pfda_chapter_7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(12345)\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "from pandas import Series, DataFrame\n",
    "import pandas\n",
    "import pandas as pd\n",
    "np.set_printoptions(precision=4, threshold=500)\n",
    "pd.options.display.max_rows = 100\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Database-style DataFrame Merges\n",
    "df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],\n",
    "                'data1': range(7)})\n",
    "df2 = DataFrame({'key': ['a', 'b', 'd'],\n",
    "                'data2': range(3)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>a</td>\n",
       "    </tr>\n",
       "    <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on = 'key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = DataFrame({'lkey': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],\n",
    "                'data1': range(7)})\n",
    "df4 = DataFrame({'rkey': ['a', 'b', 'd'],\n",
    "                'data2': range(3)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>lkey</th>\n",
       "      <th>data2</th>\n",
       "      <th>rkey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df3, df4, left_on = 'lkey', right_on = 'rkey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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\n",
       "6      3   c    NaN\n",
       "7    NaN   d      2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Many-to-many joins\n",
    "df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],\n",
    "                'data1': range(6)})\n",
    "df2 = DataFrame({'key': ['a', 'b', 'a', 'b', 'd'],\n",
    "                'data2': range(5)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data1 key  data2\n",
       "0       0   b      1\n",
       "1       0   b      3\n",
       "2       1   b      1\n",
       "3       1   b      3\n",
       "4       2   a      0\n",
       "5       2   a      2\n",
       "6       3   c    NaN\n",
       "7       4   a      0\n",
       "8       4   a      2\n",
       "9       5   b      1\n",
       "10      5   b      3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on = 'key', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\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\n",
       "6      2   a      0\n",
       "7      2   a      2\n",
       "8      4   a      0\n",
       "9      4   a      2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left = DataFrame({'key1': ['foo', 'foo', 'bar'],\n",
    "                  'key2': ['one', 'two', 'one'],\n",
    "                  'lval': [1, 2, 3]})\n",
    "right = DataFrame({'key1': ['foo', 'foo', 'bar', 'bar'],\n",
    "                   'key2': ['one', 'one', 'one', 'two'],\n",
    "                   'rval': [4, 5, 6, 7]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>lval</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>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2  lval  rval\n",
       "0  foo  one     1     4\n",
       "1  foo  one     1     5\n",
       "2  foo  two     2   NaN\n",
       "3  bar  one     3     6\n",
       "4  bar  two   NaN     7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on = ['key1', 'key2'], how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on = 'key1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the duplicate keys\n",
    "pd.merge(left, right, on = 'key1', suffixes = ('_left', '_right'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Merging on index\n",
    "left1 = DataFrame({'key': ['a', 'b', 'a', 'a', 'b', 'c'],\n",
    "                   'value': range(6)})\n",
    "right1 = DataFrame({'group_val': [3.5, 7]},\n",
    "                   index = ['a', 'b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>value</th>\n",
       "      <th>group_val</th>\n",
       "    </tr>\n",
       "  </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>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>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</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",
       "  </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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on = 'key', right_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>value</th>\n",
       "      <th>group_val</th>\n",
       "    </tr>\n",
       "  </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>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>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</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",
       "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on = 'key', right_index = True, \n",
    "         how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Hierarchical-data merges\n",
    "lefth = DataFrame({'key1': ['Ohio', 'Ohio', 'Ohio', \n",
    "                            'Nevada', 'Nevada'],\n",
    "                  'key2': [2000, 2001, 2002, 2001, 2002],\n",
    "                  'data': np.arange(5.)})\n",
    "righth = DataFrame(np.arange(12).reshape((6, 2)),\n",
    "                   index = [['Nevada', 'Nevada', 'Ohio',\n",
    "                            'Ohio', 'Ohio', 'Ohio'],\n",
    "                           [2001, 2000, 2000, 2000, 2001, 2002]],\n",
    "                  columns = ['event1', 'event2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data    key1  key2\n",
       "0     0    Ohio  2000\n",
       "1     1    Ohio  2001\n",
       "2     2    Ohio  2002\n",
       "3     3  Nevada  2001\n",
       "4     4  Nevada  2002"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>event1</th>\n",
       "      <th>event2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Nevada</th>\n",
       "      <th>2001</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             event1  event2\n",
       "Nevada 2001       0       1\n",
       "       2000       2       3\n",
       "Ohio   2000       4       5\n",
       "       2000       6       7\n",
       "       2001       8       9\n",
       "       2002      10      11"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "righth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>event1</th>\n",
       "      <th>event2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data    key1  key2  event1  event2\n",
       "0     0    Ohio  2000       4       5\n",
       "0     0    Ohio  2000       6       7\n",
       "1     1    Ohio  2001       8       9\n",
       "2     2    Ohio  2002      10      11\n",
       "3     3  Nevada  2001       0       1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(lefth, righth, left_on = ['key1', 'key2'],\n",
    "        right_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>event1</th>\n",
       "      <th>event2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data    key1  key2  event1  event2\n",
       "0     0    Ohio  2000       4       5\n",
       "0     0    Ohio  2000       6       7\n",
       "1     1    Ohio  2001       8       9\n",
       "2     2    Ohio  2002      10      11\n",
       "3     3  Nevada  2001       0       1\n",
       "4     4  Nevada  2002     NaN     NaN\n",
       "4   NaN  Nevada  2000       2       3"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left2 = DataFrame([[1., 2.], [3., 4.], [5., 6.]],\n",
    "                 index = ['a', 'c', 'e'],\n",
    "                 columns = ['Ohio', 'Nevada'])\n",
    "right2 = DataFrame([[7., 8.], [9., 10.], [11., 12.], [13, 14]],\n",
    "                  index = ['b', 'c', 'd', 'e'],\n",
    "                  columns = ['Missouri', 'Alabama'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada\n",
       "a     1       2\n",
       "c     3       4\n",
       "e     5       6"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Missouri  Alabama\n",
       "b         7        8\n",
       "c         9       10\n",
       "d        11       12\n",
       "e        13       14"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabama\n",
       "a     1       2       NaN      NaN\n",
       "b   NaN     NaN         7        8\n",
       "c     3       4         9       10\n",
       "d   NaN     NaN        11       12\n",
       "e     5       6        13       14"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left2, right2, how = 'outer', left_index = True,\n",
    "         right_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabama\n",
       "a     1       2       NaN      NaN\n",
       "b   NaN     NaN         7        8\n",
       "c     3       4         9       10\n",
       "d   NaN     NaN        11       12\n",
       "e     5       6        13       14"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More convenient joining by index\n",
    "left2.join(right2, how = 'outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>value</th>\n",
       "      <th>group_val</th>\n",
       "    </tr>\n",
       "  </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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This method also supports joining by column\n",
    "left1.join(right1, on = 'key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "another = DataFrame([[7., 8.], [9., 10.], [11., 12.], [16., 17.]],\n",
    "                   index = ['a', 'c', 'e', 'f'],\n",
    "                   columns = ['New York', 'Oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Missouri</th>\n",
       "      <th>Alabama</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</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Nevada  Missouri  Alabama  New York  Oregon\n",
       "a     1       2       NaN      NaN         7       8\n",
       "c     3       4         9       10         9      10\n",
       "e     5       6        13       14        11      12"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left2.join([right2, another])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating along an axis (rowbind)\n",
    "arr = np.arange(12).reshape((3, 4))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([arr, arr], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Concat with labelled axis\n",
    "s1 = Series([0, 1], index = ['a', 'b'])\n",
    "s2 = Series([2, 3, 4], index = ['c', 'd', 'e'])\n",
    "s3 = Series([5, 6], index = ['f', 'g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3]) # Column bind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "    0   1   2\n",
       "a   0 NaN NaN\n",
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       "e NaN   4 NaN\n",
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       "g NaN NaN   6"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.concat([s1, s2, s3], axis = 1)"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "s4 = pd.concat([s1 * 5, s3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
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    {
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    "pd.concat([s1, s4], axis = 1, join_axes = [['a', 'c', 'b', 'e']])"
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   "execution_count": 45,
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    {
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       "one    a    0\n",
       "       b    1\n",
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       "       b    1\n",
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       "       g    6\n",
       "dtype: int64"
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     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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    "result = pd.concat([s1, s1, s3], keys = ['one', 'two', 'three'])\n",
    "result"
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  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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    "result.unstack()"
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   "metadata": {
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    {
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       "   one  two  three\n",
       "a    0  NaN    NaN\n",
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       "e  NaN    4    NaN\n",
       "f  NaN  NaN      5\n",
       "g  NaN  NaN      6"
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     "execution_count": 47,
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    "pd.concat([s1, s2, s3], axis = 1, keys = ['one', 'two', 'three'])"
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  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Concat with DataFrames\n",
    "df1 = DataFrame(np.arange(6).reshape((3, 2)), \n",
    "                index = ['a', 'b', 'c'],\n",
    "                columns = ['one', 'two'])\n",
    "df2 = DataFrame(5 + np.arange(4).reshape((2, 2)),\n",
    "                index = ['a', 'c'],\n",
    "                columns = ['three', 'four'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
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       "      <th colspan=\"2\" halign=\"left\">level1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">level2</th>\n",
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       "  level1     level2     \n",
