mych07.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#  数据规整化：清洗、转换、合并、重塑"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  数据库风格的DataFrame合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame,Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = DataFrame({'key':['b','b','a','c','a','a','b'],\n",
    "                'data1':range(7)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = DataFrame({'key':['a', 'b','d'], 'data2':range(3)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
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       "  </thead>\n",
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       "      <td>2</td>\n",
       "      <td>a</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key\n",
       "0      0   b\n",
       "1      1   b\n",
       "2      2   a\n",
       "3      3   c\n",
       "4      4   a\n",
       "5      5   a\n",
       "6      6   b"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data2</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key\n",
       "0      0   a\n",
       "1      1   b\n",
       "2      2   d"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
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       "      <td>6</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      "text/plain": [
       "   data1 key  data2\n",
       "0      0   b      1\n",
       "1      1   b      1\n",
       "2      6   b      1\n",
       "3      2   a      0\n",
       "4      4   a      0\n",
       "5      5   a      0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
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       "      <th>4</th>\n",
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       "      <td>a</td>\n",
       "      <td>0</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0      0   b      1\n",
       "1      1   b      1\n",
       "2      6   b      1\n",
       "3      2   a      0\n",
       "4      4   a      0\n",
       "5      5   a      0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 如果两个对象的列名不同，也可以分别进行指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = DataFrame({'lkey':['b','b','a','c','a','a','b'],\n",
    "                'data1':range(7)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df4 = DataFrame({'rkey':['a','b','d'],\n",
    "                         'data2':range(3)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   data1 lkey\n",
       "0      0    b\n",
       "1      1    b\n",
       "2      2    a\n",
       "3      3    c\n",
       "4      4    a\n",
       "5      5    a\n",
       "6      6    b"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>rkey</th>\n",
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       "  </thead>\n",
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      "text/plain": [
       "   data2 rkey\n",
       "0      0    a\n",
       "1      1    b\n",
       "2      2    d"
      ]
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead tr:only-child th {\n",
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       "      <th></th>\n",
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       "      <th>lkey</th>\n",
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       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>b</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
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       "      <td>a</td>\n",
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      "text/plain": [
       "   data1 lkey  data2 rkey\n",
       "0      0    b      1    b\n",
       "1      1    b      1    b\n",
       "2      6    b      1    b\n",
       "3      2    a      0    a\n",
       "4      4    a      0    a\n",
       "5      5    a      0    a"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df3, df4, left_on='lkey',right_on='rkey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 默认情况下，merge做的是“inner”连接，结果中的键是交集。外连接取的是键的bingji："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>d</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0    0.0   b    1.0\n",
       "1    1.0   b    1.0\n",
       "2    6.0   b    1.0\n",
       "3    2.0   a    0.0\n",
       "4    4.0   a    0.0\n",
       "5    5.0   a    0.0\n",
       "6    3.0   c    NaN\n",
       "7    NaN   d    2.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 多对多的合并操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = DataFrame({'key':['b','b','a','c','a','b'],\n",
    "                'data1': range(6)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = DataFrame({'key':['a', 'b', 'a','b','d'],\n",
    "                'data2': range(5)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   data1 key\n",
       "0      0   b\n",
       "1      1   b\n",
       "2      2   a\n",
       "3      3   c\n",
       "4      4   a\n",
       "5      5   b"
      ]
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     "execution_count": 28,
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    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "   data2 key\n",
       "0      0   a\n",
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       "2      2   a\n",
       "3      3   b\n",
       "4      4   d"
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    "df2"
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  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "df11 = pd.merge(df1, df2, on='key', how='left')\n",
    "# 多对多连接产生的是行的笛卡尔积，由于左边的df有3个‘b’行，右边的有2个，所以最终结果中有6个‘b’行\n",
    "# 连接方式只影响出现在结果中的键"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "      <th>6</th>\n",
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       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "    data1 key  data2\n",
       "4       2   a    0.0\n",
       "5       2   a    2.0\n",
       "7       4   a    0.0\n",
       "8       4   a    2.0\n",
       "0       0   b    1.0\n",
       "1       0   b    3.0\n",
       "2       1   b    1.0\n",
       "3       1   b    3.0\n",
       "9       5   b    1.0\n",
       "10      5   b    3.0\n",
       "6       3   c    NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.sort_values('key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "   data1 key  data2\n",
       "6      2   a      0\n",
       "7      2   a      2\n",
       "8      4   a      0\n",
       "9      4   a      2\n",
       "0      0   b      1\n",
       "1      0   b      3\n",
       "2      1   b      1\n",
       "3      1   b      3\n",
       "4      5   b      1\n",
       "5      5   b      3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how='inner').sort_values('key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  要根据多个键进行合并，传入一个由列名组成的列表即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left = DataFrame({'key1':['foo','foo','bar'],\n",
    "                 'key2': ['one','two','one'],\n",
    "                 'lval':[1, 2, 3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "right = DataFrame({'key1':['foo','foo','bar','bar'],\n",
    "                  'key2':['one','one','one','two'],\n",
    "                  'rval':[4,5,6,7]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  key1 key2  lval\n",
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   "execution_count": 50,
   "metadata": {
    "collapsed": false
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    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2  lval  rval\n",
       "0  foo  one   1.0   4.0\n",
       "1  foo  one   1.0   5.0\n",
       "2  foo  two   2.0   NaN\n",
       "3  bar  one   3.0   6.0\n",
       "4  bar  two   NaN   7.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on=['key1', 'key2'], how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 在进行列-列连接时，DataFrame对象中的索引会被丢弃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": 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>key1</th>\n",
       "      <th>key2_x</th>\n",
       "      <th>lval</th>\n",
       "      <th>key2_y</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>one</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>one</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>one</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2_x  lval key2_y  rval\n",
       "0  foo    one     1    one     4\n",
       "1  foo    one     1    one     5\n",
       "2  foo    two     2    one     4\n",
       "3  foo    two     2    one     5\n",
       "4  bar    one     3    one     6\n",
       "5  bar    one     3    two     7"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": 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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2_left</th>\n",
       "      <th>lval</th>\n",
       "      <th>key2_right</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>one</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>one</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>one</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2_left  lval key2_right  rval\n",
       "0  foo       one     1        one     4\n",
       "1  foo       one     1        one     5\n",
       "2  foo       two     2        one     4\n",
       "3  foo       two     2        one     5\n",
       "4  bar       one     3        one     6\n",
       "5  bar       one     3        two     7"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key1', suffixes=('_left', '_right'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 索引上的合并"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 有时候连接键位于索引中，此时可以传入lefty_index=True或right_index=True（或两个都传）以说明索引应该被用作连接键："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left1 = DataFrame({'key':['a','b','a','a','b','c'],\n",
    "                  'value':range(6)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "right1 = DataFrame({'group_val':[3.5, 7]}, index={'a', 'b'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  value\n",
       "0   a      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   a      3\n",
       "4   b      4\n",
       "5   c      5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": 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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group_val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group_val\n",
       "a        3.5\n",
       "b        7.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "      <td>3.5</td>\n",
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       "      <th>3</th>\n",
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       "      <td>3.5</td>\n",
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       "      <th>1</th>\n",
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       "      <th>4</th>\n",
       "      <td>b</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "1   b      1        7.0\n",
       "4   b      4        7.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "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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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>b</td>\n",
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       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "1   b      1        7.0\n",
       "4   b      4        7.0\n",
       "5   c      5        NaN"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lefth = DataFrame({'key1':['Ohio','Ohio','Ohio','Nevada','Nevada'],\n",
    "                  'key2':[2000, 2001, 2002, 2001, 2002],\n",
    "                  'data': np.arange(5.)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "righth = DataFrame(np.arange(12).reshape((6,2)), index=[['Nebada','Nevada','Ohio','Ohio','Ohio','Ohio'],\n",
    "                                                       [2001,2000,2000,2000,2001,2002]],\n",
    "                                                       columns = ['event1','event2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data    key1  key2\n",
       "0   0.0    Ohio  2000\n",
       "1   1.0    Ohio  2001\n",
       "2   2.0    Ohio  2002\n",
       "3   3.0  Nevada  2001\n",
       "4   4.0  Nevada  2002"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefth"
   ]
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  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "      <th></th>\n",
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       "      <th>event1</th>\n",
       "      <th>event2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nebada</th>\n",
       "      <th>2001</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <th>2000</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Ohio</th>\n",
       "      <th>2000</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
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       "      <th>2002</th>\n",
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       "      <td>11</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "             event1  event2\n",
       "Nebada 2001       0       1\n",
       "Nevada 2000       2       3\n",
       "Ohio   2000       4       5\n",
       "       2000       6       7\n",
       "       2001       8       9\n",
       "       2002      10      11"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "righth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>Ohio</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Ohio</td>\n",
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       "      <td>11</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   data  key1  key2  event1  event2\n",
       "0   0.0  Ohio  2000       4       5\n",
       "0   0.0  Ohio  2000       6       7\n",
       "1   1.0  Ohio  2001       8       9\n",
       "2   2.0  Ohio  2002      10      11"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(lefth, righth, left_on=['key1','key2'], right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ohio</td>\n",
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       "      <th>1</th>\n",
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       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Nevada</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Nebada</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data    key1  key2  event1  event2\n",
       "0   0.0    Ohio  2000     4.0     5.0\n",
       "0   0.0    Ohio  2000     6.0     7.0\n",
       "1   1.0    Ohio  2001     8.0     9.0\n",
       "2   2.0    Ohio  2002    10.0    11.0\n",
       "3   3.0  Nevada  2001     NaN     NaN\n",
       "4   4.0  Nevada  2002     NaN     NaN\n",
       "4   NaN  Nebada  2001     0.0     1.0\n",
       "4   NaN  Nevada  2000     2.0     3.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(lefth, righth, left_on=['key1', 'key2'],\n",
    "        right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left2 = DataFrame([[1., 2.], [3., 4.], [5., 6.]], index=['a','c','e'],\n",
    "                 columns=['Ohio', 'Nevada'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "right2 = DataFrame([[7., 8.], [9., 10.],[11., 12.],[13., 14.]],\n",
    "                  index=['b','c','d','e'],\n",
    "                  columns=['Missouri','Alabana'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   Ohio  Nevada\n",
       "a   1.0     2.0\n",
       "c   3.0     4.0\n",
       "e   5.0     6.0"
      ]
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    "left2"
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  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <tbody>\n",
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       "      <th>b</th>\n",
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      "text/plain": [
       "   Missouri  Alabana\n",
       "b       7.0      8.0\n",
       "c       9.0     10.0\n",
       "d      11.0     12.0\n",
       "e      13.0     14.0"
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    "right2"
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  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>b</th>\n",
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       "      <th>c</th>\n",
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       "      <th>d</th>\n",
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       "      <th>e</th>\n",
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       "      <td>14.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabana\n",
       "a   1.0     2.0       NaN      NaN\n",
       "b   NaN     NaN       7.0      8.0\n",
       "c   3.0     4.0       9.0     10.0\n",
       "d   NaN     NaN      11.0     12.0\n",
       "e   5.0     6.0      13.0     14.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left2, right2, how='outer', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "####  join方法，更方便的实现按索引合并，它还可用于合并多个带有相同或相似索引的DataFrame对象，而不管它们之间有没有重叠的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>14.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabana\n",
       "a   1.0     2.0       NaN      NaN\n",
       "b   NaN     NaN       7.0      8.0\n",
       "c   3.0     4.0       9.0     10.0\n",
       "d   NaN     NaN      11.0     12.0\n",
       "e   5.0     6.0      13.0     14.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left2.join(right2, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DataFrame的join方式是在连接键上做左连接\n",
    "# 它还支持参数DataFrame的索引跟调用者DataFrame的某个列之间的连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  value  group_val\n",
       "0   a      0        3.5\n",
       "1   b      1        7.0\n",
       "2   a      2        3.5\n",
       "3   a      3        3.5\n",
       "4   b      4        7.0\n",
       "5   c      5        NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left1.join(right1, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "another = DataFrame([[7., 8.], [9., 10.], [11., 12.], [16., 17.]],\n",
    "                   index=['a','c','e','f'], columns=['New York', 'Oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "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>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabana</th>\n",
       "      <th>New York</th>\n",
       "      <th>Oregon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabana  New York  Oregon\n",
       "a   1.0     2.0       NaN      NaN       7.0     8.0\n",
       "c   3.0     4.0       9.0     10.0       9.0    10.0\n",
       "e   5.0     6.0      13.0     14.0      11.0    12.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left2.join([right2, another])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": 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>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabana</th>\n",
       "      <th>New York</th>\n",
       "      <th>Oregon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabana  New York  Oregon\n",
       "a   1.0     2.0       NaN      NaN       7.0     8.0\n",
       "b   NaN     NaN       7.0      8.0       NaN     NaN\n",
       "c   3.0     4.0       9.0     10.0       9.0    10.0\n",
       "d   NaN     NaN      11.0     12.0       NaN     NaN\n",
       "e   5.0     6.0      13.0     14.0      11.0    12.0\n",
       "f   NaN     NaN       NaN      NaN      16.0    17.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left2.join([right2, another], how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 轴向连接"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumPy用于合并原始数组的concatenation函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr = np.arange(12.).reshape((3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   1.,   2.,   3.],\n",
       "       [  4.,   5.,   6.,   7.],\n",
       "       [  8.,   9.,  10.,  11.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   1.,   2.,   3.,   0.,   1.,   2.,   3.],\n",
       "       [  4.,   5.,   6.,   7.,   4.,   5.,   6.,   7.],\n",
       "       [  8.,   9.,  10.,  11.,   8.,   9.,  10.,  11.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([arr, arr], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 假设有三个没有重叠索引的Series："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s1 = Series([0,1], index=['a', 'b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s2 = Series([2, 3, 4], index=['c', 'd', 'e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s3 = Series([5, 6], index=['f', 'g'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对这些对象调用concat可以将值和索引粘合在一起"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "e    4\n",
       "f    5\n",
       "g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2,s3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 默认情况下。concat的axis=0，最终产生一个新的Series\n",
    "#### 如果axis=1， 则结果就会变成一个DataFrame(axis=1是列)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": 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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2\n",
       "a  0.0  NaN  NaN\n",
       "b  1.0  NaN  NaN\n",
       "c  NaN  2.0  NaN\n",
       "d  NaN  3.0  NaN\n",
       "e  NaN  4.0  NaN\n",
       "f  NaN  NaN  5.0\n",
       "g  NaN  NaN  6.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 传入join='inner即可得到他们的交集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s4 = pd.concat([s1 * 5, s3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": 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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  1\n",
       "a  0.0  0\n",
       "b  1.0  5\n",
       "f  NaN  5\n",
       "g  NaN  6"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.concat([s1, s4], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": 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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   0  1\n",
       "a  0  0\n",
       "b  1  5"
      ]
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.concat([s1, s4], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可以通过join_axes指定要在其他轴上使用的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "     0    1\n",
       "a  0.0  0.0\n",
       "c  NaN  NaN\n",
       "b  1.0  5.0\n",
       "e  NaN  NaN"
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     "execution_count": 32,
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   ],
   "source": [
    "pd.concat([s1, s4], axis=1, join_axes=[['a', 'c',  'b', 'e']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 想在连接轴上创建一个层次化索引。可以使用keys参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = pd.concat([s1, s1, s3], keys=['one','two', 'three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    a    0\n",
       "       b    1\n",
       "two    a    0\n",
       "       b    1\n",
       "three  f    5\n",
       "       g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "         a    b    f    g\n",
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       "three  NaN  NaN  5.0  6.0"
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    "result.unstack()"
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   "cell_type": "code",
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    "collapsed": false
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    {
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       "      <th>g</th>\n",
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       "   one  two  three\n",
       "a  0.0  NaN    NaN\n",
       "b  1.0  NaN    NaN\n",
       "c  NaN  2.0    NaN\n",
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       "e  NaN  4.0    NaN\n",
       "f  NaN  NaN    5.0\n",
       "g  NaN  NaN    6.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3], axis=1, keys=['one', 'two', 'three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   one  two\n",
       "a    0    1\n",
       "b    2    3\n",
       "c    4    5"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = DataFrame(np.arange(6).reshape(3, 2), index=['a', 'b','c'],\n",
    "               columns=['one', 'two'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   three  four\n",
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     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2 = DataFrame(int(5) + np.arange(4).reshape(2, 2), index=['a', 'c'],\n",
    "               columns=['three', 'four'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
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       "      <th>c</th>\n",
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       "      <td>5</td>\n",
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      "text/plain": [
       "  level1     level2     \n",
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      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.concat([df1, df2], axis=1, keys=['level1','level2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果传入的不是列表而是一个字典，则字典的键就会被当作keys选项的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>c</th>\n",
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      "text/plain": [
       "  level1     level2     \n",
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       "a      0   1    5.0  6.0\n",
       "b      2   3    NaN  NaN\n",
       "c      4   5    7.0  8.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat({'level1':df1,'level2':df2}, axis=1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = DataFrame(np.random.randn(3, 4), columns=['a','b','c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = DataFrame(np.random.randn(2,3), columns=['b','d','a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "          a         b         c         d\n",
       "0 -2.648690 -2.928283 -1.181355  0.393985\n",
       "1 -0.905464  1.770920  0.459076 -0.550185\n",
       "2  1.169514  1.220485 -2.010192 -0.368788"
      ]
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     "execution_count": 53,
     "metadata": {},
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   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>-0.914800</td>\n",
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       "      <th>1</th>\n",
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       "          b         d         a\n",
       "0 -1.934440 -2.336940 -0.914800\n",
       "1 -1.280028 -0.139215  0.149345"
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     "execution_count": 54,
     "metadata": {},
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   "source": [
    "df2"
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  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "      <td>-0.368788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.914800</td>\n",
       "      <td>-1.934440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.336940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.149345</td>\n",
       "      <td>-1.280028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.139215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0 -2.648690 -2.928283 -1.181355  0.393985\n",
       "1 -0.905464  1.770920  0.459076 -0.550185\n",
       "2  1.169514  1.220485 -2.010192 -0.368788\n",
       "3 -0.914800 -1.934440       NaN -2.336940\n",
       "4  0.149345 -1.280028       NaN -0.139215"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 改变行索引，使原来的行索引无意义，即不保留连接轴上的索引，产生一组新索引range(total_length)\n",
    "pd.concat([df1, df2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": 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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.648690</td>\n",
       "      <td>-2.928283</td>\n",
