>1260.621540</td>\n",
       "      <td>57000</td>\n",
       "      <td>14250</td>\n",
       "      <td>9178.779875</td>\n",
       "      <td>8067</td>\n",
       "      <td>2016.750000</td>\n",
       "      <td>0.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>2500</td>\n",
       "      <td>833.333333</td>\n",
       "      <td>288.675135</td>\n",
       "      <td>9000</td>\n",
       "      <td>3000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>6049</td>\n",
       "      <td>2016.333333</td>\n",
       "      <td>0.57735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bonus                           Salary                      Year  \\\n",
       "         sum         mean          std    sum   mean          std   sum   \n",
       "Name                                                                      \n",
       "Han     1200  1200.000000          NaN   5000   5000          NaN  2016   \n",
       "July   10300  2575.000000  1260.621540  57000  14250  9178.779875  8067   \n",
       "Zewei   2500   833.333333   288.675135   9000   3000  1000.000000  6049   \n",
       "\n",
       "                             \n",
       "              mean      std  \n",
       "Name                         \n",
       "Han    2016.000000      NaN  \n",
       "July   2016.750000  0.50000  \n",
       "Zewei  2016.333333  0.57735  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.agg([np.sum, np.mean, np.std])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对每一列可以采用不同的aggregate操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <td>1200</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>10300</td>\n",
       "      <td>57000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>2500</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bonus  Salary\n",
       "Name                \n",
       "Han     1200    5000\n",
       "July   10300   57000\n",
       "Zewei   2500    9000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.agg({\"Bonus\": np.sum, \"Salary\": np.sum})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Han</th>\n",
       "      <td>2016</td>\n",
       "      <td>1200</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>2016</td>\n",
       "      <td>10300</td>\n",
       "      <td>57000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zewei</th>\n",
       "      <td>2016</td>\n",
       "      <td>2500</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Bonus  Salary\n",
       "Name                      \n",
       "Han    2016   1200    5000\n",
       "July   2016  10300   57000\n",
       "Zewei  2016   2500    9000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name.agg({\"Bonus\": np.sum, \"Salary\": np.sum, \"Year\": (lambda x: list(x)[0])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Bonus</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>Han</th>\n",
       "      <th>2016</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">July</th>\n",
       "      <th>2016</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2433.333333</td>\n",
       "      <td>1504.43788</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>3150.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15666.666667</td>\n",
       "      <td>10692.676622</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>21500.0</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Zewei</th>\n",
       "      <th>2016</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>1414.213562</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Bonus                                                           \\\n",
       "           count         mean         std     min     25%     50%     75%   \n",
       "Name  Year                                                                  \n",
       "Han   2016   1.0  1200.000000         NaN  1200.0  1200.0  1200.0  1200.0   \n",
       "July  2016   1.0  3000.000000         NaN  3000.0  3000.0  3000.0  3000.0   \n",
       "      2017   3.0  2433.333333  1504.43788  1000.0  1650.0  2300.0  3150.0   \n",
       "Zewei 2016   2.0  1000.000000     0.00000  1000.0  1000.0  1000.0  1000.0   \n",
       "      2017   1.0   500.000000         NaN   500.0   500.0   500.0   500.0   \n",
       "\n",
       "                   Salary                                                \\\n",
       "               max  count          mean           std      min      25%   \n",
       "Name  Year                                                                \n",
       "Han   2016  1200.0    1.0   5000.000000           NaN   5000.0   5000.0   \n",
       "July  2016  3000.0    1.0  10000.000000           NaN  10000.0  10000.0   \n",
       "      2017  4000.0    3.0  15666.666667  10692.676622   4000.0  11000.0   \n",
       "Zewei 2016  1000.0    2.0   3000.000000   1414.213562   2000.0   2500.0   \n",
       "      2017   500.0    1.0   3000.000000           NaN   3000.0   3000.0   \n",
       "\n",
       "                                       \n",
       "                50%      75%      max  \n",
       "Name  Year                             \n",
       "Han   2016   5000.0   5000.0   5000.0  \n",
       "July  2016  10000.0  10000.0  10000.0  \n",
       "      2017  18000.0  21500.0  25000.0  \n",
       "Zewei 2016   3000.0   3500.0   4000.0  \n",
       "      2017   3000.0   3000.0   3000.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_name_year.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### transform\n",
    "transform会把group中的每一个record都按照同样的规则转化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-22</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.953125</td>\n",
       "      <td>1.552083</td>\n",
       "      <td>1.640625</td>\n",
       "      <td>1.523430</td>\n",
       "      <td>67867200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-25</th>\n",
       "      <td>1.770833</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.640625</td>\n",
       "      <td>1.812500</td>\n",
       "      <td>1.683028</td>\n",
       "      <td>12762000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-26</th>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.869792</td>\n",
       "      <td>1.645833</td>\n",
       "      <td>1.671875</td>\n",
       "      <td>1.552448</td>\n",
       "      <td>8580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-27</th>\n",
       "      <td>1.677083</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.547611</td>\n",
       "      <td>6109200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-28</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.677083</td>\n",
       "      <td>1.651042</td>\n",
       "      <td>1.661458</td>\n",
       "      <td>1.542776</td>\n",
       "      <td>5688000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low     Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "1999-01-22  1.750000  1.953125  1.552083  1.640625   1.523430  67867200\n",
       "1999-01-25  1.770833  1.833333  1.640625  1.812500   1.683028  12762000\n",
       "1999-01-26  1.833333  1.869792  1.645833  1.671875   1.552448   8580000\n",
       "1999-01-27  1.677083  1.718750  1.583333  1.666667   1.547611   6109200\n",
       "1999-01-28  1.666667  1.677083  1.651042  1.661458   1.542776   5688000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvda = pd.read_csv(\"data/NVDA.csv\", index_col=0)\n",
    "nvda.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "key = lambda x: x[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1.950782</td>\n",
       "      <td>2.007317</td>\n",
       "      <td>1.883559</td>\n",
       "      <td>1.947230</td>\n",
       "      <td>1.808134</td>\n",
       "      <td>6.433220e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>8.781084</td>\n",
       "      <td>9.222697</td>\n",
       "      <td>8.360522</td>\n",
       "      <td>8.778826</td>\n",
       "      <td>8.151729</td>\n",
       "      <td>1.104182e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>13.091254</td>\n",
       "      <td>13.600750</td>\n",
       "      <td>12.680548</td>\n",
       "      <td>13.181552</td>\n",
       "      <td>12.239956</td>\n",
       "      <td>2.782387e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>9.690344</td>\n",
       "      <td>9.955093</td>\n",
       "      <td>9.344391</td>\n",
       "      <td>9.614749</td>\n",
       "      <td>8.927940</td>\n",
       "      <td>3.168655e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>5.902434</td>\n",
       "      <td>6.042659</td>\n",
       "      <td>5.764960</td>\n",
       "      <td>5.900344</td>\n",
       "      <td>5.478865</td>\n",
       "      <td>2.430220e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>6.484735</td>\n",
       "      <td>6.608810</td>\n",
       "      <td>6.353558</td>\n",
       "      <td>6.465913</td>\n",
       "      <td>6.004034</td>\n",
       "      <td>1.706331e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>9.512381</td>\n",
       "      <td>9.659656</td>\n",
       "      <td>9.353175</td>\n",
       "      <td>9.513823</td>\n",
       "      <td>8.834223</td>\n",
       "      <td>1.542825e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>18.057902</td>\n",
       "      <td>18.425126</td>\n",
       "      <td>17.720279</td>\n",
       "      <td>18.095963</td>\n",
       "      <td>16.803316</td>\n",
       "      <td>1.534446e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>27.762045</td>\n",
       "      <td>28.251673</td>\n",
       "      <td>27.206056</td>\n",
       "      <td>27.724542</td>\n",
       "      <td>25.744098</td>\n",
       "      <td>1.514562e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>16.004308</td>\n",
       "      <td>16.426245</td>\n",
       "      <td>15.521462</td>\n",
       "      <td>15.945613</td>\n",
       "      <td>14.806572</td>\n",
       "      <td>2.022721e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>11.825119</td>\n",
       "      <td>12.114762</td>\n",
       "      <td>11.565952</td>\n",
       "      <td>11.850873</td>\n",
       "      <td>11.004331</td>\n",
       "      <td>1.919821e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>13.576349</td>\n",
       "      <td>13.802659</td>\n",
       "      <td>13.318532</td>\n",
       "      <td>13.563175</td>\n",
       "      <td>12.594318</td>\n",
       "      <td>1.853295e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>16.912540</td>\n",
       "      <td>17.267540</td>\n",
       "      <td>16.512143</td>\n",
       "      <td>16.887540</td>\n",
       "      <td>15.681214</td>\n",
       "      <td>2.289352e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>13.526200</td>\n",
       "      <td>13.717400</td>\n",
       "      <td>13.319800</td>\n",
       "      <td>13.507880</td>\n",
       "      <td>12.551166</td>\n",
       "      <td>1.207757e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>14.173571</td>\n",
       "      <td>14.329802</td>\n",
       "      <td>14.035278</td>\n",
       "      <td>14.189127</td>\n",
       "      <td>13.412278</td>\n",
       "      <td>8.843986e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>18.543056</td>\n",
       "      <td>18.745476</td>\n",
       "      <td>18.348214</td>\n",
       "      <td>18.547064</td>\n",
       "      <td>17.875053</td>\n",
       "      <td>7.098902e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>23.680595</td>\n",
       "      <td>23.979524</td>\n",
       "      <td>23.411071</td>\n",
       "      <td>23.718254</td>\n",
       "      <td>23.262283</td>\n",
       "      <td>7.756520e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>53.630833</td>\n",
       "      <td>54.415397</td>\n",
       "      <td>52.895119</td>\n",
       "      <td>53.761190</td>\n",
       "      <td>53.475737</td>\n",
       "      <td>1.107062e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>120.481305</td>\n",
       "      <td>122.300725</td>\n",
       "      <td>118.402754</td>\n",
       "      <td>120.547971</td>\n",
       "      <td>120.436863</td>\n",
       "      <td>1.907742e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open        High         Low       Close   Adj Close        Volume\n",
       "1999    1.950782    2.007317    1.883559    1.947230    1.808134  6.433220e+06\n",
       "2000    8.781084    9.222697    8.360522    8.778826    8.151729  1.104182e+07\n",
       "2001   13.091254   13.600750   12.680548   13.181552   12.239956  2.782387e+07\n",
       "2002    9.690344    9.955093    9.344391    9.614749    8.927940  3.168655e+07\n",
       "2003    5.902434    6.042659    5.764960    5.900344    5.478865  2.430220e+07\n",
       "2004    6.484735    6.608810    6.353558    6.465913    6.004034  1.706331e+07\n",
       "2005    9.512381    9.659656    9.353175    9.513823    8.834223  1.542825e+07\n",
       "2006   18.057902   18.425126   17.720279   18.095963   16.803316  1.534446e+07\n",
       "2007   27.762045   28.251673   27.206056   27.724542   25.744098  1.514562e+07\n",
       "2008   16.004308   16.426245   15.521462   15.945613   14.806572  2.022721e+07\n",
       "2009   11.825119   12.114762   11.565952   11.850873   11.004331  1.919821e+07\n",
       "2010   13.576349   13.802659   13.318532   13.563175   12.594318  1.853295e+07\n",
       "2011   16.912540   17.267540   16.512143   16.887540   15.681214  2.289352e+07\n",
       "2012   13.526200   13.717400   13.319800   13.507880   12.551166  1.207757e+07\n",
       "2013   14.173571   14.329802   14.035278   14.189127   13.412278  8.843986e+06\n",
       "2014   18.543056   18.745476   18.348214   18.547064   17.875053  7.098902e+06\n",
       "2015   23.680595   23.979524   23.411071   23.718254   23.262283  7.756520e+06\n",
       "2016   53.630833   54.415397   52.895119   53.761190   53.475737  1.107062e+07\n",
       "2017  120.481305  122.300725  118.402754  120.547971  120.436863  1.907742e+07"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvda.groupby(key).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zscore = lambda x: (x-x.mean())/x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-22</th>\n",
       "      <td>-0.340955</td>\n",
       "      <td>-0.088217</td>\n",
       "      <td>-0.579850</td>\n",
       "      <td>-0.510124</td>\n",
       "      <td>-0.510124</td>\n",
       "      <td>7.544438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-25</th>\n",
       "      <td>-0.305578</td>\n",
       "      <td>-0.283222</td>\n",
       "      <td>-0.424964</td>\n",
       "      <td>-0.224161</td>\n",
       "      <td>-0.224161</td>\n",
       "      <td>0.777210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-26</th>\n",
       "      <td>-0.199444</td>\n",
       "      <td>-0.223871</td>\n",
       "      <td>-0.415854</td>\n",
       "      <td>-0.458130</td>\n",
       "      <td>-0.458131</td>\n",
       "      <td>0.263637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-27</th>\n",
       "      <td>-0.464778</td>\n",
       "      <td>-0.469747</td>\n",
       "      <td>-0.525185</td>\n",
       "      <td>-0.466795</td>\n",
       "      <td>-0.466798</td>\n",
       "      <td>-0.039791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-28</th>\n",
       "      <td>-0.482465</td>\n",
       "      <td>-0.537575</td>\n",
       "      <td>-0.406741</td>\n",
       "      <td>-0.475462</td>\n",
       "      <td>-0.475461</td>\n",
       "      <td>-0.091517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low     Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "1999-01-22 -0.340955 -0.088217 -0.579850 -0.510124  -0.510124  7.544438\n",
       "1999-01-25 -0.305578 -0.283222 -0.424964 -0.224161  -0.224161  0.777210\n",
       "1999-01-26 -0.199444 -0.223871 -0.415854 -0.458130  -0.458131  0.263637\n",
       "1999-01-27 -0.464778 -0.469747 -0.525185 -0.466795  -0.466798 -0.039791\n",
       "1999-01-28 -0.482465 -0.537575 -0.406741 -0.475462  -0.475461 -0.091517"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed = nvda.groupby(key).transform(zscore)\n",
    "transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来尝试一下把这些数据画出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0f059f60>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v3489TNQbsXSc9Tsv/LLbrl88wKL7e5Zk8bzi7Y3XD7TW1Wx607ygte6ote6g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\nI6+MwM9P8efxS3RqUDVf0Mk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71BWlGeSrAHHAcmA40FNr7bTRuChz13glx2ScKNytAfuS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\nsP8HRmrPDCb7k6YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c0f0328d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = pd.DataFrame({\"Original Adj Close\": nvda[\"Adj Close\"], \n",
    "                        \"Transormed Adj Close\": transformed[\"Adj Close\"]})\n",
    "compare.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-22</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-25</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-26</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-27</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-28</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open     High       Low     Close  Adj Close      Volume\n",
       "Date                                                                    \n",
       "1999-01-22  2.552084  2.53125  2.510417  2.546875   2.364945  67375200.0\n",
       "1999-01-25  2.552084  2.53125  2.510417  2.546875   2.364945  67375200.0\n",
       "1999-01-26  2.552084  2.53125  2.510417  2.546875   2.364945  67375200.0\n",
       "1999-01-27  2.552084  2.53125  2.510417  2.546875   2.364945  67375200.0\n",
       "1999-01-28  2.552084  2.53125  2.510417  2.546875   2.364945  67375200.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_range = lambda x: x.max() - x.min()\n",