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     "metadata": {},
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    "pd.concat([df1, df2], axis = 1, keys = ['level1', 'level2'])"
   ]
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  {
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   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
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       "  level1     level2     \n",
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     "execution_count": 53,
     "metadata": {},
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    "pd.concat({'level1': df1, 'level2': df2}, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
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       "upper level1     level2     \n",
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       "c          4   5      7    8"
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     "metadata": {},
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    "pd.concat([df1, df2], axis = 1, keys = ['level1', 'level2'],\n",
    "         names = ['upper', 'lower'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = DataFrame(np.random.randn(3, 4), \n",
    "               columns = ['a', 'b', 'c', 'd'])\n",
    "df2 = DataFrame(np.random.randn(2, 3), \n",
    "               columns = ['b', 'd', 'a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <td>-0.995855</td>\n",
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       "      <td>0.372807</td>\n",
       "      <td>-1.057416</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.728744</td>\n",
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       "      <td>2.104258</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.803449</td>\n",
       "      <td>2.775540</td>\n",
       "      <td>0.338320</td>\n",
       "      <td>-0.023190</td>\n",
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      "text/plain": [
       "          a         b         c         d\n",
       "0 -0.995855  0.854624  0.372807 -1.057416\n",
       "1  0.728744  1.220832  1.845024  2.104258\n",
       "2  1.803449  2.775540  0.338320 -0.023190"
      ]
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     "execution_count": 57,
     "metadata": {},
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   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>b</th>\n",
       "      <th>d</th>\n",
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       "      <td>0.486205</td>\n",
       "      <td>0.674075</td>\n",
       "      <td>1.180739</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.203408</td>\n",
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       "      <td>-1.366668</td>\n",
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       "0  0.486205  0.674075  1.180739\n",
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     "execution_count": 58,
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   "source": [
    "df2"
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  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.995855</td>\n",
       "      <td>0.854624</td>\n",
       "      <td>0.372807</td>\n",
       "      <td>-1.057416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.728744</td>\n",
       "      <td>1.220832</td>\n",
       "      <td>1.845024</td>\n",
       "      <td>2.104258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.803449</td>\n",
       "      <td>2.775540</td>\n",
       "      <td>0.338320</td>\n",
       "      <td>-0.023190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.180739</td>\n",
       "      <td>0.486205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.674075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.366668</td>\n",
       "      <td>-0.203408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.761263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0 -0.995855  0.854624  0.372807 -1.057416\n",
       "1  0.728744  1.220832  1.845024  2.104258\n",
       "2  1.803449  2.775540  0.338320 -0.023190\n",
       "3  1.180739  0.486205       NaN  0.674075\n",
       "4 -1.366668 -0.203408       NaN  0.761263"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Combining data with overlap\n",
    "a = Series([np.nan, 2.5, np.nan, 3.5, 4.5, np.nan],\n",
    "          index = ['f', 'e', 'd', 'c', 'b', 'a'])\n",
    "b = Series(np.arange(len(a), dtype = np.float64),\n",
    "          index = ['f', 'e', 'd', 'c', 'b', 'a'])\n",
    "b[-1] = np.nan"
   ]
  },
  {
   "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\n",
       "e     1\n",
       "d     2\n",
       "c     3\n",
       "b     4\n",
       "a   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  2.5,  2. ,  3.5,  4.5,  nan])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(pd.isnull(a), b, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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)})\n",
    "df2 = DataFrame({'a': [5., 4., np.nan, 3., 7.],\n",
    "                 'b': [np.nan, 3., 4., 6., 8.]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c\n",
       "0  1 NaN   2\n",
       "1  4   2   6\n",
       "2  5   4  10\n",
       "3  3   6  14\n",
       "4  7   8 NaN"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.combine_first(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshaping with hierarchical indexing\n",
    "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'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = data.stack()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jburchell/.virtualenvs/daup/lib/python2.7/site-packages/pandas/core/index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return np.sum(name == np.asarray(self.names)) > 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1</td>\n",
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       "      <th>three</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state   Ohio  Colorado\n",
       "number                \n",
       "one        0         3\n",
       "two        1         4\n",
       "three      2         5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Colorado</th>\n",
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       "</div>"
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      "text/plain": [
       "state   Ohio  Colorado\n",
       "number                \n",
       "one        0         3\n",
       "two        1         4\n",
       "three      2         5"
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     "execution_count": 77,
     "metadata": {},
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   "source": [
    "result.unstack('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s1 = Series([0, 1, 2, 3], index = ['a', 'b', 'c', 'd'])\n",
    "s2 = Series([4, 5, 6], index = ['c', 'd', 'e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2 = pd.concat([s1, s2], keys = ['one', 'two'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>a</th>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
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       "      a   b  c  d   e\n",
       "one   0   1  2  3 NaN\n",
       "two NaN NaN  4  5   6"
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     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data2.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.unstack().stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one  a     0\n",
       "     b     1\n",
       "     c     2\n",
       "     d     3\n",
       "     e   NaN\n",
       "two  a   NaN\n",
       "     b   NaN\n",
       "     c     4\n",
       "     d     5\n",
       "     e     6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.unstack().stack(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>side</th>\n",
       "      <th>left</th>\n",
       "      <th>right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
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       "      <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",
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       "      <td>2</td>\n",
       "      <td>7</td>\n",
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       "    <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",
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       "      <th>three</th>\n",
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       "</div>"
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      "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": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
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       "      <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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.unstack('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Colorado</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>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>left</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">three</th>\n",
       "      <th>left</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>right</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state         Ohio  Colorado\n",
       "number side                 \n",
       "one    left      0         3\n",
       "       right     5         8\n",
       "two    left      1         4\n",
       "       right     6         9\n",
       "three  left      2         5\n",
       "       right     7        10"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.unstack('state').stack('side')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pivoting \"long\" to \"wide\"\n",
    "data = pd.read_csv('ch07/macrodata.csv')\n",
    "periods = pd.PeriodIndex(year=data.year, quarter=data.quarter, name='date')\n",
    "data = DataFrame(data.to_records(),\n",
    "                 columns=pd.Index(['realgdp', 'infl', 'unemp'], name='item'),\n",
    "                 index=periods.to_timestamp('D', 'end'))\n",
    "\n",
    "ldata = data.stack().reset_index().rename(columns={0: 'value'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>item</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959-03-31</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2710.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-03-31</td>\n",
       "      <td>infl</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-03-31</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959-06-30</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2778.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959-06-30</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1959-06-30</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1959-09-30</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2775.488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1959-09-30</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1959-09-30</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1959-12-31</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2785.204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date     item     value\n",
       "0 1959-03-31  realgdp  2710.349\n",
       "1 1959-03-31     infl     0.000\n",
       "2 1959-03-31    unemp     5.800\n",
       "3 1959-06-30  realgdp  2778.801\n",
       "4 1959-06-30     infl     2.340\n",
       "5 1959-06-30    unemp     5.100\n",
       "6 1959-09-30  realgdp  2775.488\n",
       "7 1959-09-30     infl     2.740\n",
       "8 1959-09-30    unemp     5.300\n",
       "9 1959-12-31  realgdp  2785.204"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldata[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pivoted = ldata.pivot('date', 'item', 'value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>item</th>\n",
       "      <th>infl</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-03-31</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-30</th>\n",
       "      <td>2.34</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-30</th>\n",
       "      <td>2.74</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-31</th>\n",
       "      <td>0.27</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-31</th>\n",
       "      <td>2.31</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "item        infl   realgdp  unemp\n",
       "date                             \n",
       "1959-03-31  0.00  2710.349    5.8\n",
       "1959-06-30  2.34  2778.801    5.1\n",
       "1959-09-30  2.74  2775.488    5.3\n",
       "1959-12-31  0.27  2785.204    5.6\n",
       "1960-03-31  2.31  2847.699    5.2"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ldata['value2'] = np.random.randn(len(ldata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>item</th>\n",
       "      <th>value</th>\n",
       "      <th>value2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959-03-31</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>-0.204708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959-03-31</td>\n",
       "      <td>infl</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.478943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959-03-31</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.800</td>\n",