       "      <td>-1.181355</td>\n",
       "      <td>0.393985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.905464</td>\n",
       "      <td>1.770920</td>\n",
       "      <td>0.459076</td>\n",
       "      <td>-0.550185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.169514</td>\n",
       "      <td>1.220485</td>\n",
       "      <td>-2.010192</td>\n",
       "      <td>-0.368788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.914800</td>\n",
       "      <td>-1.934440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.336940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.149345</td>\n",
       "      <td>-1.280028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.139215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0 -2.648690 -2.928283 -1.181355  0.393985\n",
       "1 -0.905464  1.770920  0.459076 -0.550185\n",
       "2  1.169514  1.220485 -2.010192 -0.368788\n",
       "0 -0.914800 -1.934440       NaN -2.336940\n",
       "1  0.149345 -1.280028       NaN -0.139215"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 合并重叠数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = Series([np.nan, 2.5, np.nan, 3.5, 4.5, np.nan],\n",
    "          index=['f', 'e', 'd', 'c', 'b', 'a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = Series(np.arange(len(a), dtype=np.float64),\n",
    "           index=['f','e','d','c','b','a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    NaN\n",
       "e    2.5\n",
       "d    NaN\n",
       "c    3.5\n",
       "b    4.5\n",
       "a    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    0.0\n",
       "e    1.0\n",
       "d    2.0\n",
       "c    3.0\n",
       "b    4.0\n",
       "a    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b[-1] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    0.0\n",
       "e    1.0\n",
       "d    2.0\n",
       "c    3.0\n",
       "b    4.0\n",
       "a    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  2.5,  2. ,  3.5,  4.5,  nan])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(pd.isnull(a), b, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    NaN\n",
       "b    4.5\n",
       "c    3.0\n",
       "d    2.0\n",
       "e    1.0\n",
       "f    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:-2].combine_first(a[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = DataFrame({'a':[1., np.nan, 5., np.nan],\n",
    "                'b': [np.nan, 2., np.nan, 6.],\n",
    "                'c': range(2, 18, 4)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = DataFrame({'a':[5., 4., np.nan, 3., 7.],\n",
    "                'b':[np.nan, 3., 4., 6., 8.]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": 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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b   c\n",
       "0  1.0  NaN   2\n",
       "1  NaN  2.0   6\n",
       "2  5.0  NaN  10\n",
       "3  NaN  6.0  14"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": 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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b\n",
       "0  5.0  NaN\n",
       "1  4.0  3.0\n",
       "2  NaN  4.0\n",
       "3  3.0  6.0\n",
       "4  7.0  8.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": 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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b     c\n",
       "0  1.0  NaN   2.0\n",
       "1  4.0  2.0   6.0\n",
       "2  5.0  4.0  10.0\n",
       "3  3.0  6.0  14.0\n",
       "4  7.0  8.0   NaN"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.combine_first(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重塑和轴向旋转"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 重新排列表格型数据的基础运算，这些函数被称作重塑（reshape）和轴向旋转（pivot）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 重塑层次化索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. stack： 将数据的列旋转为行\n",
    "2. unstack：将数据的行旋转为列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = DataFrame(np.arange(6).reshape((2, 3)),\n",
    "                index=pd.Index(['Ohio','Colorado'], name='state'),\n",
    "                columns=pd.Index(['one', 'two','three'], name='number'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": 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>number</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "number    one  two  three\n",
       "state                    \n",
       "Ohio        0    1      2\n",
       "Colorado    3    4      5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 使用stack将列转成行，得到一个Series\n",
    "result = data.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state     number\n",
       "Ohio      one       0\n",
       "          two       1\n",
       "          three     2\n",
       "Colorado  one       3\n",
       "          two       4\n",
       "          three     5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 对于一个层次化的Series，可以使用unstack将其重排为一个DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": 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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>number</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "number    one  two  three\n",
       "state                    \n",
       "Ohio        0    1      2\n",
       "Colorado    3    4      5"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 默认情况下，unstack和stack操作的是最内层。传入分层级别的编号或名称即可对其他级别进行操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": 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>state</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state   Ohio  Colorado\n",
       "number                \n",
       "one        0         3\n",
       "two        1         4\n",
       "three      2         5"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 如果不是所有的级别值都能在各分组中找到，则unstack操作会引入确实数据\n",
    "s1 = Series([0,1,2,3], index=['a','b','c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s2 = Series([4, 5, 6], index=['c','d','e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2 = pd.concat([s1, s2], keys=['one', 'two'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one  a    0\n",
       "     b    1\n",
       "     c    2\n",
       "     d    3\n",
       "two  c    4\n",
       "     d    5\n",
       "     e    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": 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>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>one</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a    b    c    d    e\n",
       "one  0.0  1.0  2.0  3.0  NaN\n",
       "two  NaN  NaN  4.0  5.0  6.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### stack默认会滤出确实数据，因此该运算时可逆的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one  a    0.0\n",
       "     b    1.0\n",
       "     c    2.0\n",
       "     d    3.0\n",
       "two  c    4.0\n",
       "     d    5.0\n",
       "     e    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.unstack().stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one  a    0.0\n",
       "     b    1.0\n",
       "     c    2.0\n",
       "     d    3.0\n",
       "     e    NaN\n",
       "two  a    NaN\n",
       "     b    NaN\n",
       "     c    4.0\n",
       "     d    5.0\n",
       "     e    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.unstack().stack(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对DataFrame进行unstack进行操作时，作为旋转轴级将会成为结果中的最低级别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame({'left' : result, 'right':result + 5},\n",
    "              columns=pd.Index(['left', 'right'],\n",
    "                              name= 'side'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": 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>side</th>\n",
       "      <th>left</th>\n",
       "      <th>right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Ohio</th>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Colorado</th>\n",
       "      <th>one</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "side             left  right\n",
       "state    number             \n",
       "Ohio     one        0      5\n",
       "         two        1      6\n",
       "         three      2      7\n",
       "Colorado one        3      8\n",
       "         two        4      9\n",
       "         three      5     10"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": 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>\n",
       "      <th>side</th>\n",
       "      <th colspan=\"2\" halign=\"left\">left</th>\n",
       "      <th colspan=\"2\" halign=\"left\">right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "side   left          right         \n",
       "state  Ohio Colorado  Ohio Colorado\n",
       "number                             \n",
       "one       0        3     5        8\n",
       "two       1        4     6        9\n",
       "three     2        5     7       10"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.unstack('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": 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>state</th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th>side</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>left</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>left</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">three</th>\n",
       "      <th>left</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state         Colorado  Ohio\n",
       "number side                 \n",
       "one    left          3     0\n",
       "       right         8     5\n",
       "two    left          4     1\n",
       "       right         9     6\n",
       "three  left          5     2\n",
       "       right        10     7"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.unstack('state').stack('side')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将“长格式”旋转为“宽格式”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pd.load('ch07/ld')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据转换"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 移除重复数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DataFrame中经常会出现重复行：\n",
    "data = DataFrame({'k1':['one'] * 3 + ['two'] * 4,\n",
    "                 'k2':[1, 1, 2, 3, 3, 4, 4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": 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>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "1  one   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "4  two   3\n",
       "5  two   4\n",
       "6  two   4"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame的duplicated()方法返回一个布尔型Series，表示各行是否是重复行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### drop_duplicates方法返回一个移除了重复行的DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .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>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "5  two   4"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 这两个方法默认会判断全部列，也可以指定部分列进行重复项判断"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 假设只根据k1列过滤重复计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['v1'] = range(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": 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>k1</th>\n",
       "      <th>k2</th>\n",
       "      <th>v1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2  v1\n",
       "0  one   1   0\n",
       "3  two   3   3"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(['k1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 利用函数或映射进行数据转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = DataFrame({'food' : ['bacon', 'pulled pork', 'bacon','Pastrami','corned beef', 'Bacon','pastrami','honey ham','nova lox'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": 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>food</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bacon</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pastrami</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacon</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces\n",
       "0        bacon     4.0\n",
       "1  pulled pork     3.0\n",
       "2        bacon    12.0\n",
       "3     Pastrami     6.0\n",
       "4  corned beef     7.5\n",
       "5        Bacon     8.0\n",
       "6     pastrami     3.0\n",
       "7    honey ham     5.0\n",
       "8     nova lox     6.0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 假设想添加一列表示该肉类食物来源的动物类型\n",
    "# 先编写一个映射"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "meat_to_animal = {\n",
    "    'bacon' : 'pig',\n",
    "    'pulled pork':'pig',\n",
    "    'pastrami':'cow',\n",
    "    'corned beef':'cow',\n",
    "    'honey ham':'pig',\n",
    "    'nova lox':'salmon'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Series的map方法可以接受一个函数或含有映射关系的字典型对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['annimal'] = data['food'].map(str.lower).map(meat_to_animal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": 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>food</th>\n",
       "      <th>ounces</th>\n",
       "      <th>annimal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bacon</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pastrami</td>\n",
       "      <td>6.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacon</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "      <td>salmon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces annimal\n",
       "0        bacon     4.0     pig\n",
       "1  pulled pork     3.0     pig\n",
       "2        bacon    12.0     pig\n",
       "3     Pastrami     6.0     cow\n",
       "4  corned beef     7.5     cow\n",
       "5        Bacon     8.0     pig\n",
       "6     pastrami     3.0     cow\n",
       "7    honey ham     5.0     pig\n",
       "8     nova lox     6.0  salmon"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       pig\n",
       "1       pig\n",
       "2       pig\n",
       "3       cow\n",
       "4       cow\n",
       "5       pig\n",
       "6       cow\n",
       "7       pig\n",
       "8    salmon\n",
       "Name: food, dtype: object"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以传入一个能够完成全部工作的函数：\n",
    "data['food'].map(lambda x : meat_to_animal[x.lower()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['animal'] = data['food'].map(lambda x : meat_to_animal[x.lower()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": 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>food</th>\n",
       "      <th>ounces</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bacon</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pastrami</td>\n",
       "      <td>6.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacon</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "      <td>salmon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces  animal\n",
       "0        bacon     4.0     pig\n",
       "1  pulled pork     3.0     pig\n",
       "2        bacon    12.0     pig\n",
       "3     Pastrami     6.0     cow\n",
       "4  corned beef     7.5     cow\n",
       "5        Bacon     8.0     pig\n",
       "6     pastrami     3.0     cow\n",
       "7    honey ham     5.0     pig\n",
       "8     nova lox     6.0  salmon"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('annimal', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 替换值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = Series([1., -999, 2., -999., -1000., 3.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1    -999.0\n",
       "2       2.0\n",
       "3    -999.0\n",
       "4   -1000.0\n",
       "5       3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -999可能是一个表示缺失数据的标记值，要将其替换为pandas能够理解的NA值，我们可以用replace来产生一个新的Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1       NaN\n",
       "2       2.0\n",
       "3       NaN\n",
       "4   -1000.0\n",
       "5       3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace(-999, np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果希望一次传入多个值，可以传入一个由待替换值组成的列表以及一个替换值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999,-1000], np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果希望对不同的值进行不同的替换，则传入一个由替换关系组成的列表即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    0.0\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999, -1000], [np.nan, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 传入的参数也可以是字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    0.0\n",
       "5    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace({-999:np.nan, -1000:0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重命名轴索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 轴标签可以向Series中的值一样，通过函数或映射进行替换\n",
    "2. 轴可以就地修改，无需新建一个数据结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = DataFrame(np.arange(12).reshape((3, 4)),\n",
    "                index=['Ohio','Colorado','New York'],\n",
    "                columns=['one','two','three','four'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 跟Series标签一样，轴标签也有一个map方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'OHIO', u'COLORADO', u'NEW YORK'], dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.map(str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 可以直接赋值，就可以就地修改了\n",
    "data.index = data.index.map(str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": 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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OHIO</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLORADO</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW YORK</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "OHIO        0    1      2     3\n",
       "COLORADO    4    5      6     7\n",
       "NEW YORK    8    9     10    11"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果是想要创建数据集的转换版本，而不是修改原始数据，可以使用rename方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": 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>ONE</th>\n",
       "      <th>TWO</th>\n",
       "      <th>THREE</th>\n",
       "      <th>FOUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ONE  TWO  THREE  FOUR\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "New York    8    9     10    11"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index= str.title, columns=str.upper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### rename可以结合字典型对象实现对部分轴标签的更新 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": 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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>peekaboo</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OHIO</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLORADO</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW YORK</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  peekaboo  four\n",
       "OHIO        0    1         2     3\n",
       "COLORADO    4    5         6     7\n",
       "NEW YORK    8    9        10    11"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index={'HOIO': 'INDIANA'},\n",
    "           columns={'three':'peekaboo'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 离散化和面元（bin）划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 假设有一组人员数据，而你希望将他们划分为不同的年龄组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将其划分为“18-25”，“26-35”，“35-60”以及60以上几个面元"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bins = [18, 25, 35, 60, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cats = pd.cut(ages, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 返回的是一个特殊的Categorical对象，可以看做是一组表示面元名称的字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: 'labels' is deprecated. Use 'codes' instead\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.labels  # 标号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Categorical.describe of [(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Categorical.get_values of [(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.get_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 25]     5\n",
       "(35, 60]     3\n",
       "(25, 35]     3\n",
       "(60, 100]    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 区间开闭情况可以通过right=False修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[18, 26), [18, 26), [18, 26), [26, 36), [18, 26), ..., [26, 36), [61, 100), [36, 61), [36, 61), [26, 36)]\n",
       "Length: 12\n",
       "Categories (4, interval[int64]): [[18, 26) < [26, 36) < [36, 61) < [61, 100)]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(ages, [18, 26, 36, 61, 100], right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 可以自己设置面元名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grouip_names = ['Youth', 'YoungAdult', 'MiddleAged','Senior']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Youth, Youth, Youth, YoungAdult, Youth, ..., YoungAdult, Senior, MiddleAged, MiddleAged, YoungAdult]\n",
       "Length: 12\n",
       "Categories (4, object): [Youth < YoungAdult < MiddleAged < Senior]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(ages, bins, labels=grouip_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果向cut传入的是面元的数量而不是确切的面元边界，则它会根据数据的最小值和最大值计算等长面元"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.randn(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.48, 2.66], (-0.87, 0.31], (-0.87, 0.31], (1.48, 2.66], (0.31, 1.48], ..., (-0.87, 0.31], (-2.05, -0.87], (0.31, 1.48], (-0.87, 0.31], (-0.87, 0.31]]\n",
       "Length: 20\n",
       "Categories (4, interval[float64]): [(-2.05, -0.87] < (-0.87, 0.31] < (0.31, 1.48] < (1.48, 2.66]]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(data, 4, precision=2)  # 将数据分为4组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.randn(1000) # 正太分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cats = pd.qcut(data, 4) # 按四分位数进行切割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-2.978, -0.756], (-0.756, -0.0602], (-0.0602, 0.602], (-0.0602, 0.602], (0.602, 2.918], ..., (-0.756, -0.0602], (-2.978, -0.756], (-0.0602, 0.602], (-2.978, -0.756], (-0.756, -0.0602]]\n",
       "Length: 1000\n",
       "Categories (4, interval[float64]): [(-2.978, -0.756] < (-0.756, -0.0602] < (-0.0602, 0.602] < (0.602, 2.918]]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.602, 2.918]       250\n",
       "(-0.0602, 0.602]     250\n",
       "(-0.756, -0.0602]    250\n",
       "(-2.978, -0.756]     250\n",
       "dtype: int64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-1.292, -0.0602], (-1.292, -0.0602], (-0.0602, 1.168], (-0.0602, 1.168], (1.168, 2.918], ..., (-1.292, -0.0602], (-1.292, -0.0602], (-0.0602, 1.168], (-1.292, -0.0602], (-1.292, -0.0602]]\n",
       "Length: 1000\n",
       "Categories (4, interval[float64]): [(-2.978, -1.292] < (-1.292, -0.0602] < (-0.0602, 1.168] < (1.168, 2.918]]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以设置自定义的分位数（0-1之间的值，包含端点）\n",
    "pd.qcut(data, [0, 0.1, 0.5, 0.9, 1.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 检测和过滤异常值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值也叫孤立点和离群值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = DataFrame(np.random.rand(1000, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.822952</td>\n",
       "      <td>0.305272</td>\n",
       "      <td>0.598033</td>\n",
       "      <td>0.747639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.891071</td>\n",
       "      <td>0.881773</td>\n",
       "      <td>0.732910</td>\n",
       "      <td>0.119047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.553461</td>\n",
       "      <td>0.387108</td>\n",
       "      <td>0.233421</td>\n",
       "      <td>0.393985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.671267</td>\n",
       "      <td>0.055173</td>\n",
       "      <td>0.657021</td>\n",
       "      <td>0.542877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.021261</td>\n",
       "      <td>0.915287</td>\n",
       "      <td>0.458971</td>\n",
       "      <td>0.028686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.497803</td>\n",
       "      <td>0.771876</td>\n",
       "      <td>0.827811</td>\n",
       "      <td>0.940157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.901412</td>\n",
       "      <td>0.733529</td>\n",
       "      <td>0.922124</td>\n",
       "      <td>0.721472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.693455</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.872457</td>\n",
       "      <td>0.985984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.330219</td>\n",
       "      <td>0.639113</td>\n",
       "      <td>0.243929</td>\n",
       "      <td>0.771501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.794084</td>\n",
       "      <td>0.696750</td>\n",
       "      <td>0.725534</td>\n",
       "      <td>0.580586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.988459</td>\n",
       "      <td>0.555040</td>\n",
       "      <td>0.189406</td>\n",
       "      <td>0.920202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.398224</td>\n",
       "      <td>0.884291</td>\n",
       "      <td>0.211178</td>\n",
       "      <td>0.898072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.479344</td>\n",
       "      <td>0.497890</td>\n",
       "      <td>0.432494</td>\n",
       "      <td>0.547958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.158759</td>\n",
       "      <td>0.937759</td>\n",
       "      <td>0.352324</td>\n",
       "      <td>0.630257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.376726</td>\n",
       "      <td>0.792733</td>\n",
       "      <td>0.157629</td>\n",
       "      <td>0.783659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.330176</td>\n",
       "      <td>0.623384</td>\n",
       "      <td>0.168045</td>\n",
       "      <td>0.781852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.764479</td>\n",
       "      <td>0.777299</td>\n",
       "      <td>0.063609</td>\n",
       "      <td>0.465936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.923244</td>\n",
       "      <td>0.667921</td>\n",
       "      <td>0.917554</td>\n",
       "      <td>0.951361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.987056</td>\n",
       "      <td>0.101955</td>\n",
       "      <td>0.897381</td>\n",
       "      <td>0.200006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.159807</td>\n",
       "      <td>0.837541</td>\n",
       "      <td>0.453378</td>\n",
       "      <td>0.426799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.204622</td>\n",
       "      <td>0.951610</td>\n",
       "      <td>0.491157</td>\n",
       "      <td>0.588450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.773095</td>\n",
       "      <td>0.349290</td>\n",
       "      <td>0.187834</td>\n",
       "      <td>0.462360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.697931</td>\n",
       "      <td>0.457178</td>\n",
       "      <td>0.303124</td>\n",
       "      <td>0.761929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.804283</td>\n",
       "      <td>0.393852</td>\n",
       "      <td>0.540087</td>\n",
       "      <td>0.959902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.484109</td>\n",
       "      <td>0.952588</td>\n",
       "      <td>0.088975</td>\n",
       "      <td>0.706463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.232370</td>\n",
       "      <td>0.385174</td>\n",
       "      <td>0.876377</td>\n",
       "      <td>0.878568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.123252</td>\n",