    "nvda.groupby(key).transform(price_range).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "<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>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-22</th>\n",
       "      <td>3.947917</td>\n",
       "      <td>3.953125</td>\n",
       "      <td>3.84375</td>\n",
       "      <td>3.911458</td>\n",
       "      <td>3.632052</td>\n",
       "      <td>67867200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-25</th>\n",
       "      <td>3.947917</td>\n",
       "      <td>3.953125</td>\n",
       "      <td>3.84375</td>\n",
       "      <td>3.911458</td>\n",
       "      <td>3.632052</td>\n",
       "      <td>67867200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-26</th>\n",
       "      <td>3.947917</td>\n",
       "      <td>3.953125</td>\n",
       "      <td>3.84375</td>\n",
       "      <td>3.911458</td>\n",
       "      <td>3.632052</td>\n",
       "      <td>67867200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-27</th>\n",
       "      <td>3.947917</td>\n",
       "      <td>3.953125</td>\n",
       "      <td>3.84375</td>\n",
       "      <td>3.911458</td>\n",
       "      <td>3.632052</td>\n",
       "      <td>67867200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-28</th>\n",
       "      <td>3.947917</td>\n",
       "      <td>3.953125</td>\n",
       "      <td>3.84375</td>\n",
       "      <td>3.911458</td>\n",
       "      <td>3.632052</td>\n",
       "      <td>67867200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High      Low     Close  Adj Close    Volume\n",
       "Date                                                                  \n",
       "1999-01-22  3.947917  3.953125  3.84375  3.911458   3.632052  67867200\n",
       "1999-01-25  3.947917  3.953125  3.84375  3.911458   3.632052  67867200\n",
       "1999-01-26  3.947917  3.953125  3.84375  3.911458   3.632052  67867200\n",
       "1999-01-27  3.947917  3.953125  3.84375  3.911458   3.632052  67867200\n",
       "1999-01-28  3.947917  3.953125  3.84375  3.911458   3.632052  67867200"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvda.groupby(key).transform(\"max\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "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>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-22</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-25</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-26</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-27</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-28</th>\n",
       "      <td>2.552084</td>\n",
       "      <td>2.53125</td>\n",
       "      <td>2.510417</td>\n",
       "      <td>2.546875</td>\n",
       "      <td>2.364945</td>\n",
       "      <td>67375200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open     High       Low     Close  Adj Close    Volume\n",
       "Date                                                                  \n",
       "1999-01-22  2.552084  2.53125  2.510417  2.546875   2.364945  67375200\n",
       "1999-01-25  2.552084  2.53125  2.510417  2.546875   2.364945  67375200\n",
       "1999-01-26  2.552084  2.53125  2.510417  2.546875   2.364945  67375200\n",
       "1999-01-27  2.552084  2.53125  2.510417  2.546875   2.364945  67375200\n",
       "1999-01-28  2.552084  2.53125  2.510417  2.546875   2.364945  67375200"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(nvda.groupby(key).transform(\"max\") - nvda.groupby(key).transform(\"min\")).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- filter\n",
    "\n",
    "比如我们想要找出符合统计特征的一些内容，就像SQL里面的having, where语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    2\n",
       "3    2\n",
       "4    2\n",
       "6    4\n",
       "7    4\n",
       "8    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,1,2,2,2,3,4,4,5])\n",
    "s.groupby(s).filter(lambda x: x.sum() > 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "0  0  a\n",
       "1  1  a\n",
       "2  2  a\n",
       "3  3  b\n",
       "4  4  b\n",
       "5  5  b"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"A\": np.arange(8), \"B\":list(\"aaabbbcc\")})\n",
    "df.groupby(\"B\").filter(lambda x: len(x) > 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来看看均价超过100的有哪些月份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "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>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-01-22</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.953125</td>\n",
       "      <td>1.552083</td>\n",
       "      <td>1.640625</td>\n",
       "      <td>1.523430</td>\n",
       "      <td>67867200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-25</th>\n",
       "      <td>1.770833</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.640625</td>\n",
       "      <td>1.812500</td>\n",
       "      <td>1.683028</td>\n",
       "      <td>12762000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-26</th>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.869792</td>\n",
       "      <td>1.645833</td>\n",
       "      <td>1.671875</td>\n",
       "      <td>1.552448</td>\n",
       "      <td>8580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-27</th>\n",
       "      <td>1.677083</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.547611</td>\n",
       "      <td>6109200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-28</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.677083</td>\n",
       "      <td>1.651042</td>\n",
       "      <td>1.661458</td>\n",
       "      <td>1.542776</td>\n",
       "      <td>5688000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low     Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "1999-01-22  1.750000  1.953125  1.552083  1.640625   1.523430  67867200\n",
       "1999-01-25  1.770833  1.833333  1.640625  1.812500   1.683028  12762000\n",
       "1999-01-26  1.833333  1.869792  1.645833  1.671875   1.552448   8580000\n",
       "1999-01-27  1.677083  1.718750  1.583333  1.666667   1.547611   6109200\n",
       "1999-01-28  1.666667  1.677083  1.651042  1.661458   1.542776   5688000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvda.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "key_month = lambda x: x[0:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\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>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>46.750000</td>\n",
       "      <td>47.360001</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>46.660000</td>\n",
       "      <td>46.397064</td>\n",
       "      <td>5462200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-05</th>\n",
       "      <td>46.400002</td>\n",
       "      <td>47.430000</td>\n",
       "      <td>46.029999</td>\n",
       "      <td>47.349998</td>\n",
       "      <td>47.083176</td>\n",
       "      <td>9277100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-06</th>\n",
       "      <td>47.020000</td>\n",
       "      <td>47.930000</td>\n",
       "      <td>46.779999</td>\n",
       "      <td>47.650002</td>\n",
       "      <td>47.381493</td>\n",
       "      <td>6827600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-07</th>\n",
       "      <td>47.970001</td>\n",
       "      <td>49.060001</td>\n",
       "      <td>47.750000</td>\n",
       "      <td>48.889999</td>\n",
       "      <td>48.614498</td>\n",
       "      <td>9885000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-08</th>\n",
       "      <td>49.410000</td>\n",
       "      <td>51.090000</td>\n",
       "      <td>49.220001</td>\n",
       "      <td>50.849998</td>\n",
       "      <td>50.563454</td>\n",
       "      <td>12048300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2016-07-01  46.750000  47.360001  46.500000  46.660000  46.397064   5462200\n",
       "2016-07-05  46.400002  47.430000  46.029999  47.349998  47.083176   9277100\n",
       "2016-07-06  47.020000  47.930000  46.779999  47.650002  47.381493   6827600\n",
       "2016-07-07  47.970001  49.060001  47.750000  48.889999  48.614498   9885000\n",
       "2016-07-08  49.410000  51.090000  49.220001  50.849998  50.563454  12048300"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvda.groupby(key_month).filter(lambda x: x[\"Adj Close\"].mean() > 50).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x7f8c0ef3c358>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nvda_month = nvda.groupby(key_month)\n",
    "nvda_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>demeaned</th>\n",
       "      <th>original</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>2017-07-14</th>\n",
       "      <td>8.557689</td>\n",
       "      <td>164.949997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-17</th>\n",
       "      <td>7.857692</td>\n",
       "      <td>164.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18</th>\n",
       "      <td>9.567699</td>\n",
       "      <td>165.960007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-19</th>\n",
       "      <td>8.707698</td>\n",
       "      <td>165.100006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20</th>\n",
       "      <td>11.107692</td>\n",
       "      <td>167.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             demeaned    original\n",
       "Date                             \n",
       "2017-07-14   8.557689  164.949997\n",
       "2017-07-17   7.857692  164.250000\n",
       "2017-07-18   9.567699  165.960007\n",
       "2017-07-19   8.707698  165.100006\n",
       "2017-07-20  11.107692  167.500000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(group):\n",
    "    return pd.DataFrame({\"original\": group,\n",
    "                        \"demeaned\": group - group.mean()})\n",
    "nvda_month[\"Adj Close\"].apply(f).tail()"
   ]
  },
  {
   "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>original</th>\n",
       "      <th>zscore</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>2017-07-14</th>\n",
       "      <td>164.949997</td>\n",
       "      <td>0.849678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-17</th>\n",
       "      <td>164.250000</td>\n",
       "      <td>0.780176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18</th>\n",
       "      <td>165.960007</td>\n",
       "      <td>0.949960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-19</th>\n",
       "      <td>165.100006</td>\n",
       "      <td>0.864572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20</th>\n",
       "      <td>167.500000</td>\n",
       "      <td>1.102863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              original    zscore\n",
       "Date                            \n",
       "2017-07-14  164.949997  0.849678\n",
       "2017-07-17  164.250000  0.780176\n",
       "2017-07-18  165.960007  0.949960\n",
       "2017-07-19  165.100006  0.864572\n",
       "2017-07-20  167.500000  1.102863"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def zscored(group):\n",
    "    return pd.DataFrame({\"original\": group,\n",
    "                        \"zscore\": (group - group.mean()) / group.std()})\n",
    "nvda_month[\"Adj Close\"].apply(zscored).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 表格匹配与拼接\n",
    "### 2018七月在线 机器学习集训营 julyedu.com \n",
    "- concat\n",
    "- append\n",
    "- merge\n",
    "- join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           apts    cars\n",
      "Shanghai  55000  200000\n",
      "Beijing   60000  300000\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'apts': [55000, 60000],\n",
    "                   'cars': [200000, 300000],},\n",
    "                  index = ['Shanghai', 'Beijing'])\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           apts    cars\n",
      "Hangzhou  25000  150000\n",
      "Najing    20000  120000\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'cars': [150000, 120000],\n",
    "                    'apts': [25000, 20000],\n",
    "                   },\n",
    "                  index = ['Hangzhou', 'Najing'])\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            apts    cars\n",
      "Guangzhou  30000  180000\n",
      "Chongqing  10000  100000\n"
     ]
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'apts': [30000, 10000],\n",
    "                   'cars': [180000, 100000],},\n",
    "                  index = ['Guangzhou', 'Chongqing'])\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            apts    cars\n",
      "Shanghai   55000  200000\n",
      "Beijing    60000  300000\n",
      "Hangzhou   25000  150000\n",
      "Najing     20000  120000\n",
      "Guangzhou  30000  180000\n",
      "Chongqing  10000  100000\n"
     ]
    }
   ],
   "source": [
    "frames = [df1, df2, df3] \n",
    "result = pd.concat(frames)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在concatenate的时候可以指定keys，这样可以给每一个部分加上一个Key。\n",
    "\n",
    "以下的例子就构造了一个hierarchical index。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              apts    cars\n",
      "x Shanghai   55000  200000\n",
      "  Beijing    60000  300000\n",
      "y Hangzhou   25000  150000\n",
      "  Najing     20000  120000\n",
      "z Guangzhou  30000  180000\n",
      "  Chongqing  10000  100000\n"
     ]
    }
   ],
   "source": [
    "result2 = pd.concat(frames, keys=['x', 'y', 'z'])\n",
    "print(result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>apts</th>\n",
       "      <th>cars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hangzhou</th>\n",
       "      <td>25000</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Najing</th>\n",
       "      <td>20000</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           apts    cars\n",
       "Hangzhou  25000  150000\n",
       "Najing    20000  120000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2.loc[\"y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           salaries\n",
      "Suzhou        10000\n",
      "Beijing       30000\n",
      "Shanghai      30000\n",
      "Guangzhou     20000\n",
      "Tianjin       15000\n"
     ]
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'salaries': [10000, 30000, 30000, 20000, 15000]},\n",
    "                  index = ['Suzhou', 'Beijing', 'Shanghai', 'Guangzhou', 'Tianjin'])\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              apts      cars  salaries\n",
      "Beijing    60000.0  300000.0   30000.0\n",
      "Chongqing  10000.0  100000.0       NaN\n",
      "Guangzhou  30000.0  180000.0   20000.0\n",
      "Hangzhou   25000.0  150000.0       NaN\n",
      "Najing     20000.0  120000.0       NaN\n",
      "Shanghai   55000.0  200000.0   30000.0\n",
      "Suzhou         NaN       NaN   10000.0\n",
      "Tianjin        NaN       NaN   15000.0\n"
     ]
    }
   ],
   "source": [
    "result3 = pd.concat([result, df4], axis=1)\n",
    "print(result3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "复习一下前面讲过的stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beijing    apts         60000.0\n",
      "           cars        300000.0\n",
      "           salaries     30000.0\n",
      "Chongqing  apts         10000.0\n",
      "           cars        100000.0\n",
      "Guangzhou  apts         30000.0\n",
      "           cars        180000.0\n",
      "           salaries     20000.0\n",
      "Hangzhou   apts         25000.0\n",
      "           cars        150000.0\n",
      "Najing     apts         20000.0\n",
      "           cars        120000.0\n",
      "Shanghai   apts         55000.0\n",
      "           cars        200000.0\n",
      "           salaries     30000.0\n",
      "Suzhou     salaries     10000.0\n",
      "Tianjin    salaries     15000.0\n",
      "dtype: float64\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(result3.stack())\n",
    "print(type(result3.stack()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用inner可以去掉NaN,也就是说如果出现了不匹配的行就会被忽略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            apts    cars  salaries\n",
      "Shanghai   55000  200000     30000\n",
      "Beijing    60000  300000     30000\n",
      "Guangzhou  30000  180000     20000\n"
     ]
    }
   ],
   "source": [
    "result3 = pd.concat([result, df4], axis=1, join='inner')\n",
    "print(result3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用```append```来做concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           apts    cars\n",
      "Shanghai  55000  200000\n",
      "Beijing   60000  300000\n",
      "Hangzhou  25000  150000\n",
      "Najing    20000  120000\n"
     ]
    }
   ],
   "source": [
    "print(df1.append(df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              apts      cars  salaries\n",
      "Shanghai   55000.0  200000.0       NaN\n",
      "Beijing    60000.0  300000.0       NaN\n",
      "Suzhou         NaN       NaN   10000.0\n",
      "Beijing        NaN       NaN   30000.0\n",
      "Shanghai       NaN       NaN   30000.0\n",
      "Guangzhou      NaN       NaN   20000.0\n",
      "Tianjin        NaN       NaN   15000.0\n"
     ]
    }
   ],
   "source": [
    "print(df1.append(df4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series和DataFrame还可以被一起concatenate，这时候Series会先被转成DataFrame然后做Join，因为Series本来就是一个只有一维的DataFrame对吧。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shanghai    60\n",
      "Beijing     50\n",
      "Name: meal, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s1 = pd.Series([60, 50], index=['Shanghai', 'Beijing'], name='meal')\n",
    "print(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           apts    cars\n",
      "Shanghai  55000  200000\n",
      "Beijing   60000  300000\n"
     ]
    }
   ],
   "source": [
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           apts    cars  meal\n",
      "Shanghai  55000  200000    60\n",