       "      <td>-0.519439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959-06-30</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>-0.555730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959-06-30</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.340</td>\n",
       "      <td>1.965781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1959-06-30</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.100</td>\n",
       "      <td>1.393406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1959-09-30</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>0.092908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1959-09-30</td>\n",
       "      <td>infl</td>\n",
       "      <td>2.740</td>\n",
       "      <td>0.281746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1959-09-30</td>\n",
       "      <td>unemp</td>\n",
       "      <td>5.300</td>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1959-12-31</td>\n",
       "      <td>realgdp</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1.246435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date     item     value    value2\n",
       "0 1959-03-31  realgdp  2710.349 -0.204708\n",
       "1 1959-03-31     infl     0.000  0.478943\n",
       "2 1959-03-31    unemp     5.800 -0.519439\n",
       "3 1959-06-30  realgdp  2778.801 -0.555730\n",
       "4 1959-06-30     infl     2.340  1.965781\n",
       "5 1959-06-30    unemp     5.100  1.393406\n",
       "6 1959-09-30  realgdp  2775.488  0.092908\n",
       "7 1959-09-30     infl     2.740  0.281746\n",
       "8 1959-09-30    unemp     5.300  0.769023\n",
       "9 1959-12-31  realgdp  2785.204  1.246435"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ldata[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">value</th>\n",
       "      <th colspan=\"3\" halign=\"left\">value2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item</th>\n",
       "      <th>infl</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>unemp</th>\n",
       "      <th>infl</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-03-31</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.478943</td>\n",
       "      <td>-0.204708</td>\n",
       "      <td>-0.519439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-30</th>\n",
       "      <td>2.34</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.965781</td>\n",
       "      <td>-0.555730</td>\n",
       "      <td>1.393406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-30</th>\n",
       "      <td>2.74</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.281746</td>\n",
       "      <td>0.092908</td>\n",
       "      <td>0.769023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-31</th>\n",
       "      <td>0.27</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.007189</td>\n",
       "      <td>1.246435</td>\n",
       "      <td>-1.296221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-31</th>\n",
       "      <td>2.31</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.228913</td>\n",
       "      <td>0.274992</td>\n",
       "      <td>1.352917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           value                    value2                    \n",
       "item        infl   realgdp unemp      infl   realgdp     unemp\n",
       "date                                                          \n",
       "1959-03-31  0.00  2710.349   5.8  0.478943 -0.204708 -0.519439\n",
       "1959-06-30  2.34  2778.801   5.1  1.965781 -0.555730  1.393406\n",
       "1959-09-30  2.74  2775.488   5.3  0.281746  0.092908  0.769023\n",
       "1959-12-31  0.27  2785.204   5.6  1.007189  1.246435 -1.296221\n",
       "1960-03-31  2.31  2847.699   5.2  0.228913  0.274992  1.352917"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted = ldata.pivot('date', 'item')\n",
    "pivoted[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>4</td>\n",
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       "  </tbody>\n",
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      ],
      "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": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing duplicates\n",
    "data = DataFrame({'k1': ['one'] * 3 + ['two'] * 4,\n",
    "                  'k2': [1, 1, 2, 3, 3, 4, 4]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['v1'] = range(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(['k1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "      <th>v1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2  v1\n",
       "1  one   1   1\n",
       "2  one   2   2\n",
       "4  two   3   4\n",
       "6  two   4   6"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(['k1', 'k2'], keep = 'last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming data using a function or mapping\n",
    "data = DataFrame({'food': ['bacon', 'pulled pork', 'bacon',\n",
    "                          'Pastrami', 'corned beef', 'Bacon',\n",
    "                          'pastrami', 'honey ham', 'nova lox'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "meat_to_animal = {'bacon': 'pig',\n",
    "                  'pulled pork': 'pig',\n",
    "                  'pastrami': 'cow',\n",
    "                  'corned beef': 'cow',\n",
    "                  'honey ham': 'pig',\n",
    "                  'nova lox': 'salmon'\n",
    "                 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map performs element-wise transformations at the same time as \n",
    "# appending data\n",
    "data['animal'] = data['food'].map(str.lower).map(meat_to_animal)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['food'].map(lambda x: meat_to_animal[x.lower()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1    -999\n",
       "2       2\n",
       "3    -999\n",
       "4   -1000\n",
       "5       3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing values\n",
    "data = Series([1., -999., 2., -999., -1000., 3])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1     NaN\n",
       "2       2\n",
       "3     NaN\n",
       "4   -1000\n",
       "5       3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace(-999, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1   NaN\n",
       "2     2\n",
       "3   NaN\n",
       "4   NaN\n",
       "5     3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999, -1000], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1   NaN\n",
       "2     2\n",
       "3   NaN\n",
       "4     0\n",
       "5     3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999, -1000], [np.nan, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1   NaN\n",
       "2     2\n",
       "3   NaN\n",
       "4     0\n",
       "5     3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace({-999: np.nan, -1000: 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Renaming axis indexes\n",
    "data = DataFrame(np.arange(12).reshape((3, 4)),\n",
    "                 index = ['Ohio', 'Colorado', 'New York'],\n",
    "                 columns = ['one', 'two', 'three', 'four'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['OHIO', 'COLORADO', 'NEW YORK'], dtype=object)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.map(str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.index = data.index.map(str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index = str.title, columns = str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>peekaboo</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INDIANA</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",
       "INDIANA     0    1         2     3\n",
       "COLORADO    4    5         6     7\n",
       "NEW YORK    8    9        10    11"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index = {'OHIO': 'INDIANA'},\n",
    "            columns = {'three': 'peekaboo'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INDIANA</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",
       "INDIANA     0    1      2     3\n",
       "COLORADO    4    5      6     7\n",
       "NEW YORK    8    9     10    11"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = data.rename(index = {'OHIO': 'INDIANA'}, inplace = True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Discretisation and binning\n",
    "ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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, object): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [18, 25, 35, 60, 100]\n",
    "cats = pd.cut(ages, bins)\n",
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'(18, 25]', u'(25, 35]', u'(35, 60]', u'(60, 100]'], dtype='object')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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, object): [[18, 26) < [26, 36) < [36, 61) < [61, 100)]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(ages, [18, 26, 36, 61, 100], right = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_names = ['Youth', 'YoungAdult', 'MiddleAged', 'Senior']\n",
    "pd.cut(ages, bins, labels = group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.64, 1.39], (-0.11, 0.64], (-0.11, 0.64], (-0.11, 0.64], (0.64, 1.39], ..., (-1.61, -0.86], (-0.86, -0.11], (-1.61, -0.86], (0.64, 1.39], (-1.61, -0.86]]\n",
       "Length: 20\n",
       "Categories (4, object): [(-1.61, -0.86] < (-0.86, -0.11] < (-0.11, 0.64] < (0.64, 1.39]]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randn(20)\n",
    "pd.cut(data, 4, precision = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.0206, 0.641], (0.641, 3.26], [-3.745, -0.632], (-0.632, -0.0206], (0.641, 3.26], ..., (-0.632, -0.0206], (-0.632, -0.0206], (-0.0206, 0.641], (0.641, 3.26], (-0.0206, 0.641]]\n",
       "Length: 1000\n",
       "Categories (4, object): [[-3.745, -0.632] < (-0.632, -0.0206] < (-0.0206, 0.641] < (0.641, 3.26]]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randn(1000)\n",
    "cats = pd.qcut(data, 4)\n",
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.641, 3.26]        250\n",
       "(-0.0206, 0.641]     250\n",
       "(-0.632, -0.0206]    250\n",
       "[-3.745, -0.632]     250\n",
       "dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.0206, 1.302], (-0.0206, 1.302], [-3.745, -1.264], (-1.264, -0.0206], (1.302, 3.26], ..., (-1.264, -0.0206], (-1.264, -0.0206], (-0.0206, 1.302], (-0.0206, 1.302], (-0.0206, 1.302]]\n",
       "Length: 1000\n",
       "Categories (4, object): [[-3.745, -1.264] < (-1.264, -0.0206] < (-0.0206, 1.302] < (1.302, 3.26]]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(data, [0, 0.1, 0.5, 0.9, 1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <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>-0.067684</td>\n",
       "      <td>0.067924</td>\n",
       "      <td>0.025598</td>\n",
       "      <td>-0.002298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.998035</td>\n",
       "      <td>0.992106</td>\n",
       "      <td>1.006835</td>\n",
       "      <td>0.996794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.428254</td>\n",
       "      <td>-3.548824</td>\n",
       "      <td>-3.184377</td>\n",
       "      <td>-3.745356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.774890</td>\n",
       "      <td>-0.591841</td>\n",
       "      <td>-0.641675</td>\n",
       "      <td>-0.644144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.116401</td>\n",
       "      <td>0.101143</td>\n",
       "      <td>0.002073</td>\n",
       "      <td>-0.013611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.616366</td>\n",
       "      <td>0.780282</td>\n",
       "      <td>0.680391</td>\n",
       "      <td>0.654328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.366626</td>\n",
       "      <td>2.653656</td>\n",
       "      <td>3.260383</td>\n",
       "      <td>3.927528</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     -0.067684     0.067924     0.025598    -0.002298\n",
       "std       0.998035     0.992106     1.006835     0.996794\n",
       "min      -3.428254    -3.548824    -3.184377    -3.745356\n",
       "25%      -0.774890    -0.591841    -0.641675    -0.644144\n",
       "50%      -0.116401     0.101143     0.002073    -0.013611\n",
       "75%       0.616366     0.780282     0.680391     0.654328\n",
       "max       3.366626     2.653656     3.260383     3.927528"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detecting and filtering outliers\n",
    "np.random.seed(12345)\n",
    "data = DataFrame(np.random.randn(1000, 4))\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97     3.927528\n",
       "305   -3.399312\n",
       "400   -3.745356\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = data[3]\n",
    "col[np.abs(col) > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.539741</td>\n",
       "      <td>0.476985</td>\n",
       "      <td>3.248944</td>\n",
       "      <td>-1.021228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-0.774363</td>\n",
       "      <td>0.552936</td>\n",
       "      <td>0.106061</td>\n",
       "      <td>3.927528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>-0.655054</td>\n",
       "      <td>-0.565230</td>\n",
       "      <td>3.176873</td>\n",
       "      <td>0.959533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>-2.315555</td>\n",
