DataPrepare.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 준비: 다듬기, 변형, 병합, 변형"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "from pandas import Series, DataFrame\n",
    "import pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sims.gen_sim_price>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sims.gen_sim_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 병합"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터베이스 스타일 DataFrame 병합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'],\n",
    "                 'data1': range(7)})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = DataFrame({'key': ['a', 'b', 'd'],\n",
    "                 'data2': range(3)})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
       "      <th>data1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key  data1\n",
       "0      0   a      2\n",
       "1      0   a      4\n",
       "2      0   a      5\n",
       "3      1   b      0\n",
       "4      1   b      1\n",
       "5      1   b      6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df2, df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.merge(df1, df2, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "열의 공통 이름이 없는 경우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)})\n",
    "pd.merge(df3, df4, left_on='lkey', right_on='rkey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "합집합은 outer join으로 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N-대-N 조합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>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   b"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],\n",
    "                 'data1': range(6)})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>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   a\n",
       "3      3   b\n",
       "4      4   d"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = DataFrame({'key': ['a', 'b', 'a', 'b', 'd'],\n",
    "                 'data2': range(5)})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>0</td>\n",
       "      <td>b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.merge(df1, df2, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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]})\n",
    "pd.merge(left, right, on=['key1', 'key2'], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.merge(left, right, on='key1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.merge(left, right, on='key1', suffixes=('_left', '_right'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 도전과제\n",
    "\n",
    "\n",
    "주식 종목에 대해 다음 두 가지 형태의 정보가 있다. 첫 번째는, 주식 종목의 회사에 관한 프로필 정보이고, 두 번째 정보는 주식 종목의 일일 가격에 관한 정보이다. \n",
    "\n",
    "1. 주어진 두 가지 정보를 단일한 DataFrame으로 합치시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>산업분류</th>\n",
       "      <th>상호</th>\n",
       "      <th>종목코드</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>전자</td>\n",
       "      <td>SJ전자</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>소프트웨어</td>\n",
       "      <td>코드베이직</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>식품</td>\n",
       "      <td>성주식품</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    산업분류     상호  종목코드\n",
       "0     전자   SJ전자   100\n",
       "1  소프트웨어  코드베이직   101\n",
       "2     식품   성주식품   102"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_profile = DataFrame([{u'상호': u'SJ전자',\n",
    "                              u'종목코드': 100,\n",
    "                              u'산업분류': '전자'},\n",
    "                             {u'상호': u'코드베이직',\n",
    "                              u'종목코드': 101,\n",
    "                              u'산업분류': '소프트웨어'},\n",
    "                             {u'상호': u'성주식품',\n",
    "                              u'종목코드': 102,\n",
    "                              u'산업분류': '식품'}\n",
    "                            ])\n",
    "company_profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
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       "      <th>89</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1074.684056</td>\n",
       "      <td>1060.830375</td>\n",
       "      <td>824.553208</td>\n",
       "      <td>862.037705</td>\n",
       "      <td>684.909426</td>\n",
       "      <td>619.514413</td>\n",
       "      <td>592.612223</td>\n",
       "      <td>750.538802</td>\n",
       "      <td>729.419919</td>\n",
       "      <td>905.870584</td>\n",
       "      <td>...</td>\n",
       "      <td>574.694899</td>\n",
       "      <td>611.437658</td>\n",
       "      <td>560.717960</td>\n",
       "      <td>428.986204</td>\n",
       "      <td>512.004897</td>\n",
       "      <td>580.093691</td>\n",
       "      <td>553.521843</td>\n",
       "      <td>397.356167</td>\n",
       "      <td>320.893645</td>\n",
       "      <td>398.286504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>310.472015</td>\n",
       "      <td>339.638564</td>\n",
       "      <td>319.307297</td>\n",
       "      <td>377.760282</td>\n",
       "      <td>426.675872</td>\n",
       "      <td>328.653650</td>\n",
       "      <td>243.208724</td>\n",
       "      <td>198.730173</td>\n",
       "      <td>237.261499</td>\n",
       "      <td>237.094229</td>\n",
       "      <td>...</td>\n",
       "      <td>42.610295</td>\n",
       "      <td>40.666555</td>\n",
       "      <td>30.200342</td>\n",
       "      <td>22.188067</td>\n",
       "      <td>19.991217</td>\n",
       "      <td>23.328396</td>\n",
       "      <td>25.643460</td>\n",
       "      <td>23.127591</td>\n",
       "      <td>22.999909</td>\n",
       "      <td>24.910208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>810.933820</td>\n",
       "      <td>729.492895</td>\n",
       "      <td>906.290551</td>\n",
       "      <td>1149.374686</td>\n",
       "      <td>1187.061305</td>\n",
       "      <td>1382.396131</td>\n",
       "      <td>1084.537073</td>\n",
       "      <td>996.783185</td>\n",
       "      <td>1082.139676</td>\n",
       "      <td>1333.096928</td>\n",
       "      <td>...</td>\n",
       "      <td>2076.390707</td>\n",
       "      <td>2195.093438</td>\n",
       "      <td>2693.610784</td>\n",
       "      <td>2217.599065</td>\n",
       "      <td>1888.630113</td>\n",
       "      <td>2311.627720</td>\n",
       "      <td>2603.014347</td>\n",
       "      <td>2228.119544</td>\n",
       "      <td>2342.429710</td>\n",
       "      <td>2705.278008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0            1           2            3            4   \\\n",
       "100  1074.684056  1060.830375  824.553208   862.037705   684.909426   \n",
       "101   310.472015   339.638564  319.307297   377.760282   426.675872   \n",
       "102   810.933820   729.492895  906.290551  1149.374686  1187.061305   \n",
       "\n",
       "              5            6           7            8            9   \\\n",
       "100   619.514413   592.612223  750.538802   729.419919   905.870584   \n",
       "101   328.653650   243.208724  198.730173   237.261499   237.094229   \n",
       "102  1382.396131  1084.537073  996.783185  1082.139676  1333.096928   \n",
       "\n",
       "        ...                80           81           82           83  \\\n",
       "100     ...        574.694899   611.437658   560.717960   428.986204   \n",
       "101     ...         42.610295    40.666555    30.200342    22.188067   \n",
       "102     ...       2076.390707  2195.093438  2693.610784  2217.599065   \n",
       "\n",
       "              84           85           86           87           88  \\\n",
       "100   512.004897   580.093691   553.521843   397.356167   320.893645   \n",
       "101    19.991217    23.328396    25.643460    23.127591    22.999909   \n",
       "102  1888.630113  2311.627720  2603.014347  2228.119544  2342.429710   \n",
       "\n",
       "              89  \n",
       "100   398.286504  \n",
       "101    24.910208  \n",
       "102  2705.278008  \n",
       "\n",
       "[3 rows x 90 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_data = sims.gen_sim_price(num_stocks=3, num_days=90)\n",
    "price_data = price_data.T\n",
    "end_prices = DataFrame(price_data,\n",
    "         index=[100, 101, 102])\n",
    "end_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>산업분류</th>\n",
       "      <th>상호</th>\n",
       "      <th>종목코드</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>...</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>전자</td>\n",
       "      <td>SJ전자</td>\n",
       "      <td>100</td>\n",
       "      <td>1074.684056</td>\n",
       "      <td>1060.830375</td>\n",
       "      <td>824.553208</td>\n",
       "      <td>862.037705</td>\n",
       "      <td>684.909426</td>\n",
       "      <td>619.514413</td>\n",
       "      <td>592.612223</td>\n",
       "      <td>...</td>\n",
       "      <td>574.694899</td>\n",
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       "      <td>580.093691</td>\n",
       "      <td>553.521843</td>\n",
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       "      <td>320.893645</td>\n",
       "      <td>398.286504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>소프트웨어</td>\n",
       "      <td>코드베이직</td>\n",
       "      <td>101</td>\n",
       "      <td>310.472015</td>\n",
       "      <td>339.638564</td>\n",
       "      <td>319.307297</td>\n",
       "      <td>377.760282</td>\n",
       "      <td>426.675872</td>\n",
       "      <td>328.653650</td>\n",
       "      <td>243.208724</td>\n",
       "      <td>...</td>\n",
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       "      <td>40.666555</td>\n",
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       "      <td>22.188067</td>\n",
       "      <td>19.991217</td>\n",
       "      <td>23.328396</td>\n",
       "      <td>25.643460</td>\n",
       "      <td>23.127591</td>\n",
       "      <td>22.999909</td>\n",
       "      <td>24.910208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>식품</td>\n",
       "      <td>성주식품</td>\n",
       "      <td>102</td>\n",
       "      <td>810.933820</td>\n",
       "      <td>729.492895</td>\n",
       "      <td>906.290551</td>\n",
       "      <td>1149.374686</td>\n",
       "      <td>1187.061305</td>\n",
       "      <td>1382.396131</td>\n",
       "      <td>1084.537073</td>\n",
       "      <td>...</td>\n",
       "      <td>2076.390707</td>\n",
       "      <td>2195.093438</td>\n",
       "      <td>2693.610784</td>\n",
       "      <td>2217.599065</td>\n",
       "      <td>1888.630113</td>\n",
       "      <td>2311.627720</td>\n",
       "      <td>2603.014347</td>\n",
       "      <td>2228.119544</td>\n",
       "      <td>2342.429710</td>\n",
       "      <td>2705.278008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    산업분류     상호  종목코드            0            1           2            3  \\\n",
       "0     전자   SJ전자   100  1074.684056  1060.830375  824.553208   862.037705   \n",
       "1  소프트웨어  코드베이직   101   310.472015   339.638564  319.307297   377.760282   \n",
       "2     식품   성주식품   102   810.933820   729.492895  906.290551  1149.374686   \n",
       "\n",
       "             4            5            6     ...                80  \\\n",
       "0   684.909426   619.514413   592.612223     ...        574.694899   \n",
       "1   426.675872   328.653650   243.208724     ...         42.610295   \n",
       "2  1187.061305  1382.396131  1084.537073     ...       2076.390707   \n",
       "\n",
       "            81           82           83           84           85  \\\n",
       "0   611.437658   560.717960   428.986204   512.004897   580.093691   \n",
       "1    40.666555    30.200342    22.188067    19.991217    23.328396   \n",
       "2  2195.093438  2693.610784  2217.599065  1888.630113  2311.627720   \n",
       "\n",
       "            86           87           88           89  \n",
       "0   553.521843   397.356167   320.893645   398.286504  \n",
       "1    25.643460    23.127591    22.999909    24.910208  \n",
       "2  2603.014347  2228.119544  2342.429710  2705.278008  \n",
       "\n",
       "[3 rows x 93 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(company_profile, end_prices, left_on=u'종목코드', right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 인덱스 기준 합치기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  value\n",
       "0   a      0\n",
       "1   b      1\n",
       "2   a      2\n",
       "3   a      3\n",
       "4   b      4\n",
       "5   c      5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left1 = DataFrame({'key': ['a', 'b', 'a', 'a', 'b', 'c'],\n",
    "                  'value': range(6)})\n",
    "left1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group_val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group_val\n",
       "a        3.5\n",
       "b        7.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right1 = DataFrame({'group_val': [3.5, 7]}, index=['a', 'b'])\n",
    "right1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"left의 'key' 열은 right의 인덱스이다\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lefth = DataFrame({'key1': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],\n",
    "                   'key2': [2000, 2001, 2002, 2001, 2002],\n",
    "                   'data': np.arange(5.)})\n",
    "righth = DataFrame(np.arange(12).reshape((6, 2)),\n",
    "                   index=[['Nevada', 'Nevada', 'Ohio', 'Ohio', 'Ohio', 'Ohio'],\n",
    "                          [2001, 2000, 2000, 2000, 2001, 2002]],\n",
    "                   columns=['event1', 'event2'])\n",
    "lefth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "righth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.merge(lefth, righth, left_on=['key1', 'key2'], right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.merge(lefth, righth, left_on=['key1', 'key2'],\n",
    "         right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left2 = DataFrame([[1., 2.], [3., 4.], [5., 6.]], 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'], columns=['Missouri', 'Alabama'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "right2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.merge(left2, right2, how='outer', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left2.join(right2, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left1.join(right1, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "another = DataFrame([[7., 8.], [9., 10.], [11., 12.], [16., 17.]],\n",
    "                    index=['a', 'c', 'e', 'f'], columns=['New York', 'Oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left2.join([right2, another])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left2.join([right2, another], how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 연습 문제\n",
    "\n",
    "학생들의 신체 발달 정보는 학번, 나이, 신장, 체중 정보를 담고 있다. 각 학생은 또한 성취도 정보에 학번과 세 과목의 시험 점수를 갖는다. 신체발달 정보와 학업성취도의 연관성을 알아보려 한다.\n",
    "\n",
    "a. 신체발달 정보 기준으로 성취도를 merge 하시오.\n",
    "\n",
    "b. 성취도를 기준으로 신체발달 정보를 merge 하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>나이</th>\n",
       "      <th>신장</th>\n",
       "      <th>체중</th>\n",
       "      <th>학번</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>160.769723</td>\n",
       "      <td>39.470320</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>161.336181</td>\n",
       "      <td>40.388564</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>158.273799</td>\n",
       "      <td>38.218147</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>160.004215</td>\n",
       "      <td>38.378841</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>159.058502</td>\n",
       "      <td>40.673683</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>159.770833</td>\n",
       "      <td>40.922474</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17</td>\n",
       "      <td>159.426132</td>\n",
       "      <td>38.930030</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>160.677461</td>\n",
       "      <td>39.646879</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>159.536734</td>\n",
       "      <td>38.408236</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>161.732760</td>\n",
       "      <td>40.561718</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   나이          신장         체중  학번\n",
       "0  12  160.769723  39.470320   5\n",
       "1  16  161.336181  40.388564   6\n",
       "2  15  158.273799  38.218147   7\n",
       "3  16  160.004215  38.378841   8\n",
       "4  16  159.058502  40.673683   9\n",
       "5  12  159.770833  40.922474  10\n",
       "6  17  159.426132  38.930030  11\n",
       "7  16  160.677461  39.646879  12\n",
       "8  14  159.536734  38.408236  13\n",
       "9  15  161.732760  40.561718  14"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 신체발달 모의 데이터 생성\n",
    "ages = np.random.randint(12,18, size=10)\n",
    "heights = np.random.normal(160, size=10)\n",
    "weights = np.random.normal(40, size=10)\n",
    "\n",
    "physical_data = {u'신장': heights, u'체중': weights, u'나이':ages,\n",
    "                u'학번': range(5,15)}\n",
    "physical_frame = DataFrame(physical_data)\n",
    "physical_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국</th>\n",
       "      <th>영</th>\n",
       "      <th>수</th>\n",
       "      <th>학번</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.196809</td>\n",