      "Beijing   60000  300000    50\n"
     ]
    }
   ],
   "source": [
    "print(pd.concat([df1, s1], axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面讲如何append一个row到DataFrame里。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apts    18000\n",
      "cars    12000\n",
      "Name: Xiamen, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s2 = pd.Series([18000, 12000], index=['apts', 'cars'], name='Xiamen') #注意这里的name是必须要有的，因为要用作Index。\n",
    "print(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           apts    cars\n",
      "Shanghai  55000  200000\n",
      "Beijing   60000  300000\n",
      "Xiamen    18000   12000\n"
     ]
    }
   ],
   "source": [
    "print(df1.append(s2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge(Join)\n",
    "### 2018七月在线 机器学习集训营 julyedu.com "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    apts    cars      city\n",
      "0  55000  200000  Shanghai\n",
      "1  60000  300000   Beijing\n",
      "2  58000  250000  Shenzhen\n"
     ]
    }
   ],
   "source": [
    "    df1 = pd.DataFrame({'apts': [55000, 60000, 58000],\n",
    "                       'cars': [200000, 300000,250000],\n",
    "                      'city': ['Shanghai', 'Beijing','Shenzhen']})\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        city  salaries\n",
      "0     Suzhou     10000\n",
      "1    Beijing     30000\n",
      "2   Shanghai     30000\n",
      "3  Guangzhou     20000\n",
      "4    Tianjin     15000\n"
     ]
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'salaries': [10000, 30000, 30000, 20000, 15000],\n",
    "                  'city': ['Suzhou', 'Beijing', 'Shanghai', 'Guangzhou', 'Tianjin']})\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    apts    cars      city  salaries\n",
      "0  55000  200000  Shanghai     30000\n",
      "1  60000  300000   Beijing     30000\n"
     ]
    }
   ],
   "source": [
    "result = pd.merge(df1, df4, on='city')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>apts</th>\n",
       "      <th>cars</th>\n",
       "      <th>city</th>\n",
       "      <th>salaries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55000</td>\n",
       "      <td>200000</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60000</td>\n",
       "      <td>300000</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    apts    cars      city  salaries\n",
       "0  55000  200000  Shanghai     30000\n",
       "1  60000  300000   Beijing     30000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.merge(df1, df4)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      apts      cars       city  salaries\n",
      "0  55000.0  200000.0   Shanghai   30000.0\n",
      "1  60000.0  300000.0    Beijing   30000.0\n",
      "2  58000.0  250000.0   Shenzhen       NaN\n",
      "3      NaN       NaN     Suzhou   10000.0\n",
      "4      NaN       NaN  Guangzhou   20000.0\n",
      "5      NaN       NaN    Tianjin   15000.0\n"
     ]
    }
   ],
   "source": [
    "result = pd.merge(df1, df4, on='city', how='outer')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      apts      cars       city  salaries\n",
      "0  55000.0  200000.0   Shanghai     30000\n",
      "1  60000.0  300000.0    Beijing     30000\n",
      "2      NaN       NaN     Suzhou     10000\n",
      "3      NaN       NaN  Guangzhou     20000\n",
      "4      NaN       NaN    Tianjin     15000\n"
     ]
    }
   ],
   "source": [
    "result = pd.merge(df1, df4, on='city', how='right')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    apts    cars      city  salaries\n",
      "0  55000  200000  Shanghai   30000.0\n",
      "1  60000  300000   Beijing   30000.0\n",
      "2  58000  250000  Shenzhen       NaN\n"
     ]
    }
   ],
   "source": [
    "result = pd.merge(df1, df4, on='city', how='left')\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### join on index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           apts    cars\n",
      "Shanghai  55000  200000\n",
      "Beijing   60000  300000\n",
      "Shenzhen  58000  250000\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'apts': [55000, 60000, 58000],\n",
    "                   'cars': [200000, 300000,250000]},\n",
    "                  index=['Shanghai', 'Beijing','Shenzhen'])\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           salaries\n",
      "Suzhou        10000\n",
      "Beijing       30000\n",
      "Shanghai      30000\n",
      "Guangzhou     20000\n",
      "Tianjin       15000\n"
     ]
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'salaries': [10000, 30000, 30000, 20000, 15000]},\n",
    "                  index=['Suzhou', 'Beijing', 'Shanghai', 'Guangzhou', 'Tianjin'])\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           apts    cars  salaries\n",
      "Shanghai  55000  200000   30000.0\n",
      "Beijing   60000  300000   30000.0\n",
      "Shenzhen  58000  250000       NaN\n"
     ]
    }
   ],
   "source": [
    "print(df1.join(df4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              apts      cars  salaries\n",
      "Beijing    60000.0  300000.0   30000.0\n",
      "Guangzhou      NaN       NaN   20000.0\n",
      "Shanghai   55000.0  200000.0   30000.0\n",
      "Shenzhen   58000.0  250000.0       NaN\n",
      "Suzhou         NaN       NaN   10000.0\n",
      "Tianjin        NaN       NaN   15000.0\n"
     ]
    }
   ],
   "source": [
    "print(df1.join(df4, how='outer'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以用merge来写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              apts      cars  salaries\n",
      "Beijing    60000.0  300000.0   30000.0\n",
      "Guangzhou      NaN       NaN   20000.0\n",
      "Shanghai   55000.0  200000.0   30000.0\n",
      "Shenzhen   58000.0  250000.0       NaN\n",
      "Suzhou         NaN       NaN   10000.0\n",
      "Tianjin        NaN       NaN   15000.0\n"
     ]
    }
   ],
   "source": [
    "print(pd.merge(df1, df4, left_index=True, right_index=True, how='outer'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目时间\n",
    "### 2018七月在线 机器学习集训营 julyedu.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来尝试一个例子，读取一些自行车的数据\n",
    "\n",
    "bikes.csv记录了Montreal自行车路线的数据，具体有7条路线，分别记录了每条自行车路线每天有多少人经过"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# bikes = pd.read_csv(\"data/bikes.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怎么报错了？不要慌，我们Google一下这个错误是什么意思。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "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>Date;Berri 1;Brébeuf (données non disponibles);Côte-Sainte-Catherine;Maisonneuve 1;Maisonneuve 2;du Parc;Pierre-Dupuy;Rachel1;St-Urbain (données non disponibles)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2012;35;;0;38;51;26;10;16;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/01/2012;83;;1;68;153;53;6;43;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/01/2012;135;;2;104;248;89;3;58;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/01/2012;144;;1;116;318;111;8;61;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/01/2012;197;;2;124;330;97;13;95;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date;Berri 1;Brébeuf (données non disponibles);Côte-Sainte-Catherine;Maisonneuve 1;Maisonneuve 2;du Parc;Pierre-Dupuy;Rachel1;St-Urbain (données non disponibles)\n",
       "0                   01/01/2012;35;;0;38;51;26;10;16;                                                                                                               \n",
       "1                   02/01/2012;83;;1;68;153;53;6;43;                                                                                                               \n",
       "2                 03/01/2012;135;;2;104;248;89;3;58;                                                                                                               \n",
       "3                04/01/2012;144;;1;116;318;111;8;61;                                                                                                               \n",
       "4                05/01/2012;197;;2;124;330;97;13;95;                                                                                                               "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes = pd.read_csv(\"data/bikes.csv\", encoding=\"latin-1\")\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个数据读进来都揉成一团了，为什么呢？因为这个数据的格式并不是用逗号分隔的，而是用分号分隔的。所以我们要声明一下sep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Berri 1</th>\n",
       "      <th>Brébeuf (données non disponibles)</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>Maisonneuve 2</th>\n",
       "      <th>du Parc</th>\n",
       "      <th>Pierre-Dupuy</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>St-Urbain (données non disponibles)</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>153</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>248</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>318</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>330</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Brébeuf (données non disponibles)  Côte-Sainte-Catherine  \\\n",
       "Date                                                                            \n",
       "2012-01-01       35                                NaN                      0   \n",
       "2012-01-02       83                                NaN                      1   \n",
       "2012-01-03      135                                NaN                      2   \n",
       "2012-01-04      144                                NaN                      1   \n",
       "2012-01-05      197                                NaN                      2   \n",
       "\n",
       "            Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  Rachel1  \\\n",
       "Date                                                                       \n",
       "2012-01-01             38             51       26            10       16   \n",
       "2012-01-02             68            153       53             6       43   \n",
       "2012-01-03            104            248       89             3       58   \n",
       "2012-01-04            116            318      111             8       61   \n",
       "2012-01-05            124            330       97            13       95   \n",
       "\n",
       "            St-Urbain (données non disponibles)  \n",
       "Date                                             \n",
       "2012-01-01                                  NaN  \n",
       "2012-01-02                                  NaN  \n",
       "2012-01-03                                  NaN  \n",
       "2012-01-04                                  NaN  \n",
       "2012-01-05                                  NaN  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes = pd.read_csv('data/bikes.csv', sep=';', parse_dates=['Date'], \\\n",
    "                    encoding='latin1', dayfirst=True, index_col='Date')\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样读进来的数据就正常了，还有一个问题，就是数据看起来有好多的NaN。dropna可以帮我们删掉一些带NA的行或者列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310, 9)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Brébeuf (données non disponibles)</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>Maisonneuve 2</th>\n",
       "      <th>du Parc</th>\n",
       "      <th>Pierre-Dupuy</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>St-Urbain (données non disponibles)</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Berri 1, Brébeuf (données non disponibles), Côte-Sainte-Catherine, Maisonneuve 1, Maisonneuve 2, du Parc, Pierre-Dupuy, Rachel1, St-Urbain (données non disponibles)]\n",
       "Index: []"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Brébeuf (données non disponibles)</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>Maisonneuve 2</th>\n",
       "      <th>du Parc</th>\n",
       "      <th>Pierre-Dupuy</th>\n",
       "      <th>Rachel1</th>\n",
       "      <th>St-Urbain (données non disponibles)</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>153</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>248</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>318</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>330</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Brébeuf (données non disponibles)  Côte-Sainte-Catherine  \\\n",
       "Date                                                                            \n",
       "2012-01-01       35                                NaN                      0   \n",
       "2012-01-02       83                                NaN                      1   \n",
       "2012-01-03      135                                NaN                      2   \n",
       "2012-01-04      144                                NaN                      1   \n",
       "2012-01-05      197                                NaN                      2   \n",
       "\n",
       "            Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  Rachel1  \\\n",
       "Date                                                                       \n",
       "2012-01-01             38             51       26            10       16   \n",
       "2012-01-02             68            153       53             6       43   \n",
       "2012-01-03            104            248       89             3       58   \n",
       "2012-01-04            116            318      111             8       61   \n",
       "2012-01-05            124            330       97            13       95   \n",
       "\n",
       "            St-Urbain (données non disponibles)  \n",
       "Date                                             \n",
       "2012-01-01                                  NaN  \n",
       "2012-01-02                                  NaN  \n",
       "2012-01-03                                  NaN  \n",
       "2012-01-04                                  NaN  \n",
       "2012-01-05                                  NaN  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.dropna(how = \"all\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310, 9)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.dropna(how = \"all\").shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dropna默认是删掉行，如果想删掉一列，就要声明一下axis参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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",
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       "    }\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>Berri 1</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>Maisonneuve 2</th>\n",
       "      <th>du Parc</th>\n",
       "      <th>Pierre-Dupuy</th>\n",
       "      <th>Rachel1</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>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>153</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>248</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>318</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>330</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Côte-Sainte-Catherine  Maisonneuve 1  Maisonneuve 2  \\\n",
       "Date                                                                       \n",
       "2012-01-01       35                      0             38             51   \n",
       "2012-01-02       83                      1             68            153   \n",
       "2012-01-03      135                      2            104            248   \n",
       "2012-01-04      144                      1            116            318   \n",
       "2012-01-05      197                      2            124            330   \n",
       "\n",
       "            du Parc  Pierre-Dupuy  Rachel1  \n",
       "Date                                        \n",
       "2012-01-01       26            10       16  \n",
       "2012-01-02       53             6       43  \n",
       "2012-01-03       89             3       58  \n",
       "2012-01-04      111             8       61  \n",
       "2012-01-05       97            13       95  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.dropna(how=\"all\", axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310, 7)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.dropna(how=\"all\", axis=1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面给大家介绍一下如何填充缺失的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Berri 1                                35.0\n",
       "Brébeuf (données non disponibles)       NaN\n",
       "Côte-Sainte-Catherine                   0.0\n",
       "Maisonneuve 1                          38.0\n",
       "Maisonneuve 2                          51.0\n",
       "du Parc                                26.0\n",
       "Pierre-Dupuy                           10.0\n",
       "Rachel1                                16.0\n",
       "St-Urbain (données non disponibles)     NaN\n",
       "Name: 2012-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = bikes.iloc[0].copy()\n",
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Berri 1                                35.000000\n",
       "Brébeuf (données non disponibles)      25.142857\n",
       "Côte-Sainte-Catherine                   0.000000\n",
       "Maisonneuve 1                          38.000000\n",
       "Maisonneuve 2                          51.000000\n",
       "du Parc                                26.000000\n",
       "Pierre-Dupuy                           10.000000\n",
       "Rachel1                                16.000000\n",
       "St-Urbain (données non disponibles)    25.142857\n",
       "Name: 2012-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row.fillna(row.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "  <thead>\n",
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       "      <th>Berri 1</th>\n",
       "      <th>Brébeuf (données non disponibles)</th>\n",