       "      <td>0.102101</td>\n",
       "      <td>0.072329</td>\n",
       "      <td>0.118548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.095876</td>\n",
       "      <td>0.383800</td>\n",
       "      <td>0.744193</td>\n",
       "      <td>0.378601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.086249</td>\n",
       "      <td>0.568279</td>\n",
       "      <td>0.721645</td>\n",
       "      <td>0.215377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.331767</td>\n",
       "      <td>0.664377</td>\n",
       "      <td>0.578359</td>\n",
       "      <td>0.312672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>0.509567</td>\n",
       "      <td>0.550761</td>\n",
       "      <td>0.445909</td>\n",
       "      <td>0.210970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>0.834616</td>\n",
       "      <td>0.181652</td>\n",
       "      <td>0.638845</td>\n",
       "      <td>0.129637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>0.273476</td>\n",
       "      <td>0.652881</td>\n",
       "      <td>0.462116</td>\n",
       "      <td>0.624388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>0.621654</td>\n",
       "      <td>0.529435</td>\n",
       "      <td>0.798512</td>\n",
       "      <td>0.420236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>0.723825</td>\n",
       "      <td>0.003454</td>\n",
       "      <td>0.970846</td>\n",
       "      <td>0.061585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.050373</td>\n",
       "      <td>0.840215</td>\n",
       "      <td>0.196180</td>\n",
       "      <td>0.494576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.669585</td>\n",
       "      <td>0.265701</td>\n",
       "      <td>0.129782</td>\n",
       "      <td>0.332931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>0.812199</td>\n",
       "      <td>0.708708</td>\n",
       "      <td>0.054264</td>\n",
       "      <td>0.359889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.258397</td>\n",
       "      <td>0.265816</td>\n",
       "      <td>0.360538</td>\n",
       "      <td>0.645686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.092021</td>\n",
       "      <td>0.789615</td>\n",
       "      <td>0.850649</td>\n",
       "      <td>0.101120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>0.975096</td>\n",
       "      <td>0.208439</td>\n",
       "      <td>0.724710</td>\n",
       "      <td>0.330534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0.317340</td>\n",
       "      <td>0.503770</td>\n",
       "      <td>0.550153</td>\n",
       "      <td>0.859039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0.603645</td>\n",
       "      <td>0.853624</td>\n",
       "      <td>0.429459</td>\n",
       "      <td>0.667950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0.468934</td>\n",
       "      <td>0.036531</td>\n",
       "      <td>0.707722</td>\n",
       "      <td>0.285071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>0.969272</td>\n",
       "      <td>0.788089</td>\n",
       "      <td>0.634423</td>\n",
       "      <td>0.774257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>0.805812</td>\n",
       "      <td>0.692707</td>\n",
       "      <td>0.031887</td>\n",
       "      <td>0.628694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>0.887147</td>\n",
       "      <td>0.540582</td>\n",
       "      <td>0.422751</td>\n",
       "      <td>0.017596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>0.466086</td>\n",
       "      <td>0.751402</td>\n",
       "      <td>0.978571</td>\n",
       "      <td>0.234840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>0.048902</td>\n",
       "      <td>0.203822</td>\n",
       "      <td>0.105655</td>\n",
       "      <td>0.155439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>0.105373</td>\n",
       "      <td>0.542721</td>\n",
       "      <td>0.991550</td>\n",
       "      <td>0.703746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>0.644169</td>\n",
       "      <td>0.631504</td>\n",
       "      <td>0.588666</td>\n",
       "      <td>0.650756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>0.024548</td>\n",
       "      <td>0.119780</td>\n",
       "      <td>0.245846</td>\n",
       "      <td>0.088502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>0.314739</td>\n",
       "      <td>0.848045</td>\n",
       "      <td>0.311962</td>\n",
       "      <td>0.598441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>0.553419</td>\n",
       "      <td>0.338051</td>\n",
       "      <td>0.701415</td>\n",
       "      <td>0.523262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>0.035971</td>\n",
       "      <td>0.193485</td>\n",
       "      <td>0.679021</td>\n",
       "      <td>0.691591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.854871</td>\n",
       "      <td>0.695061</td>\n",
       "      <td>0.356774</td>\n",
       "      <td>0.979735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.661607</td>\n",
       "      <td>0.503878</td>\n",
       "      <td>0.654542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.998023</td>\n",
       "      <td>0.952016</td>\n",
       "      <td>0.824926</td>\n",
       "      <td>0.703714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.293444</td>\n",
       "      <td>0.310071</td>\n",
       "      <td>0.375565</td>\n",
       "      <td>0.723561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.057917</td>\n",
       "      <td>0.092781</td>\n",
       "      <td>0.689473</td>\n",
       "      <td>0.339998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "0    0.822952  0.305272  0.598033  0.747639\n",
       "1    0.891071  0.881773  0.732910  0.119047\n",
       "2    0.553461  0.387108  0.233421  0.393985\n",
       "3    0.671267  0.055173  0.657021  0.542877\n",
       "4    0.021261  0.915287  0.458971  0.028686\n",
       "5    0.497803  0.771876  0.827811  0.940157\n",
       "6    0.901412  0.733529  0.922124  0.721472\n",
       "7    0.693455  0.071844  0.872457  0.985984\n",
       "8    0.330219  0.639113  0.243929  0.771501\n",
       "9    0.794084  0.696750  0.725534  0.580586\n",
       "10   0.988459  0.555040  0.189406  0.920202\n",
       "11   0.398224  0.884291  0.211178  0.898072\n",
       "12   0.479344  0.497890  0.432494  0.547958\n",
       "13   0.158759  0.937759  0.352324  0.630257\n",
       "14   0.376726  0.792733  0.157629  0.783659\n",
       "15   0.330176  0.623384  0.168045  0.781852\n",
       "16   0.764479  0.777299  0.063609  0.465936\n",
       "17   0.923244  0.667921  0.917554  0.951361\n",
       "18   0.987056  0.101955  0.897381  0.200006\n",
       "19   0.159807  0.837541  0.453378  0.426799\n",
       "20   0.204622  0.951610  0.491157  0.588450\n",
       "21   0.773095  0.349290  0.187834  0.462360\n",
       "22   0.697931  0.457178  0.303124  0.761929\n",
       "23   0.804283  0.393852  0.540087  0.959902\n",
       "24   0.484109  0.952588  0.088975  0.706463\n",
       "25   0.232370  0.385174  0.876377  0.878568\n",
       "26   0.123252  0.102101  0.072329  0.118548\n",
       "27   0.095876  0.383800  0.744193  0.378601\n",
       "28   0.086249  0.568279  0.721645  0.215377\n",
       "29   0.331767  0.664377  0.578359  0.312672\n",
       "..        ...       ...       ...       ...\n",
       "970  0.509567  0.550761  0.445909  0.210970\n",
       "971  0.834616  0.181652  0.638845  0.129637\n",
       "972  0.273476  0.652881  0.462116  0.624388\n",
       "973  0.621654  0.529435  0.798512  0.420236\n",
       "974  0.723825  0.003454  0.970846  0.061585\n",
       "975  0.050373  0.840215  0.196180  0.494576\n",
       "976  0.669585  0.265701  0.129782  0.332931\n",
       "977  0.812199  0.708708  0.054264  0.359889\n",
       "978  0.258397  0.265816  0.360538  0.645686\n",
       "979  0.092021  0.789615  0.850649  0.101120\n",
       "980  0.975096  0.208439  0.724710  0.330534\n",
       "981  0.317340  0.503770  0.550153  0.859039\n",
       "982  0.603645  0.853624  0.429459  0.667950\n",
       "983  0.468934  0.036531  0.707722  0.285071\n",
       "984  0.969272  0.788089  0.634423  0.774257\n",
       "985  0.805812  0.692707  0.031887  0.628694\n",
       "986  0.887147  0.540582  0.422751  0.017596\n",
       "987  0.466086  0.751402  0.978571  0.234840\n",
       "988  0.048902  0.203822  0.105655  0.155439\n",
       "989  0.105373  0.542721  0.991550  0.703746\n",
       "990  0.644169  0.631504  0.588666  0.650756\n",
       "991  0.024548  0.119780  0.245846  0.088502\n",
       "992  0.314739  0.848045  0.311962  0.598441\n",
       "993  0.553419  0.338051  0.701415  0.523262\n",
       "994  0.035971  0.193485  0.679021  0.691591\n",
       "995  0.854871  0.695061  0.356774  0.979735\n",
       "996  0.986335  0.661607  0.503878  0.654542\n",
       "997  0.998023  0.952016  0.824926  0.703714\n",
       "998  0.293444  0.310071  0.375565  0.723561\n",
       "999  0.057917  0.092781  0.689473  0.339998\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.491864</td>\n",
       "      <td>0.509591</td>\n",
       "      <td>0.495459</td>\n",
       "      <td>0.510614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.289043</td>\n",
       "      <td>0.294337</td>\n",
       "      <td>0.290806</td>\n",
       "      <td>0.292637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.003314</td>\n",
       "      <td>0.001973</td>\n",
       "      <td>0.001630</td>\n",
       "      <td>0.001231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.238931</td>\n",
       "      <td>0.248048</td>\n",
       "      <td>0.228687</td>\n",
       "      <td>0.258082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.487702</td>\n",
       "      <td>0.522888</td>\n",
       "      <td>0.489536</td>\n",
       "      <td>0.495384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.742035</td>\n",
       "      <td>0.764712</td>\n",
       "      <td>0.745998</td>\n",
       "      <td>0.766676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.998598</td>\n",
       "      <td>0.999973</td>\n",
       "      <td>0.999572</td>\n",
       "      <td>0.998901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean      0.491864     0.509591     0.495459     0.510614\n",
       "std       0.289043     0.294337     0.290806     0.292637\n",
       "min       0.003314     0.001973     0.001630     0.001231\n",
       "25%       0.238931     0.248048     0.228687     0.258082\n",
       "50%       0.487702     0.522888     0.489536     0.495384\n",
       "75%       0.742035     0.764712     0.745998     0.766676\n",
       "max       0.998598     0.999973     0.999572     0.998901"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 假设想找出某列中绝对值大小超过3的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col = data[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: 3, dtype: float64)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col[np.abs(col) > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": 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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [0, 1, 2, 3]\n",
       "Index: []"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选出全部含有“超过2或-2的值”的行，可以利用布尔型DataFrame以及any方法：\n",
    "data[(np.abs(data) > 2).any(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 下面的代码可以将值限制在区间-3到3以内"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[np.abs(data) > 3] = np.sign(data) * 3   #np.sign返回的是一个由1和-1组成的数组，表示原始值的符号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.491864</td>\n",
       "      <td>0.509591</td>\n",
       "      <td>0.495459</td>\n",
       "      <td>0.510614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.289043</td>\n",
       "      <td>0.294337</td>\n",
       "      <td>0.290806</td>\n",
       "      <td>0.292637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.003314</td>\n",
       "      <td>0.001973</td>\n",
       "      <td>0.001630</td>\n",
       "      <td>0.001231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.238931</td>\n",
       "      <td>0.248048</td>\n",
       "      <td>0.228687</td>\n",
       "      <td>0.258082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.487702</td>\n",
       "      <td>0.522888</td>\n",
       "      <td>0.489536</td>\n",
       "      <td>0.495384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.742035</td>\n",
       "      <td>0.764712</td>\n",
       "      <td>0.745998</td>\n",
       "      <td>0.766676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.998598</td>\n",
       "      <td>0.999973</td>\n",
       "      <td>0.999572</td>\n",
       "      <td>0.998901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean      0.491864     0.509591     0.495459     0.510614\n",
       "std       0.289043     0.294337     0.290806     0.292637\n",
       "min       0.003314     0.001973     0.001630     0.001231\n",
       "25%       0.238931     0.248048     0.228687     0.258082\n",
       "50%       0.487702     0.522888     0.489536     0.495384\n",
       "75%       0.742035     0.764712     0.745998     0.766676\n",
       "max       0.998598     0.999973     0.999572     0.998901"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 排列和随机采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 通过需要排列的轴的长度调用permutation，可产生一个表示新殊勋的整数数组\n",
    "df = DataFrame(np.arange(5 * 4).reshape(5, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampler = np.random.permutation(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, 4, 3])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "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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       "    .dataframe thead th {\n",
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       "</style>\n",
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       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15\n",
       "4  16  17  18  19"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 然后就可以基于ix的索引操作或take函数中使用该数组了\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>3</th>\n",
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       "      <td>15</td>\n",
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      "text/plain": [
       "    0   1   2   3\n",
       "2   8   9  10  11\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "4  16  17  18  19\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.take(sampler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果不想用替换的方式选取随机子集，可以使用permutation：从permutation返回的数组中切下前k个元素，期中ke为期望的子集大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "      <th>1</th>\n",
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      "text/plain": [
       "    0   1   2   3\n",
       "0   0   1   2   3\n",
       "3  12  13  14  15\n",
       "1   4   5   6   7"
      ]
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     "execution_count": 240,
     "metadata": {},
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   ],
   "source": [
    "df.take(np.random.permutation(len(df))[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 要痛快替换的方式产生样本，最快的方式是通过np.random.randint得到一组随机整数\n",
    "bag = np.array([5, 7, -1, 6, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sampler = np.random.randint(0, len(bag), size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 2, 2, 0, 2, 3, 1, 3])"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "draws = bag.take(sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  7,  7, -1, -1,  5, -1,  6,  7,  6])"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draws"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算指标\\哑变量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 另一种常用语统计建模或机器学习的转换方式是：将分类变量（categorical variable）转换为“哑变量矩阵”（dummy matrix）或“指标矩阵”（indicator matrix）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果DataFrame的某一列中含有k个不同的值，则可以派生出一个k列矩阵或DataFrame（其值全为1和0）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas有一个get_dummies函数可以实现此功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame({'key':['b','b','a','c','a','b'],\n",
    "               'data1':range(6)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
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      "text/plain": [
       "   a  b  c\n",
       "0  0  1  0\n",
       "1  0  1  0\n",
       "2  1  0  0\n",
       "3  0  0  1\n",
       "4  1  0  0\n",
       "5  0  1  0"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['key'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": false
   },
   "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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       "      <td>3</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key\n",
       "0      0   b\n",
       "1      1   b\n",
       "2      2   a\n",
       "3      3   c\n",
       "4      4   a\n",
       "5      5   b"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 有时候想给DataFrame的列加一个前缀，以便能够跟其他数据进行合并，get_dummies的prefix参数可以实现该功能\n",
    "dummies = pd.get_dummies(df['key'], prefix='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key_a  key_b  key_c\n",
       "0      0      1      0\n",
       "1      0      1      0\n",
       "2      1      0      0\n",
       "3      0      0      1\n",
       "4      1      0      0\n",
       "5      0      1      0"
      ]
     },
     "execution_count": 251,
     "metadata": {},
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    }
   ],
   "source": [
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_with_dummy = df[['data1']].join(dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  key_a  key_b  key_c\n",
       "0      0      0      1      0\n",
       "1      1      0      1      0\n",
       "2      2      1      0      0\n",
       "3      3      0      0      1\n",
       "4      4      1      0      0\n",
       "5      5      0      1      0"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果df中某行同属于多个分类，则事情就会变得有点复杂"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mnames = ['movie_id', 'title', 'genres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "movies = pd.read_table('ch02/movielens/movies.dat', sep='::', header=None, names=mnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
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       "      <th>0</th>\n",
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       "      <td>Toy Story (1995)</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>American President, The (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Dracula: Dead and Loving It (1995)</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Balto (1995)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Nixon (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Cutthroat Island (1995)</td>\n",
       "      <td>Action|Adventure|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Casino (1995)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Sense and Sensibility (1995)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Ace Ventura: When Nature Calls (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Money Train (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>Action|Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Assassins (1995)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Powder (1995)</td>\n",
       "      <td>Drama|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Leaving Las Vegas (1995)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Othello (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Now and Then (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Persuasion (1995)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>City of Lost Children, The (1995)</td>\n",
       "      <td>Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Shanghai Triad (Yao a yao yao dao waipo qiao) ...</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3853</th>\n",
       "      <td>3923</td>\n",
       "      <td>Return of the Fly (1959)</td>\n",
       "      <td>Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3854</th>\n",
       "      <td>3924</td>\n",
       "      <td>Pajama Party (1964)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3855</th>\n",
       "      <td>3925</td>\n",
       "      <td>Stranger Than Paradise (1984)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3856</th>\n",
       "      <td>3926</td>\n",
       "      <td>Voyage to the Bottom of the Sea (1961)</td>\n",
       "      <td>Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3857</th>\n",
       "      <td>3927</td>\n",
       "      <td>Fantastic Voyage (1966)</td>\n",
       "      <td>Adventure|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3858</th>\n",
       "      <td>3928</td>\n",
       "      <td>Abbott and Costello Meet Frankenstein (1948)</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>3929</td>\n",
       "      <td>Bank Dick, The (1940)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3860</th>\n",
       "      <td>3930</td>\n",
       "      <td>Creature From the Black Lagoon, The (1954)</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3861</th>\n",
       "      <td>3931</td>\n",
       "      <td>Giant Gila Monster, The (1959)</td>\n",
       "      <td>Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3862</th>\n",
       "      <td>3932</td>\n",
       "      <td>Invisible Man, The (1933)</td>\n",
       "      <td>Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3863</th>\n",
       "      <td>3933</td>\n",
       "      <td>Killer Shrews, The (1959)</td>\n",
       "      <td>Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>3934</td>\n",
       "      <td>Kronos (1957)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3865</th>\n",
       "      <td>3935</td>\n",
       "      <td>Kronos (1973)</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3866</th>\n",
       "      <td>3936</td>\n",
       "      <td>Phantom of the Opera, The (1943)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3867</th>\n",
       "      <td>3937</td>\n",
       "      <td>Runaway (1984)</td>\n",
       "      <td>Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3868</th>\n",
       "      <td>3938</td>\n",
       "      <td>Slumber Party Massacre, The (1982)</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3869</th>\n",
       "      <td>3939</td>\n",
       "      <td>Slumber Party Massacre II, The (1987)</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3870</th>\n",
       "      <td>3940</td>\n",
       "      <td>Slumber Party Massacre III, The (1990)</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3871</th>\n",
       "      <td>3941</td>\n",
       "      <td>Sorority House Massacre (1986)</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3872</th>\n",
       "      <td>3942</td>\n",
       "      <td>Sorority House Massacre II (1990)</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3873</th>\n",
       "      <td>3943</td>\n",
       "      <td>Bamboozled (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3874</th>\n",
       "      <td>3944</td>\n",
       "      <td>Bootmen (2000)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3875</th>\n",
       "      <td>3945</td>\n",
       "      <td>Digimon: The Movie (2000)</td>\n",
       "      <td>Adventure|Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>3946</td>\n",
       "      <td>Get Carter (2000)</td>\n",
       "      <td>Action|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3877</th>\n",
       "      <td>3947</td>\n",
       "      <td>Get Carter (1971)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>3948</td>\n",
       "      <td>Meet the Parents (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>3949</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>3950</td>\n",
       "      <td>Tigerland (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>3951</td>\n",
       "      <td>Two Family House (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3883 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id                                              title  \\\n",
       "0            1                                   Toy Story (1995)   \n",
       "1            2                                     Jumanji (1995)   \n",
       "2            3                            Grumpier Old Men (1995)   \n",
       "3            4                           Waiting to Exhale (1995)   \n",
       "4            5                 Father of the Bride Part II (1995)   \n",
       "5            6                                        Heat (1995)   \n",
       "6            7                                     Sabrina (1995)   \n",
       "7            8                                Tom and Huck (1995)   \n",
       "8            9                                Sudden Death (1995)   \n",
       "9           10                                   GoldenEye (1995)   \n",
       "10          11                     American President, The (1995)   \n",
       "11          12                 Dracula: Dead and Loving It (1995)   \n",
       "12          13                                       Balto (1995)   \n",
       "13          14                                       Nixon (1995)   \n",
       "14          15                            Cutthroat Island (1995)   \n",
       "15          16                                      Casino (1995)   \n",
       "16          17                       Sense and Sensibility (1995)   \n",
       "17          18                                  Four Rooms (1995)   \n",
       "18          19              Ace Ventura: When Nature Calls (1995)   \n",
       "19          20                                 Money Train (1995)   \n",
       "20          21                                  Get Shorty (1995)   \n",
       "21          22                                     Copycat (1995)   \n",
       "22          23                                   Assassins (1995)   \n",
       "23          24                                      Powder (1995)   \n",
       "24          25                           Leaving Las Vegas (1995)   \n",
       "25          26                                     Othello (1995)   \n",
       "26          27                                Now and Then (1995)   \n",
       "27          28                                  Persuasion (1995)   \n",
       "28          29                  City of Lost Children, The (1995)   \n",
       "29          30  Shanghai Triad (Yao a yao yao dao waipo qiao) ...   \n",
       "...        ...                                                ...   \n",
       "3853      3923                           Return of the Fly (1959)   \n",
       "3854      3924                                Pajama Party (1964)   \n",
       "3855      3925                      Stranger Than Paradise (1984)   \n",
       "3856      3926             Voyage to the Bottom of the Sea (1961)   \n",
       "3857      3927                            Fantastic Voyage (1966)   \n",
       "3858      3928       Abbott and Costello Meet Frankenstein (1948)   \n",
       "3859      3929                              Bank Dick, The (1940)   \n",
       "3860      3930         Creature From the Black Lagoon, The (1954)   \n",
       "3861      3931                     Giant Gila Monster, The (1959)   \n",