       "      <td>50.674796</td>\n",
       "      <td>47.671703</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59.293858</td>\n",
       "      <td>56.726219</td>\n",
       "      <td>38.831409</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.873630</td>\n",
       "      <td>46.804611</td>\n",
       "      <td>53.811438</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.682511</td>\n",
       "      <td>36.342635</td>\n",
       "      <td>56.083811</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.960973</td>\n",
       "      <td>46.622332</td>\n",
       "      <td>44.598420</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.920529</td>\n",
       "      <td>52.748902</td>\n",
       "      <td>53.060109</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.650012</td>\n",
       "      <td>56.352235</td>\n",
       "      <td>44.707694</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>59.714633</td>\n",
       "      <td>44.866694</td>\n",
       "      <td>49.420394</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50.998477</td>\n",
       "      <td>54.667462</td>\n",
       "      <td>41.686535</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>47.790209</td>\n",
       "      <td>59.866725</td>\n",
       "      <td>51.270325</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           국          영          수  학번\n",
       "0  54.196809  50.674796  47.671703  10\n",
       "1  59.293858  56.726219  38.831409  11\n",
       "2  63.873630  46.804611  53.811438  12\n",
       "3  49.682511  36.342635  56.083811  13\n",
       "4  54.960973  46.622332  44.598420  14\n",
       "5  30.920529  52.748902  53.060109  15\n",
       "6  54.650012  56.352235  44.707694  16\n",
       "7  59.714633  44.866694  49.420394  17\n",
       "8  50.998477  54.667462  41.686535  18\n",
       "9  47.790209  59.866725  51.270325  19"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 성취도 모의 데이터 생성\n",
    "tests_frame = DataFrame(np.random.normal(50, 10, size=(10,3)),\n",
    "                       columns=[u'국', u'영', u'수'])\n",
    "tests_frame[u'학번'] = range(10,20)\n",
    "tests_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>나이</th>\n",
       "      <th>신장</th>\n",
       "      <th>체중</th>\n",
       "      <th>국</th>\n",
       "      <th>영</th>\n",
       "      <th>수</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>학번</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>5</th>\n",
       "      <td>12</td>\n",
       "      <td>160.769723</td>\n",
       "      <td>39.470320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>161.336181</td>\n",
       "      <td>40.388564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>158.273799</td>\n",
       "      <td>38.218147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>160.004215</td>\n",
       "      <td>38.378841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>159.058502</td>\n",
       "      <td>40.673683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>159.770833</td>\n",
       "      <td>40.922474</td>\n",
       "      <td>54.196809</td>\n",
       "      <td>50.674796</td>\n",
       "      <td>47.671703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>159.426132</td>\n",
       "      <td>38.930030</td>\n",
       "      <td>59.293858</td>\n",
       "      <td>56.726219</td>\n",
       "      <td>38.831409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>160.677461</td>\n",
       "      <td>39.646879</td>\n",
       "      <td>63.873630</td>\n",
       "      <td>46.804611</td>\n",
       "      <td>53.811438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>159.536734</td>\n",
       "      <td>38.408236</td>\n",
       "      <td>49.682511</td>\n",
       "      <td>36.342635</td>\n",
       "      <td>56.083811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>161.732760</td>\n",
       "      <td>40.561718</td>\n",
       "      <td>54.960973</td>\n",
       "      <td>46.622332</td>\n",
       "      <td>44.598420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.920529</td>\n",
       "      <td>52.748902</td>\n",
       "      <td>53.060109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.650012</td>\n",
       "      <td>56.352235</td>\n",
       "      <td>44.707694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.714633</td>\n",
       "      <td>44.866694</td>\n",
       "      <td>49.420394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.998477</td>\n",
       "      <td>54.667462</td>\n",
       "      <td>41.686535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.790209</td>\n",
       "      <td>59.866725</td>\n",
       "      <td>51.270325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    나이          신장         체중          국          영          수\n",
       "학번                                                            \n",
       "5   12  160.769723  39.470320        NaN        NaN        NaN\n",
       "6   16  161.336181  40.388564        NaN        NaN        NaN\n",
       "7   15  158.273799  38.218147        NaN        NaN        NaN\n",
       "8   16  160.004215  38.378841        NaN        NaN        NaN\n",
       "9   16  159.058502  40.673683        NaN        NaN        NaN\n",
       "10  12  159.770833  40.922474  54.196809  50.674796  47.671703\n",
       "11  17  159.426132  38.930030  59.293858  56.726219  38.831409\n",
       "12  16  160.677461  39.646879  63.873630  46.804611  53.811438\n",
       "13  14  159.536734  38.408236  49.682511  36.342635  56.083811\n",
       "14  15  161.732760  40.561718  54.960973  46.622332  44.598420\n",
       "15 NaN         NaN        NaN  30.920529  52.748902  53.060109\n",
       "16 NaN         NaN        NaN  54.650012  56.352235  44.707694\n",
       "17 NaN         NaN        NaN  59.714633  44.866694  49.420394\n",
       "18 NaN         NaN        NaN  50.998477  54.667462  41.686535\n",
       "19 NaN         NaN        NaN  47.790209  59.866725  51.270325"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: 문제 1\n",
    "data = pd.merge(physical_frame, tests_frame, how='outer').set_index(u'학번')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국</th>\n",
       "      <th>영</th>\n",
       "      <th>수</th>\n",
       "      <th>학번</th>\n",
       "      <th>나이</th>\n",
       "      <th>신장</th>\n",
       "      <th>체중</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54.196809</td>\n",
       "      <td>50.674796</td>\n",
       "      <td>47.671703</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>159.770833</td>\n",
       "      <td>40.922474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59.293858</td>\n",
       "      <td>56.726219</td>\n",
       "      <td>38.831409</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>159.426132</td>\n",
       "      <td>38.930030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63.873630</td>\n",
       "      <td>46.804611</td>\n",
       "      <td>53.811438</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>160.677461</td>\n",
       "      <td>39.646879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.682511</td>\n",
       "      <td>36.342635</td>\n",
       "      <td>56.083811</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>159.536734</td>\n",
       "      <td>38.408236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.960973</td>\n",
       "      <td>46.622332</td>\n",
       "      <td>44.598420</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>161.732760</td>\n",
       "      <td>40.561718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           국          영          수  학번  나이          신장         체중\n",
       "0  54.196809  50.674796  47.671703  10  12  159.770833  40.922474\n",
       "1  59.293858  56.726219  38.831409  11  17  159.426132  38.930030\n",
       "2  63.873630  46.804611  53.811438  12  16  160.677461  39.646879\n",
       "3  49.682511  36.342635  56.083811  13  14  159.536734  38.408236\n",
       "4  54.960973  46.622332  44.598420  14  15  161.732760  40.561718"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: 문제 2\n",
    "pd.merge(tests_frame, physical_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 연습문제\n",
    "\n",
    "고객 정보가 담긴 customers.csv, 구매 정보가 담긴 purchases.csv, 상품평 정보가 담긴 reviews.csv 파일의 데이터를 활용해 다음을 수행하시오.\n",
    "\n",
    "a. 구매한 물품별 배송지 정보를 구하시오.\n",
    "\n",
    "b. 각 고객이 구매한 물품명과 상품평을 표시하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 축 따라 이어붙이기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr = np.arange(12).reshape((3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.concatenate([arr, arr], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    0\n",
      "b    1\n",
      "dtype: int64\n",
      "c    2\n",
      "d    3\n",
      "e    4\n",
      "dtype: int64\n",
      "f    5\n",
      "g    6\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "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'])\n",
    "\n",
    "print(s1)\n",
    "print(s2)\n",
    "print(s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>"
      ],
      "text/plain": [
       "    0   1   2\n",
       "a   0 NaN NaN\n",
       "b   1 NaN NaN\n",
       "c NaN   2 NaN\n",
       "d NaN   3 NaN\n",
       "e NaN   4 NaN\n",
       "f NaN NaN   5\n",
       "g NaN NaN   6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s2, s3], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    5\n",
       "f    5\n",
       "g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s4 = pd.concat([s1 * 5, s3])\n",
    "s4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>g</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0  1\n",
       "a   0  0\n",
       "b   1  5\n",
       "f NaN  5\n",
       "g NaN  6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([s1, s4], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th>a</th>\n",
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       "      <th>b</th>\n",
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       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1\n",
       "a  0  0\n",
       "b  1  5"
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     "execution_count": 50,
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   ],
   "source": [
    "pd.concat([s1, s4], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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       "  <tbody>\n",
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       "      <th>a</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>b</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1\n",
       "a   0   0\n",
       "c NaN NaN\n",
       "b   1   5\n",
       "e NaN NaN"
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     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.concat([s1, s4], axis=1, join_axes=[['a', 'c', 'b', 'e']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = pd.concat([s1, s1, s3], keys=['one', 'two', 'three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Much more on the unstack function later\n",
    "result.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.concat([s1, s2, s3], axis=1, keys=['one', 'two', 'three'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame 축 따라 이어 붙이기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">level1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">level2</th>\n",
       "    </tr>\n",
       "    <tr>\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>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  level1     level2     \n",
       "     one two  three four\n",
       "a      0   1      5    6\n",
       "b      2   3    NaN  NaN\n",
       "c      4   5      7    8"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = DataFrame(np.arange(6).reshape(3, 2), index=['a', 'b', 'c'],\n",
    "                columns=['one', 'two'])\n",
    "df2 = DataFrame(5 + np.arange(4).reshape(2, 2), index=['a', 'c'],\n",
    "                columns=['three', 'four'])\n",
    "pd.concat([df1, df2], axis=1, keys=['level1', 'level2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">level1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">level2</th>\n",
       "    </tr>\n",
       "    <tr>\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>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  level1     level2     \n",
       "     one two  three four\n",
       "a      0   1      5    6\n",
       "b      2   3    NaN  NaN\n",
       "c      4   5      7    8"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat({'level1': df1, 'level2': df2}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.concat([df1, df2], axis=1, keys=['level1', 'level2'],\n",
    "          names=['upper', 'lower'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = DataFrame(np.random.randn(3, 4), columns=['a', 'b', 'c', 'd'])\n",
    "df2 = DataFrame(np.random.randn(2, 3), columns=['b', 'd', 'a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>-0.348124</td>\n",
       "      <td>0.943158</td>\n",
       "      <td>-0.117074</td>\n",
       "      <td>1.019861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.820716</td>\n",
       "      <td>-1.599333</td>\n",
       "      <td>0.298164</td>\n",
       "      <td>0.227103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.619864</td>\n",
       "      <td>0.290818</td>\n",
       "      <td>1.793764</td>\n",
       "      <td>0.165982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.853051</td>\n",
       "      <td>-0.737803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.301149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.506363</td>\n",
       "      <td>-0.198649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.426967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0 -0.348124  0.943158 -0.117074  1.019861\n",
       "1 -0.820716 -1.599333  0.298164  0.227103\n",
       "2 -0.619864  0.290818  1.793764  0.165982\n",
       "3 -1.853051 -0.737803       NaN -1.301149\n",
       "4 -1.506363 -0.198649       NaN -2.426967"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 겹치는 데이터 합치기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.where(pd.isnull(a), b, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b[:-2].combine_first(a[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c\n",
       "0  5 NaN  10"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</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 NaN   2   6\n",
       "2   5 NaN  10\n",
       "3 NaN   6  14\n",
       "0   5 NaN NaN\n",
       "1   4   3 NaN\n",
       "2 NaN   4 NaN\n",
       "3   3   6 NaN\n",
       "4   7   8 NaN"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.combine_first(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 재형성과 피벗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 계층 색인 재구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = DataFrame(np.arange(6).reshape((2, 3)),\n",
    "                 index=pd.Index(['Ohio', 'Colorado'], name='state'),\n",
    "                 columns=pd.Index(['one', 'two', 'three'], name='number'))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = data.stack()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result.unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result.unstack('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s1 = Series([0, 1, 2, 3], index=['a', 'b', 'c', 'd'])\n",
    "s2 = Series([4, 5, 6], index=['c', 'd', 'e'])\n",
    "data2 = pd.concat([s1, s2], keys=['one', 'two'])\n",
    "data2.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data2.unstack().stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data2.unstack().stack(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = DataFrame({'left': result, 'right': result + 5},\n",
    "               columns=pd.Index(['left', 'right'], name='side'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.unstack('state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.unstack('state').stack('side')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 가로 세로 방향 피벗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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'})\n",
    "wdata = ldata.pivot('date', 'item', 'value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldata[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pivoted = ldata.pivot('date', 'item', 'value')\n",
    "pivoted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ldata['value2'] = np.random.randn(len(ldata))\n",
    "ldata[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pivoted = ldata.pivot('date', 'item')\n",
    "pivoted[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pivoted['value'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unstacked = ldata.set_index(['date', 'item']).unstack('item')\n",
    "unstacked[:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 변형"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 중복 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   1\n",
       "1  one   1\n",
       "2  one   2\n",
       "3  two   3\n",
       "4  two   3\n",
       "5  two   4\n",
       "6  two   4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataFrame({'k1': ['one'] * 3 + ['two'] * 4,\n",
    "                  'k2': [1, 1, 2, 3, 3, 4, 4]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['v1'] = range(7)\n",