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       "      <th>Maisonneuve 1</th>\n",
       "      <th>Maisonneuve 2</th>\n",
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       "      <th>Rachel1</th>\n",
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       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>25.142857</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>25.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>58.142857</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>153</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>58.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>91.285714</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>248</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>91.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>108.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>318</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>108.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>122.571429</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>330</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>95</td>\n",
       "      <td>122.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Brébeuf (données non disponibles)  Côte-Sainte-Catherine  \\\n",
       "Date                                                                            \n",
       "2012-01-01       35                          25.142857                      0   \n",
       "2012-01-02       83                          58.142857                      1   \n",
       "2012-01-03      135                          91.285714                      2   \n",
       "2012-01-04      144                         108.428571                      1   \n",
       "2012-01-05      197                         122.571429                      2   \n",
       "\n",
       "            Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  Rachel1  \\\n",
       "Date                                                                       \n",
       "2012-01-01             38             51       26            10       16   \n",
       "2012-01-02             68            153       53             6       43   \n",
       "2012-01-03            104            248       89             3       58   \n",
       "2012-01-04            116            318      111             8       61   \n",
       "2012-01-05            124            330       97            13       95   \n",
       "\n",
       "            St-Urbain (données non disponibles)  \n",
       "Date                                             \n",
       "2012-01-01                            25.142857  \n",
       "2012-01-02                            58.142857  \n",
       "2012-01-03                            91.285714  \n",
       "2012-01-04                           108.428571  \n",
       "2012-01-05                           122.571429  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = bikes.mean(axis=1)\n",
    "for i, col in enumerate(bikes):\n",
    "    bikes.iloc[:, i] = bikes.iloc[:, i].fillna(m)\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面这样的话一行就可以写完"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>Brébeuf (données non disponibles)</th>\n",
       "      <th>Côte-Sainte-Catherine</th>\n",
       "      <th>Maisonneuve 1</th>\n",
       "      <th>Maisonneuve 2</th>\n",
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       "    <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>25.142857</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
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       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>25.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>58.142857</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>153</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>58.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>91.285714</td>\n",
       "      <td>2</td>\n",
       "      <td>104</td>\n",
       "      <td>248</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>91.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>108.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>318</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>108.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>122.571429</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>330</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>95</td>\n",
       "      <td>122.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>146</td>\n",
       "      <td>93.285714</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>244</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>93.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-07</th>\n",
       "      <td>98</td>\n",
       "      <td>57.285714</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>108</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>57.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-08</th>\n",
       "      <td>95</td>\n",
       "      <td>56.285714</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>98</td>\n",
       "      <td>64</td>\n",
       "      <td>11</td>\n",
       "      <td>63</td>\n",
       "      <td>56.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>244</td>\n",
       "      <td>175.142857</td>\n",
       "      <td>2</td>\n",
       "      <td>165</td>\n",
       "      <td>432</td>\n",
       "      <td>198</td>\n",
       "      <td>12</td>\n",
       "      <td>173</td>\n",
       "      <td>175.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10</th>\n",
       "      <td>397</td>\n",
       "      <td>247.857143</td>\n",
       "      <td>3</td>\n",
       "      <td>238</td>\n",
       "      <td>563</td>\n",
       "      <td>275</td>\n",
       "      <td>18</td>\n",
       "      <td>241</td>\n",
       "      <td>247.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-11</th>\n",
       "      <td>273</td>\n",
       "      <td>194.571429</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>443</td>\n",
       "      <td>258</td>\n",
       "      <td>12</td>\n",
       "      <td>194</td>\n",
       "      <td>194.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-12</th>\n",
       "      <td>157</td>\n",
       "      <td>108.857143</td>\n",
       "      <td>1</td>\n",
       "      <td>134</td>\n",
       "      <td>261</td>\n",
       "      <td>137</td>\n",
       "      <td>9</td>\n",
       "      <td>63</td>\n",
       "      <td>108.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-13</th>\n",
       "      <td>75</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>105</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-14</th>\n",
       "      <td>32</td>\n",
       "      <td>23.142857</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>56</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-15</th>\n",
       "      <td>54</td>\n",
       "      <td>23.571429</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>60</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-16</th>\n",
       "      <td>168</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>312</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-17</th>\n",
       "      <td>155</td>\n",
       "      <td>81.571429</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>256</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-18</th>\n",
       "      <td>139</td>\n",
       "      <td>66.285714</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>188</td>\n",
       "      <td>68</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>66.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-19</th>\n",
       "      <td>191</td>\n",
       "      <td>89.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "      <td>248</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>89.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-20</th>\n",
       "      <td>161</td>\n",
       "      <td>78.142857</td>\n",
       "      <td>4</td>\n",
       "      <td>96</td>\n",
       "      <td>217</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>78.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-21</th>\n",
       "      <td>53</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>70</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-22</th>\n",
       "      <td>71</td>\n",
       "      <td>32.142857</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>32.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-23</th>\n",
       "      <td>210</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>114</td>\n",
       "      <td>357</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-24</th>\n",
       "      <td>299</td>\n",
       "      <td>158.714286</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>444</td>\n",
       "      <td>174</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>158.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-25</th>\n",
       "      <td>334</td>\n",
       "      <td>169.857143</td>\n",
       "      <td>1</td>\n",
       "      <td>217</td>\n",
       "      <td>453</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>169.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-26</th>\n",
       "      <td>306</td>\n",
       "      <td>172.571429</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>495</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>172.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-27</th>\n",
       "      <td>91</td>\n",
       "      <td>63.428571</td>\n",
       "      <td>5</td>\n",
       "      <td>79</td>\n",
       "      <td>204</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-28</th>\n",
       "      <td>80</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>123</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-29</th>\n",
       "      <td>87</td>\n",
       "      <td>47.428571</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>132</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>47.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-30</th>\n",
       "      <td>219</td>\n",
       "      <td>127.142857</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>371</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>127.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-07</th>\n",
       "      <td>1580</td>\n",
       "      <td>1199.714286</td>\n",
       "      <td>660</td>\n",
       "      <td>922</td>\n",
       "      <td>1629</td>\n",
       "      <td>860</td>\n",
       "      <td>695</td>\n",
       "      <td>2052</td>\n",
       "      <td>1199.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-08</th>\n",
       "      <td>1854</td>\n",
       "      <td>1456.571429</td>\n",
       "      <td>880</td>\n",
       "      <td>987</td>\n",
       "      <td>1818</td>\n",
       "      <td>1040</td>\n",
       "      <td>1115</td>\n",
       "      <td>2502</td>\n",
       "      <td>1456.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-09</th>\n",
       "      <td>4787</td>\n",
       "      <td>3369.142857</td>\n",
       "      <td>2210</td>\n",
       "      <td>3026</td>\n",
       "      <td>5138</td>\n",
       "      <td>3418</td>\n",
       "      <td>927</td>\n",
       "      <td>4078</td>\n",
       "      <td>3369.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-10</th>\n",
       "      <td>3115</td>\n",
       "      <td>2326.428571</td>\n",
       "      <td>1537</td>\n",
       "      <td>2081</td>\n",
       "      <td>3681</td>\n",
       "      <td>2608</td>\n",
       "      <td>560</td>\n",
       "      <td>2703</td>\n",
       "      <td>2326.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-11</th>\n",
       "      <td>3746</td>\n",
       "      <td>2845.000000</td>\n",
       "      <td>1857</td>\n",
       "      <td>2569</td>\n",
       "      <td>4694</td>\n",
       "      <td>3034</td>\n",
       "      <td>558</td>\n",
       "      <td>3457</td>\n",
       "      <td>2845.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>3169</td>\n",
       "      <td>2453.000000</td>\n",
       "      <td>1460</td>\n",
       "      <td>2261</td>\n",
       "      <td>4045</td>\n",
       "      <td>2564</td>\n",
       "      <td>448</td>\n",
       "      <td>3224</td>\n",
       "      <td>2453.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-13</th>\n",
       "      <td>1783</td>\n",
       "      <td>1439.428571</td>\n",
       "      <td>802</td>\n",
       "      <td>1205</td>\n",
       "      <td>2113</td>\n",
       "      <td>1183</td>\n",
       "      <td>681</td>\n",
       "      <td>2309</td>\n",
       "      <td>1439.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-14</th>\n",
       "      <td>587</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>287</td>\n",
       "      <td>443</td>\n",
       "      <td>852</td>\n",
       "      <td>503</td>\n",
       "      <td>65</td>\n",
       "      <td>952</td>\n",
       "      <td>527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-15</th>\n",
       "      <td>3292</td>\n",
       "      <td>2547.000000</td>\n",
       "      <td>1678</td>\n",
       "      <td>2165</td>\n",
       "      <td>4197</td>\n",
       "      <td>2754</td>\n",
       "      <td>560</td>\n",
       "      <td>3183</td>\n",
       "      <td>2547.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-16</th>\n",
       "      <td>3739</td>\n",
       "      <td>2872.285714</td>\n",
       "      <td>1858</td>\n",
       "      <td>2684</td>\n",
       "      <td>4681</td>\n",
       "      <td>2997</td>\n",
       "      <td>554</td>\n",
       "      <td>3593</td>\n",
       "      <td>2872.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-17</th>\n",
       "      <td>4098</td>\n",
       "      <td>3024.000000</td>\n",
       "      <td>1964</td>\n",
       "      <td>2645</td>\n",
       "      <td>4836</td>\n",
       "      <td>3063</td>\n",
       "      <td>728</td>\n",
       "      <td>3834</td>\n",
       "      <td>3024.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-18</th>\n",
       "      <td>4671</td>\n",
       "      <td>3494.857143</td>\n",
       "      <td>2292</td>\n",
       "      <td>3129</td>\n",
       "      <td>5542</td>\n",
       "      <td>3477</td>\n",
       "      <td>1108</td>\n",
       "      <td>4245</td>\n",
       "      <td>3494.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-19</th>\n",
       "      <td>1313</td>\n",
       "      <td>1038.428571</td>\n",
       "      <td>597</td>\n",
       "      <td>885</td>\n",
       "      <td>1668</td>\n",
       "      <td>1209</td>\n",
       "      <td>111</td>\n",
       "      <td>1486</td>\n",
       "      <td>1038.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-20</th>\n",
       "      <td>2011</td>\n",
       "      <td>1514.428571</td>\n",
       "      <td>748</td>\n",
       "      <td>1323</td>\n",
       "      <td>2266</td>\n",
       "      <td>1213</td>\n",
       "      <td>797</td>\n",
       "      <td>2243</td>\n",
       "      <td>1514.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-21</th>\n",
       "      <td>1277</td>\n",
       "      <td>1045.714286</td>\n",
       "      <td>609</td>\n",
       "      <td>869</td>\n",
       "      <td>1777</td>\n",
       "      <td>898</td>\n",
       "      <td>242</td>\n",
       "      <td>1648</td>\n",
       "      <td>1045.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-22</th>\n",
       "      <td>3650</td>\n",
       "      <td>2895.000000</td>\n",
       "      <td>1819</td>\n",
       "      <td>2495</td>\n",
       "      <td>4800</td>\n",
       "      <td>3023</td>\n",
       "      <td>757</td>\n",
       "      <td>3721</td>\n",
       "      <td>2895.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-23</th>\n",
       "      <td>4177</td>\n",
       "      <td>3109.571429</td>\n",
       "      <td>1997</td>\n",
       "      <td>2795</td>\n",
       "      <td>5216</td>\n",
       "      <td>3233</td>\n",
       "      <td>795</td>\n",
       "      <td>3554</td>\n",
       "      <td>3109.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-24</th>\n",
       "      <td>3744</td>\n",
       "      <td>2920.428571</td>\n",
       "      <td>1868</td>\n",
       "      <td>2625</td>\n",
       "      <td>4900</td>\n",
       "      <td>3035</td>\n",
       "      <td>649</td>\n",
       "      <td>3622</td>\n",
       "      <td>2920.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-25</th>\n",
       "      <td>3735</td>\n",
       "      <td>2929.000000</td>\n",
       "      <td>1815</td>\n",
       "      <td>2528</td>\n",
       "      <td>5010</td>\n",
       "      <td>3017</td>\n",
       "      <td>631</td>\n",
       "      <td>3767</td>\n",
       "      <td>2929.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-26</th>\n",
       "      <td>4290</td>\n",
       "      <td>3330.142857</td>\n",
       "      <td>1987</td>\n",
       "      <td>2754</td>\n",
       "      <td>5246</td>\n",
       "      <td>3000</td>\n",
       "      <td>1456</td>\n",
       "      <td>4578</td>\n",
       "      <td>3330.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-27</th>\n",
       "      <td>1857</td>\n",
       "      <td>1519.428571</td>\n",
       "      <td>792</td>\n",
       "      <td>1244</td>\n",
       "      <td>2461</td>\n",
       "      <td>1193</td>\n",
       "      <td>618</td>\n",
       "      <td>2471</td>\n",
       "      <td>1519.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-28</th>\n",
       "      <td>1310</td>\n",
       "      <td>1130.142857</td>\n",
       "      <td>697</td>\n",
       "      <td>910</td>\n",
       "      <td>1776</td>\n",
       "      <td>955</td>\n",
       "      <td>387</td>\n",
       "      <td>1876</td>\n",
       "      <td>1130.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-29</th>\n",