       "3862      3932                          Invisible Man, The (1933)   \n",
       "3863      3933                          Killer Shrews, The (1959)   \n",
       "3864      3934                                      Kronos (1957)   \n",
       "3865      3935                                      Kronos (1973)   \n",
       "3866      3936                   Phantom of the Opera, The (1943)   \n",
       "3867      3937                                     Runaway (1984)   \n",
       "3868      3938                 Slumber Party Massacre, The (1982)   \n",
       "3869      3939              Slumber Party Massacre II, The (1987)   \n",
       "3870      3940             Slumber Party Massacre III, The (1990)   \n",
       "3871      3941                     Sorority House Massacre (1986)   \n",
       "3872      3942                  Sorority House Massacre II (1990)   \n",
       "3873      3943                                  Bamboozled (2000)   \n",
       "3874      3944                                     Bootmen (2000)   \n",
       "3875      3945                          Digimon: The Movie (2000)   \n",
       "3876      3946                                  Get Carter (2000)   \n",
       "3877      3947                                  Get Carter (1971)   \n",
       "3878      3948                            Meet the Parents (2000)   \n",
       "3879      3949                         Requiem for a Dream (2000)   \n",
       "3880      3950                                   Tigerland (2000)   \n",
       "3881      3951                            Two Family House (2000)   \n",
       "3882      3952                              Contender, The (2000)   \n",
       "\n",
       "                              genres  \n",
       "0        Animation|Children's|Comedy  \n",
       "1       Adventure|Children's|Fantasy  \n",
       "2                     Comedy|Romance  \n",
       "3                       Comedy|Drama  \n",
       "4                             Comedy  \n",
       "5              Action|Crime|Thriller  \n",
       "6                     Comedy|Romance  \n",
       "7               Adventure|Children's  \n",
       "8                             Action  \n",
       "9          Action|Adventure|Thriller  \n",
       "10              Comedy|Drama|Romance  \n",
       "11                     Comedy|Horror  \n",
       "12              Animation|Children's  \n",
       "13                             Drama  \n",
       "14          Action|Adventure|Romance  \n",
       "15                    Drama|Thriller  \n",
       "16                     Drama|Romance  \n",
       "17                          Thriller  \n",
       "18                            Comedy  \n",
       "19                            Action  \n",
       "20               Action|Comedy|Drama  \n",
       "21              Crime|Drama|Thriller  \n",
       "22                          Thriller  \n",
       "23                      Drama|Sci-Fi  \n",
       "24                     Drama|Romance  \n",
       "25                             Drama  \n",
       "26                             Drama  \n",
       "27                           Romance  \n",
       "28                  Adventure|Sci-Fi  \n",
       "29                             Drama  \n",
       "...                              ...  \n",
       "3853                   Horror|Sci-Fi  \n",
       "3854                          Comedy  \n",
       "3855                          Comedy  \n",
       "3856                Adventure|Sci-Fi  \n",
       "3857                Adventure|Sci-Fi  \n",
       "3858                   Comedy|Horror  \n",
       "3859                          Comedy  \n",
       "3860                          Horror  \n",
       "3861                   Horror|Sci-Fi  \n",
       "3862                   Horror|Sci-Fi  \n",
       "3863                   Horror|Sci-Fi  \n",
       "3864                          Sci-Fi  \n",
       "3865                          Horror  \n",
       "3866                  Drama|Thriller  \n",
       "3867                 Sci-Fi|Thriller  \n",
       "3868                          Horror  \n",
       "3869                          Horror  \n",
       "3870                          Horror  \n",
       "3871                          Horror  \n",
       "3872                          Horror  \n",
       "3873                          Comedy  \n",
       "3874                    Comedy|Drama  \n",
       "3875  Adventure|Animation|Children's  \n",
       "3876           Action|Drama|Thriller  \n",
       "3877                        Thriller  \n",
       "3878                          Comedy  \n",
       "3879                           Drama  \n",
       "3880                           Drama  \n",
       "3881                           Drama  \n",
       "3882                  Drama|Thriller  \n",
       "\n",
       "[3883 rows x 3 columns]"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genre_iter = (set(x.split('|')) for x in movies.genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object <genexpr> at 0x000000000A700750>"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genres = sorted(set.union(*genre_iter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dummies = DataFrame(np.zeros((len(movies), len(genres))), columns=genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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>3853</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3854</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3855</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3856</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3857</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3858</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3860</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3861</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3862</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3863</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3865</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3866</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3867</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3868</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3869</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3870</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3871</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3872</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3873</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3874</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3875</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3877</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3883 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Action  Adventure  Animation  Children's  Comedy  Crime  Documentary  \\\n",
       "0        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "1        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "2        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "4        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "5        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "6        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "7        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "8        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "9        0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "10       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "11       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "12       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "13       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "14       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "15       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "16       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "17       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "18       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "19       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "20       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "21       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "22       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "23       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "24       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "25       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "26       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "27       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "28       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "29       0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "...      ...        ...        ...         ...     ...    ...          ...   \n",
       "3853     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3854     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3855     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3856     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3857     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3858     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3859     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3860     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3861     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3862     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3863     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3864     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3865     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3866     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3867     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3868     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3869     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3870     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3871     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3872     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3873     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3874     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3875     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3876     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3877     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3878     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3879     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3880     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3881     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "3882     0.0        0.0        0.0         0.0     0.0    0.0          0.0   \n",
       "\n",
       "      Drama  Fantasy  Film-Noir  Horror  Musical  Mystery  Romance  Sci-Fi  \\\n",
       "0       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "1       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "2       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "4       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "5       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "6       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "7       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "8       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "9       0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "10      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "11      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "12      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "13      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "14      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "15      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "16      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "17      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "18      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "19      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "20      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "21      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "22      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "23      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "24      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "25      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "26      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "27      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "28      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "29      0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "...     ...      ...        ...     ...      ...      ...      ...     ...   \n",
       "3853    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3854    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3855    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3856    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3857    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3858    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3859    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3860    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3861    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3862    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3863    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3864    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3865    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3866    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3867    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3868    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3869    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3870    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3871    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3872    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3873    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3874    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3875    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3876    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3877    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3878    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3879    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3880    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3881    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "3882    0.0      0.0        0.0     0.0      0.0      0.0      0.0     0.0   \n",
       "\n",
       "      Thriller  War  Western  \n",
       "0          0.0  0.0      0.0  \n",
       "1          0.0  0.0      0.0  \n",
       "2          0.0  0.0      0.0  \n",
       "3          0.0  0.0      0.0  \n",
       "4          0.0  0.0      0.0  \n",
       "5          0.0  0.0      0.0  \n",
       "6          0.0  0.0      0.0  \n",
       "7          0.0  0.0      0.0  \n",
       "8          0.0  0.0      0.0  \n",
       "9          0.0  0.0      0.0  \n",
       "10         0.0  0.0      0.0  \n",
       "11         0.0  0.0      0.0  \n",
       "12         0.0  0.0      0.0  \n",
       "13         0.0  0.0      0.0  \n",
       "14         0.0  0.0      0.0  \n",
       "15         0.0  0.0      0.0  \n",
       "16         0.0  0.0      0.0  \n",
       "17         0.0  0.0      0.0  \n",
       "18         0.0  0.0      0.0  \n",
       "19         0.0  0.0      0.0  \n",
       "20         0.0  0.0      0.0  \n",
       "21         0.0  0.0      0.0  \n",
       "22         0.0  0.0      0.0  \n",
       "23         0.0  0.0      0.0  \n",
       "24         0.0  0.0      0.0  \n",
       "25         0.0  0.0      0.0  \n",
       "26         0.0  0.0      0.0  \n",
       "27         0.0  0.0      0.0  \n",
       "28         0.0  0.0      0.0  \n",
       "29         0.0  0.0      0.0  \n",
       "...        ...  ...      ...  \n",
       "3853       0.0  0.0      0.0  \n",
       "3854       0.0  0.0      0.0  \n",
       "3855       0.0  0.0      0.0  \n",
       "3856       0.0  0.0      0.0  \n",
       "3857       0.0  0.0      0.0  \n",
       "3858       0.0  0.0      0.0  \n",
       "3859       0.0  0.0      0.0  \n",
       "3860       0.0  0.0      0.0  \n",
       "3861       0.0  0.0      0.0  \n",
       "3862       0.0  0.0      0.0  \n",
       "3863       0.0  0.0      0.0  \n",
       "3864       0.0  0.0      0.0  \n",
       "3865       0.0  0.0      0.0  \n",
       "3866       0.0  0.0      0.0  \n",
       "3867       0.0  0.0      0.0  \n",
       "3868       0.0  0.0      0.0  \n",
       "3869       0.0  0.0      0.0  \n",
       "3870       0.0  0.0      0.0  \n",
       "3871       0.0  0.0      0.0  \n",
       "3872       0.0  0.0      0.0  \n",
       "3873       0.0  0.0      0.0  \n",
       "3874       0.0  0.0      0.0  \n",
       "3875       0.0  0.0      0.0  \n",
       "3876       0.0  0.0      0.0  \n",
       "3877       0.0  0.0      0.0  \n",
       "3878       0.0  0.0      0.0  \n",
       "3879       0.0  0.0      0.0  \n",
       "3880       0.0  0.0      0.0  \n",
       "3881       0.0  0.0      0.0  \n",
       "3882       0.0  0.0      0.0  \n",
       "\n",
       "[3883 rows x 18 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 接下来，迭代每一步电影比并将dummies各行设置为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i,gen in enumerate(movies.genres):\n",
    "    dummies.ix[i, gen.split('|')] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将其与movies合并起来\n",
    "movies_windic = movies.join(dummies.add_prefix('Genre_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id                                       1\n",
       "title                           Toy Story (1995)\n",
       "genres               Animation|Children's|Comedy\n",
       "Genre_Action                                   0\n",
       "Genre_Adventure                                0\n",
       "Genre_Animation                                1\n",
       "Genre_Children's                               1\n",
       "Genre_Comedy                                   1\n",
       "Genre_Crime                                    0\n",
       "Genre_Documentary                              0\n",
       "Genre_Drama                                    0\n",
       "Genre_Fantasy                                  0\n",
       "Genre_Film-Noir                                0\n",
       "Genre_Horror                                   0\n",
       "Genre_Musical                                  0\n",
       "Genre_Mystery                                  0\n",
       "Genre_Romance                                  0\n",
       "Genre_Sci-Fi                                   0\n",
       "Genre_Thriller                                 0\n",
       "Genre_War                                      0\n",
       "Genre_Western                                  0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_windic.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  对于很大的数据，用这种方式构建多成员指标变量就会变得非常慢。肯定需要编写一个能够利用DataFrame内部机制的更低级的函数才行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 一个对统计应用有用的秘诀是：结合get_dummies和诸如cut之类的离散化函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values = np.random.rand(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.27057906,  0.40433492,  0.53287655,  0.87859799,  0.09943786,\n",
       "        0.19758977,  0.01405213,  0.47045859,  0.22434885,  0.33531395])"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bins = [0, 0.2, 0.4, 0.6, 0.8, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": 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>(0.0, 0.2]</th>\n",
       "      <th>(0.2, 0.4]</th>\n",
       "      <th>(0.4, 0.6]</th>\n",
       "      <th>(0.6, 0.8]</th>\n",
       "      <th>(0.8, 1.0]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   (0.0, 0.2]  (0.2, 0.4]  (0.4, 0.6]  (0.6, 0.8]  (0.8, 1.0]\n",
       "0           0           1           0           0           0\n",
       "1           0           0           1           0           0\n",
       "2           0           0           1           0           0\n",
       "3           0           0           0           0           1\n",
       "4           1           0           0           0           0\n",
       "5           1           0           0           0           0\n",
       "6           1           0           0           0           0\n",
       "7           0           0           1           0           0\n",
       "8           0           1           0           0           0\n",
       "9           0           1           0           0           0"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(pd.cut(values, bins))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字符串操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  字符串对象方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "val = 'a,b, guido'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', ' guido']"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#strip可以去掉空格\n",
    "pieces = [x.strip() for x in val.split(',')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'guido']"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "first, second, thrid = pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a::b::guido'"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first + '::' + second + '::' + thrid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 检测子串的最佳方式是利用Python的in关键字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'guido' in val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.index(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.find(':')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### find和index的区别：如果找不到字符串，index会引发一个异常，find会返回-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "substring not found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-293-280f8b2856ce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mval\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m':'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: substring not found"
     ]
    }
   ],
   "source": [
    "val.index(':')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### count函数用于返回指定子串的出现次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.count(',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### replace用于将指定模式替换为另一个模式，它也尝尝用于删除模式：传入空字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a::b:: guido'"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.replace(',', '::')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ab guido'"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.replace(',','')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则表达式 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text = \"foo bar\\t baz \\tqux\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'qux']"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.split('\\s+', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regex = re.compile('\\s+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'qux']"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.split(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' ', '\\t ', ' \\t']"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text)      # 找到匹配regex的所有模式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### findall返回的是字符串所有的匹配项，search只返回第一个匹配项\n",
    "#### match更加严格，只匹配字符串的首部"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text = \"\"\"Dave dave@google.com\n",
    "Steve steve@gmail.com\n",
    "Rob rob@gmail.com\n",
    "Ryan ryan@yahoo.com\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pattern = r'[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,4}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regex = re.compile(pattern, flags=re.IGNORECASE)       # re.IGNORECASE作用是使正则表达式对大小不敏感"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dave@google.com', 'steve@gmail.com', 'rob@gmail.com', 'ryan@yahoo.com']"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = regex.search(text)      # search返回的是文本中第一个电子邮件地址（以特殊的匹配对象形式返回），\n",
    "#对于上述regex，匹配对象只能告诉我们模式在原字符串初中起始和结束位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_sre.SRE_Match at 0xa6a9850>"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dave@google.com'"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[m.start():m.end()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "# regex.macth返回None，因为它只匹配出现在字符串开头的模式：\n",
    "print regex.match(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dave REDACTED\n",
      "Steve REDACTED\n",
      "Rob REDACTED\n",
      "Ryan REDACTED\n"
     ]
    }
   ],
   "source": [
    "# sub方法，将匹配到的模式替换为指定字符串，并返回所得到的新字符串\n",
    "print regex.sub('REDACTED', text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 假设不仅想要找出电子邮件地址，还想将各个地址分成3部分，包括：用户名、域名以及域后缀\n",
    "#### 要实现此功能，只需要将待分段的模式的各部分用圆括号包起来即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pattern = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\\.([A-Z]{2,4})'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regex = re.compile(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 由这种正则所产生的匹配对象，可以通过其groups方法返回一个由模式各段组成的元祖："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = regex.match('wesm@bright.net')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('wesm', 'bright', 'net')"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.groups()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('dave', 'google', 'com'),\n",
       " ('steve', 'gmail', 'com'),\n",
       " ('rob', 'gmail', 'com'),\n",
       " ('ryan', 'yahoo', 'com')]"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对于带有分组功能的模式，findall会返回一个元祖列表\n",
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dave Username: dave, Domain: google, Suffix: com\n",
      "Steve Username: steve, Domain: gmail, Suffix: com\n",
      "Rob Username: rob, Domain: gmail, Suffix: com\n",
      "Ryan Username: ryan, Domain: yahoo, Suffix: com\n"
     ]
    }
   ],
   "source": [
    "# sub还能通过注入\\1, \\2之类的特殊符号访问各匹配项中的分组\n",
    "print regex.sub(r'Username: \\1, Domain: \\2, Suffix: \\3', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regex = re.compile(r\"\"\"\n",
    "(?P<username>[A-Z0-9._%+-]+)\n",
    "@\n",
    "(?P<domain>[A-Z0-9._]+)\n",
    "\\.\n",
    "(?P<suffix>[A-Z]{2,4})\n",
    "\"\"\", flags=re.IGNORECASE|re.VERBOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = regex.match('werm@bright.net')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_sre.SRE_Match at 0xa416160>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'domain': 'bright', 'suffix': 'net', 'username': 'werm'}"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.groupdict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('dave', 'google', 'com'),\n",
       " ('steve', 'gmail', 'com'),\n",
       " ('rob', 'gmail', 'com'),\n",
       " ('ryan', 'yahoo', 'com')]"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas中矢量化的字符串函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {'Dave':'dave@google.com','Stave':'stave@gmail.com',\n",
    "       'Rob':'rob@gmail.com',\n",
    "       'Wes':np.nan}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     dave@google.com\n",
       "Rob        rob@gmail.com\n",
       "Stave    stave@gmail.com\n",
       "Wes                  NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     False\n",
       "Rob      False\n",
       "Stave    False\n",
       "Wes       True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### data.map，所有字符串和正则表达式都能被应用于（传入lambda表达式或其他函数）各个值，但是如果存在NA值就会报错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     False\n",