       "      <td>0.457246</td>\n",
       "      <td>-0.025907</td>\n",
       "      <td>-3.399312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>0.050188</td>\n",
       "      <td>1.951312</td>\n",
       "      <td>3.260383</td>\n",
       "      <td>0.963301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.146326</td>\n",
       "      <td>0.508391</td>\n",
       "      <td>-0.196713</td>\n",
       "      <td>-3.745356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>-0.293333</td>\n",
       "      <td>-0.242459</td>\n",
       "      <td>-3.056990</td>\n",
       "      <td>1.918403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>-3.428254</td>\n",
       "      <td>-0.296336</td>\n",
       "      <td>-0.439938</td>\n",
       "      <td>-0.867165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>0.275144</td>\n",
       "      <td>1.179227</td>\n",
       "      <td>-3.184377</td>\n",
       "      <td>1.369891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>-0.362528</td>\n",
       "      <td>-3.548824</td>\n",
       "      <td>1.553205</td>\n",
       "      <td>-2.186301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>3.366626</td>\n",
       "      <td>-2.372214</td>\n",
       "      <td>0.851010</td>\n",
       "      <td>1.332846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "5   -0.539741  0.476985  3.248944 -1.021228\n",
       "97  -0.774363  0.552936  0.106061  3.927528\n",
       "102 -0.655054 -0.565230  3.176873  0.959533\n",
       "305 -2.315555  0.457246 -0.025907 -3.399312\n",
       "324  0.050188  1.951312  3.260383  0.963301\n",
       "400  0.146326  0.508391 -0.196713 -3.745356\n",
       "499 -0.293333 -0.242459 -3.056990  1.918403\n",
       "523 -3.428254 -0.296336 -0.439938 -0.867165\n",
       "586  0.275144  1.179227 -3.184377  1.369891\n",
       "808 -0.362528 -3.548824  1.553205 -2.186301\n",
       "900  3.366626 -2.372214  0.851010  1.332846"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(np.abs(data) > 3).any(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <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>-0.067623</td>\n",
       "      <td>0.068473</td>\n",
       "      <td>0.025153</td>\n",
       "      <td>-0.002081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.995485</td>\n",
       "      <td>0.990253</td>\n",
       "      <td>1.003977</td>\n",
       "      <td>0.989736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.774890</td>\n",
       "      <td>-0.591841</td>\n",
       "      <td>-0.641675</td>\n",
       "      <td>-0.644144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.116401</td>\n",
       "      <td>0.101143</td>\n",
       "      <td>0.002073</td>\n",
       "      <td>-0.013611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.616366</td>\n",
       "      <td>0.780282</td>\n",
       "      <td>0.680391</td>\n",
       "      <td>0.654328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.653656</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</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     -0.067623     0.068473     0.025153    -0.002081\n",
       "std       0.995485     0.990253     1.003977     0.989736\n",
       "min      -3.000000    -3.000000    -3.000000    -3.000000\n",
       "25%      -0.774890    -0.591841    -0.641675    -0.644144\n",
       "50%      -0.116401     0.101143     0.002073    -0.013611\n",
       "75%       0.616366     0.780282     0.680391     0.654328\n",
       "max       3.000000     2.653656     3.000000     3.000000"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[np.abs(data) > 3] = np.sign(data) * 3\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 3, 4])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Permutation and random sampling\n",
    "df = DataFrame(np.arange(5 * 4).reshape(5, 4))\n",
    "sampler = np.random.permutation(5)\n",
    "sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>2</th>\n",
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       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <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": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>11</td>\n",
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       "      <th>3</th>\n",
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       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
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       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3\n",
       "1   4   5   6   7\n",
       "0   0   1   2   3\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15\n",
       "4  16  17  18  19"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.take(sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
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       "      <td>14</td>\n",
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       "      <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",
       "1   4   5   6   7\n",
       "3  12  13  14  15\n",
       "4  16  17  18  19"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.take(np.random.permutation(len(df))[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 2, 2, 2, 0, 3, 0, 4, 1])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag = np.array([5, 7, -1, 6, 4])\n",
    "sampler = np.random.randint(0, len(bag), size = 10)\n",
    "sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  4, -1, -1, -1,  5,  6,  5,  4,  7])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draws = bag.take(sampler)\n",
    "draws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing indicator/dummy variables\n",
    "df = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],\n",
    "                'data1': range(6)})\n",
    "pd.get_dummies(df['key'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df['key'], prefix = 'key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key_a</th>\n",
       "      <th>key_b</th>\n",
       "      <th>key_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <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",
       "    </tr>\n",
       "  </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": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummy = df[['data1']].join(dummies)\n",
    "df_with_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table('ch02/movielens/movies.dat',\n",
    "                       sep = '::',\n",
    "                       header = None,\n",
    "                       names = mnames,\n",
    "                       engine = 'python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                               title                        genres\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy\n",
       "5         6                         Heat (1995)         Action|Crime|Thriller\n",
       "6         7                      Sabrina (1995)                Comedy|Romance\n",
       "7         8                 Tom and Huck (1995)          Adventure|Children's\n",
       "8         9                 Sudden Death (1995)                        Action\n",
       "9        10                    GoldenEye (1995)     Action|Adventure|Thriller"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "genre_iter = (set(x.split('|')) for x in movies.genres)\n",
    "genres = sorted(set.union(*genre_iter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dummies = DataFrame(np.zeros((len(movies), len(genres))), \n",
    "                    columns = genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i, gen in enumerate(movies.genres):\n",
    "    dummies.ix[i, gen.split('|')] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movies_windic = movies.join(dummies.add_prefix('Genre_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_windic.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "values = np.random.rand(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.756 ,  0.9083,  0.9659,  0.1737,  0.8759,  0.7542,  0.1635,\n",
       "        0.2378,  0.8556,  0.5874])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bins = [0, 0.2, 0.4, 0.6, 0.8, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   (0, 0.2]  (0.2, 0.4]  (0.4, 0.6]  (0.6, 0.8]  (0.8, 1]\n",
       "0         0           0           0           1         0\n",
       "1         0           0           0           0         1\n",
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       "8         0           0           0           0         1\n",
       "9         0           0           1           0         0"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(pd.cut(values, bins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# String manipulation\n",
    "val = 'a,b,  guido'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', '  guido']"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'guido']"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces = [x.strip() for x in val.split(',')]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a::b::guido'"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first, second, third = pieces\n",
    "first + '::' + second + '::' + third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a::b::guido'"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'::'.join(pieces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'guido' in val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.index(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.find(':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.count(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a::b::  guido'"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.replace(',', '::')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ab  guido'"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.replace(',', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Regular expression\n",
    "import re\n",
    "text = \"foo    bar\\t baz  \\tqux\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'qux']"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.split('\\s+', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'qux']"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex = re.compile('\\s+')\n",
    "regex.split(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['    ', '\\t ', '  \\t']"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text = \"\"\"Dave dave@google.com\n",
    "Steve steve@gmail.com\n",
    "Rob rob@gmail.com\n",
    "Ryan ryan@yahoo.com\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pattern = r'[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,4}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regex = re.compile(pattern, flags = re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dave@google.com', 'steve@gmail.com', 'rob@gmail.com', 'ryan@yahoo.com']"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_sre.SRE_Match at 0x10544b238>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = regex.search(text)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dave@google.com'"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[m.start():m.end()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print regex.match(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dave REDACTED\n",
      "Steve REDACTED\n",
      "Rob REDACTED\n",
      "Ryan REDACTED\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print regex.sub('REDACTED', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pattern = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\\.([A-Z]{2,4})'\n",
    "regex = re.compile(pattern, flags = re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('wesm', 'bright', 'net')"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = regex.match('wesm@bright.net')\n",
    "m.groups()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('dave', 'google', 'com'),\n",
       " ('steve', 'gmail', 'com'),\n",
       " ('rob', 'gmail', 'com'),\n",
       " ('ryan', 'yahoo', 'com')]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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",
      "\n"
     ]
    }
   ],
   "source": [
    "print regex.sub(r'Username: \\1, Domain: \\2, Suffix: \\3', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "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})\"\"\", flags=re.IGNORECASE|re.VERBOSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'domain': 'bright', 'suffix': 'net', 'username': 'wes'}"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = regex.match('wes@bright.net')\n",
    "m.groupdict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     dave@google.com\n",
       "Rob        rob@gmail.com\n",
       "Steve    steve@gmail.com\n",
       "Wes                  NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vectorised string functions in pandas\n",
    "data = {'Dave': 'dave@google.com',\n",
    "        'Steve': 'steve@gmail.com',\n",
    "        'Rob': 'rob@gmail.com',\n",
    "        'Wes': np.nan}\n",
    "data = Series(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     False\n",
       "Rob      False\n",
       "Steve    False\n",
       "Wes       True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     False\n",