    "data.drop_duplicates(['k1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.drop_duplicates(['k1', 'k2'], take_last=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 함수 또는 매핑으로 데이터 변형"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataFrame({'food': ['bacon', 'pulled pork', 'bacon', 'Pastrami',\n",
    "                           'corned beef', 'Bacon', 'pastrami', 'honey ham',\n",
    "                           'nova lox'],\n",
    "                  'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meat_to_animal = {\n",
    "  'bacon': 'pig',\n",
    "  'pulled pork': 'pig',\n",
    "  'pastrami': 'cow',\n",
    "  'corned beef': 'cow',\n",
    "  'honey ham': 'pig',\n",
    "  'nova lox': 'salmon'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['animal'] = data['food'].map(str.lower).map(meat_to_animal)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['food'].map(lambda x: meat_to_animal[x.lower()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 값 교체"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = Series([1., -999., 2., -999., -1000., 3.])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.replace(-999, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.replace([-999, -1000], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.replace([-999, -1000], [np.nan, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.replace({-999: np.nan, -1000: 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 축 인덱스 이름변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['OHIO', 'COLORADO', 'NEW YORK'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.map(str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = data.index.map(str.upper)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index=str.title, columns=str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index={'OHIO': 'INDIANA'},\n",
    "            columns={'three': 'peekaboo'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Always returns a reference to a DataFrame\n",
    "_ = data.rename(index={'OHIO': 'INDIANA'}, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 개별화와 양자화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (55, 65], (35, 45], (35, 45], (25, 35]]\n",
       "Length: 12\n",
       "Categories (6, object): [(18, 25] < (25, 35] < (35, 45] < (45, 55] < (55, 65] < (65, 100]]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [18, 25, 35, 45, 55, 65, 100]\n",
    "\n",
    "cats = pd.cut(ages, bins)\n",
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 2, 1, 4, 2, 2, 1], dtype=int8)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'(18, 25]', u'(25, 35]', u'(35, 45]', u'(45, 55]', u'(55, 65]', u'(65, 100]'], dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 25]     5\n",
       "(35, 45]     3\n",
       "(25, 35]     3\n",
       "(55, 65]     1\n",
       "(65, 100]    0\n",
       "(45, 55]     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 특이값 탐지와 선별"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)\n",
    "data = DataFrame(np.random.randn(1000, 4))\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "col = data[3]\n",
    "col[np.abs(col) > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data[(np.abs(data) > 3).any(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data[np.abs(data) > 3] = np.sign(data) * 3\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 치환과 무작위 표본추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.arange(5 * 4).reshape((5, 4)))\n",
    "sampler = np.random.permutation(5)\n",
    "sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.take(sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.take(np.random.permutation(len(df))[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bag = np.array([5, 7, -1, 6, 4])\n",
    "sampler = np.random.randint(0, len(bag), size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "draws = bag.take(sampler)\n",
    "draws"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 표시자/더미 변수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'b'],\n",
    "                'data1': range(6)})\n",
    "pd.get_dummies(df['key'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df['key'], prefix='key')\n",
    "df_with_dummy = df[['data1']].join(dummies)\n",
    "df_with_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table('ch07/movies.dat', sep='::', header=None,\n",
    "                        names=mnames)\n",
    "movies[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "genre_iter = (set(x.split('|')) for x in movies.genres)\n",
    "genres = sorted(set.union(*genre_iter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dummies = DataFrame(np.zeros((len(movies), len(genres))), columns=genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i, gen in enumerate(movies.genres):\n",
    "    dummies.ix[i, gen.split('|')] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "movies_windic = movies.join(dummies.add_prefix('Genre_'))\n",
    "movies_windic.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "values = np.random.rand(10)\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bins = [0, 0.2, 0.4, 0.6, 0.8, 1]\n",
    "pd.get_dummies(pd.cut(values, bins))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 문자열 조작"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 문자열 객체 메서드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val = 'a,b,  guido'\n",
    "val.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pieces = [x.strip() for x in val.split(',')]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "first, second, third = pieces\n",
    "first + '::' + second + '::' + third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "'::'.join(pieces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "'guido' in val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val.index(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val.find(':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val.index(':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val.count(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val.replace(',', '::')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "val.replace(',', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 정규식(Regular expressions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "text = \"foo    bar\\t baz  \\tqux\"\n",
    "re.split('\\s+', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regex = re.compile('\\s+')\n",
    "regex.split(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text = \"\"\"Dave dave@google.com\n",
    "Steve steve@gmail.com\n",
    "Rob rob@gmail.com\n",
    "Ryan ryan@yahoo.com\n",
    "\"\"\"\n",
    "pattern = r'[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,4}'\n",
    "\n",
    "# re.IGNORECASE makes the regex case-insensitive\n",
    "regex = re.compile(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = regex.search(text)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text[m.start():m.end()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(regex.match(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(regex.sub('REDACTED', text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = regex.match('wesm@bright.net')\n",
    "m.groups()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "regex.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(regex.sub(r'Username: \\1, Domain: \\2, Suffix: \\3', text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = regex.match('wesm@bright.net')\n",
    "m.groupdict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas의 벡터화된 문자열 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = {'Dave': 'dave@google.com', 'Steve': 'steve@gmail.com',\n",
    "        'Rob': 'rob@gmail.com', 'Wes': np.nan}\n",
    "data = Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.str.contains('gmail')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.str.findall(pattern, flags=re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matches = data.str.match(pattern, flags=re.IGNORECASE)\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matches.str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matches.str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.str[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 예제: 미국 농무부 음식 데이터베이스"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "{\n",
    "  \"id\": 21441,\n",
    "  \"description\": \"KENTUCKY FRIED CHICKEN, Fried Chicken, EXTRA CRISPY,\n",
    "Wing, meat and skin with breading\",\n",
    "  \"tags\": [\"KFC\"],\n",
    "  \"manufacturer\": \"Kentucky Fried Chicken\",\n",
    "  \"group\": \"Fast Foods\",\n",
    "  \"portions\": [\n",
    "    {\n",
    "      \"amount\": 1,\n",
    "      \"unit\": \"wing, with skin\",\n",
    "      \"grams\": 68.0\n",
    "    },\n",
    "\n",
    "    ...\n",
    "  ],\n",
    "  \"nutrients\": [\n",
    "    {\n",
    "      \"value\": 20.8,\n",
    "      \"units\": \"g\",\n",
    "      \"description\": \"Protein\",\n",
    "      \"group\": \"Composition\"\n",
    "    },\n",
    "\n",
    "    ...\n",
    "  ]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JSON 파일을 읽어들여 파이썬 객체 생성하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6636"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "db = json.load(open('pydata/ch07/foods-2011-10-03.json'))\n",
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
       "      <th>nutrients</th>\n",
       "      <th>portions</th>\n",
       "      <th>tags</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz'}]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 132.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Other', u'descri...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</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",
       "      <td>[{u'units': u'g', u'group': u'Other', u'descri...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 150.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Other', u'descri...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz'}]</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",
       "      <td>[{u'units': u'g', u'group': u'Other', u'descri...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 132.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 140.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 242.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 235.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 244.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 15.0, u'unit': u'con...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 94.0, u'unit': u'cup...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 244.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 244.0, u'unit': u'cu...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 246.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 244.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 246.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 246.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 244.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 128.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 120.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 68.0, u'unit': u'cup...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #2203]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #2237]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS # 2213]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #1049]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #1623]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #1049]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #1623]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #1480]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #1553]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</td>\n",
       "      <td>[URMIS #1464]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.35, u'unit': u'oz...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 126.0, u'unit': u'cu...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 126.0, u'unit': u'cu...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 305.0, u'unit': u'se...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 305.0, u'unit': u'se...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 245.0, u'unit': u'cu...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 126.0, u'unit': u'cu...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 243.0, u'unit': u'oz...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 243.0, u'unit': u'fl...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 30.0, u'unit': u'oz'...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 243.0, u'unit': u'fl...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 243.0, u'unit': u'fl...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 28.0, u'unit': u'sli...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 130.0, u'unit': u'cu...</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 31.2, u'unit': u'fl ...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 15.0, u'unit': u'tbs...</td>\n",
       "      <td>[]</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",
       "      <td>[{u'units': u'g', u'group': u'Composition', u'...</td>\n",
       "      <td>[{u'amount': 1, u'grams': 15.0, u'unit': u'tbs...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6636 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            description  \\\n",
       "0                                       Cheese, caraway   \n",
       "1                                       Cheese, cheddar   \n",
       "2                                          Cheese, edam   \n",
       "3                                          Cheese, feta   \n",
       "4                    Cheese, mozzarella, part skim milk   \n",
       "5      Cheese, mozzarella, part skim milk, low moisture   \n",
       "6                                        Cheese, romano   \n",
       "7                                     Cheese, roquefort   \n",
       "8     Cheese spread, pasteurized process, american, ...   \n",
       "9                           Cream, fluid, half and half   \n",
       "10    Sour dressing, non-butterfat, cultured, filled...   \n",
       "11    Milk, filled, fluid, with blend of hydrogenate...   \n",
       "12    Cream substitute, liquid, with lauric acid oil...   \n",
       "13                           Cream substitute, powdered   \n",
       "14                  Milk, producer, fluid, 3.7% milkfat   \n",
       "15    Milk, reduced fat, fluid, 2% milkfat, with add...   \n",
       "16    Milk, reduced fat, fluid, 2% milkfat, with add...   \n",
       "17    Milk, reduced fat, fluid, 2% milkfat, protein ...   \n",
       "18    Milk, lowfat, fluid, 1% milkfat, with added vi...   \n",
       "19    Milk, lowfat, fluid, 1% milkfat, with added no...   \n",
       "20    Milk, lowfat, fluid, 1% milkfat, protein forti...   \n",
       "21    Milk, nonfat, fluid, with added vitamin A and ...   \n",
       "22    Milk, nonfat, fluid, with added nonfat milk so...   \n",
       "23    Milk, nonfat, fluid, protein fortified, with a...   \n",
       "24            Milk, buttermilk, fluid, cultured, lowfat   \n",
       "25                              Milk, low sodium, fluid   \n",
       "26               Milk, dry, whole, with added vitamin D   \n",
       "27    Milk, dry, nonfat, regular, without added vita...   \n",
       "28    Milk, dry, nonfat, instant, with added vitamin...   \n",
       "29                   Milk, dry, nonfat, calcium reduced   \n",
       "...                                                 ...   \n",
       "6606  Beef, tenderloin, steak, separable lean only, ...   \n",
       "6607  Beef, top sirloin, steak, separable lean only,...   \n",
       "6608  Beef, short loin, top loin, steak, separable l...   \n",
       "6609  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6610  Beef, brisket, flat half, separable lean only,...   \n",
       "6611  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6612  Beef, brisket, flat half, separable lean only,...   \n",
       "6613  Beef, round, eye of round, roast, separable le...   \n",
       "6614  Beef, round, top round, steak, separable lean ...   \n",
       "6615  Beef, round, bottom round, roast, separable le...   \n",
       "6616  Beef, rib, small end (ribs 10-12), separable l...   \n",
       "6617  CAMPBELL Soup Company, CAMPBELL'S Red and Whit...   \n",
       "6618  CAMPBELL Soup Company, CAMPBELL's Red and Whit...   \n",
       "6619  CAMPBELL Soup Company, CAMPBELL'S SELECT Soups...   \n",
       "6620  CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...   \n",
       "6621  CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...   \n",
       "6622  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6623  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6624  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6625  CAMPBELL Soup Company, CAMPBELL'S Red and Whit...   \n",
       "6626  CAMPBELL Soup Company, V8 Vegetable Juice, Ess...   \n",
       "6627  CAMPBELL Soup Company, V8 Vegetable Juice, Spi...   \n",
       "6628  CAMPBELL Soup Company, PACE, Jalapenos Nacho S...   \n",
       "6629  CAMPBELL Soup Company, V8 60% Vegetable Juice,...   \n",
       "6630  CAMPBELL Soup Company, V8 Vegetable Juice, Low...   \n",
       "6631                             Bologna, beef, low fat   \n",
       "6632  Turkey and pork sausage, fresh, bulk, patty or...   \n",
       "6633                              Babyfood, juice, pear   \n",