       "Rob       True\n",
       "Stave     True\n",
       "Wes        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.str.contains('gmail')  # 检查各个电子邮件地址是否含有“gmail”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\\\\.([A-Z]{2,4})'"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     [(dave, google, com)]\n",
       "Rob        [(rob, gmail, com)]\n",
       "Stave    [(stave, gmail, com)]\n",
       "Wes                        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.str.findall(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 有两个办法可以实现矢量化的元素获取操作：\n",
    "1. 使用str.get\n",
    "2. 在str属性上使用索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "matches = data.str.match(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     True\n",
       "Rob      True\n",
       "Stave    True\n",
       "Wes       NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave    NaN\n",
       "Rob     NaN\n",
       "Stave   NaN\n",
       "Wes     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave    NaN\n",
       "Rob     NaN\n",
       "Stave   NaN\n",
       "Wes     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     dave@\n",
       "Rob      rob@g\n",
       "Stave    stave\n",
       "Wes        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.str[:5]   # 对字符串进行截取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实例：USDA食品数据库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db = json.load(open('ch07/foods-2011-10-03.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6636"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# db中的每个条目都是一个含有某种食物全部数据的字典\n",
    "# nutrients字段是一个字典列表，其中的每个字典对应一种营养成分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'portions',\n",
       " u'description',\n",
       " u'tags',\n",
       " u'nutrients',\n",
       " u'group',\n",
       " u'id',\n",
       " u'manufacturer']"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'description': u'Protein',\n",
       " u'group': u'Composition',\n",
       " u'units': u'g',\n",
       " u'value': 25.18}"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0]['nutrients'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nutrients = DataFrame(db[0]['nutrients'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "collapsed": 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>description</th>\n",
       "      <th>group</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   description        group units    value\n",
       "0                      Protein  Composition     g    25.18\n",
       "1            Total lipid (fat)  Composition     g    29.20\n",
       "2  Carbohydrate, by difference  Composition     g     3.06\n",
       "3                          Ash        Other     g     3.28\n",
       "4                       Energy       Energy  kcal   376.00\n",
       "5                        Water  Composition     g    39.28\n",
       "6                       Energy       Energy    kJ  1573.00"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 在将字典列表转换为DataFrame时，可以只抽取其中的一部分字段，这里，我们将抽取食物的名称、分类、编号以及制造商等信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "info_keys = ['description','group','id','manufacturer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "info = DataFrame(db, columns=info_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "collapsed": 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>description</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1008</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheese, cheddar</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1009</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese, edam</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1018</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cheese, feta</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1019</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cheese, mozzarella, part skim milk</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1028</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          description                   group    id  \\\n",
       "0                     Cheese, caraway  Dairy and Egg Products  1008   \n",
       "1                     Cheese, cheddar  Dairy and Egg Products  1009   \n",
       "2                        Cheese, edam  Dairy and Egg Products  1018   \n",
       "3                        Cheese, feta  Dairy and Egg Products  1019   \n",
       "4  Cheese, mozzarella, part skim milk  Dairy and Egg Products  1028   \n",
       "\n",
       "  manufacturer  \n",
       "0               \n",
       "1               \n",
       "2               \n",
       "3               \n",
       "4               "
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": 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>description</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1008</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheese, cheddar</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1009</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese, edam</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1018</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cheese, feta</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1019</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cheese, mozzarella, part skim milk</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1028</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cheese, mozzarella, part skim milk, low moisture</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1029</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cheese, romano</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1038</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cheese, roquefort</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1039</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cheese spread, pasteurized process, american, ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1048</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cream, fluid, half and half</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1049</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sour dressing, non-butterfat, cultured, filled...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1058</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Milk, filled, fluid, with blend of hydrogenate...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1059</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cream substitute, liquid, with lauric acid oil...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1068</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cream substitute, powdered</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1069</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Milk, producer, fluid, 3.7% milkfat</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1078</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, with add...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1079</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, with add...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1080</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, protein ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1081</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, with added vi...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1082</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, with added no...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1083</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, protein forti...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1084</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Milk, nonfat, fluid, with added vitamin A and ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1085</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Milk, nonfat, fluid, with added nonfat milk so...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1086</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Milk, nonfat, fluid, protein fortified, with a...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1087</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Milk, buttermilk, fluid, cultured, lowfat</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1088</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Milk, low sodium, fluid</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1089</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Milk, dry, whole, with added vitamin D</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1090</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Milk, dry, nonfat, regular, without added vita...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1091</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Milk, dry, nonfat, instant, with added vitamin...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1092</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Milk, dry, nonfat, calcium reduced</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1093</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6606</th>\n",
       "      <td>Beef, tenderloin, steak, separable lean only, ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23628</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6607</th>\n",
       "      <td>Beef, top sirloin, steak, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23629</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6608</th>\n",
       "      <td>Beef, short loin, top loin, steak, separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23630</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6609</th>\n",
       "      <td>Beef, chuck, arm pot roast, separable lean onl...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23631</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6610</th>\n",
       "      <td>Beef, brisket, flat half, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23632</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6611</th>\n",
       "      <td>Beef, chuck, arm pot roast, separable lean onl...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23633</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6612</th>\n",
       "      <td>Beef, brisket, flat half, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23634</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6613</th>\n",
       "      <td>Beef, round, eye of round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23635</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6614</th>\n",
       "      <td>Beef, round, top round, steak, separable lean ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23636</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6615</th>\n",
       "      <td>Beef, round, bottom round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23637</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6616</th>\n",
       "      <td>Beef, rib, small end (ribs 10-12), separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23638</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6617</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27015</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6618</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL's Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27016</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6619</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Soups...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27021</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6620</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27022</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6621</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27023</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27024</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27025</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6624</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27026</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6625</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27032</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6626</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Ess...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31010</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6627</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Spi...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31013</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6628</th>\n",
       "      <td>CAMPBELL Soup Company, PACE, Jalapenos Nacho S...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31014</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6629</th>\n",
       "      <td>CAMPBELL Soup Company, V8 60% Vegetable Juice,...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31016</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6630</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Low...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31017</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>Bologna, beef, low fat</td>\n",
       "      <td>Sausages and Luncheon Meats</td>\n",
       "      <td>42161</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6632</th>\n",
       "      <td>Turkey and pork sausage, fresh, bulk, patty or...</td>\n",
       "      <td>Sausages and Luncheon Meats</td>\n",
       "      <td>42173</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>Babyfood, juice, pear</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43408</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6634</th>\n",
       "      <td>Babyfood, dessert, banana yogurt, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43539</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6635</th>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43546</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6636 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            description  \\\n",
       "0                                       Cheese, caraway   \n",
       "1                                       Cheese, cheddar   \n",
       "2                                          Cheese, edam   \n",
       "3                                          Cheese, feta   \n",
       "4                    Cheese, mozzarella, part skim milk   \n",
       "5      Cheese, mozzarella, part skim milk, low moisture   \n",
       "6                                        Cheese, romano   \n",
       "7                                     Cheese, roquefort   \n",
       "8     Cheese spread, pasteurized process, american, ...   \n",
       "9                           Cream, fluid, half and half   \n",
       "10    Sour dressing, non-butterfat, cultured, filled...   \n",
       "11    Milk, filled, fluid, with blend of hydrogenate...   \n",
       "12    Cream substitute, liquid, with lauric acid oil...   \n",
       "13                           Cream substitute, powdered   \n",
       "14                  Milk, producer, fluid, 3.7% milkfat   \n",
       "15    Milk, reduced fat, fluid, 2% milkfat, with add...   \n",
       "16    Milk, reduced fat, fluid, 2% milkfat, with add...   \n",
       "17    Milk, reduced fat, fluid, 2% milkfat, protein ...   \n",
       "18    Milk, lowfat, fluid, 1% milkfat, with added vi...   \n",
       "19    Milk, lowfat, fluid, 1% milkfat, with added no...   \n",
       "20    Milk, lowfat, fluid, 1% milkfat, protein forti...   \n",
       "21    Milk, nonfat, fluid, with added vitamin A and ...   \n",
       "22    Milk, nonfat, fluid, with added nonfat milk so...   \n",
       "23    Milk, nonfat, fluid, protein fortified, with a...   \n",
       "24            Milk, buttermilk, fluid, cultured, lowfat   \n",
       "25                              Milk, low sodium, fluid   \n",
       "26               Milk, dry, whole, with added vitamin D   \n",
       "27    Milk, dry, nonfat, regular, without added vita...   \n",
       "28    Milk, dry, nonfat, instant, with added vitamin...   \n",
       "29                   Milk, dry, nonfat, calcium reduced   \n",
       "...                                                 ...   \n",
       "6606  Beef, tenderloin, steak, separable lean only, ...   \n",
       "6607  Beef, top sirloin, steak, separable lean only,...   \n",
       "6608  Beef, short loin, top loin, steak, separable l...   \n",
       "6609  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6610  Beef, brisket, flat half, separable lean only,...   \n",
       "6611  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6612  Beef, brisket, flat half, separable lean only,...   \n",
       "6613  Beef, round, eye of round, roast, separable le...   \n",
       "6614  Beef, round, top round, steak, separable lean ...   \n",
       "6615  Beef, round, bottom round, roast, separable le...   \n",
       "6616  Beef, rib, small end (ribs 10-12), separable l...   \n",
       "6617  CAMPBELL Soup Company, CAMPBELL'S Red and Whit...   \n",
       "6618  CAMPBELL Soup Company, CAMPBELL's Red and Whit...   \n",
       "6619  CAMPBELL Soup Company, CAMPBELL'S SELECT Soups...   \n",
       "6620  CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...   \n",
       "6621  CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...   \n",
       "6622  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6623  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6624  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6625  CAMPBELL Soup Company, CAMPBELL'S Red and Whit...   \n",
       "6626  CAMPBELL Soup Company, V8 Vegetable Juice, Ess...   \n",
       "6627  CAMPBELL Soup Company, V8 Vegetable Juice, Spi...   \n",
       "6628  CAMPBELL Soup Company, PACE, Jalapenos Nacho S...   \n",
       "6629  CAMPBELL Soup Company, V8 60% Vegetable Juice,...   \n",
       "6630  CAMPBELL Soup Company, V8 Vegetable Juice, Low...   \n",
       "6631                             Bologna, beef, low fat   \n",
       "6632  Turkey and pork sausage, fresh, bulk, patty or...   \n",
       "6633                              Babyfood, juice, pear   \n",
       "6634         Babyfood, dessert, banana yogurt, strained   \n",
       "6635              Babyfood, banana no tapioca, strained   \n",
       "\n",
       "                                  group     id       manufacturer  \n",
       "0                Dairy and Egg Products   1008                     \n",
       "1                Dairy and Egg Products   1009                     \n",
       "2                Dairy and Egg Products   1018                     \n",
       "3                Dairy and Egg Products   1019                     \n",
       "4                Dairy and Egg Products   1028                     \n",
       "5                Dairy and Egg Products   1029                     \n",
       "6                Dairy and Egg Products   1038                     \n",
       "7                Dairy and Egg Products   1039                     \n",
       "8                Dairy and Egg Products   1048                     \n",
       "9                Dairy and Egg Products   1049                     \n",
       "10               Dairy and Egg Products   1058                     \n",
       "11               Dairy and Egg Products   1059                     \n",
       "12               Dairy and Egg Products   1068                     \n",
       "13               Dairy and Egg Products   1069                     \n",
       "14               Dairy and Egg Products   1078                     \n",
       "15               Dairy and Egg Products   1079               None  \n",
       "16               Dairy and Egg Products   1080                     \n",
       "17               Dairy and Egg Products   1081                     \n",
       "18               Dairy and Egg Products   1082                     \n",
       "19               Dairy and Egg Products   1083                     \n",
       "20               Dairy and Egg Products   1084                     \n",
       "21               Dairy and Egg Products   1085                     \n",
       "22               Dairy and Egg Products   1086                     \n",
       "23               Dairy and Egg Products   1087                     \n",
       "24               Dairy and Egg Products   1088                     \n",
       "25               Dairy and Egg Products   1089                     \n",
       "26               Dairy and Egg Products   1090                     \n",
       "27               Dairy and Egg Products   1091                     \n",
       "28               Dairy and Egg Products   1092                     \n",
       "29               Dairy and Egg Products   1093                     \n",
       "...                                 ...    ...                ...  \n",
       "6606                      Beef Products  23628                     \n",
       "6607                      Beef Products  23629                     \n",
       "6608                      Beef Products  23630                     \n",
       "6609                      Beef Products  23631                     \n",
       "6610                      Beef Products  23632                     \n",
       "6611                      Beef Products  23633                     \n",
       "6612                      Beef Products  23634                     \n",
       "6613                      Beef Products  23635                     \n",
       "6614                      Beef Products  23636                     \n",
       "6615                      Beef Products  23637                     \n",
       "6616                      Beef Products  23638                     \n",
       "6617         Soups, Sauces, and Gravies  27015  Campbell Soup Co.  \n",
       "6618         Soups, Sauces, and Gravies  27016  Campbell Soup Co.  \n",
       "6619         Soups, Sauces, and Gravies  27021  Campbell Soup Co.  \n",
       "6620         Soups, Sauces, and Gravies  27022  Campbell Soup Co.  \n",
       "6621         Soups, Sauces, and Gravies  27023  Campbell Soup Co.  \n",
       "6622         Soups, Sauces, and Gravies  27024  Campbell Soup Co.  \n",
       "6623         Soups, Sauces, and Gravies  27025  Campbell Soup Co.  \n",
       "6624         Soups, Sauces, and Gravies  27026  Campbell Soup Co.  \n",
       "6625         Soups, Sauces, and Gravies  27032  Campbell Soup Co.  \n",
       "6626  Vegetables and Vegetable Products  31010  Campbell Soup Co.  \n",
       "6627  Vegetables and Vegetable Products  31013  Campbell Soup Co.  \n",
       "6628  Vegetables and Vegetable Products  31014  Campbell Soup Co.  \n",
       "6629  Vegetables and Vegetable Products  31016  Campbell Soup Co.  \n",
       "6630  Vegetables and Vegetable Products  31017  Campbell Soup Co.  \n",
       "6631        Sausages and Luncheon Meats  42161                     \n",
       "6632        Sausages and Luncheon Meats  42173                     \n",
       "6633                         Baby Foods  43408               None  \n",
       "6634                         Baby Foods  43539               None  \n",
       "6635                         Baby Foods  43546               None  \n",
       "\n",
       "[6636 rows x 4 columns]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vegetables and Vegetable Products    812\n",
       "Beef Products                        618\n",
       "Baked Products                       496\n",
       "Breakfast Cereals                    403\n",
       "Legumes and Legume Products          365\n",
       "Fast Foods                           365\n",
       "Lamb, Veal, and Game Products        345\n",
       "Sweets                               341\n",
       "Fruits and Fruit Juices              328\n",
       "Pork Products                        328\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过value_counts,可以查看书屋类别的分部情况\n",
    "pd.value_counts(info.group)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将各个食物的营养成分整合到一个大表中\n",
    "nutrients = []\n",
    "for rec in db:\n",
    "    fnuts = DataFrame(rec['nutrients'])\n",
    "    fnuts['id'] = rec['id']\n",
    "    nutrients.append(fnuts)\n",
    "\n",
    "nutrients = pd.concat(nutrients, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "collapsed": 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>description</th>\n",
       "      <th>group</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.200</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.060</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>673.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Magnesium, Mg</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Phosphorus, P</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>490.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Potassium, K</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>93.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sodium, Na</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>690.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zinc, Zn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>2.940</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copper, Cu</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Manganese, Mn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>14.500</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>1054.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>262.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>271.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.031</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.450</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pantothenic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.190</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.074</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389325</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>1.100</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389326</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>5.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389327</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389328</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389329</th>\n",
       "      <td>Carotene, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389330</th>\n",
       "      <td>Carotene, alpha</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389331</th>\n",
       "      <td>Vitamin E (alpha-tocopherol)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.250</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389332</th>\n",
       "      <td>Vitamin D</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389333</th>\n",
       "      <td>Vitamin D (D2 + D3)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389334</th>\n",
       "      <td>Cryptoxanthin, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389335</th>\n",
       "      <td>Lycopene</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389336</th>\n",