       "Rob       True\n",
       "Steve     True\n",
       "Wes        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.str.contains('gmail')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\\\\.([A-Z]{2,4})'"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     [(dave, google, com)]\n",
       "Rob        [(rob, gmail, com)]\n",
       "Steve    [(steve, gmail, com)]\n",
       "Wes                        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.str.findall(pattern, flags = re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jburchell/.virtualenvs/daup/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: In future versions of pandas, match will change to always return a bool indexer.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dave     (dave, google, com)\n",
       "Rob        (rob, gmail, com)\n",
       "Steve    (steve, gmail, com)\n",
       "Wes                      NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = data.str.match(pattern, flags = re.IGNORECASE)\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     google\n",
       "Rob       gmail\n",
       "Steve     gmail\n",
       "Wes         NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave      dave\n",
       "Rob        rob\n",
       "Steve    steve\n",
       "Wes        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dave     dave@\n",
       "Rob      rob@g\n",
       "Steve    steve\n",
       "Wes        NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.str[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6636"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example: USDA food database\n",
    "import json\n",
    "db = json.load(open('ch07/foods-2011-10-03.json'))\n",
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0]['nutrients'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients = DataFrame(db[0]['nutrients'])\n",
    "nutrients[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "info_keys = ['description', 'group', 'id', 'manufacturer']\n",
    "info = DataFrame(db, columns = info_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(info.group)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nutrients = []\n",
    "\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": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>25.18</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>29.20</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>3.06</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>3.28</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>376.00</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>39.28</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>1573.00</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>0.00</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>673.00</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>0.64</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   description        group units    value    id\n",
       "0                      Protein  Composition     g    25.18  1008\n",
       "1            Total lipid (fat)  Composition     g    29.20  1008\n",
       "2  Carbohydrate, by difference  Composition     g     3.06  1008\n",
       "3                          Ash        Other     g     3.28  1008\n",
       "4                       Energy       Energy  kcal   376.00  1008\n",
       "5                        Water  Composition     g    39.28  1008\n",
       "6                       Energy       Energy    kJ  1573.00  1008\n",
       "7         Fiber, total dietary  Composition     g     0.00  1008\n",
       "8                  Calcium, Ca     Elements    mg   673.00  1008\n",
       "9                     Iron, Fe     Elements    mg     0.64  1008"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14223"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nutrients = nutrients.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>30</th>\n",
       "      <td>Milk, buttermilk, dried</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1094</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Milk, canned, condensed, sweetened</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1095</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Milk, canned, evaporated, with added vitamin D...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1096</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Milk, canned, evaporated, nonfat, with added v...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1097</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Milk, indian buffalo, fluid</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1108</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Milk, sheep, fluid</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1109</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Yogurt, plain, skim milk, 13 grams protein per...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1118</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Yogurt, vanilla, low fat, 11 grams protein per...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1119</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Egg, whole, cooked, fried</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1128</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Egg, whole, cooked, hard-boiled</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1129</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Egg, duck, whole, fresh, raw</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1138</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Egg, goose, whole, fresh, raw</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1139</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Cheese, pasteurized process, swiss, without di...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1148</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Cheese food, pasteurized process, american, wi...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1149</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Cheese, goat, soft type</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1159</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Cheese, low fat, cheddar or colby</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1168</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Cheese, low-sodium, cheddar or colby</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1169</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Sour cream, reduced fat</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1178</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Sour cream, light</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1179</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Sour cream, fat free</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1180</td>\n",
       "      <td>None</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>6586</th>\n",
       "      <td>Beef, tenderloin, steak, separable lean only, ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23587</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6587</th>\n",
       "      <td>Beef, top sirloin, steak, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23588</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6588</th>\n",
       "      <td>Beef, short loin, top loin, steak, separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23589</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6589</th>\n",
       "      <td>Beef, round, bottom round , roast, separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23590</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6590</th>\n",
       "      <td>Beef, round, eye of round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23591</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6591</th>\n",
       "      <td>Beef, round, top round, steak, separable lean ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23592</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6592</th>\n",
       "      <td>Beef, round, bottom round, steak, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23593</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6593</th>\n",
       "      <td>Beef, short loin, top loin, steak, separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23606</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6594</th>\n",
       "      <td>Beef, brisket, flat half, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23613</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6595</th>\n",
       "      <td>Beef, chuck, arm pot roast, separable lean onl...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23614</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6596</th>\n",
       "      <td>Beef, brisket, flat half, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23615</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6597</th>\n",
       "      <td>Beef, round, eye of round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23616</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6598</th>\n",
       "      <td>Beef, round, top round, steak, separable lean ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23617</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6599</th>\n",
       "      <td>Beef, round, eye of round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23620</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6600</th>\n",
       "      <td>Beef, round, top round, steak, separable lean ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23621</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6601</th>\n",
       "      <td>Beef, round, bottom round, steak, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23622</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6602</th>\n",
       "      <td>Beef, tenderloin, steak, separable lean only, ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23624</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6603</th>\n",
       "      <td>Beef, top sirloin, steak, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23625</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6604</th>\n",
       "      <td>Beef, rib, small end (ribs 10-12), separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23626</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6605</th>\n",
       "      <td>Beef, short loin, top loin, steak, separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23627</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",
       "30                              Milk, buttermilk, dried   \n",
       "31                   Milk, canned, condensed, sweetened   \n",
       "32    Milk, canned, evaporated, with added vitamin D...   \n",
       "33    Milk, canned, evaporated, nonfat, with added v...   \n",
       "34                          Milk, indian buffalo, fluid   \n",
       "35                                   Milk, sheep, fluid   \n",
       "36    Yogurt, plain, skim milk, 13 grams protein per...   \n",
       "37    Yogurt, vanilla, low fat, 11 grams protein per...   \n",
       "38                            Egg, whole, cooked, fried   \n",
       "39                      Egg, whole, cooked, hard-boiled   \n",
       "40                         Egg, duck, whole, fresh, raw   \n",
       "41                        Egg, goose, whole, fresh, raw   \n",
       "42    Cheese, pasteurized process, swiss, without di...   \n",
       "43    Cheese food, pasteurized process, american, wi...   \n",
       "44                              Cheese, goat, soft type   \n",
       "45                    Cheese, low fat, cheddar or colby   \n",
       "46                 Cheese, low-sodium, cheddar or colby   \n",
       "47                              Sour cream, reduced fat   \n",
       "48                                    Sour cream, light   \n",
       "49                                 Sour cream, fat free   \n",
       "...                                                 ...   \n",
       "6586  Beef, tenderloin, steak, separable lean only, ...   \n",
       "6587  Beef, top sirloin, steak, separable lean only,...   \n",
       "6588  Beef, short loin, top loin, steak, separable l...   \n",
       "6589  Beef, round, bottom round , roast, separable l...   \n",
       "6590  Beef, round, eye of round, roast, separable le...   \n",
       "6591  Beef, round, top round, steak, separable lean ...   \n",
       "6592  Beef, round, bottom round, steak, separable le...   \n",
       "6593  Beef, short loin, top loin, steak, separable l...   \n",
       "6594  Beef, brisket, flat half, separable lean only,...   \n",
       "6595  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6596  Beef, brisket, flat half, separable lean only,...   \n",
       "6597  Beef, round, eye of round, roast, separable le...   \n",
       "6598  Beef, round, top round, steak, separable lean ...   \n",
       "6599  Beef, round, eye of round, roast, separable le...   \n",
       "6600  Beef, round, top round, steak, separable lean ...   \n",
       "6601  Beef, round, bottom round, steak, separable le...   \n",
       "6602  Beef, tenderloin, steak, separable lean only, ...   \n",