       "6634         Babyfood, dessert, banana yogurt, strained   \n",
       "6635              Babyfood, banana no tapioca, strained   \n",
       "\n",
       "                                  group     id       manufacturer  \\\n",
       "0                Dairy and Egg Products   1008                      \n",
       "1                Dairy and Egg Products   1009                      \n",
       "2                Dairy and Egg Products   1018                      \n",
       "3                Dairy and Egg Products   1019                      \n",
       "4                Dairy and Egg Products   1028                      \n",
       "5                Dairy and Egg Products   1029                      \n",
       "6                Dairy and Egg Products   1038                      \n",
       "7                Dairy and Egg Products   1039                      \n",
       "8                Dairy and Egg Products   1048                      \n",
       "9                Dairy and Egg Products   1049                      \n",
       "10               Dairy and Egg Products   1058                      \n",
       "11               Dairy and Egg Products   1059                      \n",
       "12               Dairy and Egg Products   1068                      \n",
       "13               Dairy and Egg Products   1069                      \n",
       "14               Dairy and Egg Products   1078                      \n",
       "15               Dairy and Egg Products   1079               None   \n",
       "16               Dairy and Egg Products   1080                      \n",
       "17               Dairy and Egg Products   1081                      \n",
       "18               Dairy and Egg Products   1082                      \n",
       "19               Dairy and Egg Products   1083                      \n",
       "20               Dairy and Egg Products   1084                      \n",
       "21               Dairy and Egg Products   1085                      \n",
       "22               Dairy and Egg Products   1086                      \n",
       "23               Dairy and Egg Products   1087                      \n",
       "24               Dairy and Egg Products   1088                      \n",
       "25               Dairy and Egg Products   1089                      \n",
       "26               Dairy and Egg Products   1090                      \n",
       "27               Dairy and Egg Products   1091                      \n",
       "28               Dairy and Egg Products   1092                      \n",
       "29               Dairy and Egg Products   1093                      \n",
       "...                                 ...    ...                ...   \n",
       "6606                      Beef Products  23628                      \n",
       "6607                      Beef Products  23629                      \n",
       "6608                      Beef Products  23630                      \n",
       "6609                      Beef Products  23631                      \n",
       "6610                      Beef Products  23632                      \n",
       "6611                      Beef Products  23633                      \n",
       "6612                      Beef Products  23634                      \n",
       "6613                      Beef Products  23635                      \n",
       "6614                      Beef Products  23636                      \n",
       "6615                      Beef Products  23637                      \n",
       "6616                      Beef Products  23638                      \n",
       "6617         Soups, Sauces, and Gravies  27015  Campbell Soup Co.   \n",
       "6618         Soups, Sauces, and Gravies  27016  Campbell Soup Co.   \n",
       "6619         Soups, Sauces, and Gravies  27021  Campbell Soup Co.   \n",
       "6620         Soups, Sauces, and Gravies  27022  Campbell Soup Co.   \n",
       "6621         Soups, Sauces, and Gravies  27023  Campbell Soup Co.   \n",
       "6622         Soups, Sauces, and Gravies  27024  Campbell Soup Co.   \n",
       "6623         Soups, Sauces, and Gravies  27025  Campbell Soup Co.   \n",
       "6624         Soups, Sauces, and Gravies  27026  Campbell Soup Co.   \n",
       "6625         Soups, Sauces, and Gravies  27032  Campbell Soup Co.   \n",
       "6626  Vegetables and Vegetable Products  31010  Campbell Soup Co.   \n",
       "6627  Vegetables and Vegetable Products  31013  Campbell Soup Co.   \n",
       "6628  Vegetables and Vegetable Products  31014  Campbell Soup Co.   \n",
       "6629  Vegetables and Vegetable Products  31016  Campbell Soup Co.   \n",
       "6630  Vegetables and Vegetable Products  31017  Campbell Soup Co.   \n",
       "6631        Sausages and Luncheon Meats  42161                      \n",
       "6632        Sausages and Luncheon Meats  42173                      \n",
       "6633                         Baby Foods  43408               None   \n",
       "6634                         Baby Foods  43539               None   \n",
       "6635                         Baby Foods  43546               None   \n",
       "\n",
       "                                              nutrients  \\\n",
       "0     [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "1     [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "2     [{u'units': u'g', u'group': u'Other', u'descri...   \n",
       "3     [{u'units': u'g', u'group': u'Other', u'descri...   \n",
       "4     [{u'units': u'g', u'group': u'Other', u'descri...   \n",
       "5     [{u'units': u'g', u'group': u'Other', u'descri...   \n",
       "6     [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "7     [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "8     [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "9     [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "10    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "11    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "12    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "13    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "14    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "15    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "16    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "17    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "18    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "19    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "20    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "21    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "22    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "23    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "24    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "25    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "26    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "27    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "28    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "29    [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "...                                                 ...   \n",
       "6606  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6607  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6608  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6609  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6610  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6611  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6612  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6613  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6614  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6615  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6616  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6617  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6618  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6619  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6620  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6621  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6622  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6623  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6624  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6625  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6626  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6627  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6628  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6629  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6630  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6631  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6632  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6633  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6634  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "6635  [{u'units': u'g', u'group': u'Composition', u'...   \n",
       "\n",
       "                                               portions            tags  \n",
       "0     [{u'amount': 1, u'grams': 28.35, u'unit': u'oz'}]              []  \n",
       "1     [{u'amount': 1, u'grams': 132.0, u'unit': u'cu...              []  \n",
       "2     [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...              []  \n",
       "3     [{u'amount': 1, u'grams': 150.0, u'unit': u'cu...              []  \n",
       "4     [{u'amount': 1, u'grams': 28.35, u'unit': u'oz'}]              []  \n",
       "5     [{u'amount': 1, u'grams': 132.0, u'unit': u'cu...              []  \n",
       "6     [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...              []  \n",
       "7     [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...              []  \n",
       "8     [{u'amount': 1, u'grams': 140.0, u'unit': u'cu...              []  \n",
       "9     [{u'amount': 1, u'grams': 242.0, u'unit': u'cu...              []  \n",
       "10    [{u'amount': 1, u'grams': 235.0, u'unit': u'cu...              []  \n",
       "11    [{u'amount': 1, u'grams': 244.0, u'unit': u'cu...              []  \n",
       "12    [{u'amount': 1, u'grams': 15.0, u'unit': u'con...              []  \n",
       "13    [{u'amount': 1, u'grams': 94.0, u'unit': u'cup...              []  \n",
       "14    [{u'amount': 1, u'grams': 244.0, u'unit': u'cu...              []  \n",
       "15    [{u'amount': 1, u'grams': 244.0, u'unit': u'cu...              []  \n",
       "16    [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "17    [{u'amount': 1, u'grams': 246.0, u'unit': u'cu...              []  \n",
       "18    [{u'amount': 1, u'grams': 244.0, u'unit': u'cu...              []  \n",
       "19    [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "20    [{u'amount': 1, u'grams': 246.0, u'unit': u'cu...              []  \n",
       "21    [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "22    [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "23    [{u'amount': 1, u'grams': 246.0, u'unit': u'cu...              []  \n",
       "24    [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "25    [{u'amount': 1, u'grams': 244.0, u'unit': u'cu...              []  \n",
       "26    [{u'amount': 1, u'grams': 128.0, u'unit': u'cu...              []  \n",
       "27    [{u'amount': 1, u'grams': 120.0, u'unit': u'cu...              []  \n",
       "28    [{u'amount': 1, u'grams': 68.0, u'unit': u'cup...              []  \n",
       "29    [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...              []  \n",
       "...                                                 ...             ...  \n",
       "6606  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #2203]  \n",
       "6607  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #2237]  \n",
       "6608  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...  [URMIS # 2213]  \n",
       "6609  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #1049]  \n",
       "6610  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #1623]  \n",
       "6611  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #1049]  \n",
       "6612  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #1623]  \n",
       "6613  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #1480]  \n",
       "6614  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #1553]  \n",
       "6615  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...   [URMIS #1464]  \n",
       "6616  [{u'amount': 1, u'grams': 28.35, u'unit': u'oz...              []  \n",
       "6617  [{u'amount': 1, u'grams': 126.0, u'unit': u'cu...              []  \n",
       "6618  [{u'amount': 1, u'grams': 126.0, u'unit': u'cu...              []  \n",
       "6619  [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "6620  [{u'amount': 1, u'grams': 305.0, u'unit': u'se...              []  \n",
       "6621  [{u'amount': 1, u'grams': 305.0, u'unit': u'se...              []  \n",
       "6622  [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "6623  [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "6624  [{u'amount': 1, u'grams': 245.0, u'unit': u'cu...              []  \n",
       "6625  [{u'amount': 1, u'grams': 126.0, u'unit': u'cu...              []  \n",
       "6626  [{u'amount': 1, u'grams': 243.0, u'unit': u'oz...              []  \n",
       "6627  [{u'amount': 1, u'grams': 243.0, u'unit': u'fl...              []  \n",
       "6628  [{u'amount': 1, u'grams': 30.0, u'unit': u'oz'...              []  \n",
       "6629  [{u'amount': 1, u'grams': 243.0, u'unit': u'fl...              []  \n",
       "6630  [{u'amount': 1, u'grams': 243.0, u'unit': u'fl...              []  \n",
       "6631  [{u'amount': 1, u'grams': 28.0, u'unit': u'sli...              []  \n",
       "6632  [{u'amount': 1, u'grams': 130.0, u'unit': u'cu...              []  \n",
       "6633  [{u'amount': 1, u'grams': 31.2, u'unit': u'fl ...              []  \n",
       "6634  [{u'amount': 1, u'grams': 15.0, u'unit': u'tbs...              []  \n",
       "6635  [{u'amount': 1, u'grams': 15.0, u'unit': u'tbs...              []  \n",
       "\n",
       "[6636 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>25.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>673.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Magnesium, Mg</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>22.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Phosphorus, P</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>490.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Potassium, K</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>93.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sodium, Na</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>690.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zinc, Zn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>2.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copper, Cu</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Manganese, Mn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>14.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>1054.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>262.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>271.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pantothenic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Pantothenic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Folate, food</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Folate, DFE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_DFE</td>\n",
       "      <td>18.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Tryptophan</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>0.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Threonine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Isoleucine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>1.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Leucine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>2.412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Lysine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>2.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Methionine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>0.659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Cystine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Phenylalanine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Tyrosine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>1.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Valine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>1.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Arginine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>0.952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Histidine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>0.884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Alanine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>0.711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Aspartic acid</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>1.618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Glutamic acid</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>6.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Glycine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>0.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Proline</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>2.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Serine</td>\n",