       "      <td>2919</td>\n",
       "      <td>2266.000000</td>\n",
       "      <td>1458</td>\n",
       "      <td>2071</td>\n",
       "      <td>3768</td>\n",
       "      <td>2440</td>\n",
       "      <td>411</td>\n",
       "      <td>2795</td>\n",
       "      <td>2266.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-30</th>\n",
       "      <td>2887</td>\n",
       "      <td>2149.142857</td>\n",
       "      <td>1251</td>\n",
       "      <td>2007</td>\n",
       "      <td>3516</td>\n",
       "      <td>2255</td>\n",
       "      <td>338</td>\n",
       "      <td>2790</td>\n",
       "      <td>2149.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-31</th>\n",
       "      <td>2634</td>\n",
       "      <td>2035.857143</td>\n",
       "      <td>1294</td>\n",
       "      <td>1835</td>\n",
       "      <td>3453</td>\n",
       "      <td>2220</td>\n",
       "      <td>245</td>\n",
       "      <td>2570</td>\n",
       "      <td>2035.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>2405</td>\n",
       "      <td>1871.142857</td>\n",
       "      <td>1208</td>\n",
       "      <td>1701</td>\n",
       "      <td>3082</td>\n",
       "      <td>2076</td>\n",
       "      <td>165</td>\n",
       "      <td>2461</td>\n",
       "      <td>1871.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-02</th>\n",
       "      <td>1582</td>\n",
       "      <td>1297.428571</td>\n",
       "      <td>737</td>\n",
       "      <td>1109</td>\n",
       "      <td>2277</td>\n",
       "      <td>1392</td>\n",
       "      <td>97</td>\n",
       "      <td>1888</td>\n",
       "      <td>1297.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-03</th>\n",
       "      <td>844</td>\n",
       "      <td>727.571429</td>\n",
       "      <td>380</td>\n",
       "      <td>612</td>\n",
       "      <td>1137</td>\n",
       "      <td>713</td>\n",
       "      <td>105</td>\n",
       "      <td>1302</td>\n",
       "      <td>727.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-04</th>\n",
       "      <td>966</td>\n",
       "      <td>808.857143</td>\n",
       "      <td>446</td>\n",
       "      <td>710</td>\n",
       "      <td>1277</td>\n",
       "      <td>692</td>\n",
       "      <td>197</td>\n",
       "      <td>1374</td>\n",
       "      <td>808.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-05</th>\n",
       "      <td>2247</td>\n",
       "      <td>1870.714286</td>\n",
       "      <td>1170</td>\n",
       "      <td>1705</td>\n",
       "      <td>3221</td>\n",
       "      <td>2143</td>\n",
       "      <td>179</td>\n",
       "      <td>2430</td>\n",
       "      <td>1870.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>310 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  Brébeuf (données non disponibles)  Côte-Sainte-Catherine  \\\n",
       "Date                                                                            \n",
       "2012-01-01       35                          25.142857                      0   \n",
       "2012-01-02       83                          58.142857                      1   \n",
       "2012-01-03      135                          91.285714                      2   \n",
       "2012-01-04      144                         108.428571                      1   \n",
       "2012-01-05      197                         122.571429                      2   \n",
       "2012-01-06      146                          93.285714                      0   \n",
       "2012-01-07       98                          57.285714                      2   \n",
       "2012-01-08       95                          56.285714                      1   \n",
       "2012-01-09      244                         175.142857                      2   \n",
       "2012-01-10      397                         247.857143                      3   \n",
       "2012-01-11      273                         194.571429                      0   \n",
       "2012-01-12      157                         108.857143                      1   \n",
       "2012-01-13       75                          41.000000                      0   \n",
       "2012-01-14       32                          23.142857                      0   \n",
       "2012-01-15       54                          23.571429                      0   \n",
       "2012-01-16      168                         108.000000                      2   \n",
       "2012-01-17      155                          81.571429                      0   \n",
       "2012-01-18      139                          66.285714                      0   \n",
       "2012-01-19      191                          89.428571                      1   \n",
       "2012-01-20      161                          78.142857                      4   \n",
       "2012-01-21       53                          29.000000                      0   \n",
       "2012-01-22       71                          32.142857                      0   \n",
       "2012-01-23      210                         112.000000                      6   \n",
       "2012-01-24      299                         158.714286                      1   \n",
       "2012-01-25      334                         169.857143                      1   \n",
       "2012-01-26      306                         172.571429                      0   \n",
       "2012-01-27       91                          63.428571                      5   \n",
       "2012-01-28       80                          44.000000                      1   \n",
       "2012-01-29       87                          47.428571                      1   \n",
       "2012-01-30      219                         127.142857                      0   \n",
       "...             ...                                ...                    ...   \n",
       "2012-10-07     1580                        1199.714286                    660   \n",
       "2012-10-08     1854                        1456.571429                    880   \n",
       "2012-10-09     4787                        3369.142857                   2210   \n",
       "2012-10-10     3115                        2326.428571                   1537   \n",
       "2012-10-11     3746                        2845.000000                   1857   \n",
       "2012-10-12     3169                        2453.000000                   1460   \n",
       "2012-10-13     1783                        1439.428571                    802   \n",
       "2012-10-14      587                         527.000000                    287   \n",
       "2012-10-15     3292                        2547.000000                   1678   \n",
       "2012-10-16     3739                        2872.285714                   1858   \n",
       "2012-10-17     4098                        3024.000000                   1964   \n",
       "2012-10-18     4671                        3494.857143                   2292   \n",
       "2012-10-19     1313                        1038.428571                    597   \n",
       "2012-10-20     2011                        1514.428571                    748   \n",
       "2012-10-21     1277                        1045.714286                    609   \n",
       "2012-10-22     3650                        2895.000000                   1819   \n",
       "2012-10-23     4177                        3109.571429                   1997   \n",
       "2012-10-24     3744                        2920.428571                   1868   \n",
       "2012-10-25     3735                        2929.000000                   1815   \n",
       "2012-10-26     4290                        3330.142857                   1987   \n",
       "2012-10-27     1857                        1519.428571                    792   \n",
       "2012-10-28     1310                        1130.142857                    697   \n",
       "2012-10-29     2919                        2266.000000                   1458   \n",
       "2012-10-30     2887                        2149.142857                   1251   \n",
       "2012-10-31     2634                        2035.857143                   1294   \n",
       "2012-11-01     2405                        1871.142857                   1208   \n",
       "2012-11-02     1582                        1297.428571                    737   \n",
       "2012-11-03      844                         727.571429                    380   \n",
       "2012-11-04      966                         808.857143                    446   \n",
       "2012-11-05     2247                        1870.714286                   1170   \n",
       "\n",
       "            Maisonneuve 1  Maisonneuve 2  du Parc  Pierre-Dupuy  Rachel1  \\\n",
       "Date                                                                       \n",
       "2012-01-01             38             51       26            10       16   \n",
       "2012-01-02             68            153       53             6       43   \n",
       "2012-01-03            104            248       89             3       58   \n",
       "2012-01-04            116            318      111             8       61   \n",
       "2012-01-05            124            330       97            13       95   \n",
       "2012-01-06             98            244       86             4       75   \n",
       "2012-01-07             80            108       53             6       54   \n",
       "2012-01-08             62             98       64            11       63   \n",
       "2012-01-09            165            432      198            12      173   \n",
       "2012-01-10            238            563      275            18      241   \n",
       "2012-01-11            182            443      258            12      194   \n",
       "2012-01-12            134            261      137             9       63   \n",
       "2012-01-13             41            105       64             2        0   \n",
       "2012-01-14             54             56       19             0        1   \n",
       "2012-01-15             33             60       18             0        0   \n",
       "2012-01-16            136            312      137             1        0   \n",
       "2012-01-17             86            256       74             0        0   \n",
       "2012-01-18             66            188       68             3        0   \n",
       "2012-01-19            104            248       79             3        0   \n",
       "2012-01-20             96            217       67             1        1   \n",
       "2012-01-21             47             70       32             1        0   \n",
       "2012-01-22             41             73       35             5        0   \n",
       "2012-01-23            114            357       91             6        0   \n",
       "2012-01-24            189            444      174             4        0   \n",
       "2012-01-25            217            453      180             4        0   \n",
       "2012-01-26            215            495      191             0        1   \n",
       "2012-01-27             79            204       65             0        0   \n",
       "2012-01-28             61            123       33             9        1   \n",
       "2012-01-29             65            132       40             7        0   \n",
       "2012-01-30            146            371      152             2        0   \n",
       "...                   ...            ...      ...           ...      ...   \n",
       "2012-10-07            922           1629      860           695     2052   \n",
       "2012-10-08            987           1818     1040          1115     2502   \n",
       "2012-10-09           3026           5138     3418           927     4078   \n",
       "2012-10-10           2081           3681     2608           560     2703   \n",
       "2012-10-11           2569           4694     3034           558     3457   \n",
       "2012-10-12           2261           4045     2564           448     3224   \n",
       "2012-10-13           1205           2113     1183           681     2309   \n",
       "2012-10-14            443            852      503            65      952   \n",
       "2012-10-15           2165           4197     2754           560     3183   \n",
       "2012-10-16           2684           4681     2997           554     3593   \n",
       "2012-10-17           2645           4836     3063           728     3834   \n",
       "2012-10-18           3129           5542     3477          1108     4245   \n",
       "2012-10-19            885           1668     1209           111     1486   \n",
       "2012-10-20           1323           2266     1213           797     2243   \n",
       "2012-10-21            869           1777      898           242     1648   \n",
       "2012-10-22           2495           4800     3023           757     3721   \n",
       "2012-10-23           2795           5216     3233           795     3554   \n",
       "2012-10-24           2625           4900     3035           649     3622   \n",
       "2012-10-25           2528           5010     3017           631     3767   \n",
       "2012-10-26           2754           5246     3000          1456     4578   \n",
       "2012-10-27           1244           2461     1193           618     2471   \n",
       "2012-10-28            910           1776      955           387     1876   \n",
       "2012-10-29           2071           3768     2440           411     2795   \n",
       "2012-10-30           2007           3516     2255           338     2790   \n",
       "2012-10-31           1835           3453     2220           245     2570   \n",
       "2012-11-01           1701           3082     2076           165     2461   \n",
       "2012-11-02           1109           2277     1392            97     1888   \n",
       "2012-11-03            612           1137      713           105     1302   \n",
       "2012-11-04            710           1277      692           197     1374   \n",
       "2012-11-05           1705           3221     2143           179     2430   \n",
       "\n",
       "            St-Urbain (données non disponibles)  \n",
       "Date                                             \n",
       "2012-01-01                            25.142857  \n",
       "2012-01-02                            58.142857  \n",
       "2012-01-03                            91.285714  \n",
       "2012-01-04                           108.428571  \n",
       "2012-01-05                           122.571429  \n",
       "2012-01-06                            93.285714  \n",
       "2012-01-07                            57.285714  \n",
       "2012-01-08                            56.285714  \n",
       "2012-01-09                           175.142857  \n",
       "2012-01-10                           247.857143  \n",
       "2012-01-11                           194.571429  \n",
       "2012-01-12                           108.857143  \n",
       "2012-01-13                            41.000000  \n",
       "2012-01-14                            23.142857  \n",
       "2012-01-15                            23.571429  \n",
       "2012-01-16                           108.000000  \n",
       "2012-01-17                            81.571429  \n",
       "2012-01-18                            66.285714  \n",
       "2012-01-19                            89.428571  \n",
       "2012-01-20                            78.142857  \n",
       "2012-01-21                            29.000000  \n",
       "2012-01-22                            32.142857  \n",
       "2012-01-23                           112.000000  \n",
       "2012-01-24                           158.714286  \n",
       "2012-01-25                           169.857143  \n",
       "2012-01-26                           172.571429  \n",
       "2012-01-27                            63.428571  \n",
       "2012-01-28                            44.000000  \n",
       "2012-01-29                            47.428571  \n",
       "2012-01-30                           127.142857  \n",
       "...                                         ...  \n",
       "2012-10-07                          1199.714286  \n",
       "2012-10-08                          1456.571429  \n",
       "2012-10-09                          3369.142857  \n",
       "2012-10-10                          2326.428571  \n",
       "2012-10-11                          2845.000000  \n",
       "2012-10-12                          2453.000000  \n",
       "2012-10-13                          1439.428571  \n",
       "2012-10-14                           527.000000  \n",
       "2012-10-15                          2547.000000  \n",
       "2012-10-16                          2872.285714  \n",
       "2012-10-17                          3024.000000  \n",
       "2012-10-18                          3494.857143  \n",
       "2012-10-19                          1038.428571  \n",
       "2012-10-20                          1514.428571  \n",
       "2012-10-21                          1045.714286  \n",
       "2012-10-22                          2895.000000  \n",
       "2012-10-23                          3109.571429  \n",
       "2012-10-24                          2920.428571  \n",
       "2012-10-25                          2929.000000  \n",
       "2012-10-26                          3330.142857  \n",
       "2012-10-27                          1519.428571  \n",
       "2012-10-28                          1130.142857  \n",
       "2012-10-29                          2266.000000  \n",
       "2012-10-30                          2149.142857  \n",
       "2012-10-31                          2035.857143  \n",
       "2012-11-01                          1871.142857  \n",
       "2012-11-02                          1297.428571  \n",