       "      <td>Lutein + zeaxanthin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>20.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389337</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>21.900</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389338</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.020</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389339</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.060</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389340</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.540</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389341</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.260</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389342</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389343</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389344</th>\n",
       "      <td>Choline, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>4.100</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389345</th>\n",
       "      <td>Vitamin K (phylloquinone)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.500</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389346</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389347</th>\n",
       "      <td>Folate, food</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389348</th>\n",
       "      <td>Folate, DFE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_DFE</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389349</th>\n",
       "      <td>Vitamin E, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389350</th>\n",
       "      <td>Vitamin B-12, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389351</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389352</th>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.072</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389353</th>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.028</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389354</th>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.041</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>389355 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               description        group    units     value  \\\n",
       "0                                  Protein  Composition        g    25.180   \n",
       "1                        Total lipid (fat)  Composition        g    29.200   \n",
       "2              Carbohydrate, by difference  Composition        g     3.060   \n",
       "3                                      Ash        Other        g     3.280   \n",
       "4                                   Energy       Energy     kcal   376.000   \n",
       "5                                    Water  Composition        g    39.280   \n",
       "6                                   Energy       Energy       kJ  1573.000   \n",
       "7                     Fiber, total dietary  Composition        g     0.000   \n",
       "8                              Calcium, Ca     Elements       mg   673.000   \n",
       "9                                 Iron, Fe     Elements       mg     0.640   \n",
       "10                           Magnesium, Mg     Elements       mg    22.000   \n",
       "11                           Phosphorus, P     Elements       mg   490.000   \n",
       "12                            Potassium, K     Elements       mg    93.000   \n",
       "13                              Sodium, Na     Elements       mg   690.000   \n",
       "14                                Zinc, Zn     Elements       mg     2.940   \n",
       "15                              Copper, Cu     Elements       mg     0.024   \n",
       "16                           Manganese, Mn     Elements       mg     0.021   \n",
       "17                            Selenium, Se     Elements      mcg    14.500   \n",
       "18                           Vitamin A, IU     Vitamins       IU  1054.000   \n",
       "19                                 Retinol     Vitamins      mcg   262.000   \n",
       "20                          Vitamin A, RAE     Vitamins  mcg_RAE   271.000   \n",
       "21          Vitamin C, total ascorbic acid     Vitamins       mg     0.000   \n",
       "22                                 Thiamin     Vitamins       mg     0.031   \n",
       "23                              Riboflavin     Vitamins       mg     0.450   \n",
       "24                                  Niacin     Vitamins       mg     0.180   \n",
       "25                        Pantothenic acid     Vitamins       mg     0.190   \n",
       "26                             Vitamin B-6     Vitamins       mg     0.074   \n",
       "27                           Folate, total     Vitamins      mcg    18.000   \n",
       "28                            Vitamin B-12     Vitamins      mcg     0.270   \n",
       "29                              Folic acid     Vitamins      mcg     0.000   \n",
       "...                                    ...          ...      ...       ...   \n",
       "389325                        Selenium, Se     Elements      mcg     1.100   \n",
       "389326                       Vitamin A, IU     Vitamins       IU     5.000   \n",
       "389327                             Retinol     Vitamins      mcg     0.000   \n",
       "389328                      Vitamin A, RAE     Vitamins  mcg_RAE     0.000   \n",
       "389329                      Carotene, beta     Vitamins      mcg     2.000   \n",
       "389330                     Carotene, alpha     Vitamins      mcg     2.000   \n",
       "389331        Vitamin E (alpha-tocopherol)     Vitamins       mg     0.250   \n",
       "389332                           Vitamin D     Vitamins       IU     0.000   \n",
       "389333                 Vitamin D (D2 + D3)     Vitamins      mcg     0.000   \n",
       "389334                 Cryptoxanthin, beta     Vitamins      mcg     0.000   \n",
       "389335                            Lycopene     Vitamins      mcg     0.000   \n",
       "389336                 Lutein + zeaxanthin     Vitamins      mcg    20.000   \n",
       "389337      Vitamin C, total ascorbic acid     Vitamins       mg    21.900   \n",
       "389338                             Thiamin     Vitamins       mg     0.020   \n",
       "389339                          Riboflavin     Vitamins       mg     0.060   \n",
       "389340                              Niacin     Vitamins       mg     0.540   \n",
       "389341                         Vitamin B-6     Vitamins       mg     0.260   \n",
       "389342                       Folate, total     Vitamins      mcg    17.000   \n",
       "389343                        Vitamin B-12     Vitamins      mcg     0.000   \n",
       "389344                      Choline, total     Vitamins       mg     4.100   \n",
       "389345           Vitamin K (phylloquinone)     Vitamins      mcg     0.500   \n",
       "389346                          Folic acid     Vitamins      mcg     0.000   \n",
       "389347                        Folate, food     Vitamins      mcg    17.000   \n",
       "389348                         Folate, DFE     Vitamins  mcg_DFE    17.000   \n",
       "389349                    Vitamin E, added     Vitamins       mg     0.000   \n",
       "389350                 Vitamin B-12, added     Vitamins      mcg     0.000   \n",
       "389351                         Cholesterol        Other       mg     0.000   \n",
       "389352        Fatty acids, total saturated        Other        g     0.072   \n",
       "389353  Fatty acids, total monounsaturated        Other        g     0.028   \n",
       "389354  Fatty acids, total polyunsaturated        Other        g     0.041   \n",
       "\n",
       "           id  \n",
       "0        1008  \n",
       "1        1008  \n",
       "2        1008  \n",
       "3        1008  \n",
       "4        1008  \n",
       "5        1008  \n",
       "6        1008  \n",
       "7        1008  \n",
       "8        1008  \n",
       "9        1008  \n",
       "10       1008  \n",
       "11       1008  \n",
       "12       1008  \n",
       "13       1008  \n",
       "14       1008  \n",
       "15       1008  \n",
       "16       1008  \n",
       "17       1008  \n",
       "18       1008  \n",
       "19       1008  \n",
       "20       1008  \n",
       "21       1008  \n",
       "22       1008  \n",
       "23       1008  \n",
       "24       1008  \n",
       "25       1008  \n",
       "26       1008  \n",
       "27       1008  \n",
       "28       1008  \n",
       "29       1008  \n",
       "...       ...  \n",
       "389325  43546  \n",
       "389326  43546  \n",
       "389327  43546  \n",
       "389328  43546  \n",
       "389329  43546  \n",
       "389330  43546  \n",
       "389331  43546  \n",
       "389332  43546  \n",
       "389333  43546  \n",
       "389334  43546  \n",
       "389335  43546  \n",
       "389336  43546  \n",
       "389337  43546  \n",
       "389338  43546  \n",
       "389339  43546  \n",
       "389340  43546  \n",
       "389341  43546  \n",
       "389342  43546  \n",
       "389343  43546  \n",
       "389344  43546  \n",
       "389345  43546  \n",
       "389346  43546  \n",
       "389347  43546  \n",
       "389348  43546  \n",
       "389349  43546  \n",
       "389350  43546  \n",
       "389351  43546  \n",
       "389352  43546  \n",
       "389353  43546  \n",
       "389354  43546  \n",
       "\n",
       "[389355 rows x 5 columns]"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14179"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 丢弃重复项\n",
    "nutrients.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nutrients = nutrients.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 由于两个df中都有“group”和“description”，为了区分，对其重命名：\n",
    "col_mapping = {'description':'food',\n",
    "              'group':'fgroup'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "info = info.rename(columns=col_mapping, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "collapsed": 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>food</th>\n",
       "      <th>fgroup</th>\n",
       "      <th>id</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1008</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheese, cheddar</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1009</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese, edam</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1018</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cheese, feta</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1019</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cheese, mozzarella, part skim milk</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1028</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cheese, mozzarella, part skim milk, low moisture</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1029</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cheese, romano</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1038</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cheese, roquefort</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1039</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cheese spread, pasteurized process, american, ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1048</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cream, fluid, half and half</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1049</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sour dressing, non-butterfat, cultured, filled...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1058</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Milk, filled, fluid, with blend of hydrogenate...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1059</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cream substitute, liquid, with lauric acid oil...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1068</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cream substitute, powdered</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1069</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Milk, producer, fluid, 3.7% milkfat</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1078</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, with add...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1079</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, with add...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1080</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, protein ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1081</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, with added vi...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1082</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, with added no...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1083</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, protein forti...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1084</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Milk, nonfat, fluid, with added vitamin A and ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1085</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Milk, nonfat, fluid, with added nonfat milk so...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1086</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Milk, nonfat, fluid, protein fortified, with a...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1087</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Milk, buttermilk, fluid, cultured, lowfat</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1088</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Milk, low sodium, fluid</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1089</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Milk, dry, whole, with added vitamin D</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1090</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Milk, dry, nonfat, regular, without added vita...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1091</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Milk, dry, nonfat, instant, with added vitamin...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1092</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Milk, dry, nonfat, calcium reduced</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1093</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6606</th>\n",
       "      <td>Beef, tenderloin, steak, separable lean only, ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23628</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6607</th>\n",
       "      <td>Beef, top sirloin, steak, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23629</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6608</th>\n",
       "      <td>Beef, short loin, top loin, steak, separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23630</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6609</th>\n",
       "      <td>Beef, chuck, arm pot roast, separable lean onl...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23631</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6610</th>\n",
       "      <td>Beef, brisket, flat half, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23632</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6611</th>\n",
       "      <td>Beef, chuck, arm pot roast, separable lean onl...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23633</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6612</th>\n",
       "      <td>Beef, brisket, flat half, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23634</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6613</th>\n",
       "      <td>Beef, round, eye of round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23635</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6614</th>\n",
       "      <td>Beef, round, top round, steak, separable lean ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23636</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6615</th>\n",
       "      <td>Beef, round, bottom round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23637</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6616</th>\n",
       "      <td>Beef, rib, small end (ribs 10-12), separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23638</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6617</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27015</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6618</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL's Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27016</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6619</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Soups...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27021</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6620</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27022</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6621</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27023</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27024</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27025</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6624</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27026</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6625</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27032</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6626</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Ess...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31010</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6627</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Spi...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31013</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6628</th>\n",
       "      <td>CAMPBELL Soup Company, PACE, Jalapenos Nacho S...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31014</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6629</th>\n",
       "      <td>CAMPBELL Soup Company, V8 60% Vegetable Juice,...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31016</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6630</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Low...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31017</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>Bologna, beef, low fat</td>\n",
       "      <td>Sausages and Luncheon Meats</td>\n",
       "      <td>42161</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6632</th>\n",
       "      <td>Turkey and pork sausage, fresh, bulk, patty or...</td>\n",
       "      <td>Sausages and Luncheon Meats</td>\n",
       "      <td>42173</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>Babyfood, juice, pear</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43408</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6634</th>\n",
       "      <td>Babyfood, dessert, banana yogurt, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43539</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6635</th>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43546</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6636 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   food  \\\n",
       "0                                       Cheese, caraway   \n",
       "1                                       Cheese, cheddar   \n",
       "2                                          Cheese, edam   \n",
       "3                                          Cheese, feta   \n",
       "4                    Cheese, mozzarella, part skim milk   \n",
       "5      Cheese, mozzarella, part skim milk, low moisture   \n",
       "6                                        Cheese, romano   \n",
       "7                                     Cheese, roquefort   \n",
       "8     Cheese spread, pasteurized process, american, ...   \n",
       "9                           Cream, fluid, half and half   \n",
       "10    Sour dressing, non-butterfat, cultured, filled...   \n",
       "11    Milk, filled, fluid, with blend of hydrogenate...   \n",
       "12    Cream substitute, liquid, with lauric acid oil...   \n",
       "13                           Cream substitute, powdered   \n",
       "14                  Milk, producer, fluid, 3.7% milkfat   \n",
       "15    Milk, reduced fat, fluid, 2% milkfat, with add...   \n",
       "16    Milk, reduced fat, fluid, 2% milkfat, with add...   \n",
       "17    Milk, reduced fat, fluid, 2% milkfat, protein ...   \n",
       "18    Milk, lowfat, fluid, 1% milkfat, with added vi...   \n",
       "19    Milk, lowfat, fluid, 1% milkfat, with added no...   \n",
       "20    Milk, lowfat, fluid, 1% milkfat, protein forti...   \n",
       "21    Milk, nonfat, fluid, with added vitamin A and ...   \n",
       "22    Milk, nonfat, fluid, with added nonfat milk so...   \n",
       "23    Milk, nonfat, fluid, protein fortified, with a...   \n",
       "24            Milk, buttermilk, fluid, cultured, lowfat   \n",
       "25                              Milk, low sodium, fluid   \n",
       "26               Milk, dry, whole, with added vitamin D   \n",
       "27    Milk, dry, nonfat, regular, without added vita...   \n",
       "28    Milk, dry, nonfat, instant, with added vitamin...   \n",
       "29                   Milk, dry, nonfat, calcium reduced   \n",
       "...                                                 ...   \n",
       "6606  Beef, tenderloin, steak, separable lean only, ...   \n",
       "6607  Beef, top sirloin, steak, separable lean only,...   \n",
       "6608  Beef, short loin, top loin, steak, separable l...   \n",
       "6609  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6610  Beef, brisket, flat half, separable lean only,...   \n",
       "6611  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6612  Beef, brisket, flat half, separable lean only,...   \n",
       "6613  Beef, round, eye of round, roast, separable le...   \n",
       "6614  Beef, round, top round, steak, separable lean ...   \n",
       "6615  Beef, round, bottom round, roast, separable le...   \n",
       "6616  Beef, rib, small end (ribs 10-12), separable l...   \n",
       "6617  CAMPBELL Soup Company, CAMPBELL'S Red and Whit...   \n",
       "6618  CAMPBELL Soup Company, CAMPBELL's Red and Whit...   \n",
       "6619  CAMPBELL Soup Company, CAMPBELL'S SELECT Soups...   \n",
       "6620  CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...   \n",
       "6621  CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...   \n",
       "6622  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6623  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6624  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6625  CAMPBELL Soup Company, CAMPBELL'S Red and Whit...   \n",
       "6626  CAMPBELL Soup Company, V8 Vegetable Juice, Ess...   \n",
       "6627  CAMPBELL Soup Company, V8 Vegetable Juice, Spi...   \n",
       "6628  CAMPBELL Soup Company, PACE, Jalapenos Nacho S...   \n",
       "6629  CAMPBELL Soup Company, V8 60% Vegetable Juice,...   \n",
       "6630  CAMPBELL Soup Company, V8 Vegetable Juice, Low...   \n",
       "6631                             Bologna, beef, low fat   \n",
       "6632  Turkey and pork sausage, fresh, bulk, patty or...   \n",
       "6633                              Babyfood, juice, pear   \n",
       "6634         Babyfood, dessert, banana yogurt, strained   \n",
       "6635              Babyfood, banana no tapioca, strained   \n",
       "\n",
       "                                 fgroup     id       manufacturer  \n",
       "0                Dairy and Egg Products   1008                     \n",
       "1                Dairy and Egg Products   1009                     \n",
       "2                Dairy and Egg Products   1018                     \n",
       "3                Dairy and Egg Products   1019                     \n",
       "4                Dairy and Egg Products   1028                     \n",
       "5                Dairy and Egg Products   1029                     \n",
       "6                Dairy and Egg Products   1038                     \n",
       "7                Dairy and Egg Products   1039                     \n",
       "8                Dairy and Egg Products   1048                     \n",
       "9                Dairy and Egg Products   1049                     \n",
       "10               Dairy and Egg Products   1058                     \n",
       "11               Dairy and Egg Products   1059                     \n",
       "12               Dairy and Egg Products   1068                     \n",
       "13               Dairy and Egg Products   1069                     \n",
       "14               Dairy and Egg Products   1078                     \n",
       "15               Dairy and Egg Products   1079               None  \n",
       "16               Dairy and Egg Products   1080                     \n",
       "17               Dairy and Egg Products   1081                     \n",
       "18               Dairy and Egg Products   1082                     \n",
       "19               Dairy and Egg Products   1083                     \n",
       "20               Dairy and Egg Products   1084                     \n",
       "21               Dairy and Egg Products   1085                     \n",
       "22               Dairy and Egg Products   1086                     \n",
       "23               Dairy and Egg Products   1087                     \n",
       "24               Dairy and Egg Products   1088                     \n",
       "25               Dairy and Egg Products   1089                     \n",
       "26               Dairy and Egg Products   1090                     \n",
       "27               Dairy and Egg Products   1091                     \n",
       "28               Dairy and Egg Products   1092                     \n",
       "29               Dairy and Egg Products   1093                     \n",
       "...                                 ...    ...                ...  \n",
       "6606                      Beef Products  23628                     \n",
       "6607                      Beef Products  23629                     \n",
       "6608                      Beef Products  23630                     \n",
       "6609                      Beef Products  23631                     \n",
       "6610                      Beef Products  23632                     \n",
       "6611                      Beef Products  23633                     \n",
       "6612                      Beef Products  23634                     \n",
       "6613                      Beef Products  23635                     \n",
       "6614                      Beef Products  23636                     \n",
       "6615                      Beef Products  23637                     \n",
       "6616                      Beef Products  23638                     \n",
       "6617         Soups, Sauces, and Gravies  27015  Campbell Soup Co.  \n",
       "6618         Soups, Sauces, and Gravies  27016  Campbell Soup Co.  \n",
       "6619         Soups, Sauces, and Gravies  27021  Campbell Soup Co.  \n",
       "6620         Soups, Sauces, and Gravies  27022  Campbell Soup Co.  \n",
       "6621         Soups, Sauces, and Gravies  27023  Campbell Soup Co.  \n",
       "6622         Soups, Sauces, and Gravies  27024  Campbell Soup Co.  \n",
       "6623         Soups, Sauces, and Gravies  27025  Campbell Soup Co.  \n",
       "6624         Soups, Sauces, and Gravies  27026  Campbell Soup Co.  \n",
       "6625         Soups, Sauces, and Gravies  27032  Campbell Soup Co.  \n",
       "6626  Vegetables and Vegetable Products  31010  Campbell Soup Co.  \n",