       "      <td>Amino Acids</td>\n",
       "      <td>g</td>\n",
       "      <td>1.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>mg</td>\n",
       "      <td>93.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>18.584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>8.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            description        group    units     value\n",
       "0                               Protein  Composition        g    25.180\n",
       "1                     Total lipid (fat)  Composition        g    29.200\n",
       "2           Carbohydrate, by difference  Composition        g     3.060\n",
       "3                                   Ash        Other        g     3.280\n",
       "4                                Energy       Energy     kcal   376.000\n",
       "5                                 Water  Composition        g    39.280\n",
       "6                                Energy       Energy       kJ  1573.000\n",
       "7                  Fiber, total dietary  Composition        g     0.000\n",
       "8                           Calcium, Ca     Elements       mg   673.000\n",
       "9                              Iron, Fe     Elements       mg     0.640\n",
       "10                        Magnesium, Mg     Elements       mg    22.000\n",
       "11                        Phosphorus, P     Elements       mg   490.000\n",
       "12                         Potassium, K     Elements       mg    93.000\n",
       "13                           Sodium, Na     Elements       mg   690.000\n",
       "14                             Zinc, Zn     Elements       mg     2.940\n",
       "15                           Copper, Cu     Elements       mg     0.024\n",
       "16                        Manganese, Mn     Elements       mg     0.021\n",
       "17                         Selenium, Se     Elements      mcg    14.500\n",
       "18                        Vitamin A, IU     Vitamins       IU  1054.000\n",
       "19                              Retinol     Vitamins      mcg   262.000\n",
       "20                       Vitamin A, RAE     Vitamins  mcg_RAE   271.000\n",
       "21       Vitamin C, total ascorbic acid     Vitamins       mg     0.000\n",
       "22                              Thiamin     Vitamins       mg     0.031\n",
       "23                           Riboflavin     Vitamins       mg     0.450\n",
       "24                               Niacin     Vitamins       mg     0.180\n",
       "25                     Pantothenic acid     Vitamins       mg     0.190\n",
       "26                          Vitamin B-6     Vitamins       mg     0.074\n",
       "27                        Folate, total     Vitamins      mcg    18.000\n",
       "28                         Vitamin B-12     Vitamins      mcg     0.270\n",
       "29                           Folic acid     Vitamins      mcg     0.000\n",
       "..                                  ...          ...      ...       ...\n",
       "132                              Niacin     Vitamins       mg     0.180\n",
       "133                    Pantothenic acid     Vitamins       mg     0.190\n",
       "134                         Vitamin B-6     Vitamins       mg     0.074\n",
       "135                       Folate, total     Vitamins      mcg    18.000\n",
       "136                        Vitamin B-12     Vitamins      mcg     0.270\n",
       "137                          Folic acid     Vitamins      mcg     0.000\n",
       "138                        Folate, food     Vitamins      mcg    18.000\n",
       "139                         Folate, DFE     Vitamins  mcg_DFE    18.000\n",
       "140                          Tryptophan  Amino Acids        g     0.324\n",
       "141                           Threonine  Amino Acids        g     0.896\n",
       "142                          Isoleucine  Amino Acids        g     1.563\n",
       "143                             Leucine  Amino Acids        g     2.412\n",
       "144                              Lysine  Amino Acids        g     2.095\n",
       "145                          Methionine  Amino Acids        g     0.659\n",
       "146                             Cystine  Amino Acids        g     0.126\n",
       "147                       Phenylalanine  Amino Acids        g     1.326\n",
       "148                            Tyrosine  Amino Acids        g     1.216\n",
       "149                              Valine  Amino Acids        g     1.682\n",
       "150                            Arginine  Amino Acids        g     0.952\n",
       "151                           Histidine  Amino Acids        g     0.884\n",
       "152                             Alanine  Amino Acids        g     0.711\n",
       "153                       Aspartic acid  Amino Acids        g     1.618\n",
       "154                       Glutamic acid  Amino Acids        g     6.160\n",
       "155                             Glycine  Amino Acids        g     0.439\n",
       "156                             Proline  Amino Acids        g     2.838\n",
       "157                              Serine  Amino Acids        g     1.472\n",
       "158                         Cholesterol        Other       mg    93.000\n",
       "159        Fatty acids, total saturated        Other        g    18.584\n",
       "160  Fatty acids, total monounsaturated        Other        g     8.275\n",
       "161  Fatty acids, total polyunsaturated        Other        g     0.830\n",
       "\n",
       "[162 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(db[0]['nutrients'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients = DataFrame(db[0]['nutrients'])\n",
    "nutrients[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_keys = ['description', 'group', 'id', 'manufacturer']\n",
    "info = DataFrame(db, columns=info_keys)\n",
    "info[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "음식 분류별 개수 통계 산출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(info.group)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터의 각 음식의 nutrients 정보를 추출해, 모든 음식의 영양 정보를 포함하는 단일한 DataFrame 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.200</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.060</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>673.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Magnesium, Mg</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Phosphorus, P</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>490.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Potassium, K</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>93.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sodium, Na</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>690.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zinc, Zn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>2.940</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copper, Cu</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Manganese, Mn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>14.500</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>1054.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>262.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>271.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.031</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.450</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pantothenic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.190</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.074</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389325</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>1.100</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389326</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>5.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389327</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389328</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389329</th>\n",
       "      <td>Carotene, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389330</th>\n",
       "      <td>Carotene, alpha</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389331</th>\n",
       "      <td>Vitamin E (alpha-tocopherol)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.250</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389332</th>\n",
       "      <td>Vitamin D</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389333</th>\n",
       "      <td>Vitamin D (D2 + D3)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389334</th>\n",
       "      <td>Cryptoxanthin, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389335</th>\n",
       "      <td>Lycopene</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389336</th>\n",
       "      <td>Lutein + zeaxanthin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>20.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389337</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>21.900</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389338</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.020</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389339</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.060</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389340</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.540</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389341</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.260</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389342</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389343</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389344</th>\n",
       "      <td>Choline, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>4.100</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389345</th>\n",
       "      <td>Vitamin K (phylloquinone)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.500</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389346</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389347</th>\n",
       "      <td>Folate, food</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389348</th>\n",
       "      <td>Folate, DFE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_DFE</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389349</th>\n",
       "      <td>Vitamin E, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389350</th>\n",
       "      <td>Vitamin B-12, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389351</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389352</th>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.072</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389353</th>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.028</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389354</th>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.041</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>389355 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               description        group    units     value  \\\n",
       "0                                  Protein  Composition        g    25.180   \n",
       "1                        Total lipid (fat)  Composition        g    29.200   \n",
       "2              Carbohydrate, by difference  Composition        g     3.060   \n",
       "3                                      Ash        Other        g     3.280   \n",
       "4                                   Energy       Energy     kcal   376.000   \n",
       "5                                    Water  Composition        g    39.280   \n",
       "6                                   Energy       Energy       kJ  1573.000   \n",
       "7                     Fiber, total dietary  Composition        g     0.000   \n",
       "8                              Calcium, Ca     Elements       mg   673.000   \n",
       "9                                 Iron, Fe     Elements       mg     0.640   \n",
       "10                           Magnesium, Mg     Elements       mg    22.000   \n",
       "11                           Phosphorus, P     Elements       mg   490.000   \n",
       "12                            Potassium, K     Elements       mg    93.000   \n",
       "13                              Sodium, Na     Elements       mg   690.000   \n",
       "14                                Zinc, Zn     Elements       mg     2.940   \n",
       "15                              Copper, Cu     Elements       mg     0.024   \n",
       "16                           Manganese, Mn     Elements       mg     0.021   \n",
       "17                            Selenium, Se     Elements      mcg    14.500   \n",
       "18                           Vitamin A, IU     Vitamins       IU  1054.000   \n",
       "19                                 Retinol     Vitamins      mcg   262.000   \n",
       "20                          Vitamin A, RAE     Vitamins  mcg_RAE   271.000   \n",
       "21          Vitamin C, total ascorbic acid     Vitamins       mg     0.000   \n",
       "22                                 Thiamin     Vitamins       mg     0.031   \n",
       "23                              Riboflavin     Vitamins       mg     0.450   \n",
       "24                                  Niacin     Vitamins       mg     0.180   \n",
       "25                        Pantothenic acid     Vitamins       mg     0.190   \n",
       "26                             Vitamin B-6     Vitamins       mg     0.074   \n",
       "27                           Folate, total     Vitamins      mcg    18.000   \n",
       "28                            Vitamin B-12     Vitamins      mcg     0.270   \n",
       "29                              Folic acid     Vitamins      mcg     0.000   \n",
       "...                                    ...          ...      ...       ...   \n",
       "389325                        Selenium, Se     Elements      mcg     1.100   \n",
       "389326                       Vitamin A, IU     Vitamins       IU     5.000   \n",
       "389327                             Retinol     Vitamins      mcg     0.000   \n",
       "389328                      Vitamin A, RAE     Vitamins  mcg_RAE     0.000   \n",
       "389329                      Carotene, beta     Vitamins      mcg     2.000   \n",
       "389330                     Carotene, alpha     Vitamins      mcg     2.000   \n",
       "389331        Vitamin E (alpha-tocopherol)     Vitamins       mg     0.250   \n",
       "389332                           Vitamin D     Vitamins       IU     0.000   \n",
       "389333                 Vitamin D (D2 + D3)     Vitamins      mcg     0.000   \n",
       "389334                 Cryptoxanthin, beta     Vitamins      mcg     0.000   \n",
       "389335                            Lycopene     Vitamins      mcg     0.000   \n",
       "389336                 Lutein + zeaxanthin     Vitamins      mcg    20.000   \n",
       "389337      Vitamin C, total ascorbic acid     Vitamins       mg    21.900   \n",
       "389338                             Thiamin     Vitamins       mg     0.020   \n",
       "389339                          Riboflavin     Vitamins       mg     0.060   \n",
       "389340                              Niacin     Vitamins       mg     0.540   \n",
       "389341                         Vitamin B-6     Vitamins       mg     0.260   \n",
       "389342                       Folate, total     Vitamins      mcg    17.000   \n",
       "389343                        Vitamin B-12     Vitamins      mcg     0.000   \n",
       "389344                      Choline, total     Vitamins       mg     4.100   \n",
       "389345           Vitamin K (phylloquinone)     Vitamins      mcg     0.500   \n",
       "389346                          Folic acid     Vitamins      mcg     0.000   \n",
       "389347                        Folate, food     Vitamins      mcg    17.000   \n",
       "389348                         Folate, DFE     Vitamins  mcg_DFE    17.000   \n",
       "389349                    Vitamin E, added     Vitamins       mg     0.000   \n",
       "389350                 Vitamin B-12, added     Vitamins      mcg     0.000   \n",
       "389351                         Cholesterol        Other       mg     0.000   \n",
       "389352        Fatty acids, total saturated        Other        g     0.072   \n",
       "389353  Fatty acids, total monounsaturated        Other        g     0.028   \n",
       "389354  Fatty acids, total polyunsaturated        Other        g     0.041   \n",
       "\n",
       "           id  \n",
       "0        1008  \n",
       "1        1008  \n",
       "2        1008  \n",
       "3        1008  \n",
       "4        1008  \n",
       "5        1008  \n",
       "6        1008  \n",
       "7        1008  \n",
       "8        1008  \n",
       "9        1008  \n",
       "10       1008  \n",
       "11       1008  \n",
       "12       1008  \n",
       "13       1008  \n",
       "14       1008  \n",
       "15       1008  \n",
       "16       1008  \n",
       "17       1008  \n",
       "18       1008  \n",
       "19       1008  \n",
       "20       1008  \n",
       "21       1008  \n",
       "22       1008  \n",
       "23       1008  \n",
       "24       1008  \n",
       "25       1008  \n",
       "26       1008  \n",
       "27       1008  \n",
       "28       1008  \n",
       "29       1008  \n",
       "...       ...  \n",
       "389325  43546  \n",
       "389326  43546  \n",