       "2012-11-03                           727.571429  \n",
       "2012-11-04                           808.857143  \n",
       "2012-11-05                          1870.714286  \n",
       "\n",
       "[310 rows x 9 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.fillna(bikes.mean(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1\n",
       "Date               \n",
       "2012-01-01       35\n",
       "2012-01-02       83\n",
       "2012-01-03      135\n",
       "2012-01-04      144\n",
       "2012-01-05      197"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes = bikes[[\"Berri 1\"]].copy()\n",
    "berri_bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04',\n",
       "               '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08',\n",
       "               '2012-01-09', '2012-01-10',\n",
       "               ...\n",
       "               '2012-10-27', '2012-10-28', '2012-10-29', '2012-10-30',\n",
       "               '2012-10-31', '2012-11-01', '2012-11-02', '2012-11-03',\n",
       "               '2012-11-04', '2012-11-05'],\n",
       "              dtype='datetime64[ns]', name='Date', length=310, freq=None)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "            ...\n",
       "            5, 6, 0, 1, 2, 3, 4, 5, 6, 0],\n",
       "           dtype='int64', name='Date', length=310)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "      <th>weekday</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>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  weekday\n",
       "Date                        \n",
       "2012-01-01       35        6\n",
       "2012-01-02       83        0\n",
       "2012-01-03      135        1\n",
       "2012-01-04      144        2\n",
       "2012-01-05      197        3"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.loc[:, \"weekday\"] = berri_bikes.index.weekday\n",
    "berri_bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2984.400000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3075.113636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3476.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3639.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3222.068182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2308.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2206.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Berri 1\n",
       "weekday             \n",
       "0        2984.400000\n",
       "1        3075.113636\n",
       "2        3476.636364\n",
       "3        3639.340909\n",
       "4        3222.068182\n",
       "5        2308.590909\n",
       "6        2206.888889"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts = berri_bikes.groupby(\"weekday\").mean()\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Berri 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>2984.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>3075.113636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>3476.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>3639.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>3222.068182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>2308.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>2206.888889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Berri 1\n",
       "Monday     2984.400000\n",
       "Tuesday    3075.113636\n",
       "Wednesday  3476.636364\n",
       "Thursday   3639.340909\n",
       "Friday     3222.068182\n",
       "Saturday   2308.590909\n",
       "Sunday     2206.888889"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts.index = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>bikes_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>25.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>58.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>91.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>108.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>122.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             bikes_avg\n",
       "Date                  \n",
       "2012-01-01   25.142857\n",
       "2012-01-02   58.142857\n",
       "2012-01-03   91.285714\n",
       "2012-01-04  108.428571\n",
       "2012-01-05  122.571429"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_avg = bikes.mean(axis=1).to_frame()\n",
    "bikes_avg.columns = [\"bikes_avg\"]\n",
    "bikes_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "            ...\n",
       "            5, 6, 0, 1, 2, 3, 4, 5, 6, 0],\n",
       "           dtype='int64', name='Date', length=310)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_avg.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "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>bikes_avg</th>\n",
       "      <th>weekday</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>2012-01-01</th>\n",
       "      <td>25.142857</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>58.142857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>91.285714</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>108.428571</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>122.571429</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             bikes_avg  weekday\n",
       "Date                           \n",
       "2012-01-01   25.142857        6\n",
       "2012-01-02   58.142857        0\n",
       "2012-01-03   91.285714        1\n",
       "2012-01-04  108.428571        2\n",
       "2012-01-05  122.571429        3"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_avg.loc[:, \"weekday\"] = bikes_avg.index.weekday\n",
    "bikes_avg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后我们再来求平均，看看一周当中每天总共有多少自行车通过"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_avg = bikes_avg.groupby(\"weekday\").mean()\n",
    "weekday_avg.index = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "weekday_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0ef06908>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MAETEuszsHY2f1Q3mr5b5q1Pn7DB28nd7GWc9e5ZuZgMbuvzzJElD6PbM/m+AtRFxBPA+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\n/H/tDGdupPJ7rgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c0eeee7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "weekday_avg.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0eea62e8>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eRluDfG+cYp89vf2Y6x1G5bF9D3HMtT79aldQNdg/cmIB9/yvj+N3/uUxAMD5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\ngFX2xhjTAPw/8Qs0B/1LTa0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c0ed9b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes_avg[\"bikes_avg\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0ccbae48>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bPk8Ib2uAonHZA0rBUxWVwzVesgsyYj9Ei3giYZWWOi85hRkJawLM5NNPdrH5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t/40xFO7Ohm/ppKbvrhOVz62QWUpZvHNPWqob7pHhvx1jaCWglnvWocT8jv0rXnm3i+/CVi+/az\nZunZxCMR3vjDb/Oap0il6P7pT/FPqyGaiOOsqOJwdQmvPfgLkgOja9ksrU3H2VsGskVKTfv7ePon\nOzi0qZ0lF9Vy7ddX4KpQ73Vnuvaj1AT+nm5M1j40A+A481/PKDHrSpEcCZBgTqKHqFBIxOQxHZN4\nJIXGqEET/v/Ye+8wqcrz//91zvQ+uzOzvcPCLr13EAtKAAUVS+xRElsSjUYTE6OffJJPNMVojJpY\nvim2RBGNBWJFBERAQBDpu2yvs2V6PeX3x+wuIiy71CX5+b6uvXaumXOeec6Zc+5zP/f9vt93Klxm\nisaIp8WxMbBGHSB90JUAdNa9yhlXXc+IM89l4ztv0jBpLL5XXz2qJOppYdjbaoOk5VjQ6A6dztLN\ndRh1IgtG952xTra20vanP1FxzhwqH76J6nM+JjRPodKylNDaj3rdb0pJOoM8Fp7fUNunfG93Vj0u\nqAgBLyZbCDoNaOzHpkj5VXQnUAXTRFRRwDg0iSWWwN26h0hCZkdjAPtxFinFQl0CYF2hmCEZFvyx\nCE6rnfKFFyFpRPY8/7cTcjx9oayLO98odRUd9SPOHo8kSXRdKl/22MPrPiF8hoIa0CL5s0jP7b8z\n0Bfs1qFoTTLRTGOqSKmXUIzZrmf8NwqZdH4x1zwwjVnfHIrDc3AeoOpLqo7Nqz5AEUX02X7sMT2u\nmI1dkTdIFO9AX1SE9PfnmLRwMXs+WYO3K1F5JPheeYX4nj10TByLxQ0X3345oybPoMpmZNmdt5KM\nHeoMlGXZMWhFPqv19RQprXx2F4moxAW3jWH6xYMPuje7OezmSKq1nmAPoa8TMZQeHyvsWGAxFaPq\nQWuSyY95CSkyqqIiJw9fvZuISkgaAbuUSrYaNUkSVhWr6fhzMccLo2dsl4zvZwiCwJzv3MqQKTPY\nFvVRrcQJfrCy32MNuGFXVRVvXRDPYcIwsaTMG1sbmTs8C7ux9wRd5NNPqf/+bVScdTatf/wDwbkd\ntN+WxGASKZbHkSxU2f36zSRbD1/0kSrRLmRbnY+aaByNVuzVY++O30kaCPqaMRpDGELHzsz5KroN\ne8wwBYC4oxZ3KEq0uRpRlfm0quO4OynFwl1CVFYde5pDjDHGCWtF0rPzKJo4Fa0gUvXFtj7DWCcC\n3dICFeHUsfTHsMfCEhEU3FY9ti9dF+3bXyVZrNJRkU1u2fATyqe2ulLtDqVchaygl7ZePHaAKQsH\nMXF+cU/7u6+iO1Ff5DbTvHkTok5GtsdJN5Qx8sz3cMRN7PA9i/66ISQqKhmkahE1Wr748L0jzlH2\n+/E+8geMEydS01rJ4Pl7+GzvDUxeOIgJBYNpCPn5x93fJ+w72HPXa0VG5jr4rLYTZ4YZnVFD8Wg3\nl/9sMvnlhyZDu6tO6WxG1MsoTgVz0IagPbaGE8cDszMlC2xwJkiLdeBPHlm6Nx6ViKJiTwbRiBqE\nzJSTY0ufeGom3AdSMr4Bkv79iKKGed+7k6LR49ie5+GLF5/t9zgDbtjDvgTRYPKwidN3d7YQiEm9\n6nsnW1ppuOMOaq6+hsjGjViuOZPQg9A5MUiOVMCk2esombMUdyAN/5w4Vfdf16uwzsXj8zDqRF7Y\nUIvd0zszpluHwmrREaMVQYA08VBWwrEivYtV0BHWYY6LhDRVeJIqsiwzVu9jQ1UHOoMGk+3Yuezd\ncgIJTYq2V9ZZgSoIeIaWodXrKSgto9liwL9ixQk7rt7QLS2w0xvCZNf3i/IYjyTxyfJBFadqIkF7\n5l6EBDRsM5FbduLCMADW3LNTL9wJ0jpbevXY+4OqtjDZDiNmvZa26v1Ys6IgCjgzz0Zj8jB65gos\nCR2VrjdhZibBp55i0LiJ7FzzIdIRluPexx9HDgSIX7IIR2ktglFBowh8VvkjxtwwlUkhiY7mBl78\n6R201x+s5z62wMkXjQFEg4YbHprJvJtH9doPt9EfI82sI9BYiy0zNR+75dgkG44XZk/qgWtwxDHF\n/Pi6pXt70YtJRCRCioxDDmLRG5ALUg9/a+7AJU6/jNyht6MKsPeTqwHQaHVccOdPyHCksz7avy5d\ncBoY9rauitPDSQks3VRHrtPE1K+US6uSRMff/87+efMIvP8B3osWELtnOPvHvkXUmGCE4xrKz1uF\nxpRquFE++yW0EjRP3kvrow8fdh7dJdqvb23EcgT53u4LJtOcRJ+e8nyz8k/c016v1+NwOGhvb8cp\n5ODX+skpLkFQYZTYwqaaDhRF7WLGHGsoJnUzNnX1fnW2pmhsmRNSN8mQOXOJ67RUvXLsWhVHg/Js\nO3uag9hdxj4pj6qqEgtLtCekg+LrwQ3vERkno292oyQ05J1gw66zFyPGRAyuGELUTyIUOUgP/WjQ\nLf6VbG3FF4uQlhtBUFScXRWyOlshY6a8hl4Sabm4lqjaQBFaYsEA+zcfvglDvKKCzhdexHnpJeyv\n2UjGqA4yYhlMGPM8GkVgW809DL31PCbvayDh9/OPn91F0749PfuPLUgjISnsagqg6UNzpluHva2u\nBldO6gGXNnTBMZ2L44XRMx5RUbE4EgjJCB2JLrmJpkNXm6qqEo9KdCZlXGoIi6IilwjokqBPGzgO\n+5dhLZxPkTiOZn0zbVt/C4DOYOTCn/wvtnj/yRIDbti9dUEQwJ13cCimyR9lbUUbF4/LPahoKfLZ\nZ1QtvoSWBx7ENH4cbd//Fi0F7+O1fYjsNzNu+Itkjr//oLH0zkGUZ19PskClpu5pQqsPTx+7akoh\n0aRMiyIR8EYPKwbWHWP36EMY0+KoioBrwuLjPQ0HweVy0d7ejsM5AUkrYBjtwBmNkd5ZhS+SpMIb\nwu4yHnsoJtRd4JMyorK3AVQV2fcsweo3GDRuEoIgUNPSSGzv3hN2XL2hW1pAa9f3qReTjKdiqG0J\n6SDxr9bNfwcdxOOj0OoNZBQf2mfzeGFKWro0Y/RHpDweCaqqst8bosRjIbppE0GTHltOGHvC2OOI\nQKph8tixz6FFoON2CfG9lzAbTWx/71CBMFVVaXngQUSLBedNNxLRvYsgqAwe9wimrKmMG/VXRFVg\nt/ZxsuaPZOqO/YiiyIZ/HWBafDmB2hcafVGyu8S/TM4gmjawTTy5ao69QdDoMSW0mJ1xkkqCGjGJ\nPcPE+3/bSWvNwRXAPddOPIk1EcAYjpLMkbDJ1lOq5tgXimb8DWtMy+7mP5MMprSsrMXFzM4d3O8x\nBvxovLVBnBlm9MaD43OvbmlAVWFxFxtG6uyk6Wc/o+abVyD7fOQ++gc6LpxBh+kZ0ocEEOqK2fpK\nIct+/yzemqpDvidj7E/JjHkIzpOpfuh2kk2H6l6PynMyKs/Bh4Egiqzw3E/XsW5ZBdEv0QqDXa/T\nhBAme4Rk0IQ+p+gEnpEDht1ZeAkA8aw6XIEIiZZaDHKMDV1x9mDHsXHZu0Mxe30RnGYdQX8HNqPI\n3ugrbNv9A0Spibyhw2hxWvEtPXbBf1VVkTo6+tyuO4Ea0UOoM4Z8GL33bnRXncYElcIvcdh9sV0I\nSWis0pFdOhSN9tiKpo4EpzEPozNOyKwlJ9zWK+XxSOiMJAnEJIrdVgIbNxAza9C6YjgNh960pqyp\njBn2OIJBpf3bfnLbaqne/hlVv34Qqa2tZ7vQh6sIf/wxnu/eSuXWF3CW+kkPD8aUlZIcNufMYuyI\npxGA+hnrMORoyApFqd2+tSe0k+0wkWU39lSgHglN/hg5RomI34cuLYyhSYsuI+Ooz8WJgllwoHfG\niWk12GIBxl0zFKNZx5uPbqOj8YDn3r3a9oYDaKQYBn8nCbeMVd9/VdJTAVFvpbzs/0joVPZ9fGXP\n+/k/+EH/x+jPRoIgZAqCsKbrtU4QhLcEQVgnCML1vb3XX7TVhQ7hr3fre08uTqfAZUaVZepuWILv\ntX+RfsP1DFr+Fo36ehqjv8KSEWWY7SrOuvZ9Lv7x/xILBXnhJz9g8/LXD9G1Hjr9RXQSdFwcoP7O\n2w9LH7pqciGfhMIMv6GMkrEePnu/lmfv/YT1/6okFk7i7Uh5uaZEByZrENFvOOGiRy6Xi3g8jmIb\ngTmuIWDbj7vLux4htPJpVQeuHAuK3Lf+9+EQC6W64Oz1hhjl0BBQZNwFMqogENfDznWXMHjKVMIG\nHQ3/Xo4SP7Z4cvvTz7Bv2nRqrr2OwL//jZo4vCHslhbwqqnuNsEjyPd2C1HFBLXHY5f9fiKZUYwd\nOrzV9Sc8vt4NZ/oYBA3EckRyQ95eKY9HQlVbKnFa4rbQ8tkWzFlRECHNc3gOuLVgLmMGPYDiUHEt\n8SPqVL5Y8QYVZ51N0333E9u7l5ZfP4i+pATnZZfR6P87Slxk+JlPHTSOJe8sxpb/CVWA9pt9pPmq\nScZj1O/c3rNNd0elIyGSkPBHk3ikTjQGCRwKltipqzY9HMz6bES7REyvITvkJaKFC24fg6AReOPR\nrT0J+e4aCTmROkajJYKqFbDZj62d5cmEvWQxBYygSd9A+/ZHATAOGdLv/fs07IIgpAF/B7rdo+8B\nm1RVnQYsEATB1st7fSIWThLsiB0iJbCpppPq9khP0tT/5pvEdu4k54EHyLzrLqo2P01N+EF0RpnR\n2T8he+LPASgaM55rfvsYRWPGs+rZp1n2wP2EOg94jDpHCeVZS0jmqbRmb6H14UcOmdOC0dnYjFpe\n2dfCnOuH8837JlM00sXmd2p49qfrqN/WhoSKGm7GZAphjh+9fnxfSImBQVtbG25dKX5TiPR0K1pB\nZAQtbKzqoHi0G51Rw441jUc9fiyUSMn1NgeZkGglbNDhyAuDqjJIM5V2YxCL5p8ANAkKwXePzMY4\nHGSfj/annsJQVkayro6GH9zBvjPPovX3Dx8iRWrWayl2WaiOpQzlkRKosS4hqph4QK7Xv3YFyXwV\nbTIXVVXILTs58VJr5gwApHy1y2M/esPe3ee0SJek3duCNScCiopj0OW97uMovYwRnpuI2ROUzWug\nedhg7IsW4f/Xv6i6YCHJmloy77mHlu2PoPFE0DSXYXAc2hvAWnAuY4f+AVWvItwYxGBJsH/Lpz2f\njy1wUtsROeJxdVMdbREvZk/qtSNt5FGfhxMJs2UIgggap0xuuJX2UBxnhpkLvj8GKSHz+h+2EvbH\ne8KoWiWV1zN4UsdpzZw1YHM/EopnPIc5LrK74VGk8NHJfPTHY5eBy4DugNVsoDurtg6Y0Mt7B0EQ\nhO8IgrBJEIRNXq8X6F2qd+mmOsx6Dd8YkYUSj+P9w6MYR4zAPn8e+1fdw/7YY6hJkXGlf8Az4oaD\n9jXbHSz84b2cs+QWGnbv5G93fZcdH33Q4717xt5DViKL0Ddkmt97ho7nXzh4f72Wi8fl8ea2Rsb/\n4j1ufH0bK50KmrnZGHItJNrjRAVQ1EYEQcVlPr7mGodDN+Wxvb0dV84FqKIAU0244xLOziqaAzFa\nokmGTsqiYnNrT3iiv4iGkogmDcG4RFFnJaoooHN3Yo3rKJz5LBlxNw2ajeSNMNGakYZv6dFLh7b/\n5a8o4TA5v/41g957l/ynnsQ0ejTtzzxD5Zw51N54I5HNm3u2L8u2sSuYYiIdifJ4QDpWi8OUCre0\nbXsZRIgpQxBEkZzSk8PQMOfMRpVByJTIDbUdo8ceRisKpO3fRdCox5YdwZYwoLVkHXE/z5i7GWqc\niz47hH3YDpKLFzH4w5W4b7kF9y23YJ46gb1NfyHm0zN08v/0Oo6taAFjB/8O1aJSOK6JyvVre4p5\nujsqbT2C197dEk/rb8WemTr+9NGXHM0pOOGwpKfkso3OONkxb0+IzJ1nZcF3RxMJJHjjD1sJdl1X\nejm1yhUzJQRZxZxzxsBMvA9oDA6GDbqfmE6hYu0VR7Vvn4ZdVdWAqqpfXu9bgG6XKwBk9vLeV8d5\nSlXVCaqqTvB4UkmittouKYGCA6GYSEJi+edNzB+ZjcWgpfP555GamvDceQcVK69jv/wy8Q4D40f8\nFVfp4cv4BUFg9Jx5XPCTX9CZO5jXX/4nz9/zA2q7JGmHTH8RvSQQWCLT/OAv8DdaavYAACAASURB\nVD76x4Mq1e6eO5R755dzTpe40vu7Wnjwk/3c397C32wx3k9LgjXleWWWzu7rFB41HA4HGo0mFWcf\nfCUaSSVW0kF6aztKoB170s/Gqg6GzcxBlhT2rD+6p3ksnCSpSYWPTK3VIKjI9jAObT6CKFI+cxnG\nhIhn3FYCFoWOLZtJVFf3e3yprY2O557DPm8exqFDEDQarLNmkf/E4wx+/z3cN99EbOdOar/9HeRQ\n6hooz7Kz2xdF1Ar4j5AU7paO9bgO9MX1J/eArNJUYyKjqAS96cTVFXwZotaIJqxH506QETu25Ol+\nb5gCl5n45s2EbHrMGVHS9P1L9OZNf4J8aRju4T52bbgbrcuF5/vfw/P979G08S4ki4RvewG5w8Yd\ncRx7yUVkygWYBkcIhdrpaEzpkIzIcaAVhZ6OSodDd8cxqa0BmzuMtgUsYwfWMJq7+p8aHAlciU46\nwgdCflklDubdNBJfa4SP/pEiAliUEDqNBiVXwZrUI+pOzvVyIuAYchUFShkNuho6dj7Z7/2OJXka\nArrvKmvXGId7r09464JY0wwHFXL8e3sz4YTMJRPykTo7afvzk1jPOIPG0K+pFT8mUG1j3Jhn8Qzu\nWx700+1fIAsiCU8urYrA0l/8hNd+878EAiLl2TeT8KjI13loe+IJmn/+c1Q5RV8z67UsmVnCrxeP\nYulN09j8szlsu+9cXr1lGj+6fBQ/viAfY1oMVRFwjrmwv+et3+juf9re3o6ot5IuuwhldOIKpTza\noVITG6s68OTbyCiys2NNw1Fpu8RCScKkVjDx9hZM6XEULTjTUkVRWmseI4Y8AAaZgtlNtDot+JYt\n6/f47U8/jZpI4P7urcjxg3MAupwcPN//PvmPP44aiRB46y0AyrLtIIDBoT+ix969OsnxpMIwifp6\nonkJzJ16mvdVn7T4ejfMihNTegy9GqOq1nvUlMeqtjAlbguRTZuQi1UEDaR5zuz3/qVnv4banIZu\n0H7qPr4bACncTGVgBeEmE3mDLutXzien5DoEPTiKg1Su/xgAk15Debb9iHH2Jn8MVJVgcz16VxRj\nmx5Rf/xKq8cDnaMEbTJVpOSQgrR/5YGbX57OeTeMQOqqRk0Xw1gEkWS+ilUcuKRvf1Ey4zlMcZFd\nNb/t9z7HYtg3AzO6Xo8Gqnt5r0+01QUP4a8v3VxHkcvMxKI02p98CiUcxnTVKGrF7XTscTBmzJPk\nlI3vc+yqqip27tzJGWecwahRowhanGSfNY/6ndv5+w9vZdtmC46IlY5RDTiXXIrvny/RcMedKL0k\n+BxmHeMK0lg8Po9ifRiTPYIUMqIxHl0D4f7C5XLR1sV8cDunkzCBwZ3EbDAyTG3uEQQbPjOHzuYI\nTRX9S6Kqqko0lKRTksmzaAhEQqTnpAyps3BRz3b2QZcw2HA2jqIQkbkivtf+1S+timRzM53/+CeO\nRQupq/w+H60Zy773FyCFDs4FGEeOxFBWRudLL6Oqak/TjaRJc0S9mHAoSRKVgoyUYQ+sfptkgYpG\nykFKJigceXxdrPqC0zoInUUm5NLSsGMfZz/0Ea9v7d+DVZIVqtvDlFoF/Ht2Y8iJgArOI8TXvwpB\no2X4hL8QajSzN7qMzp1PUbPhFpJ6aPgkk7Lp/fOeHaVXYYgKuEs6qVh5IIcytsDJtjofci9sqyZ/\njHx9DFUIIdoULPKJK847HphlA0ZHAr0aP+xKqmSsh3O+VU6TGWxyEIsYQbGCbYAKq44GGpOL8qIf\nEzP033k7FsP+d+DngiD8ARgGbOjlvSMiGZfpbIkcVHFa2x5h/f4OFo/PI9nQQOcLL2BftJA9nY8h\nxTTke26iZPzkPieoKApvv/02DoeDGTNmsGjRIkaOHMneplZGXn0To86ey7b3/s1nH+QiaSA8ZgsZ\nP/oRwXfeoe7GG5FDRy6lb6yqwmwJoAka+5zLscLtdtPZ2Yksy7hKU3kEeYqBDFXE7qumyhukNRij\ndEImeqOGHWv71xtRSijISYXmeIJpop+QQYs9J5ySP804ODWSP/1JaLVjndxK0Oal8+W+C5ba/vTn\nFM3xjBZqxJ1YEjpqxV2s+3gmDZ/chiqnHpyCIJB22aXEd+0itn07uU4TNqMWn6gSaOtdvrejI0pc\noEfytn3Hq6CB9o4cjFYbhaNOrmF3Zaeuv1ge/HpKGg6Tjtv+uZVFT6xjU/Wh1E5VVfmiwc//Ld/J\n9F+vJC4pjA/VEzTosGRHMER0aK1HR7fLHjKKwI6pJIM6Pq9/kFp5K/E6JwZdKRlF/QvrCBotWcYR\nmAuitIUaiEdS1/zYAifhhMy+1uBh92vyRynWBDF7upyBzElHNfeTBYvowuiMI2kg0nZ4im3xuAz+\nYYiii/owm1PhJqt7yqmc5jEjrfwG8qT+12b027Crqjq7638NMAf4GDhHVVX5cO/1NV5bfQhUDtKI\neWVLPYKQ6sDufeQPoNGgnBUgZo3TuS2PCRdc16+5btmyhZaWFs4991x0Oh2iKLJo0SKGDx/OqjVr\nsAwfy7W/fYxE0EmsNp06dmCZP4LsBx8gsvFTaq+99oj864bK3ZhMQezy8TfX6A0ulwtFUfD5fBjc\nI7HFdMTKY6Q3tkI8SkbCy6bqTnQGDUMmZ1G52dtTeHQkRLsSS/WROKOijYQNOgyZYZzqoUtSQRQZ\nPOR3SBEd/u/INP3+l/jfeKPXsRN1dfiWLUN3xVAqNCtxxexMPHcbEwsfxCwb2R19i43vjKRz1/8D\nwH7++QhmM50vv4wgCJRn2WlMJkjG5F4Twn5/nKigUuiyoMoyfnU/KCoVm0MMmTL9pPDXvwx73rkA\nSLkqxbEO3vzeDH67eBTN/iiL//wJt7ywmZr2MHUdER5buY85D69mwR/X8rd11YzMdfLYFWMZ1b6f\noN2AJTNKmvbYku/DZ53P3rcKQRFQBahc46Zs2syjot5mD7kVQQTH4AD7VrwJwITCdAQB7nhp22GL\nlZr9MbLlzhQjRgHXxCsP2WYgYDbmo7XIxM0imubDOzl1HRF0UhRBSmBIT+VxrF2/538CBs/8Z7+3\nPaYCJVVVG1VVffnLSdXDvXckfFVKICkrvPRpLbNKPaQ17Cfw1ls4rr6QiuTbhFuMjJz6Y7T9iOVF\no1E++OADCgsLGTbsAO1No9Fw0UUXUV5ezjvvvENlYzMzL7+GyjUuFFWgcsvtOBctIu/xx4hXVlJz\nxZW9dp4JRqsRBBWPu/+80qPFl5kxAC5DOdGsJI6upr0l8caexhvDZ+YiSwq71x9adPVVdBv/oKqQ\n6a1Ca5fApOCwHV52OH/MbJo+LkGxKrTfk6Tm0R/hf2v5Ybdte+xxpGyoH7cZS1xHzpBn2LNhA+b8\nhYyfu53htm+SFCW2NP2K7f+eRCK0A8f8eQSWr0AOBinPtrEv0iUG1ksFajiU7OGwx3buJF4koenU\nk4hIlE+f3efxHy/0aUNRoyJClkyiqgqNKHDJhHw+/OFsfnDOED7c7eWshz5i5m8+5Hfv7iXdrOf/\nLhzBxp+cwzPXTmDBqBwimzaTGG5E1KpkZB3bnMtnzkaJm5Hqr8DaeRmJoI6hU4+OtmfJn4M1oiW9\n1Me+rorW/HQzf7pyHO3hOBf9aR33vLodX+RAeLLRF8UZa8fmiaJtFTAOOj044GZ76l5XMhSMrYcn\nE1S1hXF0qTrqMhIYIgI624lTAD3ZOBq9+AGrPPXWBTFadVi7ZEI/2NVCSyDOVZMLaP3t79CkpeEr\nX4usVwnvGc6wM87u17gfffQRsViMuXPnHuK9aDQaLr74YoYOHcqKFSuI2dNJzyync5eHZl0jwarX\nsc2eTcFf/h9SRwfV37yC+L59h3yHakkxOTxlJ6+M+hDDnncRiAIMkXCluxkqHzDs7jwrmcV2dqxp\n7DPW223YowJovPVYsrqW1HmHZxiJoobckvOofncwglWl7a4EFS/fQeDtg0vb45WVdHz4Bp23RhFV\ngVDDZTz/s5+z4tHf8ve7bqVm+1ayJv6SqWdspFgYT5u2jc1br8B+0QWo0Sj+N9+kPNtOi5Ja7Pnb\nehFhiySRtQJOs57gmvdJFKlEOl1YXe6Txl//KjQRE1pPgo4Vy/G/mfJ0zXott51Tykd3zWbJzGLu\nOm8oa390Ji/fNJUrJxeS1tXKUYnFiG7fjlIQRT3K+PqXYbLaGDxxKrvX7WPP5giewmJceUdvpLId\n0zFnxGmV6pC71Dznjsjmgztnc8P0Yl7eVMdZD33E0k11hOISgZiEIdSC0R3F3Gk84cV5xwqzOxVG\nFNwy9o6mnhyBqqrsaPTzqxW7+Mlr23HKXSGmLAmrfOJrUE4XDJxhr01J9XZfGM+vryXHYWRy214i\n69djWTKbRn0l7bucTL3wx4hi3y3xvF4vGzduZNy4cWRnH16/XavVcskll1BaWsqKFSvIO+M86j9N\ng6RIxa77ADCPG0fh888BUH3V1US2fNazv5RIoHembkpz/snrGGOxWDCZTD0JVMfgy9AmVeIjVTK1\nRmyBBioa22nt6mYzfGYuvpYITRVHrhzslhOICzJhXxuWrAgaScWaP7fXfQZPnIK/Vke89lqMMRO+\n6yR2rP8evn8fCMu0PPoQHd9JkLSo7HmvlG0rtzLq7LlccOdPEASBZb+6jzcf+TWRqErJmS8z0nMz\ncT2ElH9jGFaO76WXKcuy4RdTN+RXPfZEVKKlOoAaldGZUvIT7XvfAi00VugpmzbrlOl9mAQPxrQE\nypiRNN51Nx3PHpBTzbAbuecb5dx65mDy0