       "6603  Beef, top sirloin, steak, separable lean only,...   \n",
       "6604  Beef, rib, small end (ribs 10-12), separable l...   \n",
       "6605  Beef, short loin, top loin, steak, separable l...   \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",
       "30               Dairy and Egg Products   1094                     \n",
       "31               Dairy and Egg Products   1095                     \n",
       "32               Dairy and Egg Products   1096                     \n",
       "33               Dairy and Egg Products   1097                     \n",
       "34               Dairy and Egg Products   1108                     \n",
       "35               Dairy and Egg Products   1109                     \n",
       "36               Dairy and Egg Products   1118                     \n",
       "37               Dairy and Egg Products   1119                     \n",
       "38               Dairy and Egg Products   1128                     \n",
       "39               Dairy and Egg Products   1129                     \n",
       "40               Dairy and Egg Products   1138                     \n",
       "41               Dairy and Egg Products   1139                     \n",
       "42               Dairy and Egg Products   1148                     \n",
       "43               Dairy and Egg Products   1149                     \n",
       "44               Dairy and Egg Products   1159                     \n",
       "45               Dairy and Egg Products   1168                     \n",
       "46               Dairy and Egg Products   1169                     \n",
       "47               Dairy and Egg Products   1178               None  \n",
       "48               Dairy and Egg Products   1179               None  \n",
       "49               Dairy and Egg Products   1180               None  \n",
       "...                                 ...    ...                ...  \n",
       "6586                      Beef Products  23587                     \n",
       "6587                      Beef Products  23588                     \n",
       "6588                      Beef Products  23589                     \n",
       "6589                      Beef Products  23590                     \n",
       "6590                      Beef Products  23591                     \n",
       "6591                      Beef Products  23592                     \n",
       "6592                      Beef Products  23593                     \n",
       "6593                      Beef Products  23606                     \n",
       "6594                      Beef Products  23613                     \n",
       "6595                      Beef Products  23614                     \n",
       "6596                      Beef Products  23615                     \n",
       "6597                      Beef Products  23616                     \n",
       "6598                      Beef Products  23617                     \n",
       "6599                      Beef Products  23620                     \n",
       "6600                      Beef Products  23621                     \n",
       "6601                      Beef Products  23622                     \n",
       "6602                      Beef Products  23624                     \n",
       "6603                      Beef Products  23625                     \n",
       "6604                      Beef Products  23626                     \n",
       "6605                      Beef Products  23627                     \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": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_mapping = {'description': 'food',\n",
    "               'group': 'fgroup'}\n",
    "info = info.rename(columns = col_mapping, copy = False)\n",
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>25.18</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>29.20</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>3.06</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>3.28</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>376.00</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>39.28</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>1573.00</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>0.00</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>673.00</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>0.64</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      nutrient     nutgroup units    value    id\n",
       "0                      Protein  Composition     g    25.18  1008\n",
       "1            Total lipid (fat)  Composition     g    29.20  1008\n",
       "2  Carbohydrate, by difference  Composition     g     3.06  1008\n",
       "3                          Ash        Other     g     3.28  1008\n",
       "4                       Energy       Energy  kcal   376.00  1008\n",
       "5                        Water  Composition     g    39.28  1008\n",
       "6                       Energy       Energy    kJ  1573.00  1008\n",
       "7         Fiber, total dietary  Composition     g     0.00  1008\n",
       "8                  Calcium, Ca     Elements    mg   673.00  1008\n",
       "9                     Iron, Fe     Elements    mg     0.64  1008"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_mapping = {'description': 'nutrient',\n",
    "               'group': 'nutgroup'}\n",
    "nutrients = nutrients.rename(columns = col_mapping, copy = False)\n",
    "nutrients[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>25.18</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>29.20</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>3.06</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>3.28</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>376.00</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>39.28</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>1573.00</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>0.00</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>673.00</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>0.64</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      nutrient     nutgroup units    value    id  \\\n",
       "0                      Protein  Composition     g    25.18  1008   \n",
       "1            Total lipid (fat)  Composition     g    29.20  1008   \n",
       "2  Carbohydrate, by difference  Composition     g     3.06  1008   \n",
       "3                          Ash        Other     g     3.28  1008   \n",
       "4                       Energy       Energy  kcal   376.00  1008   \n",
       "5                        Water  Composition     g    39.28  1008   \n",
       "6                       Energy       Energy    kJ  1573.00  1008   \n",
       "7         Fiber, total dietary  Composition     g     0.00  1008   \n",
       "8                  Calcium, Ca     Elements    mg   673.00  1008   \n",
       "9                     Iron, Fe     Elements    mg     0.64  1008   \n",
       "\n",
       "              food                  fgroup manufacturer  \n",
       "0  Cheese, caraway  Dairy and Egg Products               \n",
       "1  Cheese, caraway  Dairy and Egg Products               \n",
       "2  Cheese, caraway  Dairy and Egg Products               \n",
       "3  Cheese, caraway  Dairy and Egg Products               \n",
       "4  Cheese, caraway  Dairy and Egg Products               \n",
       "5  Cheese, caraway  Dairy and Egg Products               \n",
       "6  Cheese, caraway  Dairy and Egg Products               \n",
       "7  Cheese, caraway  Dairy and Egg Products               \n",
       "8  Cheese, caraway  Dairy and Egg Products               \n",
       "9  Cheese, caraway  Dairy and Egg Products               "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata = pd.merge(nutrients, info, on = 'id', how = 'outer')\n",
    "ndata[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bd84390>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AUtAsSZKkwygFzRZ9JM2OlNMJkWa7eRe1WypIFueuihTXw8vO12lz41hOqXV+U0mjJT0p\nabykC2MZpVuoN8YkSZJk/pBLMws/71QCPyVtD5xEVQCDpMXM7IOu6Cw0Qz5lZmt34vaheGzMbSXt\nfhiPpdnDzB6Oul3wOI52M466coxJkiTJ/CM9IosWyxIb6ElqkjRG0g34Bnd9JJ0Wol0TJR0Y1w1Q\niVBaEUlrhYfiU3jsx0fDC7OlpG9HmxMkXVvxYEjaTS4bP0EufrY48Etgj7i3ekfh7wEjKpMQADP7\ns5m9HIGyf5D0cNhREXM7QK1F0Jaucd3A8LSMq+U1kguaPawUNEuSJJm/tCc0kseCfeBpq4/hwavT\ngaFR34QHwa4Rnw/Eg00BlgQewUXA6gmlTcZ1Q8YDQ6J+DVoLh61QKB8PHBrlSbh0PMAy8XN/4Mwa\n4/gzsFONcyfSItK2HL4z8NK0FUGrdV0/YMmoXxt4pDjGKJ9FCprlsYAfSbKwEX+3dd9juTSz8DPT\nWpZmNsOFxDaMc2PN7Pkobw8MiYwX8HTgwcC/qC2UtgrwF2BnM3sy6qqDjoZIOgH3xgzAs4AA7gcu\nkXQNcF3h3npBS7XObQ/sJKmyQeCSwOr4f85FEbSy6z4OvAScLWljXGdlnZI+HiAFzZIFmszaShZN\nciKyCGFmD0laSdJKUVUtEHaomd1VrIgU3VpCadPx9NitcP2QMkYAXzGzyZL2J+JTzOwQSZsCOwDj\nJA2rcX+Fx/H4kRtrnP+6mf29yvbPlIyx7Lpm4EUz21dSX+Dd6sYtBc2SJEnmmc4ImuVEZBFC0np4\n3M+rJafvAL4raaSZfSBpHdwbUk8o7X1cZv0OSW+Z2VUl7Q4AXooYkH2iTSQNMrOxwNjIlPkYVSJi\nVZwd194S9yFpZ9yzcgcuk//9qB9qZo/R9itireuWqdgF7IcvR7VC0lpm9ixwlqTVcUGzOhORJEmS\npJqmpiaamprmfj7uuOPavScnIgs//eT7toC/mPc3MwtRseJawkV4TMR4SQJexqXc6wqlmdk7knbE\ng0Fn4HEjxXaPwUXQXomfA6L+VLkSq4C7zWySpH8CPwt7TzSzPxU6elnSN4DTY2loDr7p3u147Mnv\nwsY+wLO4Im31GGtddw7wZ0n70VpAjsL9u0vahxQ0S5Ikma+koFmSNIBS0CxJkqTDKAXNegeSjpI0\nJdJOH4vYjHrXHySXM1/gkDQi9EParZf0VvV1DbTf4XuSJEmS7iOXZhZyQhNjBzxtd5akFfBskZqY\n2fnzxbjOUb3cUq++Qy4K+aZ+6dZIkiRZgEiPyMLPqsB/zWwWgJm9ZmYvAkh6TtIpIVT2sFp20m2W\ndESUB4egWUUifs2o/7FaxM+ao66/pFvi2smSdq82RtJ3agicjZB0hqT7JT1T8W7IOVsu634XnjJc\ny41X071Xw96Bkp6SdImkKXjALJJ+Ex6kuysZRpIOk/R43F8WlJskSZJ0A+kRWfi5E/iFpKeAu4Gr\nzWx0nDNgupltFEsxvwN2orV34Qo8cPQGSUsAfeVS8YPNbNPwItwg1xlZGXjBzHYAkLRMiT1/NrML\n4/zxwLfwjBiAVc1sC0mfwNN0/wzsjOt6fAKfVE0FLi5pV8Bpko4u1Fn0U8vef+JaKfsWMnH644Jm\nP5J0DHAsnmXzU2BgeJXKxoXH+CZJ95KxSElvIz0iCzlm9jauv3Egnrlydeh5VKh8u/8j0EraXNIA\nYDUzuyHaet/MZuLCYNtHdss4XF11MJ4x83lJJ0va0szeLDFpiFxafhKwN7B+xVRcHA0zewL4cNR/\nDrgyRPheBP5aa6jAkWY2tHLQ4iGpZS/A85VJSDAHuDrKlwNbRnkScKWkvXHRsxom5JFHdx5J0vtI\nj8gigJlVUl3vle8muz9wSdmlHWj2JDO7oLpS0lA8JuUESfeY2fFVl4ygROAseL/YVMGmRl0N9a5r\nY6+kgbQVPKtur/JMdsAnRTvhCqtDzKxqQtJcKDeRgmZJkiStSUGzXohcmMwKaqJDgecKl+wBnBI/\nH6jchqduvyXpX5K+GkszS+JesjuA4yVdYWZvS/ooPolYDHjdzK6Q9Aa+7FJNtcDZP9sZwmjgIEmX\n4F6SbfDloo5Qy94y+gC74V6RvYAx8jWX1c1slKT7gW8A/XEBtgLNHTQrSZKkd5GCZr2TAbga6HL4\nBnh/x5dpKiwvaSIua75n1BX9wPsC50v6JS7mtauZ3RVxHA9GXMSMuG4wHqcxB3/RH1JiTy2Bs0q/\nrcpmdr2kbfHYkH/QMlkqo9qjU2mjzN59KPd3vw1sGrEm/8EnaIsBl0laFp+knVG+7JQxIkmSJF1N\nCpotwsj3jRlmZq/1tC0LO0pBsyRJkg6jFDTr9QwERkWq7TWVVNqOUhEBk7SGpD3bu76dtgZKmikX\nXntc0rmah3QUeYryCp24b57HkiRJksw7ORFZtHnLzDYysyH4UsrBnWyn4gpYE4+raIOkjizzPR1Z\nLxvhWTVfm4e2OuumqDmWJEmSZP6RMSK9h/vw1NrlgeH4i/gd4MDIcGkGZpjZrwFCAOzLZvaPQhsn\nA+tFmuwlwOvALnhgZ19JzwPXVdKBJV2B65rcWGZQ7Pj7ADA4MmwqbfWR9PUadq6IpySvBjxIBG5E\nhsxNMelC0pFAfzM7TtJg4DxgJTyOZveqsYzANViGA4vjE/RdzOzpor2pI5J0hFzKS5LGSI9ILyA8\nDF/EtTJ+CYwzs42BnwOXxmWlgaBV/BQYEzoev8MnAUPxl3YTLkR2QPS5LK5bcnMdu5YGtgu7im1t\nU8fOY4HRZrYhcD2weo3mi4GqVwBnmdkngc/iu+sWx3IGcBDwu/DUDAP+VbvJPPJo70iSpFFyIrJo\n0y++8T8CPA/8AdgCuAzAzEYCK0r6UIPtVbsEDLjTzKZHe6OBtUM2fU/g2tA4qWZQ2HUfcLOZ3RH1\nd1XaqmPnVrgQGWZ2K+6VqWlvHdG26rE8CPxc0k9whdV36z6JJEmSpEvIpZlFm5nxDX8usbxQtsbw\nAa0npks12Mc7VZ8vxVN99yC8IyU8U20XPqmpFh/ryJ4z1fb3owNfTc3sKkkPATsCt0o6KCZABZoL\n5SZS0CxJkqQ1KWiWNMIYXHr9BElNwCtmNkPSc/hLGEmb4LEZ1cwAit6TsgnBCNwD828ze7IDdlW3\nVcvO0XiQ6a8kfQlYPq7/D7BKZNC8HWO5tY5o25vFsUha08ym4ZosqwNDgDoTkSRJkqSaFDRLqinz\nCDQDfwiRs7dxOXjwDej2iyDVh4GnStqZCMyWNAGfcLxe3YeZvSxpKh6/0RG7qhfXa9l5HHBVpN4+\ngC85EZvV/RIYC7yAC6RVaCPahselFMeypHxjwFl4DMmv2pqYwapJkiRdTQqaJV1KBKBOAoaa2Yye\ntqerSEGzJEmSjpOCZkmHkTQ7xMY6LIIm6VTgJeDMWpMQSQdIeqUgaPbtebB1YGzy15l7t5a0eftX\nJkmSJN1JTkSSat6JlNYOiaBFivDjwCVmdmadSw24KoJVm4ATJa1c1VbfTlneMbbBU3mTJEmSHiRj\nRJJ6NCKCNijq/4HvgguApB2Ao4AdS/a6EYCZvSLpGWCgpNPwjfk+Cdwn6XJchKwf8Azwv2Y2XdIw\nPA3ZgDsL/R2A76vz/fh8M3Camd0r6Yt4zEdffDO+b+O6IbMl7QN8H/gI8AtgNvCGmW1d/TBS0CwB\nUqgsSbqYnIgkpRRE0G6jRVzsa5K2wVN0K+m36wFbmtl7oY6KpJ2BHwJfMrM36vSxFrAWUFEwXQ3Y\n3MxM0iTge2Y2RtJxuJDZD/EJ0XfN7L5YCqqFARbelguArczseUnLxYTmPFxJ9jdhyyRgezN7UdIy\ntZtMejc5GU2SriYnIkk1FRE0gNG49+Fh4Ovg4mKSKuJiBtxoZu/F9QK2BT4FfN7M3ippX8AekrYE\n3sO9K69LMuBPMQlZFljWzMbEPZcAfyrU3xf1lwFfqjMWAZvhSqyV7JrpVecr3A9cIuka4Lo6bSZJ\nkiRdSE5Ekmo6IoIGrQXNDF9GWRNYFxhXcr0BfzSzw9ppq5UJDdTXEmRryI1hZodI2hTYARgnaVjb\nJaXmQrmJFDRLkiRpTQqaJd1FLXGx6gmCcF2PHwPXSdrNzKaWXFPXv21mb0h6XdKW4f3YFxgV9dMl\nbWFm94dNFZ4DDgmbPgZsik9CHgLOkTTQzJ6TtEJMMGYAc5dgJA0ys7HA2BBK+xhQZyKSJEmSVJOC\nZklX0BERtGoRMgPMzJ6StDe+nLJjKJZS455afe8PnBe6JM8A34z6b4YtlWBVwzu9T9I0XMjsCcIb\nY2b/lXQgPjHqgyuwfgG4CbhW0leAw4AfSlobnyTdbWaT2pqX8QFJkiRdTQqaJUkDpKBZkiRJx1lk\nBc0kHSVpiqSJIYy1aU/b1FNIKgsIrVnfRX02Szqiu9ov6e8ASXMkbVeo+1rUfb2TbX5V0ie6zsok\nSZKkMyx0E5FQw9wBlxDfGNgO+GfPWtWjNLLMMb/67M7+JgPfKNTtCUyYhzZ3BtafF6OSJEmSeWeh\nm4gAqwL/NbNZAGb2mpm9CCDpGEljQ578/MoNkkaFEBaSVopYAiRtIOnh8KpMlDQo6q+X9Gh4Xb5T\naOdbkp6Key6UdFbUryzp2uh7rKTPRv3W0fZjksZLGlA9mDp9vSXpBEkTJD0oaZWoXzM+T5J0Qkce\nXJ3ncICk6yTdJulvkk4p3PNFSePCjrsKza0vaaSkZyR9v3D9PoVnel7EZSBpz7B5sqST2xtnCWOA\nTSUtFs9xEL4Jn6KdYTG+RyXdLmnVqP9O/E4mxO+oX/x+dgJOi9/LWpIOk0vOT5R0VY3nl8cieiRJ\n0oOY2UJ1AP2Bx/DdYX8PfK5wbvlC+VJc1RN8O/dNorwSMC3KZwF7RXkxYKliO7iq52R8q/nVgGnA\ncnHtaHxPFYArgS2ivDowNco34gJdAEsDfUvG06av+DwH2CHKpwBHFdrcJ8rfxUW5yp5Tm/o6z+EA\nPCD0Q8CSeAbKR4GVccXUNeK65eJnM667sTiwIvBfXLX0E2Ff37juHDzjZTU8m2bFuO4e4Kv1xlll\n9/7xuzod94bthaugDsf1TRbHd+JdMa7fA7g4yisU2jkeODTKw4GvF869ACwe