       "389327  43546  \n",
       "389328  43546  \n",
       "389329  43546  \n",
       "389330  43546  \n",
       "389331  43546  \n",
       "389332  43546  \n",
       "389333  43546  \n",
       "389334  43546  \n",
       "389335  43546  \n",
       "389336  43546  \n",
       "389337  43546  \n",
       "389338  43546  \n",
       "389339  43546  \n",
       "389340  43546  \n",
       "389341  43546  \n",
       "389342  43546  \n",
       "389343  43546  \n",
       "389344  43546  \n",
       "389345  43546  \n",
       "389346  43546  \n",
       "389347  43546  \n",
       "389348  43546  \n",
       "389349  43546  \n",
       "389350  43546  \n",
       "389351  43546  \n",
       "389352  43546  \n",
       "389353  43546  \n",
       "389354  43546  \n",
       "\n",
       "[389355 rows x 5 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients = []\n",
    "\n",
    "for rec in db:\n",
    "    fnuts = DataFrame(rec['nutrients'])\n",
    "    fnuts['id'] = rec['id']\n",
    "    nutrients.append(fnuts)\n",
    "\n",
    "# 각각의 DatFrame을 인덱스 축 따라 이어붙이기\n",
    "nutrients = pd.concat(nutrients, ignore_index=True)\n",
    "nutrients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "중복데이터가 많음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14179"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "중복제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients = nutrients.drop_duplicates()\n",
    "nutrients.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "열 제목 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>food</th>\n",
       "      <th>fgroup</th>\n",
       "      <th>id</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1008</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheese, cheddar</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1009</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese, edam</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1018</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cheese, feta</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1019</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cheese, mozzarella, part skim milk</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1028</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cheese, mozzarella, part skim milk, low moisture</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1029</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cheese, romano</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1038</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cheese, roquefort</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1039</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cheese spread, pasteurized process, american, ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1048</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cream, fluid, half and half</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1049</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sour dressing, non-butterfat, cultured, filled...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1058</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Milk, filled, fluid, with blend of hydrogenate...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1059</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cream substitute, liquid, with lauric acid oil...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1068</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cream substitute, powdered</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1069</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Milk, producer, fluid, 3.7% milkfat</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1078</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, with add...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1079</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, with add...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1080</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Milk, reduced fat, fluid, 2% milkfat, protein ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1081</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, with added vi...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1082</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, with added no...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1083</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Milk, lowfat, fluid, 1% milkfat, protein forti...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1084</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Milk, nonfat, fluid, with added vitamin A and ...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1085</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Milk, nonfat, fluid, with added nonfat milk so...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1086</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Milk, nonfat, fluid, protein fortified, with a...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1087</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Milk, buttermilk, fluid, cultured, lowfat</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1088</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Milk, low sodium, fluid</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1089</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Milk, dry, whole, with added vitamin D</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1090</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Milk, dry, nonfat, regular, without added vita...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1091</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Milk, dry, nonfat, instant, with added vitamin...</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1092</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Milk, dry, nonfat, calcium reduced</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1093</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6606</th>\n",
       "      <td>Beef, tenderloin, steak, separable lean only, ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23628</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6607</th>\n",
       "      <td>Beef, top sirloin, steak, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23629</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6608</th>\n",
       "      <td>Beef, short loin, top loin, steak, separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23630</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6609</th>\n",
       "      <td>Beef, chuck, arm pot roast, separable lean onl...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23631</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6610</th>\n",
       "      <td>Beef, brisket, flat half, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23632</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6611</th>\n",
       "      <td>Beef, chuck, arm pot roast, separable lean onl...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23633</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6612</th>\n",
       "      <td>Beef, brisket, flat half, separable lean only,...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23634</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6613</th>\n",
       "      <td>Beef, round, eye of round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23635</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6614</th>\n",
       "      <td>Beef, round, top round, steak, separable lean ...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23636</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6615</th>\n",
       "      <td>Beef, round, bottom round, roast, separable le...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23637</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6616</th>\n",
       "      <td>Beef, rib, small end (ribs 10-12), separable l...</td>\n",
       "      <td>Beef Products</td>\n",
       "      <td>23638</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6617</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27015</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6618</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL's Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27016</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6619</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Soups...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27021</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6620</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27022</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6621</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27023</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6622</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27024</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27025</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6624</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27026</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6625</th>\n",
       "      <td>CAMPBELL Soup Company, CAMPBELL'S Red and Whit...</td>\n",
       "      <td>Soups, Sauces, and Gravies</td>\n",
       "      <td>27032</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6626</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Ess...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31010</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6627</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Spi...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31013</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6628</th>\n",
       "      <td>CAMPBELL Soup Company, PACE, Jalapenos Nacho S...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31014</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6629</th>\n",
       "      <td>CAMPBELL Soup Company, V8 60% Vegetable Juice,...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31016</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6630</th>\n",
       "      <td>CAMPBELL Soup Company, V8 Vegetable Juice, Low...</td>\n",
       "      <td>Vegetables and Vegetable Products</td>\n",
       "      <td>31017</td>\n",
       "      <td>Campbell Soup Co.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>Bologna, beef, low fat</td>\n",
       "      <td>Sausages and Luncheon Meats</td>\n",
       "      <td>42161</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6632</th>\n",
       "      <td>Turkey and pork sausage, fresh, bulk, patty or...</td>\n",
       "      <td>Sausages and Luncheon Meats</td>\n",
       "      <td>42173</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>Babyfood, juice, pear</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43408</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6634</th>\n",
       "      <td>Babyfood, dessert, banana yogurt, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43539</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6635</th>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>43546</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6636 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   food  \\\n",
       "0                                       Cheese, caraway   \n",
       "1                                       Cheese, cheddar   \n",
       "2                                          Cheese, edam   \n",
       "3                                          Cheese, feta   \n",
       "4                    Cheese, mozzarella, part skim milk   \n",
       "5      Cheese, mozzarella, part skim milk, low moisture   \n",
       "6                                        Cheese, romano   \n",
       "7                                     Cheese, roquefort   \n",
       "8     Cheese spread, pasteurized process, american, ...   \n",
       "9                           Cream, fluid, half and half   \n",
       "10    Sour dressing, non-butterfat, cultured, filled...   \n",
       "11    Milk, filled, fluid, with blend of hydrogenate...   \n",
       "12    Cream substitute, liquid, with lauric acid oil...   \n",
       "13                           Cream substitute, powdered   \n",
       "14                  Milk, producer, fluid, 3.7% milkfat   \n",
       "15    Milk, reduced fat, fluid, 2% milkfat, with add...   \n",
       "16    Milk, reduced fat, fluid, 2% milkfat, with add...   \n",
       "17    Milk, reduced fat, fluid, 2% milkfat, protein ...   \n",
       "18    Milk, lowfat, fluid, 1% milkfat, with added vi...   \n",
       "19    Milk, lowfat, fluid, 1% milkfat, with added no...   \n",
       "20    Milk, lowfat, fluid, 1% milkfat, protein forti...   \n",
       "21    Milk, nonfat, fluid, with added vitamin A and ...   \n",
       "22    Milk, nonfat, fluid, with added nonfat milk so...   \n",
       "23    Milk, nonfat, fluid, protein fortified, with a...   \n",
       "24            Milk, buttermilk, fluid, cultured, lowfat   \n",
       "25                              Milk, low sodium, fluid   \n",
       "26               Milk, dry, whole, with added vitamin D   \n",
       "27    Milk, dry, nonfat, regular, without added vita...   \n",
       "28    Milk, dry, nonfat, instant, with added vitamin...   \n",
       "29                   Milk, dry, nonfat, calcium reduced   \n",
       "...                                                 ...   \n",
       "6606  Beef, tenderloin, steak, separable lean only, ...   \n",
       "6607  Beef, top sirloin, steak, separable lean only,...   \n",
       "6608  Beef, short loin, top loin, steak, separable l...   \n",
       "6609  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6610  Beef, brisket, flat half, separable lean only,...   \n",
       "6611  Beef, chuck, arm pot roast, separable lean onl...   \n",
       "6612  Beef, brisket, flat half, separable lean only,...   \n",
       "6613  Beef, round, eye of round, roast, separable le...   \n",
       "6614  Beef, round, top round, steak, separable lean ...   \n",
       "6615  Beef, round, bottom round, roast, separable le...   \n",
       "6616  Beef, rib, small end (ribs 10-12), separable l...   \n",
       "6617  CAMPBELL Soup Company, CAMPBELL'S Red and Whit...   \n",
       "6618  CAMPBELL Soup Company, CAMPBELL's Red and Whit...   \n",
       "6619  CAMPBELL Soup Company, CAMPBELL'S SELECT Soups...   \n",
       "6620  CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...   \n",
       "6621  CAMPBELL Soup Company, CAMPBELL'S SOUP AT HAND...   \n",
       "6622  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6623  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6624  CAMPBELL Soup Company, CAMPBELL'S SELECT Gold ...   \n",
       "6625  CAMPBELL Soup Company, CAMPBELL'S Red and Whit...   \n",
       "6626  CAMPBELL Soup Company, V8 Vegetable Juice, Ess...   \n",
       "6627  CAMPBELL Soup Company, V8 Vegetable Juice, Spi...   \n",
       "6628  CAMPBELL Soup Company, PACE, Jalapenos Nacho S...   \n",
       "6629  CAMPBELL Soup Company, V8 60% Vegetable Juice,...   \n",
       "6630  CAMPBELL Soup Company, V8 Vegetable Juice, Low...   \n",
       "6631                             Bologna, beef, low fat   \n",
       "6632  Turkey and pork sausage, fresh, bulk, patty or...   \n",
       "6633                              Babyfood, juice, pear   \n",
       "6634         Babyfood, dessert, banana yogurt, strained   \n",
       "6635              Babyfood, banana no tapioca, strained   \n",
       "\n",
       "                                 fgroup     id       manufacturer  \n",
       "0                Dairy and Egg Products   1008                     \n",
       "1                Dairy and Egg Products   1009                     \n",
       "2                Dairy and Egg Products   1018                     \n",
       "3                Dairy and Egg Products   1019                     \n",
       "4                Dairy and Egg Products   1028                     \n",
       "5                Dairy and Egg Products   1029                     \n",
       "6                Dairy and Egg Products   1038                     \n",
       "7                Dairy and Egg Products   1039                     \n",
       "8                Dairy and Egg Products   1048                     \n",
       "9                Dairy and Egg Products   1049                     \n",
       "10               Dairy and Egg Products   1058                     \n",
       "11               Dairy and Egg Products   1059                     \n",
       "12               Dairy and Egg Products   1068                     \n",
       "13               Dairy and Egg Products   1069                     \n",
       "14               Dairy and Egg Products   1078                     \n",
       "15               Dairy and Egg Products   1079               None  \n",
       "16               Dairy and Egg Products   1080                     \n",
       "17               Dairy and Egg Products   1081                     \n",
       "18               Dairy and Egg Products   1082                     \n",
       "19               Dairy and Egg Products   1083                     \n",
       "20               Dairy and Egg Products   1084                     \n",
       "21               Dairy and Egg Products   1085                     \n",
       "22               Dairy and Egg Products   1086                     \n",
       "23               Dairy and Egg Products   1087                     \n",