g6l0UW3fY6aTKLJDCME9Ojsxcc8jxFnziEWDtFaVdnv\npOlXkTnsDlDAWhZk/z8O1HRYDVruXTCMt743g2K3hbte+ZyLn1iHqMpoEw1oLAo2zelTim/KnAoq\nGB0JMiNedjT6efzDCs57ZDXzH13LX9ZWMTLXwfWjHAhaBdWjYrOeeD2h0wUDY9hV6GgM94RhqtrC\nrK1o45sTcml76CG0BXk0Zr6JnBDRBmZSNPrIvFxIPZnffvttdDodZ511ZIOr1Wq59NJLKSkp4aMN\nG8kYfzaNmzPoMIYOKt8tfPFFtE4ntddf3yMc5q2vxWQLI4eMaAwnhxHTjW7NGEiJHbmULOLDFdLa\nOkCRyY018rd11QAMnpCB3qTli9VHrkTt9tiHakMEtQK27AgOyY6g6T3MVThqHIMnTuHzDzfxyT8L\n6djhJjJLYXPLD2l57U9Etm9nX+GHJEtUqj7IxZ0xlWt/+zjnLLmF0knTuOa3jzP90qvYv2kDf/3B\nzWxe/i/Shn0XQwIaW5aRdullxPfsoai1GlkE1ShStc3Lqhd289pDW/jr3Wt5+gereeXBTeglMKTp\nUSIRAro6UCDYaDxmw3YscNrLELUq4hWTsM2ZQ8uvHqD14Uf6xYyJfPopYbMWc2YUU/L4qHaFI0Zj\n96TGGDr12Pq7GlwjcMSspJUG2Pf+oQJj5dl2lt44ld8sHkVrMIYz6cPsTnn2zoLTp7BHY3CgjYkY\nnAnSkz4ueOxjfvvOHuxGHb9YNIKNPz2HZ66dSLoawp6eABFs6X3blf9UDIhhl5IKiqz2MGJe3FCD\nVhRY2LqN+N696K7LI2CO0PhpBjO++b1+jbl3714qKyuZPXs2Foulz+11Oh2XXXYZubm5VAajtDUO\nQQ5pqah7HFVOVTfq83IpfPEFDCUl1N1yK/433qBq61bMZj+akKmPbzh+fNmwA7hdZyBbwRjaiUan\nZ4a5g+fX1xCKS+j0GoZOzqLys9aeBOnhEAsnUYFxspeIQ4M+LYHTVHbEeWh1Ohb+8F5ufvoF5t3y\nQ4zyRdStLEDOUtmqf5h31lwNYxL4t+cw58pHufBH9x8UFtDqdEy5+HKufegJ8sqGserZZ3jhp3dh\nT46gwxhCN8mNYDYTefUVil0WgkaR1pogFVtaUWSFguHpjF1QxK4SPX+xxRg/t4jI5s0kBikkOww4\nPYX9ZoOcCGQUpXjnvvYt5D7yMM5LL6X9ySdpvu/+nlqI3hDZtInoJBeiTsWVdnyMEkEUmXH5NYyf\nvxBHxrHTDnM952GwJ2mztxKvrDzkc1EUuHRCPivvnM1dE+2YPDGQwTX1muOZ/gmHWTFjcCRwqhHu\nOm8oa+4+k1dunsbVUwpJ7wqF+VtbsDlTDyZb9qnv0XqqMDCGPZG6+D35NmJJmaWb61lUaCL26MMY\nxg+nNm0N0TYDGekLySzuu2WVJEm88847uN1uJk3q/81iMBi48sorcblchDJLqNmST8go0bz5pz3b\naF0uCp79O+YJE2i8+0dUrXofoylAmubkCx919z+Nd4lwpQ9ZAqpKojRBTn4hueF6AjGJlz5NSXsO\nn5mDIqns/qT3StRQIMUqGRJqgAIZQQBnZv8ucJPVRvnMMzn/9h9x9f+8S4FwG0JEwF4WRLPfzsJb\nVx6RR+7MzOLCH/8PF9z5E6LhEGuXK6CqNFc9imP+fAIrVjA6TcM7TonrfzeDJQ/N4uK7JzD64kH8\nqqqBt30BfnbFaBaMziG4diWJYpXOBgtlM844pbFeZ9F5qAqEEzUIGg1ZP/8fXDfdiG/pUhpu/8Eh\nomlqIkF0xw46X3qZ6NatxEtTD968Udcd91zKZ8xm9jXfPq4xPCPuAAkMI6M0vvhir9ulWfTkCwEs\nnhi6VgFdWu5xfe+Jhk2ficGRQJXj3Dw5h/z0Q0NhAW8rZmcEMQ7GjL6p0/+pGDDDrjNocHhMLP+8\nCV8kydWfLkWJREheFiZpUGn8JIcZl1/X51iJRIK1a9fS0dHBeeedh0bTdyz+yzCZTFx99dU4nE6a\n1KlE201Udiw7qGJSY7WS9cdH2XnWTJrcCqKokpl78tkA3QnU7ji73jkIW9hAbLhClsVOuKWeWRkq\nf1lbRVJWcOVaySqxs3Nt70nU9vYYUUHF6a3GnBMFBezFFx/13DRaHWXnfZczZi6nqGoSMy77oF9U\nQ0EQKJ00jUvu/T/iARG11UGT9AX2xQtRYzHOqNlEtS+KpE0Z6rqOCIv/tI6K1hBPXzuBRWNTxqSj\n8l3QQajJfErDMACi3oYa1CM5Wumo2owgCGTcfjuZP7mH4HvvUfft7+Bbtoymn/+cqksuZc/4CVRf\nvJjm++9HNBpRsoIkOvVYMk9ulWx/obVk4Yi5cQ4KUrn6/V6L9AC8Nfsxu6NYgn2vik81LJYSNHqF\nRLpIbOfOQz5XFYWAtwW9O44poDutNNhPNAbIsCu4860IosDzG2q4MLQX/doPsd04jwbzHjr2Ohg6\n/oqe+CGkaIwVFRVs2LCB5cuX8+yzz/L73/+eX/3qV6xatYohQ4ZQeozNdG02G9dccw0ms5WKminE\n9Sr161MhIFmWee/VV/jdg79ie0YOZmeXol3escU0jwb5+alwRuWXlsdu22iShSpZkVY0Wi1nK3tp\n8EVZ/nmK6tidRG3c6+uav4K3LsjOtY2semE37VUBooKK7G3EkhnFGDUcFY3qqzBkDWbQDf9Aazu6\nFUx6Ti5jzp1P1RYHCR2E5eUYhw+nZMP7oKrsaQ6yuznAxX9aR2ckyQtLpnDm0NT1IHm9hG1eVBUs\nhmGkZeUc8/yPFUOzrkJvT7Jpy9Uk/akOVOnXXEPOb39DZMsWmn56L4E330K0WEi/9hpyH3mYQe+/\nx6DV7yO6oxA4eTUQx4LCwkvRGmU6h8QIffBBr9t1Nm9HY1KwnwbNn78KizOlMinliDTddz9K5GBm\nVcjXgSwl0WRIWHEPxBRPGU5991kgmZRx59vY0ehnX0Uj/7NxKfryMrzFb6PIIu2fF3L+by7t2X7/\n/v0sW7aMcBcdS6/X43a7KSoqwuVy4Xa7j9mod8PpdHLd9dfz5BNP0NGWTUX6x+xbUUZUMiGJesrG\narALCnaTj5CqYs4++VKfdrud3Nxcdu/ezaxZqe9zF15KVfWnRKOfUjb9KvasX0PZiOE8uXo/C8fk\nMHh8BmuX7mP1S3vRGTS01YV6GlcYzFoSNg0Vfj/ThATmjCgu9dR3lu/GlMXfZOeaD1DCjdTHXqH4\nsnuJ3Xc/ZYU1/GNjPu/tbMas17L0pqkMyTxATQt/8gmRoSqxdgNlU3tn85xMFEz9KfX/aCDsepdP\nPprHlFnL0TsH4Tj/fExjx4GURFdQcIhX2LHrOUSdikUcuPN+OLiG3Yz63p8RR8dpf/ll7N/4xiHb\nJKIRVGNKmTW99PQr7DFnToVWEPNFEh9W0fLAA2T/4hc9nwdaW9HbkwgGFbujfABnevIxIIZdVVLx\n9WfX13LLjjfQR4IYb5xBtf5zGtdlMOEbV2O0WlEUhTVr1rBq1SpcLhcXXnghmZmZWK3WkxJT9Xg8\nXPHNy3np5QA5BXvQaeNohRhp+ggOcxAFCUlU8CRcaIxpJ/z7D4eysjI++OAD/H4/DocDW+EFaHfe\nRTjbz5gz57Djo/dZbG3gl7WZrK1oY3qhg0GWJvY2pZExKJ2Rs3PJKLTjKbRhdxv55tMbKAg2ESsE\ns1Yl3Tn9lBzH4WCy2phy0RXs+qIacWIbQ8vciGYzC2s38OstRZS4LTx7w6RDaIOhj1cjnaUQ2m1m\nyDUnf+XUGyZf8ihLH7wI16SdbFk3j3HT3kLvLEWf13vsuXH/m2AC9xHopQMBUWfGHs1FLaqn7tmt\nZG7ejGncuJ77TJaSbH13BSZPFCRwjjs6GdlTAUP6KJBAtURIX7KEjqefxjJ1KvZ58wh1dlC1dTMm\nV9eKu/i/N3EKA2TYAcyZRur++h7X1Gwi/aZvsU99ioRPT7xlKGPOW0AkEuHVV1+loqKCkSNHsmDB\nAgwGw0mfV8mQoUwun8T6dRKjRoxkzuVXYDhJMrD9Qbdh37NnD5MmTULQaHGGXbQPa8fm95E3bAT+\nL9aQmfNN3nzpfXI3vEjWvgqyAJvuXHJvfwhBm/qZH3p3DxuqOrhO6yNYlEpgO4pPbL/Wo8WY8+ax\nfdXLqEobjfsfwX7++cxY9irr5l7NI0um4rIe/JurqkpH9SoEHRiVfKxpA9e9R6PVMnPxg7z11K0M\n/kYtW9bNZ9zUN9Gn9S4s5YvvJRbTkzl54B5IvWFQ+fVsbf45/ikaaq68Cv3gQdjmzacxy8WnK98h\n4G2hbF4EfauIxmgf6OkeAkGjRRPVobPF6Rw8juqN5Wx+4veEXnuBcCCVM8ubkGL0WAtPrwfricaA\nxNh1Bg1r9jfwnU0voxQWExq+gbhBpXZNNvNuvZumlhb+/Oc/U1VVxfz587noootOiVHvxlkXX8Y9\nv3uEBd9aMqBGHVKrCJfLxe7du3vey8g5G9UMbZ89z7h5Cwm2e7mj9g2uef4XxHwB8p96MpXIe/dd\nmu67H1VReH59DX9cWcHlE/MZFmlGm5+AkBZD2pGpjicbGq2O6Zd+l0CtlbrkThwXL0ArJfm9bi/p\npkP9jkRFBZ15qZBcSfnRJ31PNHLLhlE0ZBGVbxcS0UlsWX8+8c7dB22jygnC9Stp3fK/xC0hws0W\n0rJPfV6gL6SXXYUc1CKMjZNx373UW00se/c1PnjpWURvG2dPnIHJHcMWO7n1G8cDg2zD6Eyw/LHf\nsVNIEtZpcQXCzL7qei7/+W/It/nRdwinbMU9UBgQjz0ty0zsiUdxxQLk3PtdtsTup7PCzsjpS6jz\nBXj35Vew2+1cf/315OYODKXqdCmVhpTX/sknnxCNRjGZTHjG3gqfvExHZCOl4hLMikq4cRcfFk+m\n7rIl/GbWdJg1CzkQpO2xx6hLiNynnczZZZn8ctEIvvjTbizzoxgjx540PZEYPGEK21cNRi3aSmfk\ndcwTJ9L2x8fofO55rGecgfWss7BMn47GaiG8bh2RMpA79Az5xqK+Bz8FmHnFdVTcsYHOzW6E8ZvY\nsv58csxTCUX3E1baCOsSKF2NTVQNqL5cxKNkb50KCKKIJVqMmLWP9z79C9EhcTw2PTl6CUNrO7H2\nnagmsNsH1hk4Ejz2PCJ8ziU/vY/MwSOIr1pFwx134ppSi2fBhez7QsLqO30ba5woDIhhj/gCTA+t\nwTtvMVHfw8h6AVqnEChIZ/3bbzN06FAWLVqEyXTyi4D+E1BWVsbHH3/Mvn37GDVqFFprHqYmLUGP\nn7qrrqa4JI8dVhOxeZezbI+f23xRcp0m3LfeQlN9C/Z/LeX2yUmW/O8vEYIBOvXtaE0yaeLpQbcT\nBIFpi37BZzsvZE/768x+cj2h1asJrlxJcNUq/K+/jqDTYZ4yhURrA5pbkqiNHowWa9+DnwKY7Q5m\nXXkd7/75UYqGXk7Q+k8q5I8xABZs5FGC1TIci2cq/3zoFfLL++4nMFAoHf1dPm+4Dc+k6p73ol1/\nAJq4StaMmwZiav2CxT4EQp8TavwhhuRZpE27Esfii2l/6im0xU7kdLD4Tg8N+ZOJgUmeNjfRWD6W\nIZeY2Bv10b45i7K51/P6W8uZMGEC8+fPP6085oFGbm4uVquV3bt3M2pUij+fLg2ioWgP0o1pzLzi\nOfbd/V2K2rciMJK/rK3iZwuGUekNcY1xGt8vrWfOhteJ/XMY6pAhxEpBD2QNPvHdn44VWaXDEFfn\nohbX4K3+gMy552OfOxdVkohs2UJo5YcEV66kwdKEqFPJTju5uutHixFnnMMXH77P+uXVXP3LFZjM\nhh4dGFVVadq3h09XrSLYFsBdUDSwkz0CMsoXMN5oR6dNotFZEXUWRL0NUWtF1NuOKD1xOiC99Fu4\nP3mPVl09Tf7nYMtzWKZpEC1agmsfgHlgHeDm26cCwtG0VDtRGGE0qd994ElKB/0fMUkkL/1h3t6w\nDYfDwZIlS9BqByyne9rizTffZPv27dx1113odDrkWIAd756D19pOvlxKff0itr33NvXn3sG/90f4\n163T+dZfPyUhKyz79iTEX9xL8L33ME0Yz44J29ANiTFn7p7TqkijvWI1n1V/C6kml7k3rD7kcymZ\n4P1nvoFuaDVThi3DknV6GXdvbTXP//g2hs06i/Nuuo2Oxnp2rV3F7rUf4avCDQAAGaZJREFU4Wtp\nQqvTUzJhMnOW3IrRenqsNv5boSQjBKv+RWfjW3RGduDTBXvCYZPcT2Ibdc4Az/DYIAjCZlVVJ/S1\n3YBY0Eari1F5S4laJYyVs9nib0KSJBYvXvy1Ue8FZWVlbN68maqqKoYMGYLGaGfk/HXs+2ABddp9\npLv/hoqbmdJeliWyueCxtWhFkX9+ZwoFGXaUh35H/U03E163Ds2FCnKH5bQy6gCuwbPQbLEjZTaz\n/YM3UdHR0dhAZ2M9nU0N+L2NFJ3rRRvUnnZGHcBTUMS4eQvZ9OartOyvwFtThSCI5I8YxeSLLqN0\n0jQM5v/++O7pAFFnxjHkChxDrqAIUBIhGv/xM3zrN2D9w8mvQRloDIgVdaUbCDt2Ea+zYRl0JTWr\nV7Nw4ULc7v/uarDjQXFxMXq9nt27dzNkSKrBh6DRUnrOCvSrr6bSsp7h50fY/9G7nDH+NtZV+3nm\nmvGMyE0xGES9nrzH/sj+712DLu0zhODhZY0HGuUll7Mj8BSfrf8VUkSDOT2BNStB/tAYhdYkiJCd\nOLauQ6cC0xZfQfXWzQiiyBlX30DZtFlY00+PJPX/nyHqreRd+zB51w70TI4MORAnXh3ANNJ9XOHo\ngXGPk01ADnnFd/H6h2sYOXIkY8acfh7Y6QStVktpaSl79uxBURTELm9bEEWKZr+Afv2d7PK8Ru7s\nHdxs3sO9F1xCaebBjQREsxntdUMh+RkOR5+ruQFB5tgfsPfdp8mdkuoUJSgqpqQGi+rAIuRhtpbh\nLr91gGfZO3RGI9f+7vGBnsbX+A+E1B7F+/R2ZF8c02gP6YuHIOiObVU9IIZd1MuYO4fyYWMrTqfz\n62RpP1FWVsaOHTtoaGjo0ZHpRs6Uh9BtzWCb8jS+8G8YHs0i1paLKsVQ5SiKFEWRo7R4V6JYBTJK\nFw7QURwZgkbPuOGPEWnbhMUzGVPWdETd1+GLr/HfjaQ3QtvT21ElBev0HEIfN+L1x3FdPQyN5ej7\n+A6Mxy6L1CuXEgzWcP3112M0GgdkGv9pKC0tRRRFdu/efYhhB/CM+RHp/wriNb7Exv296Ng7Idxg\nxj2t9+rIgYa1YC7Wgv/uysCv8TW6kWwO431mOwCe74xCl2VBX2CnY+kevH/ahvu64WjdR0f9HpDs\nmagrYteeKs466yzy8k6f9lonC6qikqgPIvkO36C5vzAajRQXF7Nr165eZXlHzbuP2reH07mlEKpG\noa0bj6FpGlbvmTh93yD42Vi8n5Zhsv739nv8Gl/jPwWJhhDepz4HUegx6gDm0R48S0aiRJK0PrGV\neE3gqMYdEI89EAhQUlLCtGnTBuLrTwmUhEx8n4/oznZiuztQwqmWdLpsC8ZhLkzl6ehyj17MrKys\njOXLl9PW1obH4znkc61ez6QF32HDa0tpjkVIRKOoysHypSXjJh77gX2Nr/E1TgjitQHa/rID0ajB\n8+2RaF0He+WGIgcZt4yh7W878D79OemX9n+VPSA89vz8fHXnzp3YbCfPa1TiEvHqAMn6EIYSB/oi\n+wmP46uqCpKKEpdQEwpKTCJRHyS2s4NYhQ8kBcGowTg0HVNZOnIwQXRnO4maAKgg2vWYytMxlrvQ\n51kRLbo+5xgIBPj973/P2WefzcyZfQtJqaqKlEyQjEZJRKMkYlEcGVlf0+6+xtcYQMSr/bT9dQei\nVYdnyUi0ab2Ho+VwkvbndpKoDpD/61mnL4/dZXKibUwi5ybQWE9MJZuSkEnUBIjv9xOv9JGoD4Jy\n4HN9vg3rrDxMw10I4tEZeFVWSTaGiO/3Eav0I7VGUOIyalwG5dAHoybNgHVyFsZyF4ZiO4LmQMTL\nNisPOZwktruD2M52Ip+1Et6QamUnGLXoPCa03X9uM7pMM1qPqcfgf1mjvT+GXRAEdHoDOr0Bs+P0\nau7w3wo5nARFTT2oj/Ja+0+CqqoooSSSN0LSG0UJJhD0GgSDBtGgOei1xmFAY+vfvS4H4kS2eYl+\n0Y5o0mIocWAY5ESXbRmQ86lEUqtt0dy/JKaaVIjXBkjUBlBCyZStSKTsRbfdSHqjaNMMeJaMROM4\nssChxqLDc8NIOl7Z2+85D4jHPiq7TF1x7dMAaJwG9LlWdHk29HlWdDnWfmWBu09ejyGvC4Ksggj6\nPBuGEieGQQ50OVai270E1zQgt8fQuoxYZ+ZiGZ+JoDu8EJOSkJG8UeL7fcQr/cSr/CkjDmgzTOhz\nbQjGrovXoEHUaxAMWgSDJmWYM839Xh2oSYV4tZ9kSwTJG0HyRkm2RVECB9qTad0mzGM8mMdmoHWZ\nWL16NStXruSOO+7Abj/95FNPNVRZRY1LBCIBrDY7WsPRswhOBBKNIYKr64l+7k05FSJorHpEux6N\n3YDGrkdj16PPs6EvsiPqTy8hMMkXJ7qtlejODlDVAwbaoO251hEF5I4Yya5rtfu+6A+0HhOGQc6U\noS5xHOTUKTGJ6BftRLa2Eq/0gZoKW6pJBaktpVQjGLU9+xpKHOgyzQc5TScKSiRJvCrQc/8nm1Nq\nopp0I/o8K/pcG7o8K/pcK6JRiyopJOqDKVux30e8JghdzW0OetB96b/Gpsc+p7DfDztI5epEjdgv\nj/2EGnZBEP4fUA6sUFX1l71tN2H8ePXjpR+QqA+RaAiRrA8itR9ILIpmLVq3Ca0n5a3q3CkPVolK\nqZNX6SNe23XyBNDlWnsMuaHIjmg4dCGiKirRHW0EVzeQrAsiWrSYx2eBAEoggRyIIwcSyIHEQRer\n1m3q8hgcGEqcR/VDdENSJKJSFLPWjEbs382sxCUkb5REQ4joNi/xKj+oqZVHpFTH39YtZf78+Uyc\nOLDxclVVUWMy6mFWLgCCRkAwaHoedFEpSm2glqpAFUk5SXl6OcWO4l7Pi6qqyP44ya5rJdEQQgkm\nUBIyakxGjicRpIP3iWkTyFYBk9OCJc2J1qFHtOp79fYEnYgu24Iuy4Kg7d1QqKpKpa+Sjc0b2dSy\nCUVVGJ4+nPGRYWRvNyPvDyHoNVgmZaF1G3uuJzmQSF1jwThKuGuyGiHlgHRdV4ZCW6+OxuEgdcRS\n98F+P3I4idZlROcxo3EbaTC0siG8mc2tm1NzdA1nuGs4w1zDcBoPXrUpUYno9raUQe26xnS5VkST\ntsu7lA7yNFFB49Cn7s2u+1LX9VrjMKAmD/ZMu/9LbV2OUpUfNZEyetpMM8ZBzlSIclcHSAqadGPK\niRmTgS4jFS6U/XHi+/3Euo+3o8tWiALadGPPClfXZS80TgNKRDpw3gNx5GDX7xCVEPSHGlrRoEEO\nJYnv95NsDIEKaEUMRXYMJQ4QBZINIRL1QeTOA83KNS4jSiCBmkwdky7b0rPCMBQ7EA8jPa2oCpFk\nBIvOclTh4aScRK/Vn1rDLgjCRcAFqqpeJwjCE8DDqqruO9y248aPU99Z8w7hZJiwFCaSjBANhlGb\nY4heGV2nisGvwRLQY44dbEhVVNrtIdoygviy48SyQW8xIikSESmSGrPrL5KMEJWj2PV2MkwZeMwe\nMkwZ5HSkk75Vg1ARRRVBtkDcLBM2RPHpQni1nbTq2vG6gyhWEYvOgkVnwawzY9FZ0AgaIskIYenA\n9xz0vV+aRyQZISanLkSdqKPAVkCRo4giexHFjmKKHEUU2ApQUQ8dSwoTl+IYtUbsMTNplTpMe1TE\n1iRL9Z9gM1qZUzgVyQySESSDiqRTSGoV0Aro9fpD/nQ6LXEpTpvfiy/QQSDkIxQOEA6HiEeiiBoN\nJosFi9mK1ebAaU0jzZGOy+pGDKoo7THkthhyewy5PY7cHkON9e61KajENAk6dSG8mk68Qid+MUxI\nEyEqJEBQ0Iga3OZ0MqwZ5FhzyLPn4oibkRqjSI1h1EiXMRQFNB4jQVOU5mQrtbF6OhUfMU2cNLuL\nvLQ8IsEwoU4/xqiOdMmOR07HKdnQqP9fe/ceI1d53nH8+5y5z+ysd9eXBRsMTqFURYGmoam5JRsk\nBChNiyoUUpmmQFvaKKmURI0SK4QQIdKqVSxaFGhJ3ECSNoWmStMKaEtLAEuURM6tJU2iWoltasf1\nrnc9s5eZObenf5zjvTE7Hts7c2Znn480mss5M/Pbnfc8c+Y9l3flgh2ieAS4jk9jRPGGBR3OIENZ\nZrN1Ds8d4n+mD/CDyg+Z8ibxHZ8tpc38wvRl7Dx6OVvdLZxIVdg/8kOmtrqMDpzHQG4AJ+2QyqQW\nrjMOaXUYmSwx/H95CkcdMuOKKJAS0tuKhBvSBEWlUQio5z3m8g2mczWC0GfoeJ7BYzkKRyE1HS23\nUkzjDoRMT03TCDzmpMEcDaadGidTM7iOj49PKAGhhDhph2KhyGBhkE2zg2yYyJLRNKlyhszPlim/\ncZThbZubftGqKmEQUtf6fPuf8+aY8+eY8+bwQo98Ok8pHS0rxUyRUjpadlKkcF2XRq1B7UiV2uGT\n1I5WqU3MIimheNEQpUs3UrhgkFwuF7XVXIbp2WkmKhNMViapVCtMV6epTc3iVVwKYYaBoEDZyzEw\nl6MUZCmSI69ZHJYWTCmmccoZnEIadcP5rpFTt1EIUxBuzcG2HOFolmA4hRd4uA2XIFxo49oICE+6\nBFMNwoqLk0/jnJcjHE0R5BVXXFxxqVNnYnaCiekJJmcnqcxWqNaqzNXmol90KRjID1AulhkqDTFS\nGmFjeSOlbInxk+NMnZyiOlOlNlPDr/lIQ9j7kb1dL+x/Dvyzqj4jIrcBZVX9fLN5CzsKesn9l5z2\nNYvpIhudEXb4F7DdOw/X8Xi1eIBxJucL6HIpSc0X4FK6RD6dp+pWOT53nEbQWDJvLszgio/Kwv8g\nn8qzpbiFwewgNb+2pHgH+voCVkwXX1f4TzXsxY8X00VO1E9wsHKQg9WDvFZ9DV/9171eO7Y3zuea\n8V/G83rrp7zpHblUlpKTxwkFL/Rw1cfTAF/a7zrpRSEh9VSdRqpBJsxQCAqkdB0sB2lI5VPc9+H7\nur7xtAQciW9XgSWVW0TuAe4B2HTRJj6+8+NLvtGXFMZMiUK6gCOt+89CDaPi682SkhSlTIlcKtf0\n542qMu1NMz43zvG544zXxpmqTzGSH5lfk99c3MxApvkuiKpKI2gw680SaNB2xpX4oc+RmSMcrBzk\n8PRhHFn4ZbD4iyGfzlP36wtr8/EXTWW6wtRPpqKujpSQ1TR5P03Oz1DwsmR8hyAM40uAH18HYQiO\nQzaTJZfNk8vlyecKpDIpJOWgCvghrudSb9RoNOq4bh3P8/AyAW4uwMsFhKmFL0NByKQyZJ1sdJ3K\nknWiSz6TZ7g0PL8WNr82Rhq8ENd1cT0X13WpNWr8tHqMo9WjzIZzhE7zlY5NhU1cWL6QtNNe81VV\nZv3ZFbuLVJQwFRKmQgInwMODuYDcSSg18mwsbCLn5HADP1qD8z3cwCM1mKW0bYhcfunf5uIy687i\nuV70f3M9XNfFd31c18ULPBphAzeIbruhixu6BEFAOpUm62TJhRlyfoaMnybjpRAV/EHFLQS48XO8\nwMMNXHLZHJedfxk7tuygXC4zMDBAJtN8O0MYhDRmakxMjjMRTjJdn2GmFl1qjRq1eo16o77icRKt\nPmtHHLzQi75IAndJRh8fJ+0gGYmu0wvXAOoroR+iXnwd38/lcpQGSpTLZYYGhxguD1PMRitKbugy\n485Qma1wsnKS6nSVmZkZ6nMr529FRJbkk7TgZOLbK220VchKlqxGl3SYJh2mcUIHJ3QYzA9SKpSW\ntI9cLkc6ncbzonbRaDTmr2dqMzT8BpuHNrNhcAPlcplyuUw2G/Vc3Pfh+9r7W1Zxjf3PgC+r6itx\nt8zPqeqnms171VVX6f79+1flfY0xZr1o97S9q7lJ+VvAdfHtK4GDq/jaxhhj2rSaXTH/AOwTka3A\nLcDOVXxtY4wxbVq1NXZVrQJjwCvA21W1slqvbYwxpn2reuSpqk4BT63maxpjjDkzvTU2mjHGmHNm\nhd0YY/qMFXZjjOkzVtiNMabPJHJ2RxGpAd9fYfIGYKU9alZ7WjffyzJaxk5l3A4c7vGMa+H/uBYy\nXq6qpx8nT1W7fgHGW0x7rFvTuvleltEydjBj15anXvlfreOMK37Wiy9JdcWcbDHtn7o4rZvvdbbT\neiVHq2m9kqPVtF7J0Wra2b5eN5enXvlftZrWKzlaTevEZz0vqa6Y/drG+Q6MMadny9P60e5nndQa\n+2MJva8x/ciWp/Wjrc86kTV2YzpFREaANwPfUdWJpPOczlrL2+9E5HzgcuAbqjqddJ6z1RO7O4rI\nXhF5WUTuFZENIvKsiDwnIl8VkdUZ7bpDlmXfISJPi8g+Efl00tnaJSKjIrJv2WP/KCJvSipTO5q0\nlYuAp4G3AF8Xkc0JR2wpLiJL8i5uTwnHa0uzvCLyiIi8M8lc7Vrc9kXkCuBJ4FrgxV6vPa0kXtjj\nc7enVPUaYCtwJ7BHVW8EjgE3JxivpSbZ9wAPqOr1wAUiMpZkvnaIyDDwBNFAKace2wX8WFW/k1iw\n9uxiaVu5Evigqj4I/Avwi0mGa8PlLM17A4vak4hcmmi601je/kXkUhG5HjhPVVttAOwJTdr+zwN3\nqeongR8DO5LKdq4SL+xEZ4Q8deKw54Gqqj4X398MHE8iVJvGWJr9BuDb8f3jRPuj9roAuJ1o1KtT\nXQOfBqZE5O1JBjsdVX1keVvRaKCXtxKtBf9HculOT1X/bVnem1janq5b8cm9YYyleceAzwIHReTX\nEsp0Jpa0fVX9W+CQiLwDGAYOJJjtnPRCYV8+pN4ogIhcDQyr6itJBWvD8ux/Cnwi/hl6M/DvSQVr\nl6pWdekplj8I/B3wl8B7RORXk0nWvsVtRaJxDW8HPKIFt6ctyys0WRZ62PL2vwX4b+BPgLeIyB8k\nFawdTdo+wADwLmASWLMbIHuhsM8Ap46kGgCceK3xYeDuxFK1Z3n2EHgW+B3gCVWdSSrYOXgT8BlV\nPUa0NjaWbJzWlrcVjbwPeBn4lSSztWNZ3p0sWxYSC9ae5e3/AaKDa44BXwJ6+hdfM6p6UlV/C6gD\nv5R0nrPVCw1n+ZB6/0tUUHar6qHEUrWn2XCA3yU6xHtPQpnO1QHgDfHtq4Ce/QzijVvzbUVEPiIi\n74knD9HmwRxJaZL3j1lbw0sub//3skbaTjMi8mjcLQZroP20kvjujiIyCOwj6ra4BfgL4H7ge/Es\nj6rqk8mka61J9p3Ah4ADqvrFJLOdKRF5QVXH4qENP0e0fWAO+PVe3e1LRN4LfIpFbYXo11IOeBV4\nnybdwFuIN949xULe3cBLLGpPTboKesYK7f9zRF1IGeA2VT2y8iv0hkVtfwfwRaIumH9V1QcSjnbW\nEi/sMN/AbwRein/GrRlrObvpPWutPa21vOtFTxR2Y4wxq6cX+tiNMcasIivsxhjTZ6ywG2NMn7HC\nbowxfcYKuzHG9Bkr7MYY02essBtjTJ+xwm6MMX3GCrsxxvQZK+zGGNNnrLAbY0yf6UphF5H7ReSO\nbryXMf0oXoZ+ICIvxJf3rzDPWALxTI9JJx3AGNO2B1X1S0mHML2vq4VdRJ4B8sAhVb1LRO4nOm/z\ndUTn/77ZTv1pzOmJSBH4AtFwdP8Vj8IE8CER+STwU2CXqvb88IBm9XWzj30H8BmiE/JfLCKnxnO8\nRFXfBvwN0WDQxpjmPhZ3wzwC3AO8qqpvBc4XkSviefbHy1MFeGdSQU2yOrbGLiLvBo6p6gvxQz7R\n6DZ3ASMsjJX4hfj6OJDtVB5j+sB8V4yIPApcE/epDwHb4nm+EV9/G/iZric0PaGTa+xF4Nr49g6i\ntfGvAL8BzC6abxZjzJn6EfCQqo4RjTV6OH78zfH1FfT+mKmmQzpZ2J8ErhWRF+P7DxCN6fh8fH9b\n02cZY9rxWeAWEXkJ+H3gtfjx6+NlbhT4WlLhTLJsaDxjjOkzdoCSMcb0GSvsxhjTZ1a9sIvIBhF5\nVkSeE5GvikhWRPaKyMsicu+i+UZFZN+i+9vjXbmeF5HHRERWO5sxxqwHnVhj3wXsUdUbgWPAu4GU\nql4DbBWRS0VkGHgCKC163u8B71XVG4ALgTd2IJsxxvS9jm48FZGvAINEu2U9IyK3AWXg7wEBvhbv\nrrX8ed8EblXVox0LZ4wxfapjfewicjUwTLQb1pH44SowqqpVVa2s8Lzbge9bUTfGmLPTkcIuIiPA\nw8DdwAwLR5kOtHpPEXkD8IfABzqRyxhj1oNObDzNAk8Bu1X1EPAtopN8AVzJCkfDxf3uXwbuXmlt\n3hhjzOl1Yo39t4kOa/6YiLxA1Jf+myKyB3gX8PQKz/sosB14ON475m0dyGaMMX2vK0eexmvjNwIv\n2Wl5jTGms+yUAsYY02fsyFNjjOkzVtiNMabPWGE364aIPC4i3xWR/SLyuy3mGxORi7uXzJjVZYXd\nrDfvB24CPrFoOLnlxoCLuxXImNVmhd2sO6p6gmi321vjXWtfFJEHAUTk88CdwEMi8tfxY6Pxie1e\nFpHdiQU3pk1W2M16dQJQomHl3kE88LOq3gU8DnxAVXfF8+4GnoxPZHeriGzsflxj2texwayN6XEj\nROcx+ijRaS/KLea9DLhaRO4kOiPpVqIvBmN6khV2s+6IyBBwC/BN4I+A7wH/uWiWGtFg7MTjAvyI\n6EykXxeRO4DJ7iY25szYAUpm3RCRx4nOV9QAHmKhK2Yc2ALcpKpHROQS4K+ALHAH0Rr9XmAI+Alw\np6r6Xf8DjGmTFXZjjOkztvHUGGP6jBV2Y4zpM1bYjTGmz1hhN8aYPmOF3Rhj+owVdmOM6TNW2I0x\nps/8P63K91tg3+CQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c0ccc4358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes.iloc[:50, :].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目时间\n",
    "### 2018七月在线 机器学习集训营 julyedu.com \n",
    "下面我们通过一个做股票分析的小项目带着大家熟悉一下pandas表格拼接和处理的操作。可参考以下博客文章。\n",
    "\n",
    "[stock analysis](https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2004-08-19', '2004-08-20', '2004-08-23', '2004-08-24',\n",
       "               '2004-08-25', '2004-08-26', '2004-08-27', '2004-08-30',\n",
       "               '2004-08-31', '2004-09-01',\n",
       "               ...\n",
       "               '2017-07-07', '2017-07-10', '2017-07-11', '2017-07-12',\n",
       "               '2017-07-13', '2017-07-14', '2017-07-17', '2017-07-18',\n",
       "               '2017-07-19', '2017-07-20'],\n",
       "              dtype='datetime64[ns]', name='Date', length=3253, freq=None)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goog = pd.read_csv(\"data/GOOG.csv\", index_col=0, parse_dates=[\"Date\"])\n",
    "goog.reindex(pd.to_datetime(goog.index))\n",
    "goog.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0cb953c8>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fnFK59s2lTH5nOS/M2wLgtgKpJvZgb4mC60dkOMpXPziWC7rGEm2JIikummhL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\nRAAJ9kIIEQEk2AshRASQYC+EEBHg/wGBiSMihoWkBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c0cba3668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "goog[\"Adj Close\"].plot(grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0c2a25f8>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fnFK59s2lTH5nOS/M2wLgtgKpJvZgb4mC60dkOMpXPziWC7rGEm2JIikummhL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\nRAAJ9kIIEQEk2AshRASQYC+EEBHg/wGBiSMihoWkBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c0ef3c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "goog[\"Close\"].plot(grid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```shift```这个function可以帮我们移动时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "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>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\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>2004-08-20</th>\n",
       "      <td>49.813286</td>\n",
       "      <td>51.835709</td>\n",
       "      <td>47.800831</td>\n",
       "      <td>49.982655</td>\n",
       "      <td>49.982655</td>\n",
       "      <td>44871300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>50.316402</td>\n",
       "      <td>54.336334</td>\n",