       "6627  Vegetables and Vegetable Products  31013  Campbell Soup Co.  \n",
       "6628  Vegetables and Vegetable Products  31014  Campbell Soup Co.  \n",
       "6629  Vegetables and Vegetable Products  31016  Campbell Soup Co.  \n",
       "6630  Vegetables and Vegetable Products  31017  Campbell Soup Co.  \n",
       "6631        Sausages and Luncheon Meats  42161                     \n",
       "6632        Sausages and Luncheon Meats  42173                     \n",
       "6633                         Baby Foods  43408               None  \n",
       "6634                         Baby Foods  43539               None  \n",
       "6635                         Baby Foods  43546               None  \n",
       "\n",
       "[6636 rows x 4 columns]"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col_mapping = {'description':'nutrient',\n",
    "              'group':'nutgroup'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nutrients = nutrients.rename(columns=col_mapping, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "collapsed": 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>nutrient</th>\n",
       "      <th>nutgroup</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.200</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.060</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>673.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Magnesium, Mg</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Phosphorus, P</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>490.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Potassium, K</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>93.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sodium, Na</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>690.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zinc, Zn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>2.940</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copper, Cu</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Manganese, Mn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>14.500</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>1054.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>262.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>271.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.031</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.450</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pantothenic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.190</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.074</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389325</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>1.100</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389326</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>5.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389327</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389328</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389329</th>\n",
       "      <td>Carotene, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389330</th>\n",
       "      <td>Carotene, alpha</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389331</th>\n",
       "      <td>Vitamin E (alpha-tocopherol)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.250</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389332</th>\n",
       "      <td>Vitamin D</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389333</th>\n",
       "      <td>Vitamin D (D2 + D3)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389334</th>\n",
       "      <td>Cryptoxanthin, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389335</th>\n",
       "      <td>Lycopene</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389336</th>\n",
       "      <td>Lutein + zeaxanthin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>20.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389337</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>21.900</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389338</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.020</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389339</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.060</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389340</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.540</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389341</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.260</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389342</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389343</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389344</th>\n",
       "      <td>Choline, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>4.100</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389345</th>\n",
       "      <td>Vitamin K (phylloquinone)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.500</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389346</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389347</th>\n",
       "      <td>Folate, food</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389348</th>\n",
       "      <td>Folate, DFE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_DFE</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389349</th>\n",
       "      <td>Vitamin E, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389350</th>\n",
       "      <td>Vitamin B-12, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389351</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389352</th>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.072</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389353</th>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.028</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389354</th>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.041</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375176 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  nutrient     nutgroup    units     value  \\\n",
       "0                                  Protein  Composition        g    25.180   \n",
       "1                        Total lipid (fat)  Composition        g    29.200   \n",
       "2              Carbohydrate, by difference  Composition        g     3.060   \n",
       "3                                      Ash        Other        g     3.280   \n",
       "4                                   Energy       Energy     kcal   376.000   \n",
       "5                                    Water  Composition        g    39.280   \n",
       "6                                   Energy       Energy       kJ  1573.000   \n",
       "7                     Fiber, total dietary  Composition        g     0.000   \n",
       "8                              Calcium, Ca     Elements       mg   673.000   \n",
       "9                                 Iron, Fe     Elements       mg     0.640   \n",
       "10                           Magnesium, Mg     Elements       mg    22.000   \n",
       "11                           Phosphorus, P     Elements       mg   490.000   \n",
       "12                            Potassium, K     Elements       mg    93.000   \n",
       "13                              Sodium, Na     Elements       mg   690.000   \n",
       "14                                Zinc, Zn     Elements       mg     2.940   \n",
       "15                              Copper, Cu     Elements       mg     0.024   \n",
       "16                           Manganese, Mn     Elements       mg     0.021   \n",
       "17                            Selenium, Se     Elements      mcg    14.500   \n",
       "18                           Vitamin A, IU     Vitamins       IU  1054.000   \n",
       "19                                 Retinol     Vitamins      mcg   262.000   \n",
       "20                          Vitamin A, RAE     Vitamins  mcg_RAE   271.000   \n",
       "21          Vitamin C, total ascorbic acid     Vitamins       mg     0.000   \n",
       "22                                 Thiamin     Vitamins       mg     0.031   \n",
       "23                              Riboflavin     Vitamins       mg     0.450   \n",
       "24                                  Niacin     Vitamins       mg     0.180   \n",
       "25                        Pantothenic acid     Vitamins       mg     0.190   \n",
       "26                             Vitamin B-6     Vitamins       mg     0.074   \n",
       "27                           Folate, total     Vitamins      mcg    18.000   \n",
       "28                            Vitamin B-12     Vitamins      mcg     0.270   \n",
       "29                              Folic acid     Vitamins      mcg     0.000   \n",
       "...                                    ...          ...      ...       ...   \n",
       "389325                        Selenium, Se     Elements      mcg     1.100   \n",
       "389326                       Vitamin A, IU     Vitamins       IU     5.000   \n",
       "389327                             Retinol     Vitamins      mcg     0.000   \n",
       "389328                      Vitamin A, RAE     Vitamins  mcg_RAE     0.000   \n",
       "389329                      Carotene, beta     Vitamins      mcg     2.000   \n",
       "389330                     Carotene, alpha     Vitamins      mcg     2.000   \n",
       "389331        Vitamin E (alpha-tocopherol)     Vitamins       mg     0.250   \n",
       "389332                           Vitamin D     Vitamins       IU     0.000   \n",
       "389333                 Vitamin D (D2 + D3)     Vitamins      mcg     0.000   \n",
       "389334                 Cryptoxanthin, beta     Vitamins      mcg     0.000   \n",
       "389335                            Lycopene     Vitamins      mcg     0.000   \n",
       "389336                 Lutein + zeaxanthin     Vitamins      mcg    20.000   \n",
       "389337      Vitamin C, total ascorbic acid     Vitamins       mg    21.900   \n",
       "389338                             Thiamin     Vitamins       mg     0.020   \n",
       "389339                          Riboflavin     Vitamins       mg     0.060   \n",
       "389340                              Niacin     Vitamins       mg     0.540   \n",
       "389341                         Vitamin B-6     Vitamins       mg     0.260   \n",
       "389342                       Folate, total     Vitamins      mcg    17.000   \n",
       "389343                        Vitamin B-12     Vitamins      mcg     0.000   \n",
       "389344                      Choline, total     Vitamins       mg     4.100   \n",
       "389345           Vitamin K (phylloquinone)     Vitamins      mcg     0.500   \n",
       "389346                          Folic acid     Vitamins      mcg     0.000   \n",
       "389347                        Folate, food     Vitamins      mcg    17.000   \n",
       "389348                         Folate, DFE     Vitamins  mcg_DFE    17.000   \n",
       "389349                    Vitamin E, added     Vitamins       mg     0.000   \n",
       "389350                 Vitamin B-12, added     Vitamins      mcg     0.000   \n",
       "389351                         Cholesterol        Other       mg     0.000   \n",
       "389352        Fatty acids, total saturated        Other        g     0.072   \n",
       "389353  Fatty acids, total monounsaturated        Other        g     0.028   \n",
       "389354  Fatty acids, total polyunsaturated        Other        g     0.041   \n",
       "\n",
       "           id  \n",
       "0        1008  \n",
       "1        1008  \n",
       "2        1008  \n",
       "3        1008  \n",
       "4        1008  \n",
       "5        1008  \n",
       "6        1008  \n",
       "7        1008  \n",
       "8        1008  \n",
       "9        1008  \n",
       "10       1008  \n",
       "11       1008  \n",
       "12       1008  \n",
       "13       1008  \n",
       "14       1008  \n",
       "15       1008  \n",
       "16       1008  \n",
       "17       1008  \n",
       "18       1008  \n",
       "19       1008  \n",
       "20       1008  \n",
       "21       1008  \n",
       "22       1008  \n",
       "23       1008  \n",
       "24       1008  \n",
       "25       1008  \n",
       "26       1008  \n",
       "27       1008  \n",
       "28       1008  \n",
       "29       1008  \n",
       "...       ...  \n",
       "389325  43546  \n",
       "389326  43546  \n",
       "389327  43546  \n",
       "389328  43546  \n",
       "389329  43546  \n",
       "389330  43546  \n",
       "389331  43546  \n",
       "389332  43546  \n",
       "389333  43546  \n",
       "389334  43546  \n",
       "389335  43546  \n",
       "389336  43546  \n",
       "389337  43546  \n",
       "389338  43546  \n",
       "389339  43546  \n",
       "389340  43546  \n",
       "389341  43546  \n",
       "389342  43546  \n",
       "389343  43546  \n",
       "389344  43546  \n",
       "389345  43546  \n",
       "389346  43546  \n",
       "389347  43546  \n",
       "389348  43546  \n",
       "389349  43546  \n",
       "389350  43546  \n",
       "389351  43546  \n",
       "389352  43546  \n",
       "389353  43546  \n",
       "389354  43546  \n",
       "\n",
       "[375176 rows x 5 columns]"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 合并info和nutrients\n",
    "ndata = pd.merge(nutrients, info, on='id', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nutrient</th>\n",
       "      <th>nutgroup</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "      <th>food</th>\n",
       "      <th>fgroup</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.180</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.200</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.060</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.280</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.280</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>673.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Magnesium, Mg</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Phosphorus, P</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>490.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Potassium, K</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>93.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sodium, Na</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>690.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zinc, Zn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>2.940</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copper, Cu</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Manganese, Mn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>14.500</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>1054.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>262.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>271.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.031</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.450</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pantothenic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.190</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.074</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375146</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>1.100</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375147</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>5.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375148</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375149</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375150</th>\n",
       "      <td>Carotene, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375151</th>\n",
       "      <td>Carotene, alpha</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375152</th>\n",
       "      <td>Vitamin E (alpha-tocopherol)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.250</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375153</th>\n",
       "      <td>Vitamin D</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375154</th>\n",
       "      <td>Vitamin D (D2 + D3)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375155</th>\n",
       "      <td>Cryptoxanthin, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375156</th>\n",
       "      <td>Lycopene</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375157</th>\n",
       "      <td>Lutein + zeaxanthin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>20.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375158</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>21.900</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375159</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.020</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375160</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.060</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375161</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.540</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375162</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.260</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375163</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375164</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375165</th>\n",
       "      <td>Choline, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>4.100</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375166</th>\n",
       "      <td>Vitamin K (phylloquinone)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.500</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375167</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375168</th>\n",
       "      <td>Folate, food</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375169</th>\n",
       "      <td>Folate, DFE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_DFE</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375170</th>\n",
       "      <td>Vitamin E, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375171</th>\n",
       "      <td>Vitamin B-12, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375172</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375173</th>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.072</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375174</th>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.028</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375175</th>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.041</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375176 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  nutrient     nutgroup    units     value  \\\n",
       "0                                  Protein  Composition        g    25.180   \n",
       "1                        Total lipid (fat)  Composition        g    29.200   \n",
       "2              Carbohydrate, by difference  Composition        g     3.060   \n",
       "3                                      Ash        Other        g     3.280   \n",
       "4                                   Energy       Energy     kcal   376.000   \n",
       "5                                    Water  Composition        g    39.280   \n",
       "6                                   Energy       Energy       kJ  1573.000   \n",
       "7                     Fiber, total dietary  Composition        g     0.000   \n",
       "8                              Calcium, Ca     Elements       mg   673.000   \n",
       "9                                 Iron, Fe     Elements       mg     0.640   \n",
       "10                           Magnesium, Mg     Elements       mg    22.000   \n",
       "11                           Phosphorus, P     Elements       mg   490.000   \n",
       "12                            Potassium, K     Elements       mg    93.000   \n",
       "13                              Sodium, Na     Elements       mg   690.000   \n",
       "14                                Zinc, Zn     Elements       mg     2.940   \n",
       "15                              Copper, Cu     Elements       mg     0.024   \n",
       "16                           Manganese, Mn     Elements       mg     0.021   \n",
       "17                            Selenium, Se     Elements      mcg    14.500   \n",
       "18                           Vitamin A, IU     Vitamins       IU  1054.000   \n",
       "19                                 Retinol     Vitamins      mcg   262.000   \n",
       "20                          Vitamin A, RAE     Vitamins  mcg_RAE   271.000   \n",
       "21          Vitamin C, total ascorbic acid     Vitamins       mg     0.000   \n",
       "22                                 Thiamin     Vitamins       mg     0.031   \n",
       "23                              Riboflavin     Vitamins       mg     0.450   \n",
       "24                                  Niacin     Vitamins       mg     0.180   \n",
       "25                        Pantothenic acid     Vitamins       mg     0.190   \n",
       "26                             Vitamin B-6     Vitamins       mg     0.074   \n",
       "27                           Folate, total     Vitamins      mcg    18.000   \n",
       "28                            Vitamin B-12     Vitamins      mcg     0.270   \n",
       "29                              Folic acid     Vitamins      mcg     0.000   \n",
       "...                                    ...          ...      ...       ...   \n",
       "375146                        Selenium, Se     Elements      mcg     1.100   \n",
       "375147                       Vitamin A, IU     Vitamins       IU     5.000   \n",
       "375148                             Retinol     Vitamins      mcg     0.000   \n",
       "375149                      Vitamin A, RAE     Vitamins  mcg_RAE     0.000   \n",
       "375150                      Carotene, beta     Vitamins      mcg     2.000   \n",
       "375151                     Carotene, alpha     Vitamins      mcg     2.000   \n",
       "375152        Vitamin E (alpha-tocopherol)     Vitamins       mg     0.250   \n",
       "375153                           Vitamin D     Vitamins       IU     0.000   \n",
       "375154                 Vitamin D (D2 + D3)     Vitamins      mcg     0.000   \n",
       "375155                 Cryptoxanthin, beta     Vitamins      mcg     0.000   \n",
       "375156                            Lycopene     Vitamins      mcg     0.000   \n",
       "375157                 Lutein + zeaxanthin     Vitamins      mcg    20.000   \n",
       "375158      Vitamin C, total ascorbic acid     Vitamins       mg    21.900   \n",
       "375159                             Thiamin     Vitamins       mg     0.020   \n",
       "375160                          Riboflavin     Vitamins       mg     0.060   \n",
       "375161                              Niacin     Vitamins       mg     0.540   \n",
       "375162                         Vitamin B-6     Vitamins       mg     0.260   \n",
       "375163                       Folate, total     Vitamins      mcg    17.000   \n",
       "375164                        Vitamin B-12     Vitamins      mcg     0.000   \n",
       "375165                      Choline, total     Vitamins       mg     4.100   \n",
       "375166           Vitamin K (phylloquinone)     Vitamins      mcg     0.500   \n",
       "375167                          Folic acid     Vitamins      mcg     0.000   \n",
       "375168                        Folate, food     Vitamins      mcg    17.000   \n",
       "375169                         Folate, DFE     Vitamins  mcg_DFE    17.000   \n",
       "375170                    Vitamin E, added     Vitamins       mg     0.000   \n",
       "375171                 Vitamin B-12, added     Vitamins      mcg     0.000   \n",
       "375172                         Cholesterol        Other       mg     0.000   \n",
       "375173        Fatty acids, total saturated        Other        g     0.072   \n",
       "375174  Fatty acids, total monounsaturated        Other        g     0.028   \n",
       "375175  Fatty acids, total polyunsaturated        Other        g     0.041   \n",
       "\n",
       "           id                                   food                  fgroup  \\\n",
       "0        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "1        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "2        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "3        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "4        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "5        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "6        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "7        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "8        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "9        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "10       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "11       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "12       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "13       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "14       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "15       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "16       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "17       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "18       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "19       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "20       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "21       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "22       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "23       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "24       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "25       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "26       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "27       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "28       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "29       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "...       ...                                    ...                     ...   \n",
       "375146  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375147  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375148  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375149  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375150  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375151  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375152  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375153  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375154  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375155  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375156  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375157  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375158  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375159  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375160  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375161  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375162  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375163  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375164  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375165  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375166  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375167  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375168  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375169  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375170  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375171  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375172  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375173  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375174  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375175  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "\n",