       "24               Dairy and Egg Products   1088                     \n",
       "25               Dairy and Egg Products   1089                     \n",
       "26               Dairy and Egg Products   1090                     \n",
       "27               Dairy and Egg Products   1091                     \n",
       "28               Dairy and Egg Products   1092                     \n",
       "29               Dairy and Egg Products   1093                     \n",
       "...                                 ...    ...                ...  \n",
       "6606                      Beef Products  23628                     \n",
       "6607                      Beef Products  23629                     \n",
       "6608                      Beef Products  23630                     \n",
       "6609                      Beef Products  23631                     \n",
       "6610                      Beef Products  23632                     \n",
       "6611                      Beef Products  23633                     \n",
       "6612                      Beef Products  23634                     \n",
       "6613                      Beef Products  23635                     \n",
       "6614                      Beef Products  23636                     \n",
       "6615                      Beef Products  23637                     \n",
       "6616                      Beef Products  23638                     \n",
       "6617         Soups, Sauces, and Gravies  27015  Campbell Soup Co.  \n",
       "6618         Soups, Sauces, and Gravies  27016  Campbell Soup Co.  \n",
       "6619         Soups, Sauces, and Gravies  27021  Campbell Soup Co.  \n",
       "6620         Soups, Sauces, and Gravies  27022  Campbell Soup Co.  \n",
       "6621         Soups, Sauces, and Gravies  27023  Campbell Soup Co.  \n",
       "6622         Soups, Sauces, and Gravies  27024  Campbell Soup Co.  \n",
       "6623         Soups, Sauces, and Gravies  27025  Campbell Soup Co.  \n",
       "6624         Soups, Sauces, and Gravies  27026  Campbell Soup Co.  \n",
       "6625         Soups, Sauces, and Gravies  27032  Campbell Soup Co.  \n",
       "6626  Vegetables and Vegetable Products  31010  Campbell Soup Co.  \n",
       "6627  Vegetables and Vegetable Products  31013  Campbell Soup Co.  \n",
       "6628  Vegetables and Vegetable Products  31014  Campbell Soup Co.  \n",
       "6629  Vegetables and Vegetable Products  31016  Campbell Soup Co.  \n",
       "6630  Vegetables and Vegetable Products  31017  Campbell Soup Co.  \n",
       "6631        Sausages and Luncheon Meats  42161                     \n",
       "6632        Sausages and Luncheon Meats  42173                     \n",
       "6633                         Baby Foods  43408               None  \n",
       "6634                         Baby Foods  43539               None  \n",
       "6635                         Baby Foods  43546               None  \n",
       "\n",
       "[6636 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "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": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nutrient</th>\n",
       "      <th>nutgroup</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.200</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.060</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>673.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Magnesium, Mg</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Phosphorus, P</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>490.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Potassium, K</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>93.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sodium, Na</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>690.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zinc, Zn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>2.940</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copper, Cu</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Manganese, Mn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>14.500</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>1054.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>262.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>271.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.031</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.450</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pantothenic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.190</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.074</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389325</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>1.100</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389326</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>5.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389327</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389328</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389329</th>\n",
       "      <td>Carotene, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389330</th>\n",
       "      <td>Carotene, alpha</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389331</th>\n",
       "      <td>Vitamin E (alpha-tocopherol)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.250</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389332</th>\n",
       "      <td>Vitamin D</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389333</th>\n",
       "      <td>Vitamin D (D2 + D3)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389334</th>\n",
       "      <td>Cryptoxanthin, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389335</th>\n",
       "      <td>Lycopene</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389336</th>\n",
       "      <td>Lutein + zeaxanthin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>20.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389337</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>21.900</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389338</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.020</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389339</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.060</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389340</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.540</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389341</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.260</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389342</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389343</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389344</th>\n",
       "      <td>Choline, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>4.100</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389345</th>\n",
       "      <td>Vitamin K (phylloquinone)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.500</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389346</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389347</th>\n",
       "      <td>Folate, food</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389348</th>\n",
       "      <td>Folate, DFE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_DFE</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389349</th>\n",
       "      <td>Vitamin E, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389350</th>\n",
       "      <td>Vitamin B-12, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389351</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389352</th>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.072</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389353</th>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.028</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389354</th>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.041</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375176 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  nutrient     nutgroup    units     value  \\\n",
       "0                                  Protein  Composition        g    25.180   \n",
       "1                        Total lipid (fat)  Composition        g    29.200   \n",
       "2              Carbohydrate, by difference  Composition        g     3.060   \n",
       "3                                      Ash        Other        g     3.280   \n",
       "4                                   Energy       Energy     kcal   376.000   \n",
       "5                                    Water  Composition        g    39.280   \n",
       "6                                   Energy       Energy       kJ  1573.000   \n",
       "7                     Fiber, total dietary  Composition        g     0.000   \n",
       "8                              Calcium, Ca     Elements       mg   673.000   \n",
       "9                                 Iron, Fe     Elements       mg     0.640   \n",
       "10                           Magnesium, Mg     Elements       mg    22.000   \n",
       "11                           Phosphorus, P     Elements       mg   490.000   \n",
       "12                            Potassium, K     Elements       mg    93.000   \n",
       "13                              Sodium, Na     Elements       mg   690.000   \n",
       "14                                Zinc, Zn     Elements       mg     2.940   \n",
       "15                              Copper, Cu     Elements       mg     0.024   \n",
       "16                           Manganese, Mn     Elements       mg     0.021   \n",
       "17                            Selenium, Se     Elements      mcg    14.500   \n",
       "18                           Vitamin A, IU     Vitamins       IU  1054.000   \n",
       "19                                 Retinol     Vitamins      mcg   262.000   \n",
       "20                          Vitamin A, RAE     Vitamins  mcg_RAE   271.000   \n",
       "21          Vitamin C, total ascorbic acid     Vitamins       mg     0.000   \n",
       "22                                 Thiamin     Vitamins       mg     0.031   \n",
       "23                              Riboflavin     Vitamins       mg     0.450   \n",
       "24                                  Niacin     Vitamins       mg     0.180   \n",
       "25                        Pantothenic acid     Vitamins       mg     0.190   \n",
       "26                             Vitamin B-6     Vitamins       mg     0.074   \n",
       "27                           Folate, total     Vitamins      mcg    18.000   \n",
       "28                            Vitamin B-12     Vitamins      mcg     0.270   \n",
       "29                              Folic acid     Vitamins      mcg     0.000   \n",
       "...                                    ...          ...      ...       ...   \n",
       "389325                        Selenium, Se     Elements      mcg     1.100   \n",
       "389326                       Vitamin A, IU     Vitamins       IU     5.000   \n",
       "389327                             Retinol     Vitamins      mcg     0.000   \n",
       "389328                      Vitamin A, RAE     Vitamins  mcg_RAE     0.000   \n",
       "389329                      Carotene, beta     Vitamins      mcg     2.000   \n",
       "389330                     Carotene, alpha     Vitamins      mcg     2.000   \n",
       "389331        Vitamin E (alpha-tocopherol)     Vitamins       mg     0.250   \n",
       "389332                           Vitamin D     Vitamins       IU     0.000   \n",
       "389333                 Vitamin D (D2 + D3)     Vitamins      mcg     0.000   \n",
       "389334                 Cryptoxanthin, beta     Vitamins      mcg     0.000   \n",
       "389335                            Lycopene     Vitamins      mcg     0.000   \n",
       "389336                 Lutein + zeaxanthin     Vitamins      mcg    20.000   \n",
       "389337      Vitamin C, total ascorbic acid     Vitamins       mg    21.900   \n",
       "389338                             Thiamin     Vitamins       mg     0.020   \n",
       "389339                          Riboflavin     Vitamins       mg     0.060   \n",
       "389340                              Niacin     Vitamins       mg     0.540   \n",
       "389341                         Vitamin B-6     Vitamins       mg     0.260   \n",
       "389342                       Folate, total     Vitamins      mcg    17.000   \n",
       "389343                        Vitamin B-12     Vitamins      mcg     0.000   \n",
       "389344                      Choline, total     Vitamins       mg     4.100   \n",
       "389345           Vitamin K (phylloquinone)     Vitamins      mcg     0.500   \n",
       "389346                          Folic acid     Vitamins      mcg     0.000   \n",
       "389347                        Folate, food     Vitamins      mcg    17.000   \n",
       "389348                         Folate, DFE     Vitamins  mcg_DFE    17.000   \n",
       "389349                    Vitamin E, added     Vitamins       mg     0.000   \n",
       "389350                 Vitamin B-12, added     Vitamins      mcg     0.000   \n",
       "389351                         Cholesterol        Other       mg     0.000   \n",
       "389352        Fatty acids, total saturated        Other        g     0.072   \n",
       "389353  Fatty acids, total monounsaturated        Other        g     0.028   \n",
       "389354  Fatty acids, total polyunsaturated        Other        g     0.041   \n",
       "\n",
       "           id  \n",
       "0        1008  \n",
       "1        1008  \n",
       "2        1008  \n",
       "3        1008  \n",
       "4        1008  \n",
       "5        1008  \n",
       "6        1008  \n",
       "7        1008  \n",
       "8        1008  \n",
       "9        1008  \n",
       "10       1008  \n",
       "11       1008  \n",
       "12       1008  \n",
       "13       1008  \n",
       "14       1008  \n",
       "15       1008  \n",
       "16       1008  \n",
       "17       1008  \n",
       "18       1008  \n",
       "19       1008  \n",
       "20       1008  \n",
       "21       1008  \n",
       "22       1008  \n",
       "23       1008  \n",
       "24       1008  \n",
       "25       1008  \n",
       "26       1008  \n",
       "27       1008  \n",
       "28       1008  \n",
       "29       1008  \n",
       "...       ...  \n",
       "389325  43546  \n",
       "389326  43546  \n",
       "389327  43546  \n",
       "389328  43546  \n",
       "389329  43546  \n",
       "389330  43546  \n",
       "389331  43546  \n",
       "389332  43546  \n",
       "389333  43546  \n",
       "389334  43546  \n",
       "389335  43546  \n",
       "389336  43546  \n",
       "389337  43546  \n",
       "389338  43546  \n",
       "389339  43546  \n",
       "389340  43546  \n",
       "389341  43546  \n",
       "389342  43546  \n",
       "389343  43546  \n",
       "389344  43546  \n",
       "389345  43546  \n",
       "389346  43546  \n",
       "389347  43546  \n",
       "389348  43546  \n",
       "389349  43546  \n",
       "389350  43546  \n",
       "389351  43546  \n",
       "389352  43546  \n",
       "389353  43546  \n",
       "389354  43546  \n",
       "\n",
       "[375176 rows x 5 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_mapping = {'description' : 'nutrient',\n",
    "               'group' : 'nutgroup'}\n",
    "nutrients = nutrients.rename(columns=col_mapping, copy=False)\n",
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>25.180</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.200</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.060</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.280</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.280</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>673.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Magnesium, Mg</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Phosphorus, P</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>490.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Potassium, K</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>93.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sodium, Na</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>690.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zinc, Zn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>2.940</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copper, Cu</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.024</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Manganese, Mn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>14.500</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>1054.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>262.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>271.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.031</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.450</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pantothenic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.190</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.074</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>18.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.270</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1008</td>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375146</th>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mcg</td>\n",