       "      <td>50.062355</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>22942800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.168217</td>\n",
       "      <td>56.528118</td>\n",
       "      <td>54.321388</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>18342800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>55.412300</td>\n",
       "      <td>55.591629</td>\n",
       "      <td>51.591621</td>\n",
       "      <td>52.239193</td>\n",
       "      <td>52.239193</td>\n",
       "      <td>15319700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close      Volume\n",
       "Date                                                                         \n",
       "2004-08-19        NaN        NaN        NaN        NaN        NaN         NaN\n",
       "2004-08-20  49.813286  51.835709  47.800831  49.982655  49.982655  44871300.0\n",
       "2004-08-23  50.316402  54.336334  50.062355  53.952770  53.952770  22942800.0\n",
       "2004-08-24  55.168217  56.528118  54.321388  54.495735  54.495735  18342800.0\n",
       "2004-08-25  55.412300  55.591629  51.591621  52.239193  52.239193  15319700.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goog.shift(1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>Adj 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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>49.813286</td>\n",
       "      <td>51.835709</td>\n",
       "      <td>47.800831</td>\n",
       "      <td>49.982655</td>\n",
       "      <td>49.982655</td>\n",
       "      <td>44871300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>50.316402</td>\n",
       "      <td>54.336334</td>\n",
       "      <td>50.062355</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>22942800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>55.168217</td>\n",
       "      <td>56.528118</td>\n",
       "      <td>54.321388</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>18342800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.412300</td>\n",
       "      <td>55.591629</td>\n",
       "      <td>51.591621</td>\n",
       "      <td>52.239193</td>\n",
       "      <td>52.239193</td>\n",
       "      <td>15319700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.284027</td>\n",
       "      <td>53.798351</td>\n",
       "      <td>51.746044</td>\n",
       "      <td>52.802086</td>\n",
       "      <td>52.802086</td>\n",
       "      <td>9232100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2004-08-19  49.813286  51.835709  47.800831  49.982655  49.982655  44871300\n",
       "2004-08-20  50.316402  54.336334  50.062355  53.952770  53.952770  22942800\n",
       "2004-08-23  55.168217  56.528118  54.321388  54.495735  54.495735  18342800\n",
       "2004-08-24  55.412300  55.591629  51.591621  52.239193  52.239193  15319700\n",
       "2004-08-25  52.284027  53.798351  51.746044  52.802086  52.802086   9232100"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goog.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "datetime indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "2004-08-19         NaN\n",
      "2004-08-20    0.076433\n",
      "2004-08-23    0.010013\n",
      "2004-08-24   -0.042289\n",
      "2004-08-25    0.010718\n",
      "Name: log-return, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "goog[\"log-return\"] = np.log(goog[\"Adj Close\"] / goog[\"Adj Close\"].shift(1))\n",
    "print(goog[\"log-return\"].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0c222198>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IRgO4AcB6InrFpMwUg20JS+OaVcwLMRHz5vUd3RaB0WHq433cFsFT3NWzOTo2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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c0c297f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "goog[\"log-return\"].plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Empty 'DataFrame': no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-106-454380685462>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0maapl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"data/AAPL.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0maapl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maapl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0maapl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Adj Close\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   2451\u001b[0m                            \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2452\u001b[0m                            \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2453\u001b[0;31m                            **kwds)\n\u001b[0m\u001b[1;32m   2454\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m   1892\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1893\u001b[0m                  \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1894\u001b[0;31m                  **kwds)\n\u001b[0m\u001b[1;32m   1895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m   1692\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1694\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1695\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1696\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    241\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m             raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 352\u001b[0;31m                             'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
     ]
    }
   ],
   "source": [
    "aapl = pd.read_csv(\"data/AAPL.csv\", index_col=0)\n",
    "aapl.index = pd.to_datetime(aapl.index)\n",
    "aapl[\"Adj Close\"].plot(grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怎么回事？出错了？我们尝试着一起来debug一下看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl[\"Adj Close\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for i in range(aapl[\"Adj Close\"].shape[0]):\n",
    "#     print(i, float(aapl[\"Adj Close\"][i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'null'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl[\"Adj Close\"][165]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aapl[\"Adj Close\"][aapl[\"Adj Close\"] == \"null\"] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1980-12-12    0.423252\n",
       "1980-12-15    0.401170\n",
       "1980-12-16    0.371726\n",
       "1980-12-17    0.380927\n",
       "1980-12-18    0.391969\n",
       "Name: Adj Close, dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl[\"Adj Close\"].bfill().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "aapl[\"Adj Close\"] = aapl[\"Adj Close\"].apply(lambda x: float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c06c3c2e8>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pfT3TlXXFGDNElS9pv0l8409Pi7nNHy+utCI8AFmL3ZhBzh8M88aOBg47sL3r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\nbowxg4wVdmOMGWT+H9anWyQpTvIOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c06c50a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aapl[\"Adj Close\"].plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "msft = pd.read_csv(\"data/MSFT.csv\", index_col=0)\n",
    "msft.index = pd.to_datetime(msft.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stocks = pd.DataFrame({\"AAPL\": aapl[\"Adj Close\"].bfill(),\n",
    "                      \"MSFT\": msft[\"Adj Close\"].bfill(),\n",
    "                      \"GOOG\": goog[\"Adj Close\"].bfill()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>0.423252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>0.401170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>0.371726</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>0.380927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>0.391969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL  GOOG  MSFT\n",
       "Date                            \n",
       "1980-12-12  0.423252   NaN   NaN\n",
       "1980-12-15  0.401170   NaN   NaN\n",
       "1980-12-16  0.371726   NaN   NaN\n",
       "1980-12-17  0.380927   NaN   NaN\n",
       "1980-12-18  0.391969   NaN   NaN"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c0c31ecc0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nfyK29+DMxXBgbL1sdkbzV26iuGf0RP7Ee+t54r31AJxy8F6cMzBym0076xn/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c0c1ec630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stocks = pd.to_numeric(stocks)\n",
    "stocks.plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_stocks = stocks[stocks.index >= stocks[\"GOOG\"].first_valid_index()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c06f36f28>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2lVSiKQweyvfa7gecB/cdgE7udKkzPoJek6D/ORDf3fvczPlNP+Y6osMlaDQaTVXKHtRq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c06fb0b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_stocks.plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    float64\n",
       "GOOG    float64\n",
       "MSFT    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2lVSiKQweyvfa7gecB/cdgE7udKkzPoJek6D/ORDf3fvczPlNP+Y6osMlaDQaTVXKHtRq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c06f3b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_stocks.plot(grid=True)\n",
    "valid_stocks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c06ef32b0>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+b1Cf8zRJxN5D8AlHl8fLS87AzN4UEzNXJvqPNLgJKGmsZGT/PjGVIRbDzEPT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\nR9M+OAc/hQSyiy7mes9nGK0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DNxJJxRMAOLzH4YbHHy0t49M/fer//blnDN94DuWmYwdx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\nPJO8eXp7FgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c06fa3ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_stocks_lr = np.log(valid_stocks / valid_stocks.shift(1))\n",
    "valid_stocks_lr.plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c06dd0cf8>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAIBmECRBTVUn0lKrddfnAAAAsOgESVBTstoGAABAQwmSoKa8FxwtVUFSMcurAQAAgOkR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uKRtrTibdcOr4GA4IHVB7BursPDIS5sFLoesyo6kmZSJ9rGiCR23KiyiLZNIm4kLS7aNgGRFLgA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M1Z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fSDIyuL7LqwuvIhBszLZe4PcKD1z/AB++8cOr+llTNfEDeeNGRNJo2uu9P10IuPMn5GLj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\nHST1qpQQi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c06b497b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_stocks_lr[valid_stocks_lr.index > pd.to_datetime(\"2017-01-01\")].plot(figsize=(20, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>1.981210</td>\n",
       "      <td>49.982655</td>\n",
       "      <td>18.360205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>1.987016</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>18.414366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>2.005080</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>18.495850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>2.061206</td>\n",
       "      <td>52.239193</td>\n",
       "      <td>18.495850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>2.132171</td>\n",
       "      <td>52.802086</td>\n",
       "      <td>18.706341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       GOOG       MSFT\n",
       "Date                                      \n",
       "2004-08-19  1.981210  49.982655  18.360205\n",
       "2004-08-20  1.987016  53.952770  18.414366\n",
       "2004-08-23  2.005080  54.495735  18.495850\n",
       "2004-08-24  2.061206  52.239193  18.495850\n",
       "2004-08-25  2.132171  52.802086  18.706341"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c06b5b320>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e34UDEuyFEH1d+aFe/3AWJNgLIfqS4n2Qt6HutdbSshdCiF7nw1/CM6fBx/ea9WO95eCv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\nexGRHFCzFxHJgT9YZOm2GILGrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c06b67a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_stocks = valid_stocks.resample(\"M\").last()\n",
    "monthly_stocks.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2004</th>\n",
       "      <th>8</th>\n",
       "      <td>2.225070</td>\n",
       "      <td>50.993862</td>\n",
       "      <td>18.536587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.499898</td>\n",
       "      <td>64.558022</td>\n",
       "      <td>18.774242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.380507</td>\n",
       "      <td>94.964050</td>\n",
       "      <td>18.991516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.325631</td>\n",
       "      <td>90.650223</td>\n",
       "      <td>20.288960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.154669</td>\n",
       "      <td>96.035034</td>\n",
       "      <td>20.220860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAPL       GOOG       MSFT\n",
       "Date Date                                \n",
       "2004 8     2.225070  50.993862  18.536587\n",
       "     9     2.499898  64.558022  18.774242\n",
       "     10    3.380507  94.964050  18.991516\n",
       "     11    4.325631  90.650223  20.288960\n",
       "     12    4.154669  96.035034  20.220860"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_stocks = valid_stocks.groupby([valid_stocks.index.year, valid_stocks.index.month]).last()\n",
    "monthly_stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2004-8', '2004-9', '2004-10', '2004-11', '2004-12', '2005-1', '2005-2', '2005-3', '2005-4', '2005-5', '2005-6', '2005-7', '2005-8', '2005-9', '2005-10', '2005-11', '2005-12', '2006-1', '2006-2', '2006-3', '2006-4', '2006-5', '2006-6', '2006-7', '2006-8', '2006-9', '2006-10', '2006-11', '2006-12', '2007-1', '2007-2', '2007-3', '2007-4', '2007-5', '2007-6', '2007-7', '2007-8', '2007-9', '2007-10', '2007-11', '2007-12', '2008-1', '2008-2', '2008-3', '2008-4', '2008-5', '2008-6', '2008-7', '2008-8', '2008-9', '2008-10', '2008-11', '2008-12', '2009-1', '2009-2', '2009-3', '2009-4', '2009-5', '2009-6', '2009-7', '2009-8', '2009-9', '2009-10', '2009-11', '2009-12', '2010-1', '2010-2', '2010-3', '2010-4', '2010-5', '2010-6', '2010-7', '2010-8', '2010-9', '2010-10', '2010-11', '2010-12', '2011-1', '2011-2', '2011-3', '2011-4', '2011-5', '2011-6', '2011-7', '2011-8', '2011-9', '2011-10', '2011-11', '2011-12', '2012-1', '2012-2', '2012-3', '2012-4', '2012-5', '2012-6', '2012-7', '2012-8', '2012-9', '2012-10', '2012-11', '2012-12', '2013-1', '2013-2', '2013-3', '2013-4', '2013-5', '2013-6', '2013-7', '2013-8', '2013-9', '2013-10', '2013-11', '2013-12', '2014-1', '2014-2', '2014-3', '2014-4', '2014-5', '2014-6', '2014-7', '2014-8', '2014-9', '2014-10', '2014-11', '2014-12', '2015-1', '2015-2', '2015-3', '2015-4', '2015-5', '2015-6', '2015-7', '2015-8', '2015-9', '2015-10', '2015-11', '2015-12', '2016-1', '2016-2', '2016-3', '2016-4', '2016-5', '2016-6', '2016-7', '2016-8', '2016-9', '2016-10', '2016-11', '2016-12', '2017-1', '2017-2', '2017-3', '2017-4', '2017-5', '2017-6', '2017-7']\n"
     ]
    }
   ],
   "source": [
    "index = [ str(i[0]) + \"-\" + str(i[1]) for i in monthly_stocks.index.values]\n",
    "print(index)\n",
    "index = pd.core.indexes.period.PeriodIndex(index, freq=\"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "monthly_stocks.index = index #= monthly_stocks.index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08</th>\n",
       "      <td>2.225070</td>\n",
       "      <td>50.993862</td>\n",
       "      <td>18.536587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-09</th>\n",
       "      <td>2.499898</td>\n",
       "      <td>64.558022</td>\n",
       "      <td>18.774242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-10</th>\n",
       "      <td>3.380507</td>\n",
       "      <td>94.964050</td>\n",
       "      <td>18.991516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-11</th>\n",
       "      <td>4.325631</td>\n",
       "      <td>90.650223</td>\n",
       "      <td>20.288960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12</th>\n",
       "      <td>4.154669</td>\n",
       "      <td>96.035034</td>\n",
       "      <td>20.220860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             AAPL       GOOG       MSFT\n",
       "2004-08  2.225070  50.993862  18.536587\n",
       "2004-09  2.499898  64.558022  18.774242\n",
       "2004-10  3.380507  94.964050  18.991516\n",
       "2004-11  4.325631  90.650223  20.288960\n",
       "2004-12  4.154669  96.035034  20.220860"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c06bce390>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uYvnsIeg/DW78nBUzX4RJP9QfpJe+oLtQ6gJvc3JX6+2W9xqXu/J1kB81H276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\nAW/2zjnXBv4DnKULPmeNaRYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c06b8b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_stocks.plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "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>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-08</th>\n",
       "      <td>49.653461</td>\n",
       "      <td>269.469971</td>\n",
       "      <td>22.714565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL        GOOG       MSFT\n",
       "2011-08  49.653461  269.469971  22.714565"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_stocks[\"2011-8\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 读入NVDA.csv中的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 请计算nvda股票Adj Close每天的log return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 请找出nvda股票涨幅最大的10天"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 将这十天的股票信息都输出来"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 把英伟达每个月最后一天的股价记录下来，做成一张新的月线图，然后把Adj Close画成一张图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用concat方法把英伟达的Adj Close与其他三只股票拼接在一起"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 从Google有数据的那一天起开始画出四只股票的Adj Close折线图"
   ]
  }
 ],
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