       "       manufacturer  \n",
       "0                    \n",
       "1                    \n",
       "2                    \n",
       "3                    \n",
       "4                    \n",
       "5                    \n",
       "6                    \n",
       "7                    \n",
       "8                    \n",
       "9                    \n",
       "10                   \n",
       "11                   \n",
       "12                   \n",
       "13                   \n",
       "14                   \n",
       "15                   \n",
       "16                   \n",
       "17                   \n",
       "18                   \n",
       "19                   \n",
       "20                   \n",
       "21                   \n",
       "22                   \n",
       "23                   \n",
       "24                   \n",
       "25                   \n",
       "26                   \n",
       "27                   \n",
       "28                   \n",
       "29                   \n",
       "...             ...  \n",
       "375146         None  \n",
       "375147         None  \n",
       "375148         None  \n",
       "375149         None  \n",
       "375150         None  \n",
       "375151         None  \n",
       "375152         None  \n",
       "375153         None  \n",
       "375154         None  \n",
       "375155         None  \n",
       "375156         None  \n",
       "375157         None  \n",
       "375158         None  \n",
       "375159         None  \n",
       "375160         None  \n",
       "375161         None  \n",
       "375162         None  \n",
       "375163         None  \n",
       "375164         None  \n",
       "375165         None  \n",
       "375166         None  \n",
       "375167         None  \n",
       "375168         None  \n",
       "375169         None  \n",
       "375170         None  \n",
       "375171         None  \n",
       "375172         None  \n",
       "375173         None  \n",
       "375174         None  \n",
       "375175         None  \n",
       "\n",
       "[375176 rows x 8 columns]"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "nutrient                                       Glycine\n",
       "nutgroup                                   Amino Acids\n",
       "units                                                g\n",
       "value                                             0.04\n",
       "id                                                6158\n",
       "food            Soup, tomato bisque, canned, condensed\n",
       "fgroup                      Soups, Sauces, and Gravies\n",
       "manufacturer                                          \n",
       "Name: 30000, dtype: object"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata.ix[30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26455c50>"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DkiRJ0nFSB2UhQdKUkoEzWtLjklqp7tvq3OPq6bhIOljSM0WBtSPzLS/pR02u/4+kP0j6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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24a70d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Series.sort_values((result['Zinc, Zn'])).plot(kind='barh')  # 书中的order（）函数不可用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 找出各营养成分最丰富的食物\n",
    "by_nutrient = ndata.groupby(['nutgroup', 'nutrient'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_maximum = lambda x: x.xs(x.value.idxmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_minimum = lambda x:x.xs(x.value.idxmin())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_foods = by_nutrient.apply(get_maximum)[['value','food']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 让food小一点\n",
    "max_foods.food = max_foods.food.str[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "nutrient\n",
       "Alanine                           Gelatins, dry powder, unsweetened\n",
       "Arginine                               Seeds, sesame flour, low-fat\n",
       "Aspartic acid                                   Soy protein isolate\n",
       "Cystine                Seeds, cottonseed flour, low fat (glandless)\n",
       "Glutamic acid                                   Soy protein isolate\n",
       "Glycine                           Gelatins, dry powder, unsweetened\n",
       "Histidine                Whale, beluga, meat, dried (Alaska Native)\n",
       "Hydroxyproline    KENTUCKY FRIED CHICKEN, Fried Chicken, ORIGINA...\n",
       "Isoleucine        Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Leucine           Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Lysine            Seal, bearded (Oogruk), meat, dried (Alaska Na...\n",
       "Methionine                    Fish, cod, Atlantic, dried and salted\n",
       "Phenylalanine     Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Proline                           Gelatins, dry powder, unsweetened\n",
       "Serine            Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Threonine         Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Tryptophan         Sea lion, Steller, meat with fat (Alaska Native)\n",
       "Tyrosine          Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Valine            Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Name: food, dtype: object"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_foods.ix['Amino Acids']['food']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": 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></th>\n",
       "      <th>value</th>\n",
       "      <th>food</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nutgroup</th>\n",
       "      <th>nutrient</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">Amino Acids</th>\n",
       "      <th>Alanine</th>\n",
       "      <td>8.009</td>\n",
       "      <td>Gelatins, dry powder, unsweetened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arginine</th>\n",
       "      <td>7.436</td>\n",
       "      <td>Seeds, sesame flour, low-fat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aspartic acid</th>\n",
       "      <td>10.203</td>\n",
       "      <td>Soy protein isolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cystine</th>\n",
       "      <td>1.307</td>\n",
       "      <td>Seeds, cottonseed flour, low fat (glandless)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glutamic acid</th>\n",
       "      <td>17.452</td>\n",
       "      <td>Soy protein isolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycine</th>\n",
       "      <td>19.049</td>\n",
       "      <td>Gelatins, dry powder, unsweetened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Histidine</th>\n",
       "      <td>2.999</td>\n",
       "      <td>Whale, beluga, meat, dried (Alaska Native)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydroxyproline</th>\n",
       "      <td>0.803</td>\n",
       "      <td>KENTUCKY FRIED CHICKEN, Fried Chicken, ORIGINA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isoleucine</th>\n",
       "      <td>4.300</td>\n",
       "      <td>Soy protein isolate, PROTEIN TECHNOLOGIES INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leucine</th>\n",
       "      <td>7.200</td>\n",
       "      <td>Soy protein isolate, PROTEIN TECHNOLOGIES INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lysine</th>\n",
       "      <td>6.690</td>\n",
       "      <td>Seal, bearded (Oogruk), meat, dried (Alaska Na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Methionine</th>\n",
       "      <td>1.859</td>\n",
       "      <td>Fish, cod, Atlantic, dried and salted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenylalanine</th>\n",
       "      <td>4.600</td>\n",
       "      <td>Soy protein isolate, PROTEIN TECHNOLOGIES INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proline</th>\n",
       "      <td>12.295</td>\n",
       "      <td>Gelatins, dry powder, unsweetened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serine</th>\n",
       "      <td>4.600</td>\n",
       "      <td>Soy protein isolate, PROTEIN TECHNOLOGIES INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Threonine</th>\n",
       "      <td>3.300</td>\n",
       "      <td>Soy protein isolate, PROTEIN TECHNOLOGIES INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tryptophan</th>\n",
       "      <td>1.600</td>\n",
       "      <td>Sea lion, Steller, meat with fat (Alaska Native)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyrosine</th>\n",
       "      <td>3.300</td>\n",
       "      <td>Soy protein isolate, PROTEIN TECHNOLOGIES INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valine</th>\n",
       "      <td>4.500</td>\n",
       "      <td>Soy protein isolate, PROTEIN TECHNOLOGIES INTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Composition</th>\n",
       "      <th>Adjusted Protein</th>\n",
       "      <td>12.900</td>\n",
       "      <td>Baking chocolate, unsweetened, squares</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carbohydrate, by difference</th>\n",
       "      <td>100.000</td>\n",
       "      <td>Sweeteners, tabletop, fructose, dry, powder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiber, total dietary</th>\n",
       "      <td>79.000</td>\n",
       "      <td>Corn bran, crude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein</th>\n",
       "      <td>88.320</td>\n",
       "      <td>Soy protein isolate, potassium type, crude pro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sugars, total</th>\n",
       "      <td>99.800</td>\n",
       "      <td>Sugars, granulated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total lipid (fat)</th>\n",
       "      <td>100.000</td>\n",
       "      <td>Oil, wheat germ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Water</th>\n",
       "      <td>100.000</td>\n",
       "      <td>Water, bottled, POLAND SPRING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Elements</th>\n",
       "      <th>Calcium, Ca</th>\n",
       "      <td>7364.000</td>\n",
       "      <td>Leavening agents, baking powder, double-acting...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copper, Cu</th>\n",
       "      <td>15.050</td>\n",
       "      <td>Veal, variety meats and by-products, liver, co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fluoride, F</th>\n",
       "      <td>584.000</td>\n",
       "      <td>Tea, instant, sweetened with sugar, lemon-flav...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iron, Fe</th>\n",
       "      <td>87.470</td>\n",
       "      <td>Salad dressing, russian dressing, low calorie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">Vitamins</th>\n",
       "      <th>Cryptoxanthin, beta</th>\n",
       "      <td>6186.000</td>\n",
       "      <td>Spices, paprika</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dihydrophylloquinone</th>\n",
       "      <td>103.800</td>\n",
       "      <td>Margarine, 80% fat, stick, includes regular an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folate, DFE</th>\n",
       "      <td>2630.000</td>\n",
       "      <td>Cereals ready-to-eat, QUAKER, CAP'N CRUNCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folate, food</th>\n",
       "      <td>2340.000</td>\n",
       "      <td>Leavening agents, yeast, baker's, active dry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folate, total</th>\n",
       "      <td>2340.000</td>\n",
       "      <td>Leavening agents, yeast, baker's, active dry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folic acid</th>\n",
       "      <td>1538.000</td>\n",
       "      <td>Cereals ready-to-eat, QUAKER, CAP'N CRUNCH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lutein + zeaxanthin</th>\n",
       "      <td>39550.000</td>\n",
       "      <td>Kale, raw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lycopene</th>\n",
       "      <td>46260.000</td>\n",
       "      <td>Tomato powder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Menaquinone-4</th>\n",
       "      <td>33.200</td>\n",
       "      <td>Chicken, broilers or fryers, drumstick, meat a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niacin</th>\n",
       "      <td>97.000</td>\n",
       "      <td>Yeast extract spread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pantothenic acid</th>\n",
       "      <td>35.000</td>\n",
       "      <td>Cereals ready-to-eat, KELLOGG, KELLOGG'S Compl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retinol</th>\n",
       "      <td>30000.000</td>\n",
       "      <td>Fish oil, cod liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riboflavin</th>\n",
       "      <td>14.300</td>\n",
       "      <td>Yeast extract spread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thiamin</th>\n",
       "      <td>20.000</td>\n",
       "      <td>MORNINGSTAR FARMS Hot and Spicy Veggie Sausage...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tocopherol, beta</th>\n",
       "      <td>6.490</td>\n",
       "      <td>Yellow pond lily, Wocas, dried seeds (Pacific ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tocopherol, delta</th>\n",
       "      <td>30.880</td>\n",
       "      <td>Oil, cooking and salad, ENOVA, 80% diglycerides</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tocopherol, gamma</th>\n",
       "      <td>100.880</td>\n",
       "      <td>Oil, cooking and salad, ENOVA, 80% diglycerides</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin A, IU</th>\n",
       "      <td>100000.000</td>\n",
       "      <td>Fish oil, cod liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin A, RAE</th>\n",
       "      <td>30000.000</td>\n",
       "      <td>Fish oil, cod liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin B-12</th>\n",
       "      <td>98.890</td>\n",
       "      <td>Mollusks, clam, mixed species, cooked, moist heat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin B-12, added</th>\n",
       "      <td>24.000</td>\n",
       "      <td>Cereals ready-to-eat, KELLOGG, KELLOGG'S ALL-B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin B-6</th>\n",
       "      <td>12.000</td>\n",
       "      <td>Cereals ready-to-eat, KELLOGG, KELLOGG'S ALL-B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin C, total ascorbic acid</th>\n",
       "      <td>2400.000</td>\n",
       "      <td>Orange-flavor drink, breakfast type, low calor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin D</th>\n",
       "      <td>10000.000</td>\n",
       "      <td>Fish oil, cod liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin D (D2 + D3)</th>\n",
       "      <td>250.000</td>\n",
       "      <td>Fish oil, cod liver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin D2 (ergocalciferol)</th>\n",
       "      <td>28.100</td>\n",
       "      <td>Mushrooms, maitake, raw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin D3 (cholecalciferol)</th>\n",
       "      <td>27.400</td>\n",
       "      <td>Fish, halibut, Greenland, raw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin E (alpha-tocopherol)</th>\n",
       "      <td>149.400</td>\n",
       "      <td>Oil, wheat germ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin E, added</th>\n",
       "      <td>46.550</td>\n",
       "      <td>Cereals ready-to-eat, GENERAL MILLS, Multi-Gra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin K (phylloquinone)</th>\n",
       "      <td>1714.500</td>\n",
       "      <td>Spices, sage, ground</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 value  \\\n",
       "nutgroup    nutrient                                     \n",
       "Amino Acids Alanine                              8.009   \n",
       "            Arginine                             7.436   \n",
       "            Aspartic acid                       10.203   \n",
       "            Cystine                              1.307   \n",
       "            Glutamic acid                       17.452   \n",
       "            Glycine                             19.049   \n",
       "            Histidine                            2.999   \n",
       "            Hydroxyproline                       0.803   \n",
       "            Isoleucine                           4.300   \n",
       "            Leucine                              7.200   \n",
       "            Lysine                               6.690   \n",
       "            Methionine                           1.859   \n",
       "            Phenylalanine                        4.600   \n",
       "            Proline                             12.295   \n",
       "            Serine                               4.600   \n",
       "            Threonine                            3.300   \n",
       "            Tryptophan                           1.600   \n",
       "            Tyrosine                             3.300   \n",
       "            Valine                               4.500   \n",
       "Composition Adjusted Protein                    12.900   \n",
       "            Carbohydrate, by difference        100.000   \n",
       "            Fiber, total dietary                79.000   \n",
       "            Protein                             88.320   \n",
       "            Sugars, total                       99.800   \n",
       "            Total lipid (fat)                  100.000   \n",
       "            Water                              100.000   \n",
       "Elements    Calcium, Ca                       7364.000   \n",
       "            Copper, Cu                          15.050   \n",
       "            Fluoride, F                        584.000   \n",
       "            Iron, Fe                            87.470   \n",
       "...                                                ...   \n",
       "Vitamins    Cryptoxanthin, beta               6186.000   \n",
       "            Dihydrophylloquinone               103.800   \n",
       "            Folate, DFE                       2630.000   \n",
       "            Folate, food                      2340.000   \n",
       "            Folate, total                     2340.000   \n",
       "            Folic acid                        1538.000   \n",
       "            Lutein + zeaxanthin              39550.000   \n",
       "            Lycopene                         46260.000   \n",
       "            Menaquinone-4                       33.200   \n",
       "            Niacin                              97.000   \n",
       "            Pantothenic acid                    35.000   \n",
       "            Retinol                          30000.000   \n",
       "            Riboflavin                          14.300   \n",
       "            Thiamin                             20.000   \n",
       "            Tocopherol, beta                     6.490   \n",
       "            Tocopherol, delta                   30.880   \n",
       "            Tocopherol, gamma                  100.880   \n",
       "            Vitamin A, IU                   100000.000   \n",
       "            Vitamin A, RAE                   30000.000   \n",
       "            Vitamin B-12                        98.890   \n",
       "            Vitamin B-12, added                 24.000   \n",
       "            Vitamin B-6                         12.000   \n",
       "            Vitamin C, total ascorbic acid    2400.000   \n",
       "            Vitamin D                        10000.000   \n",
       "            Vitamin D (D2 + D3)                250.000   \n",
       "            Vitamin D2 (ergocalciferol)         28.100   \n",
       "            Vitamin D3 (cholecalciferol)        27.400   \n",
       "            Vitamin E (alpha-tocopherol)       149.400   \n",
       "            Vitamin E, added                    46.550   \n",
       "            Vitamin K (phylloquinone)         1714.500   \n",
       "\n",
       "                                                                                         food  \n",
       "nutgroup    nutrient                                                                           \n",
       "Amino Acids Alanine                                         Gelatins, dry powder, unsweetened  \n",
       "            Arginine                                             Seeds, sesame flour, low-fat  \n",
       "            Aspartic acid                                                 Soy protein isolate  \n",
       "            Cystine                              Seeds, cottonseed flour, low fat (glandless)  \n",
       "            Glutamic acid                                                 Soy protein isolate  \n",
       "            Glycine                                         Gelatins, dry powder, unsweetened  \n",
       "            Histidine                              Whale, beluga, meat, dried (Alaska Native)  \n",
       "            Hydroxyproline                  KENTUCKY FRIED CHICKEN, Fried Chicken, ORIGINA...  \n",
       "            Isoleucine                      Soy protein isolate, PROTEIN TECHNOLOGIES INTE...  \n",
       "            Leucine                         Soy protein isolate, PROTEIN TECHNOLOGIES INTE...  \n",
       "            Lysine                          Seal, bearded (Oogruk), meat, dried (Alaska Na...  \n",
       "            Methionine                                  Fish, cod, Atlantic, dried and salted  \n",
       "            Phenylalanine                   Soy protein isolate, PROTEIN TECHNOLOGIES INTE...  \n",
       "            Proline                                         Gelatins, dry powder, unsweetened  \n",
       "            Serine                          Soy protein isolate, PROTEIN TECHNOLOGIES INTE...  \n",
       "            Threonine                       Soy protein isolate, PROTEIN TECHNOLOGIES INTE...  \n",
       "            Tryptophan                       Sea lion, Steller, meat with fat (Alaska Native)  \n",
       "            Tyrosine                        Soy protein isolate, PROTEIN TECHNOLOGIES INTE...  \n",
       "            Valine                          Soy protein isolate, PROTEIN TECHNOLOGIES INTE...  \n",
       "Composition Adjusted Protein                           Baking chocolate, unsweetened, squares  \n",
       "            Carbohydrate, by difference           Sweeteners, tabletop, fructose, dry, powder  \n",
       "            Fiber, total dietary                                             Corn bran, crude  \n",
       "            Protein                         Soy protein isolate, potassium type, crude pro...  \n",
       "            Sugars, total                                                  Sugars, granulated  \n",
       "            Total lipid (fat)                                                 Oil, wheat germ  \n",
       "            Water                                               Water, bottled, POLAND SPRING  \n",
       "Elements    Calcium, Ca                     Leavening agents, baking powder, double-acting...  \n",
       "            Copper, Cu                      Veal, variety meats and by-products, liver, co...  \n",
       "            Fluoride, F                     Tea, instant, sweetened with sugar, lemon-flav...  \n",
       "            Iron, Fe                            Salad dressing, russian dressing, low calorie  \n",
       "...                                                                                       ...  \n",
       "Vitamins    Cryptoxanthin, beta                                               Spices, paprika  \n",
       "            Dihydrophylloquinone            Margarine, 80% fat, stick, includes regular an...  \n",
       "            Folate, DFE                            Cereals ready-to-eat, QUAKER, CAP'N CRUNCH  \n",
       "            Folate, food                         Leavening agents, yeast, baker's, active dry  \n",
       "            Folate, total                        Leavening agents, yeast, baker's, active dry  \n",
       "            Folic acid                             Cereals ready-to-eat, QUAKER, CAP'N CRUNCH  \n",
       "            Lutein + zeaxanthin                                                     Kale, raw  \n",
       "            Lycopene                                                            Tomato powder  \n",
       "            Menaquinone-4                   Chicken, broilers or fryers, drumstick, meat a...  \n",
       "            Niacin                                                       Yeast extract spread  \n",
       "            Pantothenic acid                Cereals ready-to-eat, KELLOGG, KELLOGG'S Compl...  \n",
       "            Retinol                                                       Fish oil, cod liver  \n",
       "            Riboflavin                                                   Yeast extract spread  \n",
       "            Thiamin                         MORNINGSTAR FARMS Hot and Spicy Veggie Sausage...  \n",
       "            Tocopherol, beta                Yellow pond lily, Wocas, dried seeds (Pacific ...  \n",
       "            Tocopherol, delta                 Oil, cooking and salad, ENOVA, 80% diglycerides  \n",
       "            Tocopherol, gamma                 Oil, cooking and salad, ENOVA, 80% diglycerides  \n",
       "            Vitamin A, IU                                                 Fish oil, cod liver  \n",
       "            Vitamin A, RAE                                                Fish oil, cod liver  \n",
       "            Vitamin B-12                    Mollusks, clam, mixed species, cooked, moist heat  \n",
       "            Vitamin B-12, added             Cereals ready-to-eat, KELLOGG, KELLOGG'S ALL-B...  \n",
       "            Vitamin B-6                     Cereals ready-to-eat, KELLOGG, KELLOGG'S ALL-B...  \n",
       "            Vitamin C, total ascorbic acid  Orange-flavor drink, breakfast type, low calor...  \n",
       "            Vitamin D                                                     Fish oil, cod liver  \n",
       "            Vitamin D (D2 + D3)                                           Fish oil, cod liver  \n",
       "            Vitamin D2 (ergocalciferol)                               Mushrooms, maitake, raw  \n",
       "            Vitamin D3 (cholecalciferol)                        Fish, halibut, Greenland, raw  \n",
       "            Vitamin E (alpha-tocopherol)                                      Oil, wheat germ  \n",
       "            Vitamin E, added                Cereals ready-to-eat, GENERAL MILLS, Multi-Gra...  \n",
       "            Vitamin K (phylloquinone)                                    Spices, sage, ground  \n",
       "\n",
       "[94 rows x 2 columns]"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_foods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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