       "      <td>1.100</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375147</th>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>5.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375148</th>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375149</th>\n",
       "      <td>Vitamin A, RAE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_RAE</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375150</th>\n",
       "      <td>Carotene, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375151</th>\n",
       "      <td>Carotene, alpha</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>2.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375152</th>\n",
       "      <td>Vitamin E (alpha-tocopherol)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.250</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375153</th>\n",
       "      <td>Vitamin D</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>IU</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375154</th>\n",
       "      <td>Vitamin D (D2 + D3)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375155</th>\n",
       "      <td>Cryptoxanthin, beta</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375156</th>\n",
       "      <td>Lycopene</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375157</th>\n",
       "      <td>Lutein + zeaxanthin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>20.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375158</th>\n",
       "      <td>Vitamin C, total ascorbic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>21.900</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375159</th>\n",
       "      <td>Thiamin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.020</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375160</th>\n",
       "      <td>Riboflavin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.060</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375161</th>\n",
       "      <td>Niacin</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.540</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375162</th>\n",
       "      <td>Vitamin B-6</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.260</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375163</th>\n",
       "      <td>Folate, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375164</th>\n",
       "      <td>Vitamin B-12</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375165</th>\n",
       "      <td>Choline, total</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>4.100</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375166</th>\n",
       "      <td>Vitamin K (phylloquinone)</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.500</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375167</th>\n",
       "      <td>Folic acid</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375168</th>\n",
       "      <td>Folate, food</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375169</th>\n",
       "      <td>Folate, DFE</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg_DFE</td>\n",
       "      <td>17.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375170</th>\n",
       "      <td>Vitamin E, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375171</th>\n",
       "      <td>Vitamin B-12, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375172</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375173</th>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.072</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375174</th>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.028</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375175</th>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.041</td>\n",
       "      <td>43546</td>\n",
       "      <td>Babyfood, banana no tapioca, strained</td>\n",
       "      <td>Baby Foods</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375176 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  nutrient     nutgroup    units     value  \\\n",
       "0                                  Protein  Composition        g    25.180   \n",
       "1                        Total lipid (fat)  Composition        g    29.200   \n",
       "2              Carbohydrate, by difference  Composition        g     3.060   \n",
       "3                                      Ash        Other        g     3.280   \n",
       "4                                   Energy       Energy     kcal   376.000   \n",
       "5                                    Water  Composition        g    39.280   \n",
       "6                                   Energy       Energy       kJ  1573.000   \n",
       "7                     Fiber, total dietary  Composition        g     0.000   \n",
       "8                              Calcium, Ca     Elements       mg   673.000   \n",
       "9                                 Iron, Fe     Elements       mg     0.640   \n",
       "10                           Magnesium, Mg     Elements       mg    22.000   \n",
       "11                           Phosphorus, P     Elements       mg   490.000   \n",
       "12                            Potassium, K     Elements       mg    93.000   \n",
       "13                              Sodium, Na     Elements       mg   690.000   \n",
       "14                                Zinc, Zn     Elements       mg     2.940   \n",
       "15                              Copper, Cu     Elements       mg     0.024   \n",
       "16                           Manganese, Mn     Elements       mg     0.021   \n",
       "17                            Selenium, Se     Elements      mcg    14.500   \n",
       "18                           Vitamin A, IU     Vitamins       IU  1054.000   \n",
       "19                                 Retinol     Vitamins      mcg   262.000   \n",
       "20                          Vitamin A, RAE     Vitamins  mcg_RAE   271.000   \n",
       "21          Vitamin C, total ascorbic acid     Vitamins       mg     0.000   \n",
       "22                                 Thiamin     Vitamins       mg     0.031   \n",
       "23                              Riboflavin     Vitamins       mg     0.450   \n",
       "24                                  Niacin     Vitamins       mg     0.180   \n",
       "25                        Pantothenic acid     Vitamins       mg     0.190   \n",
       "26                             Vitamin B-6     Vitamins       mg     0.074   \n",
       "27                           Folate, total     Vitamins      mcg    18.000   \n",
       "28                            Vitamin B-12     Vitamins      mcg     0.270   \n",
       "29                              Folic acid     Vitamins      mcg     0.000   \n",
       "...                                    ...          ...      ...       ...   \n",
       "375146                        Selenium, Se     Elements      mcg     1.100   \n",
       "375147                       Vitamin A, IU     Vitamins       IU     5.000   \n",
       "375148                             Retinol     Vitamins      mcg     0.000   \n",
       "375149                      Vitamin A, RAE     Vitamins  mcg_RAE     0.000   \n",
       "375150                      Carotene, beta     Vitamins      mcg     2.000   \n",
       "375151                     Carotene, alpha     Vitamins      mcg     2.000   \n",
       "375152        Vitamin E (alpha-tocopherol)     Vitamins       mg     0.250   \n",
       "375153                           Vitamin D     Vitamins       IU     0.000   \n",
       "375154                 Vitamin D (D2 + D3)     Vitamins      mcg     0.000   \n",
       "375155                 Cryptoxanthin, beta     Vitamins      mcg     0.000   \n",
       "375156                            Lycopene     Vitamins      mcg     0.000   \n",
       "375157                 Lutein + zeaxanthin     Vitamins      mcg    20.000   \n",
       "375158      Vitamin C, total ascorbic acid     Vitamins       mg    21.900   \n",
       "375159                             Thiamin     Vitamins       mg     0.020   \n",
       "375160                          Riboflavin     Vitamins       mg     0.060   \n",
       "375161                              Niacin     Vitamins       mg     0.540   \n",
       "375162                         Vitamin B-6     Vitamins       mg     0.260   \n",
       "375163                       Folate, total     Vitamins      mcg    17.000   \n",
       "375164                        Vitamin B-12     Vitamins      mcg     0.000   \n",
       "375165                      Choline, total     Vitamins       mg     4.100   \n",
       "375166           Vitamin K (phylloquinone)     Vitamins      mcg     0.500   \n",
       "375167                          Folic acid     Vitamins      mcg     0.000   \n",
       "375168                        Folate, food     Vitamins      mcg    17.000   \n",
       "375169                         Folate, DFE     Vitamins  mcg_DFE    17.000   \n",
       "375170                    Vitamin E, added     Vitamins       mg     0.000   \n",
       "375171                 Vitamin B-12, added     Vitamins      mcg     0.000   \n",
       "375172                         Cholesterol        Other       mg     0.000   \n",
       "375173        Fatty acids, total saturated        Other        g     0.072   \n",
       "375174  Fatty acids, total monounsaturated        Other        g     0.028   \n",
       "375175  Fatty acids, total polyunsaturated        Other        g     0.041   \n",
       "\n",
       "           id                                   food                  fgroup  \\\n",
       "0        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "1        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "2        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "3        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "4        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "5        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "6        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "7        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "8        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "9        1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "10       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "11       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "12       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "13       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "14       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "15       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "16       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "17       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "18       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "19       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "20       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "21       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "22       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "23       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "24       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "25       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "26       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "27       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "28       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "29       1008                        Cheese, caraway  Dairy and Egg Products   \n",
       "...       ...                                    ...                     ...   \n",
       "375146  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375147  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375148  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375149  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375150  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375151  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375152  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375153  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375154  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375155  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375156  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375157  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375158  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375159  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375160  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375161  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375162  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375163  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375164  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375165  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375166  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375167  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375168  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375169  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375170  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375171  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375172  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375173  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375174  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "375175  43546  Babyfood, banana no tapioca, strained              Baby Foods   \n",
       "\n",
       "       manufacturer  \n",
       "0                    \n",
       "1                    \n",
       "2                    \n",
       "3                    \n",
       "4                    \n",
       "5                    \n",
       "6                    \n",
       "7                    \n",
       "8                    \n",
       "9                    \n",
       "10                   \n",
       "11                   \n",
       "12                   \n",
       "13                   \n",
       "14                   \n",
       "15                   \n",
       "16                   \n",
       "17                   \n",
       "18                   \n",
       "19                   \n",
       "20                   \n",
       "21                   \n",
       "22                   \n",
       "23                   \n",
       "24                   \n",
       "25                   \n",
       "26                   \n",
       "27                   \n",
       "28                   \n",
       "29                   \n",
       "...             ...  \n",
       "375146         None  \n",
       "375147         None  \n",
       "375148         None  \n",
       "375149         None  \n",
       "375150         None  \n",
       "375151         None  \n",
       "375152         None  \n",
       "375153         None  \n",
       "375154         None  \n",
       "375155         None  \n",
       "375156         None  \n",
       "375157         None  \n",
       "375158         None  \n",
       "375159         None  \n",
       "375160         None  \n",
       "375161         None  \n",
       "375162         None  \n",
       "375163         None  \n",
       "375164         None  \n",
       "375165         None  \n",
       "375166         None  \n",
       "375167         None  \n",
       "375168         None  \n",
       "375169         None  \n",
       "375170         None  \n",
       "375171         None  \n",
       "375172         None  \n",
       "375173         None  \n",
       "375174         None  \n",
       "375175         None  \n",
       "\n",
       "[375176 rows x 8 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata = pd.merge(nutrients, info, on='id', how='outer')\n",
    "ndata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nutrient                                           Vitamin A, RAE\n",
       "nutgroup                                                 Vitamins\n",
       "units                                                     mcg_RAE\n",
       "value                                                         473\n",
       "id                                                           3950\n",
       "description     Infant formula, ABBOTT NUTRITION, SIMILAC, ADV...\n",
       "group                                                  Baby Foods\n",
       "manufacturer                                                 None\n",
       "Name: 30000, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata.ix[30000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그래프 생성: 영영소 그룹에 따른 아연 함유량"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fdf5e90>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+CxiTa29oLj0Z2KLCvgYQ5erLo7UBbGgG/7BGpOyiWGX2r8y+mS38m6HWkUZE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120841c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = ndata.groupby(['nutrient', 'fgroup'])['value'].\\\n",
    "    quantile(0.5)\n",
    "result['Zinc, Zn'].order().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 영양소가 많이 들어있는 음식"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "by_nutrient = ndata.groupby(['nutgroup', 'nutrient'])\n",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "아미노산(Amino acids)가 많이 들어있는 음식 정보 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "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": 33,
     "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
}