5WVKbDCwPBbJA0uS\npHuIf1/UOxY6j4iZvQ0MAw7E5bqvVih6AttKekiukrkt7bveHwB+LuknwEAzezfqD5dvD/8gnsa5\nDp4Oeq+ZTTezD4A/0ZJG8T/A2XIhsBuAD0nqj7+sfyv3GCxvZrNLbCj29XFg7ah/38xuifI4YGCU\nPwtUvrFf3s74OsI9ZjbDXJxsavRXSwzMgJvNbJaZvQq8jHuqtsN/N4/Gs9gGWBMXOBtlZq/GM7gC\n+Fw74yzjanxJ5hu0PAPhmiUbAHdHv0fhEylwifox8TexN63/JopfhScBV8qzfcp+T0mSJEk3sFCm\n75rZHOBe4F757qv7S/oj/g18EzN7QdKxtIhaFcWuliq0c5Wkh4AdgVslHYS/ZLcDNjOzdyWNjHus\nygwV6gR8xszer7rmFPmeJzsA90v6gpk9NbcB1+Qo6wtgVqGdOXTN76r0OQTvFcqzo7/qMRcpjrVy\nPfimdz8vXihPkW1VVWi74XGa2SOSNgTeNrO/V7nUHzezsk3sRgBfMd8bZ39aq5AVx7cDPjnaCThK\n0pC2E8fmQrmJFDRLkiRpTa8QNJO0Du7q+XtUDcWXEiqThVflMQS7AdfENc/h38ofBXYttLWWmT0L\nnCVpdWAjfPnl9ZgYrId7BQx4BPidpOWAt4Bd8BgFcD2Lw/ClAyR90swmyEWyHgcel/Rp/Jv73IkI\nLqhV3Vd73I97BK6gtaBXI5Q+hxoYtcXAal1/D3CDpN+ab2i3AjAAGAucKWlFYHrYX2+H3mqKM46f\nATOr+n0KWFnSZmb2kKTFgbXNxdQGAC9F3T60BDbPFTSTz2hWN7NRkirPtz/wZmszmjtgcpIkSe+j\ntwiaDcAnDsvh3/D/ju9X8oakC4EpwEv4/igVTgeukQtb3ULLN+Hd5buvzgJexHdofQc4WNJU/AX3\nIICZ/VvSifhL9TXgSVpeVIcBv5cLfi2Ge2u+iy+7bIN/05+CbyBX5PayvgKrKlc+H44vIfwUXwaq\n5bVYWlL5SQyLAAAgAElEQVQxm+jXdZ5Dsf2WTmuLgVXbV7n+CUlHA3fG9bPwDerGSvoZHqMifFnn\npnbGSVm9md1e0u8sSbvik51l8d/Bb/ElpmPwv4VX4mclYPiPwIWxbLYncHHcK+AMM6uahEAKmiVJ\nknQ9KWjWAST1N7O35TvTXocHRN7Q03Yl3Y9S0CxJkqTDaFEVNOtBmiMYcjLwbE5Cugd1o2CdpOdi\nyShJkiRZAFgYl2Z6DDP7cU/bsKij1oJ1s2LSsGQXdpFujSRJkgWI9IgkCxqlgnXhyWiWi6tNkrQu\ngKRNJT0gFya7P4KZkdRX0ulyAbWJkr5X7EQubHabXKRuaUm3yEXPJkvavcywrhTQ6s1HkiRJkfSI\nJAsadwK/kPQUcDdwtZmNxj0Zr5jZMEmHAEcC38F32t3KzGZL+h/gRDwj6EBcXG5jM5sjaflCHx/C\nNUkuMbPLJe0CvGBmOwBIWqbctHSmzDs5EUmSpDU5EUkWKCIYeBiwFS6IdrWk/4vT18XP8biiKrjS\n7aWSBuMzhcrf9HbAuaE5g5m9HvXCs41OMbOKKNok4HS59PzNZnZfuXXNhXITqSOSJEnSms7oiGTW\nTLJAE96KA4ANgWFm9pqkTwGnmdk2kkYAj5rZ2ZIGAiPNbE1J1wLnmdndVe1Nw1OXlzGz/Qr1y+Gx\nKd/BVWaPr7rP0iPSFYj8PydJeg/KrJlkYUPSOpLWLlRVBOtqsQzw7ygfUKi/CzhIUt9ot7g08wvg\ndUm/j3MfAd41sytwrZVN5mUMSZIkSePkRCRZ0BgAjFDshAusR1tJ06Lw2anASZLG4xvqVeovwjfs\nmyTfy2fPVg2YHQ70k+80PAR4WJ6afQy+OV4JymOejyRJktbk0kySNIBS0CxJkqTD5NLMfEbSbLkA\n15RIBf2R2slXlLSapD/NLxtL+h8o3ziwrH5mjKdy7NNFfVae02RJ10jqNw9tjYrg1o7et2xk3yRJ\nkiQ9SE5EupZ3zGyomW0IfB74EnBsvRvM7N9mtlt1vVxGvqd5OsZTOS7vonYrz2kIvovvwcWTHRx7\nrf1p2mN5fD+gJEmSpAfJiUg3YWav4FoWh8JcD8NouSDXOLmCaCuPhKQDJN0o6R7gbkmXSPpqpU1J\nV0j6SrEfSf0l3a0Woa+vFNp9QtIF4aG5Q9JScW6YXORrAp14GctFwJ6S9LCkCyWdFfWDJD0Udpwg\naUYDzY0BBkvaWtIYSTcAUyQtKWl4tDVeUlP00U/SHyVNlXQdMNebIumtQnlXScOj/GFJ14eXakI8\n+5OBQeGZOUXSqvH7qXhqtiwZdx4pYJYkSRezIHzrXmQxs2lyhc+V8Z1rP29m78mzQq4EPl1y21Bg\niJlNl/Q54IfADfKdYTcH9q26/l1gZzObIWklfAffG+PcYGAPMztQ0tXALsAVwHB8V9z7JJ1aZwiD\n5AGcFQ4FpgFHh51vAX8FJsT5M4DfmtnVkg5q7/nIPR9fBm4tjH0DM3te0hHAbDPbSK6ieqdcNfUQ\n4C0zW1/SEFxTpEL1Tr4VzsTTeneW7wo8APhp9DU0bPkRcLuZnSh/e/Zva3HGiHScnIgkSVKfnIjM\nP5YAzpa0MTAbWKfGdXea2XQAMxst6ZyYYOwKXFsR6CrQB88a2QqYA6wmaZU4N83MJkV5HDAwJjTL\nFkS7LsOXkMp4pvKiriDpa8Coio3y+JZKuu1mQMVjcxWeCltGv8IEZzTwB2ALYKyZPR/1W+ATCMzs\nKUnP489sK3zCg5lNljSJ9tkG2CfumQO8qbYb3z0C/EHS4sBfzGxi22aaC+UmUtAsSZKkNZ0RNMuJ\nSDciaS38W/0rkpqBF81sX7m2xbs1bnun6vOluBdkD1rrZFTYG1gJ2CRkzqcBS8W59wrXzaawjFE0\ns5GxFLCqezrzlXdmyQQH4O2q62q1Xau+6LKoHmtdO81sTEzmdsTTh39jZpe1vqq5XhNJkiS9nqam\nJpqamuZ+Pu6449q9J2NEuolYjjkPOCuqlgFeivJ+uOZFI4wAfgCYmT1Zcn4Z4OWYhGwDrFGvMTN7\nA5guaYuo2rtBOyo8CmwtablYWtmlcO4h3HMD8I0OtlvNmIptsSSzOvAk7kHZK+o3BDYq3PMfSevF\n8svOtExM7sGXdIilsmWAGfieM0T96vheNhfhGiStJkpJkiRJ95Aeka6lsuSwOPAB7s34bZw7B/iz\npP2A2/H4igpW+NkqEMHMXpY0Fbi+Rp9XADfFEsWj+CZw1e1Wf/4mvgxh+CZztYIfqmNELg4p9ROB\nscBr+OTgzTj/A+ByST8H7gDeqNFuWX/VYz8HODfG9QGwv5nNknQuMDyeyRMx5go/A24GXon6SpzH\n4cAFkr6Fe4YONrOH5bv1TgZuA6YAP5Y0C5+k7EcbMt4hSZKkq0lBswUcSUvjm7INNbNGslC6HUn9\nY3O6xfCN6C42sxvkeiBv4fYuh6fIbmRm/+hA2z8AzjezmSXnRgGrApVzx5vZddXXdXAsz+HLWq+1\nc10KmiVJknQQpaDZwo18W/upwJkLyiQkaA5PyWTgWTO7IeorwmJ9gOfxF3zDk5DgcGDpGucM2Kug\nazJPk5BCm0mSJEkPkUszCzCxc+zAnrajGjP7cY36+yS9Y2YbV+ok9QduwL0jiwNHm9mNUX8N8FE8\nXuZ44MPAasBISa+Y2XYl3bSaWUf2yx+ANfFA3wMjm6ZW/Yp4Rs9qeKqzCna2ssfMrun400mSJEk6\nQi7NJF2KpA9wTwnAs8DuwNJFnRMzW1vSLsAXzOzAuO9Dcc00YFjZUknV0owB/wMchwfrHh/Bur8x\ns6FykbWy+jOj/gRJX8ZjSlbCU3yL9ixjZm8W+s5/KDXI/0OSJKlFI0szORFJuhRJM8ysmI2yOB6w\nW9E5WQf3UiyLB8peDdxc0TVpZyIyEjjCzMYX6sYDXzez5+LzP4ANgHtr1I/GBeAq9a/iOigrltlT\n6MdyFacM5UQkSZKaNDIRyaWZpLsp1Tkxs79LGgrsAJwg6R4zO76B9sr+oDuqN9KmvjF7mgvlJlLQ\nLEmSpDUpaJYsiJTqnEj6CPC6mV0h6Q3gf+P6GXFPrSyW6q/fFb2RE+T70bwSSzy16is6JL+S9CU8\ndqXMnm+17bq5E8NPkiTpPXRG0CwnIklXUz1RqKVzMgQ4TdIcYBYtO/BeANwu6YUawarVNOOaKBNx\nZdb926k/DrhK0p7AA3h2T7U97xMCaEmSJEn3kjEiSdIAGaxam/w/JEmSWnSJjoh8a/ebJP1X0iuS\nbog9VOY7Kmzz3huRNCKyTRqqXxCR1CzpX5IekzRZ0k7z0FaTpJs6ee9XJX2iI/eYWR4lR5IkybzQ\niKDZlbi+wkdw7YU/4ToMPUFv/1+vjQR8O/ULIkak0gK74VofrYhNAbubnYH150M/SZIkSR0amYj0\nM7PLzGxWHJfTsrtrjxMem9skPSpptKR1C/UPSZok6QRJM6K+1bdoSWdL2j/Kz0k6Mb6tPyppE0l3\nSnpa0kGFe34saaykifJddZHUX9ItkibEN/3dS2z9Ttw3QdK1IYle8WicId/75JmKd0PO2ZKelHQX\nsAoNZoLIN3c7rWBnRR+jj6RzJD0RY7ul0N9zciEwJH0q0mUrXoxL4vk+J+nrkk6PZ3ubXOodScMk\njYpnd7ukVevZar6J3weSVo77fivpEeBwSdtJGh99XCxpiejji2H7OHwyURlvs6QjCp+nyDeyQ9J+\n8QwmSLpU0ubATnhMyHhJa0k6TNLjcV1PTbSTJEl6HY0Eq94m6f9o8YLsEXUrAJTpPcxnLgAOMrOn\nJX0G3yxtO+AM4LdmdnVxElFC0ZtgwPMhfPUbfOfbzfEt5acA50vaHhhsZpvKd3m9Qb59/MrAC2a2\nA7ggVklffzazC+P88XhmxtlxblUz2yKWC24E/oy/aNcBPoELeU0FLm7wuXwLmB52LgncJ+lO4FPA\nGmb2CUkfxoNHK23W86qsiYt+bYDvsruzmR0p6TpgB0m34jsN72Rmr0raA/gVpdknTvy+ZpvZK/IY\njMXN7NOSlgL+Bmwbv9dLgEMknY//vrcxs2ckXU3r310Riz42AI4CNjez1yQtZ2bTJd0I3GQhEy/p\np8BA8431yn53SL1707tchkmSpDtoZCKyB/6f+oE16nskXgRA0gB8ovCnwktiifi5GfCVKF8FnN5g\nszfGz8lAfzN7G3hb0nuSlgW2B7ZXy660/YHBwH3AryWdTIkgVjBE0gm4mNcAfBde8Of4FwAzeyIm\nCACfA640fwO8KOmvDY6BsHOIpF3j8zK4cNcW+FIbZvafitejHQy4LVJwpwB9zOyOODcZl6FfB5+k\n3B2/i77Av0vaEvBDSfvgqbp7FM5dHT/XBaaZ2dPx+RLge8CoqH8m6i+n7d9ldV/bAtdUJsxmNr3q\nfIVJwJWS/kL8LtrSm1/EvXsSliRJ99HuRMTMBs4HOzpLH/xb/9AO3PMBrZek+lWdfy9+VtI4KXyu\nPK+TzOyC6obVvkDXCOAr5nue7E9rRaxiX5X/9Y3G3wBlb8lDzeyuKhu/XNVmsVx8NtXLb+8DmNkc\nSbMK9ZXnIuBxM/tsA3b+xsx+U3Lu7Rr3NLIcVf17rdhf7xkWn9kO+MRvJ+AoSUPMbHbry5sL5SZS\n0CxJkqQ13SJoFi/MNi85M7u0Qz11A2b2pqRpknY1s2vlX8WHmNkkfPlgV/zb/zcKtz0PrB8xB0vj\n35hHlzRf9vIy4A7geElXmNnbkj6Kv6QXo11BLAYAL8llz/cB/tnOEEcDB8XSxIfxpZEralxbbe8d\nwHcljTSzDyStA/wLuB/YP9pcBdga9ywAPIcv3dwOFLNwGpkMPQWsLGkzM3soxri2mU1twNbq+qeA\ngZIGhfdjX9wb8mTUr2VmzwJ7Fu59DtgRQNIm+FKSAX8Frpf0m1iaWd7MXqdFOI34u1ndzEZJuh//\ne+kPvEkrmht4DEmSJL2X7hI0+zQtE5F++It7PNATE5GlJRVf3r/G1TPPlXQ0vrvrVbib/QfA5ZJ+\njr+U3wAws39KugaP+ZiGj6WM6kwUi/vvijiOB2MJYgb+ohxM+4JYxwAPA6/EzwHV7Vf1db2kbfHY\nkH/gAly1OF/S76L8D2BLfMlkfLxoXwa+hseebBdt/jPG/0bcdxxwsaQ38Rd/Mf6ijX3FzxFbsStw\nZixhLYbvMVM2Eam1xlEZ97uSvokvuS0GjAXOiz4OBG6R9A6uqto/7v0zsF8sHT2MT2Yws6mSfgXc\nK2l2jPd/gT8CF0r6Pj6huTjsFnCGFTa8S5IkSbqPDguaSVoOuNrMvtA9JnUNkvqZ2cwofwPYw8x2\nbue2XoGk/uHNWRF/aX/WzF7uabsWZJSCZhmsmiRJh1FXCJqV8A7u9l7QGRbpmhNx+fAj2ruhs0ha\nVdIf5Wm+j8pTYtfurv6iz7mptlX1AySdG7aMC3u+XXXZzRFsOxr4ZdkkJMZQmj0yv1ANwbKof0Oe\nZj1V0i860fbW8jTehulp4bCePpIkSbqDRmJEii+CPrgI1DXdZlEXEVkrn+zufmLZ43pguJl9I+o2\nwmM6/t7g/VjH/6evdf1FwNNmNjjaX4mWDeWIvraRtJiZfVCz8UhDXoAZbWY7SVoamCDpJjN7rN27\nWtgGX1Z7sHvMS5IkSRqhkRiRX8dPwzMT/mFm7QVZ9ia2Ad4vZtFEsCzg4me4guiSwPVm1ixpIB63\n8hAwDPiyXHej1XVx//XAx/EskDMsdEjKkDQI+HRlQhS2/Bc4Nc43AcfjO9uuC6wX6aofq25f0nPA\nJnhA5214PMZngReAr0Ycx2HAQfjfxVQzKwaPEuO8lJY4jkPN7MGwoxmPldkQGGdm+8Q9X8RjS97B\nU6LrYmbvyMXNBkvaEc966Qc8YGYHRZtFOx8H/i8+z4404u8DywFH4+nfrwJ753JVkiRJ99NI+u4o\nuUJmJWi13W/5vYwNgXFlJ1Rb/OyfeHDrvmY2ttZ1ZjYG+F8ze12uwjpW0rWR9VHGBsDEduwdCmxg\nZpVdZ79Zo/2ix2UwHmNzoFxEbBc8e6c9EbD/AJ83s/diqepK/O8I3Fu1PvAicL+kz+KBpLUEy0qJ\nOJfNgF8Cd1ukTMsVVHc0s5ur7TTPtjoPmGGRRiwXOtssyt8GfgIcWdVXPVMWSXJJJkmS7qaRTe92\nxwMadwN2x19Wu3W3YQsR9f6nLoqfjcO9EIPj3PNmNraB6w6XNAFfQvg4LkrWkC2Sfh5xFC8UqscW\nJiGNtj+t4OUZh2fjQIsI2N7A7JL7lgAukjQJX84rbjI31sz+HUtSE/C4o/VoK1hW6+2/laTxuGfp\nJDN7AthWIeuPZ3dV9pKpZWex7Y/LJe8n4ROQDdp2ab3sSJIk6X4aWZo5Gnf3vwwgaWXgHnzzu8Rd\n/bvWOd9G/CyWLKrFu8qua8JTbTeLpZCR1N/n5wlgY0ky50TgRMU+O8HcfjvQ/nuF8mxaRODaEwH7\nIfCime0r38ju3TptLkbbt189F8QYM5u7c69cFv73wDAze0HSsfXsLGnvLOB0M7tZ0taUioYUq5pI\nQbMkSZLWdIugGf4yeKXw+VVS73kuZvZX+UZ53ynEV2yEx1bUEj+rptZ1y+Aiae9KWg9fgqhny9OS\nHsWVXY8xV0HtR+3fV4faLxJBtu2JgC2Di6gB7IfLvtc0n/qCZe1RmUC9Kpf+3w24poadAygImhVs\nrUjSH1DeRXMHzEmSJOl9dJeg2e3AHZKuxF9oe+DBi0kLOwO/k2+c9i4ulPaDiHOoFj/bhyrft5WL\npO2DP/uDJU3FBboayfD4NnAa8LSkV4GZwI8rXdHa69Bo+2UbyvUFLlN9EbBzgD9L2i/6eqtOm0Qs\nSS3Bsur+rere6ZIuxIXqXsKXE6lh5xuRDXatpK/iwarNuIDa67ga6xptu835d5IkSVdTV9Asvk1+\nHA8w3CKqx5jZ9fPBtiRZYIjVrp42I0mSZKFCDQiaNTIRmWxmG3a1ccnCgVwWfVKh6iozO1XSD4Dz\nrUW99i0zG1DaSHm7q+HeiYYCnyWNwOM8KnL0F5vZ2Y32V6PNUcARZlaa9VR1bU5EkiRJOkgjE5G6\nSzNmZnJ1zk0LGR5J7+IdK9/d+HDgMnzpBzqYZmFm/8bjOBq+BTjSzK7rSD8NtJmziyRJkh6kEYn3\nzfDYhWclTY5jUrt3JYssIRC2GjBS0j2F+hPksvoPSlol6kZIOkPS/ZKekbRL1A+UNDnKfSWdHn9b\nEyUdWqvrKjuWkjRc0iRJ4yMLqF59P7kU/1RJ1xFZNZL6hJ2T454fdOXzSpIkSWrTSLDqAr25XdLt\n9At9kwonmtmZkn4INJnZa1HfH3jQzI6WdArwHeBXcW5VM9siAnJvxHfKLXIgsDqwcWT6LF9ih/Dd\njY+Oz/vif5uzzWwjSesCd0paB/hejfpDgLfMbP1I4a3svDwUWM3MhgBEYGtbA3qRoFkuQyVJMr9o\nZCJSth36jJK6ZNFkZo2lmWreN7NbojwO+HyUDfgLgJk9IenDJfduB5xrZnPiujLl2DZLM5J+CZwZ\n9zwl6XlgHTywuqx+K+CMqC969p4B1pJ0JnALcGf5EHvLy7n3TLiSJOl5GpmIjMe/rVZeDssDL0l6\nCfhOI4F+Sa9gVqE8h9Z/W0XtlFpvuUbefmXXdLS9NvWR+rsx7mE5GFcQ/lbbW5sL5SZS0CxJkqQ1\n3SVodhdwrZndAXP3T9kVGA6cC2zaMTOTRYSKINhr7V3YAHcBB0kaaWazJS1fZz+dImOAvfFYlXXw\nCfOTdepHA3tF/YbARjB3v5pZZnadpL/hQbglNHd+hEmSJL2AzgiaNRKsunllEgJgZndG3YP4XiLJ\nok0/+X41lePEqL8AuL0QrFpct6jORmmvfBHwD2CSfN+bWoqq1Wsj5wB9Yonlj8D+ZjarTv25wIAQ\ncDsOeDTa+Sg+OXkMn4T8rLx79ZIjSZJk/lFXRwRA0l3A3fh/6MLd1tvjbuxHzGyT7jYySXqa1BFJ\nkiTpOI3oiNT0iEiquKfvwNVV/wJcj7u598Sls3fvGlOTeUHS7PBWTJJ0Xey10tE2Npb0pe6wryts\nkNQk6Y2CZ6ZGQGmH+jtA0lnz2k6SJEnSeerFiAwL9cv98Ki84ozGzOx94OlutC1pnLmiY6FAehDw\n6w62MRQYRhftIySpTyULpgttuNfMvjJvliVJkiQLEvViRM4D7gHWxdMxH606kgWTB4FBAJIGSbpN\n0qOSRoemBpJ2C/GuCZJGSVoc+CWwR3gbdpf0aUkPhCDY/RH02caLIOlmSZ+L8lshTDYB2FzSMZLG\nRl/nF+4ZJelkSQ9LekrSliU2lKmutnHvSdozPEGTJZ3cQP03o8+Hgc8W6ovP5N7OPfokSZKko9T0\niJjZmcCZks4zs4Pno01JJ5HUF4/fqQSQXgAcZGZPS/oMHsS5HXAMsL2ZvShpGTObJekYYJiZHRZt\nfQjYKrJY/gc4Ec+WKtuJt8LSwENmdmS0MdXMjo/ypZJ2NLOb456+ZvaZWIo51sw+X21DCVsVxNWu\nAS4BTgY2AabjwmVfBR6pUT8WT33ZBNfHGUmLqFmrZ1Lj+dYwa8El41qSJFnQaTd9NychCwUV9dOP\nAs8B50WcyOb41vaV6ypZTvcDl0i6BqgIhFWnTCwHXCppMD5xWKxwXS1m01o1dVtJP8YnKCsAU4Cb\n41yl3/HAwBo2VDPGzHaqfIjJxUgzezU+X4FvjGfAqJJ6quqvxoXOoPyZVLGwvdQXvolTkiS9j0Z0\nRJIFn5lmNlRSPzy4+Kt4ptP0MlVUMztE0qbADsA4ScNK2jweuMfMdpa0BjAq6j+g9ZLeUoXyu5XU\nEklLAb/HPRwvSDq26tr34udsOv93aLR+23Za3KzsmRTk64PmQrmJFDRLkiRpTXcJmiULCWY2U74h\n3ZV4ltM0Sbua2bVyt8gQM5skaVDspjw2lkY+hi9VfKjQ3DLAv6P8zUL9c8Ah0d7HqC1oV5l0vBre\nmd3w5ZR6VNvQHo/gy4cr4ksw38Cl3evVnyFpBVyQbTfgMfB4mpJnUmcikiRJklTTXYJmyYLP3DUD\nM5uAZzPtjquLfiuCR6cAlYyTUyuBnMD9ZjYJj5dYvxKsCpwKnCRpPJ6qbdH+fcA0YCq+b0tR4r9o\nx3Tgwuj3duDhBuwv2lAdrFotkoaZvYiLj40EJgCPmtlNZvZSnfpmPKD3PuDxQnNlz6SKnhYaS2Gy\nJEkWPdoVNEuSJAXNkiRJOoPmRdBsYUYtAl+PRfrpGpLub+C+rSQ9HvesJulPda4dGN+euxVJzZKO\nKKlfN9JgH5M0tZIeW51e22AfoyRtEuXnYukCSYdF25dJ2knST+u00VC/0deTkSZ7nyItuDPUejYN\n3vuDiKlJkiRJepBFNUbknZIgzS0auG9v4EQzuyI+l2lZzG9qfQ0/E/i1md0EIN/Erd71jfZRLB8C\nbGdmlViRmzphZ9l1e5nZeEnfAU7Dg2vnosbF0ObFRXE4vq/MzHloI0mSJJlHFkmPSBmS3oqfTfGt\n/E+SnpB0edR/G594HB8egDUkTYlzG8jFtx6TNFHSoGi2r6QLJE2RdEdkilT3u5Okh8LLcpekVaK+\nWdIfJI2U9Iyk7xfuOUouujUGF5QrY1XghcoHM5tSOLeaXMjsb5JOKbS7vVykbJykayT1r/24dB6w\nFr6x3Q+KHg9VCaJV7qnVbx3GAIOjzWoxtB9FH5MlHV7n2VSydEYpsn8krSRpWpT7RruT43d3aDzr\n1fCN7u6R1EfSiLhmkqQfNGB7kiRJ0gUsqh6Riq4GwLNmtgutvz1/ElgfeBG4X9IWZnaRpC2Am2I7\n+IGFew4GzjCzKyUthj+3VYG1gW+Y2YFyTYpdgCtozRgz2wzmTnZ+AhwZ59YBtsEzVJ6SdE7Ytgew\nMbA4rrNRpmT7W+Cvkh4A7gSGm9kb+ITgk3G8H+2eiafLHoV7OGbGMsuP8DTdaszMDpb0BaDJzF6T\ntH/hedQS/2rTr5m9QFsq64U7AZWg0LliaDGhOADPyOkDPCxXO+1b59m0CWYNDsT3R9rYzOZIWt7M\nXpf0o8LYhgGrmdkQAEnLlrSzwAqaZexKkiQLM4vqRGRmmX5GgbGV5Yb4Br4GLmgF5ekGDwBHSfoY\ncF0olQJMK2RXjKNFmKvIx+UiWavigmLPRr0Bt8T29K9Kejmu2Sr6eBd4V9KNZTaZ2QhJdwBfxJc2\nDpK0cZy+x8xmxPimhl3L45OvB8L2JWJcHaFiR5n4l9Xot3oiIuAKSTPx7JuKJ6gohrZlPIOZ0dZ1\n+HPpQ9tn0x7bAedWlnrM7PWSa54B1ooJ2y34xK6EBfGFv2BOjpIkSRplUZ2ItMd7hXK7glpmdpWk\nh4AdgVslHYS/RKvbKQt+PAs43cxulrQ1rcUo3i+xo1GRrkr66nBguDxwdsO4v9b47jKzvWq11yg1\nBNFU0m/fstuJGJGq+rliaNR/BrXKRaG16iWyum9rM5suaSN8Uncwnvr8rbZXNhfKTaSgWZIkSWtS\n0Kzz1H1RSVrLzJ4FzpK0OjCEFs9GexSFwQ5op08DRgMjJJ2ELz/siG9AWG3TF4C/xj4xqwIrAv8C\nNqrR7kPA7+XCXc9EfMhqZvb3Bscx1161Ff/6OOXugo4qnVYYgz+Dk/HJxdeAfaJc69k8B3wKX6rZ\ntdDWXbi3aGTsm7N8eEVm4L+b1+TCZ7NiSe5veBBrCc3tmJ0kSdK76Yyg2aI6ESl7KdbKDKl3rlLe\nXdI+wCw8ruRX+F4s9dqp0Izv9/I68Fd8GahybZvrzeyxiDeZCLyMb9RWxva4Sui78flIM3tZUq12\n/yvpAOAqSUtG9VFAvYlI9bOofD5V0tr4hOJuM5so6ZMl/dZay6j7+4lnMIKWsV9oZhNh7v4wZc/m\ndOAaSQfiyyuV9i7CY3EmSZqFbwR4Tvy8XdILwA9xr1LFo/KzcrNzGSRJkqSrSUGzJGkApaBZkiRJ\nhxmW0xMAACAASURBVFFvFTRLylGL0NuESOHdvJ3r50m0TZEyXceOyfI04k4LixXTdjt437KSDuls\nv0mSJEnXkBOR3sU7ZjbUzD4J/B9wUjf3V8uFULFjCB6we3DxZKRId6SPzrgqlge+24n7kiRJki4k\nJyK9l2WJ3WUlDZB0d3hJJkn6SvXFktaSi7INkzRILlz2qKTRktaNa9aU9GC0cUKDdowBBkvaWtIY\nSTcAUyQtKWl4tDVeUlP00U/SH+XS89dRyFQqemAk7SppeJQ/LOn68ARNCE/QycCg8MycImnVGEvF\nU7NlZx5qkiRJ0jEW1WDVpJyK0NtSwEeAbaN+JrCzmc2QtBK+O+1cjY6YaFwF7G9mkyXdAxwUeiqf\nwYM/t8N34/29mV0uqV1vQ3g+vgzcGlVDgQ3M7Hn5HjKzzWyj6P9O+b40hwBvmdn6kobgomYVagUd\nnwmMNLOdIyB1APDT6Gto2PIj4HYzO1EutNJGdVYLoKBZxq0kSbKwkxOR3sXMwot3M+BSXHukD3CS\npK2AObhU+ypxzyrAX/CJypOSBgCb45lAlXaXiJ+fBXaO8uVALZn3ovLtaOAP+F5AY83s+ajfAp9A\nYGZPSXoez37ZCp/wEJOiSbTPNnj6LyFs9qZiY78CjwB/kLQ48P/tnXmYXFXxv98PCUgIhF1BBMMq\nW8QQBGRzQInKpigS2UUFXFBc4AeKyERUcIuACMiagARQiOwGAhI2ISxJSCBhkUWWL0tE0AQMQlK/\nP6o6c6fn9vRM6KFnhnqfp585fe6559S93cmtPqfqc66oZOm0p7c99HufY5QkSdJd0hF5h2Jmd8n3\nZFkVFyZbBdg8tDaeoE0U7BXgH7gD8BDutLxSR7m2Hh2Ub8OpebWqXXd1SIqeQnUAbD1Rs9vCEdsN\n1yoZY2ZVeiKthXILKWiWJEnSnhQ0S7qMpA1xp+IlXNjrxXBCdqRN6wQ8mPSzwPWS5oXK7BOS9jKz\ny2IZY1hI3d8BfAHfb2e/t2jibdHHzbEksxbuCN0K7Bv1m9JewO2FuK5H8JmZf0f9TfiSzimSBuDL\nLnOB5Qr3Yy3g2dhz6F34MlEnjkiSJElSTQqaJfUoLokIj/lYKOki4OpY5rgXmF04x8zsNUm7AZMk\nzcUdhDMk/RBXOL0Y37zuCGC8fEO9K+m+oFmx/vQYYwYu335QqMiegYuPzQo7ixsCHgNcA8yJ+kqc\nxxHAWZK+jEvPf9XMpki6I9KT/wI8ABwVomdzgQM7mphLIUmSJI0mBc2SpAukoFmSJEn36beCZpIW\nSrqw8H6gpDmSrl7M/hZbuCsEtR6KtM9p8h1pO2v/fkn7LM5YvQFJYyV9rqR+a0l3xT2YJen4qN89\nZkjK+ioVPOtk7NbIpkHSaEkf666dSZIkSe+iry7NvApsImnp2BJ+Z3zDt2b8ZK21m2wt1sZjHC6u\nPiBpoJm92UjjeoBaAmLjgL0ik0XAhgBmdjVQy0Hs7udV3I/m+K62TZIkSXovfXJGJLgOz/YA2Ad/\nsAtA0mBJ50maEmJYe0T90BCtuk81JM4lbRLnTZN0v6T1umBLh2mn+EV+SsQhPFb4dX4SsH30/21J\nB0m6KrQ5JklapobtAyT9UtLdYdehUb+6uiHEJemQ6GO6pMsU8uq17JVzWsz6TMLTecum2VYFngcP\nKjGz2XH+FyX9Nsprq4bgmaSjCtfWWqg/VtLDkm4DPkA4GMUZD0knSXowzv1FodsdSu5/6Vjxnbk2\n7stMSXt3dh+TJEmSxtBXZ0QALgV+JOkaYBhwLp5iCr6r7E1m9iVJKwBTJN0IvADsbGavy3ePHQ98\nuKrfrwKnmNl4ueBWvXsk4CJJ/433N5hZZSliNTPbVtJGuEDY5biQ1pFmtjv4gxrP0BhmZq9I+lkN\n2/fH02a3lGd13C7pBjyjpVMhriouN7OzY+wTgC8Dp3Vi7564fsdGwGrALPxeV/Mb4GFJk4GJwDgz\ne532MxOlgmeSRgLrxbUtAVwpT6V9DRgFbIYHxU6lLTjVAJO0MvAZM9sw+hpS6bbsejoZa1U8a2bX\nqn4WoV4kaJbxKkmS9Bf6rCMSSwBD8dmQa6sOjwR2l3RkvH8XsCb+i/00SZvh2RMblHT9N+BYSe8D\nJpjZ3+uZQvnSjOFCYJjZbEnvifrqp5kBk8zslU5sXyvqh0naK+qHAOvRJSGudgyL2YjlcYXRiXXs\n3QEYH5Gaz0n6a+lNMDtBnn0zEl962gcXEiteby3Bs5HASLVl9AwG1sfTayfE8tt8SVfRkVfi2Ll4\nxsw1da6nbKz1gNuBX0s6CbjGzG4vucqyS28CvcchSpIkeav0WUckuAr4FfBR/Bdtkc+a2aPFipiG\nf87MDpDrScyv7jB0Mu7Cha2uk3SYmd28mPb9rzh8J+2qhbzKbAc43MwmVZ+sukJc7RgL7BGO3EG0\nV+Uqs9fq2L4IM3scOFPS2cAcdVQv7YwTzeysYoWkI6rGrrZDoX2yJS4xvxdweJSh9v3vMFaMNxxf\n7vuJpJvM7IT2LVoL5RZS0CxJkqQ9iyNo1pdjRMClwVvN7MGq+uuBb1XexAMGfBbh+SgfCAyo7lDS\nOmb2hJn9FtfCGBb1N0lavYYd3fmJ2k5Iq+TcWrZfD3w9louQtIE8nmQtYI6ZnQOcgy/zIOkCSdXL\nTuCzIM/HDMr+1P+ZfyswStIScf07ljWStGvh7Qa49sfLVc0qgmfQXvDseuBLkgZHX2vIFV9vBT4j\naWlJy+HOVvW4g4EVzOwvwHfxZZzOKB0rrm2+mV2EO7ebdzy1tfBqqTNMkiTJO4+WlhZaW1sXvbpC\nX50RMQAze5a2+IZiNscJwMlyMawlgMeBPXCRrMslHYgvScyr7hPYW9L+wBvAc8BPI5ZgXWK32hKK\nMSJzzGxkVZ/F8v3AAknT8dmJl6va1bL9HGAoMDViQV7ElzlaKBfiGgY8W2LrccAUXPRrCu6YVNu4\nqGxmf5a0Ex4b8hS+dFXG/pLG4HEdbwL7mZlJKn4upYJnZjYp4jjujJmfucD+ZjZN0qVxz14E7q4a\n03Cn7kpJS+NO3XfqXE/ZWAfgyzO/lLQQn0n5WsdLzCWRJEmSRpOCZl1A0ibAwWZ2ZN3GvYAItDzb\nzEY125b+glLQLEmSpNuoC4Jm6Ygki42kBbi0u/Dg38PN7M5O2g8FrjazYYs53mQ8c6cy+3SCmU1Y\nnL4KfT6Jb/ZXa7ar0i4dkSRJkm7SFUekry7NJL2D1yq76EZa7In0bPBEd8XjutpnkiRJ0iT6erBq\n0ntYnoihkbSspBvlonEzFKJswUBJf5DLwP9J0iBJO0n6c6WBpJ0l1ZrpaOdZS1pJ0hVycbI7JQ2r\nU7+ypBskPRDZPUURvBQ0S5IkeZvJpZlksZH0JjATWBpYHdjJzKZGavQyZjZX0irAnWa2fizNPA5s\na2Z3hvbHLDP7taTZwHZm9pKk8cBFZnZt1XiTaVuaMeDjwGjgxdAx2REYY2bD5WquZfWnRv1PJO2C\n646sgmcDfcLMKoq1Q8zsP4Wxe8U/lPz3miRJX6IrSzM5I5K8Ff5rZsPNbCPgk8AFUb8EcKKk+4FJ\nwHslvTuOPV2II/kDUJGkvxA4QK4muzXwl5LxKkszw82sEtexbZxL6L2sHKm+teq3j3Exs+toSzGe\nAewsl4vfruiEtB++ma8kSZL+R8aIJA3BzO6StErof+yKzzJsHoJjT+CzJtD+iarC+/PxzfHmA380\ns4U1hirzrGt5212uN7NHU9AsSZLkrbE4gmbpiCQNQdKG+EzIS7hw3IvhhOwIvL/QdC1JW5vZXbgU\n/G0AZvacpP8DfkibMmoZ1VMDt+HiaD+R1ILruMyVb5JXVn9rjPtTSZ8CVgz7VwdeNrOLJP0b34On\nitau3o4kSZJ3JC0tLbS0tCx6P3r06LrnpCOSvBUGqW3PFgEHmdlC+Z4zV4co273A7MI5DwPfkHQe\n8CBwRuHYeGAVM3u4Gza04nvt3I9L5R9Up340cLGkfXBxtn9E/TBS0CxJkuRtJ4NVk16DpNOA+8zs\n/GbbUk3qiCRJknSfDFZN+gyS7gM2Bf4g6dhIr71f0jT5pnaNHu+jkj7S6H6TJEmS7pFLM0mvwMxG\nAIRzsCsw3MzekO/g+64eGHJHfJ+ZmkqwSZIkSc+TMyJJb2M14J9m9gZApOiuIelyAEmflvSapIHy\nXXkfi/p1Jf1F0r2SbpX0gahfVdJlku6O1zaS3g8cBnwnZly2k/T5EDKbLumWMsMkNfSVJEmS5IxI\n0vu4AfiRpIeBG4FL8aDSD8Xx7XERtS2BJYG7ov4s4DAz+7ukrfCdlj8GnAL8xszukLQWMNHMNpZ0\nJjDXzMYARGDtyMjeGVJuWiNjRNIRSZIkgXREkl6Gmb0qaQTucOyIOyLHAI9FivCHgTHADsAA4DZJ\ng4FtgD8VZhqWir8fBzYq1C8X7aG9N3AHME7SH4G3tJFekiRJ0nXSEUl6HSFmdgtwi6SZeOrtLcAu\nwBvATcA4fGnxSNwhebmyAV8VArYys/+1q6xaGjGzr0VQ7K7AfZJGdNyRt7VQbiEFzZIkSdqzOIJm\nmb6b9CokbQCYmT0a73+CC6Rdjku2jzWzH0m6C1jVzNaNdnfgSzCXyb2MYWY2IzRNppnZr6Ldh8xs\nuqTvAkPMrDXq1zWzSrzJ3cBXzGxGwS5r9NJM/ttLkqS/k+m7SV9kWWCspAdDjGxDfCribuDdwK3R\n7n48VqTCfsCXJU0HHgAqO/5+C9giUoEfBA6N+quBPSVNlbQd8Av5TsEzgTuKTkgbauArSZIkgZwR\nSZIukYJmSZIk3aepMyKS/ippZFXdtyWd3sAxNov9Quq1+6J8W/iyY/MaZc/iImmspM9V1R0kaXxV\n3SqSXpS0ZAPH/kEX25XepzLb6/TTKumZSJudKWn3rp5b0leLpKsX89xPS9poccdOkiRJGkNPLs1c\nDHyhqm4Uvp9IoxiOBzDWo7Ofsr3hZ27ZPu8T8G3pBxXq9gKuqmhsNIjvd7FdrfvU3T3qDRgTgaWf\nB86rbiBpQDf6W1z2BDZ+G8ZJkiRJOqEnHZHLgV0lDQSQNBR4r5ndLmmkpL9Juk/SHyvplJJ2kTQ7\nRKlOrfzalTRY0nmSpsSa/h4xK/BjYFT8ut5b0oej36mS7ojAxwprSrpZ0iOSflRmsKSj5KJX90tq\nLYx9rVzoaqakvUvOOyTOmy4XzxoU9WMlnRK2PFaZOZBzmqSHJE3CYx/aTV2Z2Vw8U6Q4Y/AFfMO2\nDiJd0e+qkibJ5dHPlvSkXJkUSfvH/Zsm6UxJS0g6idi4TtKF0e6KuP8PSDqk6jrHRP2NklYpHorj\nIyRNjvMnSlqt7D5X2pvZQ8CbYfdkSb+RdA9whKSPxec4Q9K5kpaKMT4Z35H7cGeiYlurpO8V3j8g\n1w1B0oHxmU6XdIFcvXV3fJO7qZLWkfQtRVyKpItLjU4xsyRJksZjZj32wgMC94jyMcAvgJXxB+yg\nqD8aOA5YGngKeH/Uj8d//QP8DNgvyivgO7gug6d1nloYbzlgQJQ/DlwW5S8C/4dv+b40HuS4eRyb\nG39HAr+P8hJh+/bAZ4GzCmMMKbnOlQrlE4DDozwWuDTKGwGPRvmzuHCXgNWBl4HPlvT7OWBClN8L\nPBu2jQe2jfq1gFlRPg04OsqfABYCK8XYVxXuzenAAcXrL4y5YvwdFPep8n4hsE+UjwN+G+Xz43qW\nxIXHVo76UcC5Jdd0PPC9KG8FPBPlm4HTolz5LqwX78cBRxTq1436SwvfkUX9xvuZcW82ie/LSpXv\nT9HuQvtngSU7+YwNrEEvLEmS5J1A/H/Xqa/Q0zoileWZq/AH05eAj+BT4n+LX4ZL4Q+wDwCPm9k/\nCudWMhxGArtLOjLevwt/yFSnIKwAXCBpPXwJoHh9N5jZywCSJuBOxtTC8ZHASLVtaz8YWA+4Hfh1\nzB5cY2a3l1znMHma6fJ41sfEqDfgCgAzmy3pPVG/AzA+PqTnJP21pE+A64DTJS0H7I07Vgsl1RLp\n2hb4TIx3vaSX4/jHgBHAvXHOIOD5GmMeIekzUV4TWB/PWFmIP/gB/kB70S/hn98mwI0xxgDc+atG\nuLT6/vheL6MKxyr9fwB4wsz+Hu/HAd8AJkf9YwU7DqU2AnYC/mihCWJmr1QdrzADGC/pCuIzS5Ik\nSXqennZErgJ+I2k4sIyZTZO0BjDJzPYtNpS0WdW51fPXn7XQliics1VVmxOAm8xsT/l+IpNr2CX8\nwVrNiWZ2VofGbv+uwE8k3WRmJ1Q1GYvP/MyUdBDtla6KQlqVazI6Xl8HzOy/kibiMw6jgO8U+qkl\n0lXdb+X9ODPrNDBVUgvutGxtZvMl3YzPQnRoSnlcyINmtk1nY9AWIzKm5NirtUzrQv2btF9qrNjd\n2b0uXsOuuIO4O3CspGFmtqB989ZCuYUUNEuSJGnP4gia9aiOiJnNw6fcz6ctSHUKsK2kihDVYEnr\n49Pn64QDAf7grTworsf1IIhzKgqac/HlmApDaPsVfnCVOTtLWlEev/FpXNK7yPXAl9QWr7JGxC6s\nDsw3s4uAXwGbl1zqssDz8riV/Sl/SBe5FY9tWSL637GTthcD3wXebWaVfVVuoP39qDhxd+AzJ8gz\nllYMW24C9pK0ahxbqRI/AbyhiOPB79/L4YRsCGxdsGMJPLgUYF/gtsIxwz+/VSVtHWMsKalWMGg9\nx+JhYGjlOwIcgDuVD0X9OlG/T+HcJ4nPRtLmwNph11+BzxdiZVaM9nPjepF7cGuZ2WR8CXF5fEas\nitbCq6XGJSRJkrxzaWlpobW1ddGrK7wdgmYXA8PiL2Y2B4/ZuFguWPU34ANmNh/4OjBR0r3Af+IF\nPtOxZAQuPgCMjvqbgY0j2HJvPAblRElT8aWBikNg+PLC5bgQ1mVmNrVwDDObhDtLd8o3QPsj7uQM\nA6bEks1xYUs1x+EO1u3A7KpjVl02sz8DjwKz8GWHv3Vy/27E40guLdRVi3QdFvWj8eWlmXiGzfN4\nDMhs4IfADXHPb8B3uQXfLG6GPFh1IjBQ0izgRODOwpivAltG3y14oHDbhXkmz17Az+WiYtPwZbgy\nOsvAIb4LB+N7x8zAZzvONLPX8aWYayNY9YVCX5cDK8X34xu4M4OZzQJ+isvFTwd+He0vAY6KftYH\nLoyxpgKnmFnlu1cgxcySJEkaTa8SNJM02MxejfLvgEfM7JQmm9VniMySBWa2IDJDfmdmZTM4STdR\nCpolSZJ0G/VBifdDYnbjQXza/Pc9MYikYyO98/4Yb8ueGKcb9mwt6a6wZZak4xezq7WAe+KX/ynA\nIXXav63IU3RHlNQPlPQzeWr1tHh1SWiti+NeK2lIo/pLkiRJGkev2n3XzE4GTu7JMWKmYFdguJm9\nEbED7+rJMbvAOGCvCHYVvr9Kt4ksk948A1JL/OwnuJbKpmb2P0nLAt+rbhT3hu5OTZjZrotha5Ik\nSfI20NtmRN4OVgP+GTENmNm/zOw5ANUW0SoKg20R2SQVEa0L5SJqj0j6StSvLulWtcmYb1fHplWJ\ndNpIvZ4d/WypEoE2VUnWS7pG0kej/Em5UNx0STdGXQdBuKjfRG0iZ/fL055rIuk4uYDaTEm/L9RP\nlnRS9PVw5XolDZJ0SczyTMDThlXV5zLAV4BvVrKAzGyemY2O40Ojz3G4Nsiakk6XdE/MarUWrvuP\nhX4Xyb+rvrDbALn43Mz47L9d4/pTsCxJkqTBvBMdkRvwh9nDkn4naQcASUvj2T17m9kH8dmir8U5\nnf0C3xTPevkI8CN5Fsy+wERzGfMPAtPr2PQb4GFJEyQdKqkyQzMb2D7iPI7Hhd3K7DHA5FkxZ+Gp\nzh/Cg0cBjsXTmrfCdTV+GQ7AYXhg5nBcZ+SZOnaeZmZbmtkwXJF1t8L4A6L/b4et4PdvnpltHHUj\nSmxfD3iqEhtUg/XweJdNzewp4Fgz+zCwGfBRSZsCk4Ct1CaJP4oIkK6MKd9bZm9gm7jmBfiuvZvh\nqr/D4rM/v9wM6+IrSZIk6SrvOEckHngj8OyLOcClcu2PMhGtHep1B1xpZq+b2Ut4Fs+WeIbOwRHr\n8cFIY+7MphOALXAnaV/aBNFWAC6TZ6qMoW1vlLKf3MLTbW+tiMIVxLtGAsfIM39upk0Q7k7gB5L+\nHzA0slU6Yyd5LMsM3KEppudWBM6mAkOjvD0uOoaZzcRFwzolZnumSXpKrjkD8A8zu7vQbJQ822Uq\nLqK2cWh+TAT2kKcj7wJcWeya9sJu0+L92sDjeOr4qZI+QVu2VpIkSdLD9KoYkbcLM1uIy8zfEg/5\ng/B00yJF0a6iWFaZwFeRhWZ2m6Ttgd2AsZLGmNmFdWx6HDhT0tnAnFhKqCXQ1pl4Vy06CMIBD0m6\nK+y8TtJhZnZz2ckxY/Q7YISZPRtOVvFevB5/F9D+e1VvneLvwFqSlo0lmbH4PZuJp2BDQehM0tp4\n/MgWZvZvSecX7LgEOBz4F3BvjVmWUmE3SR8EPgl8FZ81+XLHU1sL5RZSSyRJkqQ9vU7QrDciaQO5\ngFqF4bgYVpmI1i1RfhKfsQDf/2VRd8CnJb1L0sr4k+keuVjYHDM7BzgnxkC+4dqHS2wqBlNugDsa\nr1BboO1J4ENy1sRnYQy4C9hBvsEglbgIagjCSVrbzJ4ws9/iswfDov6mWGIqUnnYvyQPJv089bkV\nn+Ehlk8+WN3AzF4DzgVOqyxJyXffXapGn0Nwx+Q/csn8T1WNtzmeLVS9cV1NYbf47Aaa2QRcE6ZG\nwG8rKWiWJElSm8URNHsnzogsC/xW0gr4A/9R4FAze11SRURrIL68cmacMxo4V9J/8FmJolDaDHy5\nYxXgx2b2vKQDcbGsN3AFzwOj/TB8c7Vq9pc0BngtbNov9pT5BTBO0g+Bayvjmu9g/AQuiDYbuC/q\n/ynpUGCCpCVwwa9P4DMrJ8eSyhL4UsQewN6SDgDeAJ4DfhrnrYvPKizCzF6J2ZoH8MDaKZ3c48r9\nOQM4Xy6QNhu4t0b7Y8PGByTNBf6Ly+Y/B6xR6A8zuz+WVR4CnsZF5CrHFki6Bp/hOpA2KvdtdtzL\nG+I638BF9OaHnRXH/JhyMzMINUmSpNH0KkGzvkYsT8wzs193oe0Q4GwzG1WvbTORtAlwsJkdWbfx\nOwiloFmSJEm3UR8UNOuLdOnpZGb/qTghkhaoTbhrmtr2famLpMNiFqMS2Fm9hPKWMLMHazkhqkob\nrqqfU7iesd0ZUyE4Jml5SV/rpF2nQb/R5uzIjkmSJEn6ADkj0gQkzTWz5Woc67Jol1zP5Egzu6/B\nJtYa74t4sOo3q+oPivpv1ThvoJm92YX+hwJXR3pw2fGa962nyRmRJEmS7pMzIn0EtRftmoHrnMwr\nHN8rskMqImrfk/Q5PID2IrlI2dJyUbEH5eJkvywZpzOBtAmS/iIXZvt54ZyDw7YpwDadXUbVWBWx\nt9uBCyQdpI4ibBUNlycjYPQkYN2YVfk5NVBBrCzenxbOUDsZeTVY3E0pXpYkSdJw3onBqr2BQRFw\nCR44+l1ctOuAil6GpA679hbKZmaXSzoc+J6ZTY0H+WfMbMM4v2xvlYpA2gJJH8cF0iqiZ5sBHwL+\nh4urnQosxFNENse1NW7GtTuqEa7tUVGQPSXs3AjYNgKBD6o6p8M1AUcDm4TYWHcoKolVi7ttb2b/\nkAcnQ5u425eibko4KRVxt/HyYOWSfxtlMyLpiCRJkrwV0hFpDv8tPmxjSaJatKurVJ6E/wbmSzoX\nuCZe1ayAz06shz9Vi5//TWY2N+yZhYuSrQpMDrE2JF2KpxdXY8AlxaWZCOS90sxeL2lf71reKvXE\n3XaXVImDKYq7HSvpfcCEgrBdgdZCuYVM4U2SJGnP4uiIpCPSe6gW3yr+/B5EbSqpqW/KdxH+GD7L\ncXiUi9QSSIM2QTJoEyWrngLozFEoO/ZaoVxLhG1xqO6r7P70gLhba/ctTZIkeQfR0tJCS0vLovej\nR4+ue07GiPReXpC0oVzbYs9CvWh76M/FBb6QNBhYwcz+gi/1bFbSZy2BtDIM1wr5qFz4a0lqi5h1\nZSbjSTqKsFUzF+hKMOo/gI0lLRXLKzuV2N4QcbckSZKkZ8kZkeZQ9mu9uu4YfHllDi4ENrjQrtJ2\nLC4L/xqxt4pcil3Ad0rGKBVIq+qzzSAXZ2vFly1ewWXwa9ne6TWZ2R0qEWGrGu+lCKKdCVxnZkdX\njkXcxuvR7mn5TrsPAE9QErfSCHG3jpeT8SBJkiSNJtN3kz6BpM2A35vZ1k0aP9N3kyRJukmm7yZv\nK2oTapseabMfaVC/XwXGAz9sRH9JkiRJ7yFnRJKGoYLgmKSRwA/MrKWHxuqy8FuDxssZkSRJkm6S\nMyJJM1mewsZ5ko6SdHeIhbVG3UmSvl5o0yrpe520Lwq/zcSF306XdI+kByrtou0ukmZLulfSqRUB\ntEYJmiVJkiSNIYNVk0ZSEWpbGlgd2BEWzY6sZ2ZbRuDolZK2By4BTgZOj/M/D4zspP3TdBR+O9bM\nXpY0ALhR0jB8R+UzaRMzG09b4GwDBM3SEUmSJGkU6YgkjWSRUJukrYELgU1xEbGRBTXZwbijcb6k\nd8s37ns38LKZPSvpO2XtcUekWvhtlKRD8O/y6sDGwADg8YqYGXAxcGiUGyJoNnny5Ha58kmSJMni\nCZpljEjSMFS1KZ2k53E9jqOBR8zsrJJzRgP/BFYDnjOz0yT9qqy9qjbFk7Q2cAOwhZn9W74fz2Rg\nOj670RLt9gAOMbPdJd0L7FMiaFbpbzfgm0A7QTNJVpwRyX83SZIk9ckYkaRpSNoQ/379ExcR+1KI\nriFpDfleMACXAvvgarB/irrO2hcZgivS/kfSe4BP4d7Cw8A6cvVYgFG0eREpaJYkSdKLyKWZpJEU\nN/MTcFCkmkyStBFwZwR6zgX2B+aY2SxJywLPmNkLAGZWq3074TQzuz/Gewhftrk96udHEOxEyQZt\nuQAABoJJREFUSa8C9xTOS0GzJEmSXkQuzSSdImkBMKNQ9Wkze6qk3fLAvmZ2xtto22R89+H7quqX\nAsbgSqqVzf3GmtmP4/gdZrZt9VJPnbEyfTdJkqSb5NJM0gheM7PhhVcHJyRYEfh6jWM9RS1p+Z8B\nI4B5tM1w7LboJLNt3xbrkiRJkrqkI5J0i9DhuDGUU2dUdDiAk4B1Q4fj55JWk3RrvJ8pabuSvo4L\nrZCZkn5fqJ8cGiNTQjdku6gfJOkSSbMkTcB33VVVn8sAXwR2Dsdpk3A85kuqpBPPK7Glro5IkiRJ\n0njSEUnqMSgeztMkXQ7MB/Y0sxH4rre/jnZHA4/Fw/9oYF9gYqTzfhDPZKnmNDPbMpZGBkmqzFoY\nMMDMtgK+DRwf9V8D5pnZxlE3go4zIusBT5lZtbNxL7BJof9qvopn2gyPfp+pbpBCZkmSJI0ng1WT\neizSBgGQtCRwYgiMLQTeK+nddIzkvAc4L9pfYWb3l/S9k6SjgGWAlfDddK+JYxPi71RgaJS3B04B\nMLOZEXDaKP5GXR2RJEmSpNGkI5J0l/2AVYDNzWyBpCdwJdV2mNlt4azsBoyVNMbMLqwcl7Q08Dtg\nRIiYHV/Vz+vxdwHtv6f1piUeA9aStGzVrMgI4OpaJ5nZxZLuCnuvk9ROR6RCa2srAC0tLSloliRJ\nUsXiCJrl0kzSXYYAL4YTsiNQ0eqYCxTFzNbC03PPAc4Bhlf1U3E6Xor03c93Yexb8SUfJG2KL/m0\nw8xeBcYBY0IeHkkHAoPKHIuCvet0RUektbWV1tbWfueEdPc/jr5GXl/fpj9fX3+7tpaWlkX/T1Z+\nuNUjHZGkHtXxFBcBW8SyyAHAbAAzewm4IwJPfwG0ANMlTQX2JpZUFnVq9gpwNr4cMxGY0gUbzgCW\nlTQLGI3HfZTxfTyW5RFJjwCfA/ascU2V8t7yjfOm4bEkF3RiT7+jv/1nWE1eX9+mP19ff762rpJL\nM0mnmNmQqvcvAdvUaLtfVVWnD3MzOw44rqR+x0L5n8A6UZ6Pq7DWs/l/uHrqt2ocHxJ/nyRmVczs\nJDzzJ0mSJHkbyRmRJOkiKWiWJEnSeFJZNUm6gG96lyRJknSXesqq6YgkSZIkSdI0cmkmSZIkSZKm\nkY5IkiRJkiRNIx2RJKmDpE9KekjSo5KObrY9jUTSeZJekDSz2bb0BJLWlHSzpAcjPbs0k6ovImnp\n2B9peuy/dGKzbeoJJA2ILSZqChL2VSQ9GXt2TZN0d7PtaTSSVpB0maTZ8R3durRdxogkSW0kDQAe\nBj4OPItL1+9jZrObaliDCPXbecAFsedPv0LSasBqZjY9hPPuAz7Tjz6/ZczsNUkDgduBI83s9mbb\n1UgkfRdXRl7OzPao174vEcrUI8zsX822pSeQNA64xczOi+/oYDP7d3W7nBFJks7ZEvi7mT1pZm8A\nlwCfbrJNDcPMbgNebrYdPYWZPW9m06M8Dxfge29zrWocZvZaFJcCBgD96oEWez/tgqsz99ddJ/vl\ndUlaHtjezM4DMLM3y5wQSEckSeqxBvB04f0zUZf0MSQNxbca6EzFt08haQlJ04EXgJvNbFazbWow\nvwGOwjfY7I8YcKOkeyUd0mxjGszawBxJ50uaKulsScuUNUxHJEk6J9cu+wGxLHMZcETVZoh9GjNb\naGYfAt4H7CCppckmNQxJu+H7Wk2jn84aANvG7uafAr4RS6X9hYHA5sDpZrY58CpwTFnDdESSpHOe\nBdYsvF8TnxVJ+giSlgQuB/5gZlc0256eIKa8rwW2aLYtDWQbYI+Io7gY2ElSv9oDysyei79zgD/j\nS8H9hWeAZ8zsnnh/Ge6YdCAdkSTpnHuB9SUNlbQUMAq4qsk2JV1EkoBzgVlmdnKz7WkkklaRtEKU\nBwE7A9Oaa1XjMLMfmNmaZrY28AXgr2Z2YLPtahSSlpG0XJQHAyOBfpO9ZmbPA09L2iCqPg48WNY2\nN71Lkk4wszclHQ5cjwcDnttfMi4AJF0MfBRYWdLTwI/M7Pwmm9VItgX2B2bEzsoA3zeziU20qVGs\nDoyTtAT+o/JCM7upyTb1JP1tmfQ9wJ/dV2YgcJGZ3dBckxrON4GL4kfcY8DBZY0yfTdJkiRJkqaR\nSzNJkiRJkjSNdESSJEmSJGka6YgkSZIkSdI00hFJkiRJkqRppCOSJEmSJEnTSEckSZIkSZKmkY5I\nkiRJkiRNIx2RJEmSJEmaxv8HjFaHehUz6j4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd92210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result['Zinc, Zn'].sort_values().plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "by_nutrient = ndata.groupby(['nutgroup', 'nutrient'])\n",
    "get_maximum = lambda x: x.xs(x.value.idxmax())\n",
    "get_minimum = lambda x: x.xs(x.value.idxmin())\n",
    "\n",
    "max_foods = by_nutrient.apply(get_maximum)[['value', 'food']]\n",
    "\n",
    "# Make the food a little smaller\n",
    "max_foods.food = max_foods.food.str[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "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": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_foods.ix['Amino Acids']['food']"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}