Getting Started with pandas (Ch.5).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Pandas 자료 구조 소개"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series([4,7,-5,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  7, -5,  3])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3], dtype='int64')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj2 = Series([4,7,-5,3], index=['d', 'b', 'a', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "a   -5\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'd', u'b', u'a', u'c'], dtype='object')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index   #type은 object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 배열에서 값을선택하거나 대입할 때는 색인을 이용해서 접근이 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obj2['d'] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a   -5\n",
       "d    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[['c', 'a', 'd']]   #다음과 같은색인 검색이 가능, c, a, d를 넣으면 다음과 같이 출력이 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    6\n",
       "b    7\n",
       "a   -5\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    6\n",
       "b    7\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[obj2>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    12\n",
       "b    14\n",
       "a   -10\n",
       "c     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d     403.428793\n",
       "b    1096.633158\n",
       "a       0.006738\n",
       "c      20.085537\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(obj2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 다양한 표현도 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'b' in obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'e' in obj2   #obj2에 b, e가 있으면 True, 없으면 False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 파이썬 사전형에 데이터를 저장해야 한다면 다음과 같이 바꿀 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj3 = Series(sdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio      35000\n",
       "Oregon    16000\n",
       "Texas     71000\n",
       "Utah       5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 사전 객체만 가지고 Series 객체를 생성하면 생성된 Series 객체의 색인은 사전의 키 값이 순서대로 들어간다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "states = ['California', 'Ohio', 'Oregon', 'Texas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj4 = Series(sdata, index=states)   #obj4에 sdata값을 넣고, 색인 검색으로 state에 있는 값을 찾도록 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California      NaN\n",
       "Ohio          35000\n",
       "Oregon        16000\n",
       "Texas         71000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(obj4)   #null이면 True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    False\n",
       "Ohio           True\n",
       "Oregon         True\n",
       "Texas          True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(obj4)   #null이면 False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.isnull()   #Series는 인스턴스 메서드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio      35000\n",
       "Oregon    16000\n",
       "Texas     71000\n",
       "Utah       5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California      NaN\n",
       "Ohio          35000\n",
       "Oregon        16000\n",
       "Texas         71000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California       NaN\n",
       "Ohio           70000\n",
       "Oregon         32000\n",
       "Texas         142000\n",
       "Utah             NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 + obj4   #다음과 같은 연산이 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- name속성이 있는데, 이 속성은 pandas의 기능에서 중요한 부분을 차지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj4.name = 'population'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj4.index.name = 'state'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "California      NaN\n",
       "Ohio          35000\n",
       "Oregon        16000\n",
       "Texas         71000\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj.index = ['Bob', 'Steve', 'Jeff', 'Ryan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bob      4\n",
       "Steve    7\n",
       "Jeff    -5\n",
       "Ryan     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],\n",
    "       'year': [2000, 2001, 2002, 2001, 2002],\n",
    "       'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}\n",
    "frame = DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.7</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.6</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.4</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.9</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop   state  year\n",
       "0  1.5    Ohio  2000\n",
       "1  1.7    Ohio  2001\n",
       "2  3.6    Ohio  2002\n",
       "3  2.4  Nevada  2001\n",
       "4  2.9  Nevada  2002"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame   #위의 데이터를 다음과 같이 DataFrame으로 만듬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop\n",
       "0  2000    Ohio  1.5\n",
       "1  2001    Ohio  1.7\n",
       "2  2002    Ohio  3.6\n",
       "3  2001  Nevada  2.4\n",
       "4  2002  Nevada  2.9"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(data, columns=['year', 'state', 'pop'])   #다음과 같이 배치가 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frame2 = DataFrame(data, columns=['year', 'state', 'pop', 'debt'],\n",
    "                  index=['one', 'two', 'three', 'four', 'five'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop debt\n",
       "one    2000    Ohio  1.5  NaN\n",
       "two    2001    Ohio  1.7  NaN\n",
       "three  2002    Ohio  3.6  NaN\n",
       "four   2001  Nevada  2.4  NaN\n",
       "five   2002  Nevada  2.9  NaN"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'year', u'state', u'pop', u'debt'], dtype='object')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.columns   #column에 대한 정보가 나온다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one        Ohio\n",
       "two        Ohio\n",
       "three      Ohio\n",
       "four     Nevada\n",
       "five     Nevada\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      2000\n",
       "two      2001\n",
       "three    2002\n",
       "four     2001\n",
       "five     2002\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year     2002\n",
       "state    Ohio\n",
       "pop       3.6\n",
       "debt      NaN\n",
       "Name: three, dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.ix['three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame2['debt'] = 16.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5  16.5\n",
       "two    2001    Ohio  1.7  16.5\n",
       "three  2002    Ohio  3.6  16.5\n",
       "four   2001  Nevada  2.4  16.5\n",
       "five   2002  Nevada  2.9  16.5"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5  16.5\n",
       "two    2001    Ohio  1.7  16.5\n",
       "three  2002    Ohio  3.6  16.5\n",
       "four   2001  Nevada  2.4  16.5\n",
       "five   2002  Nevada  2.9  16.5"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame2['debt'] = np.arange(5.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5     0\n",
       "two    2001    Ohio  1.7     1\n",
       "three  2002    Ohio  3.6     2\n",
       "four   2001  Nevada  2.4     3\n",
       "five   2002  Nevada  2.9     4"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "val = Series([-1.2, -1.5, -1.7], index=['two', 'four', 'five'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame2['debt'] = val   #val에 다음과 같이 시리즈를 만든후 frame2에 debt에 인덱스에 위치한 곳에 다음의 값들을 대입"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5   NaN\n",
       "two    2001    Ohio  1.7  -1.2\n",
       "three  2002    Ohio  3.6   NaN\n",
       "four   2001  Nevada  2.4  -1.5\n",
       "five   2002  Nevada  2.9  -1.7"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame2['eastern'] = frame2.state == 'Ohio'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "      <th>eastern</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt eastern\n",
       "one    2000    Ohio  1.5   NaN    True\n",
       "two    2001    Ohio  1.7  -1.2    True\n",
       "three  2002    Ohio  3.6   NaN    True\n",
       "four   2001  Nevada  2.4  -1.5   False\n",
       "five   2002  Nevada  2.9  -1.7   False"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del frame2['eastern']   #del이라는 예약어를 통해서 column삭제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'year', u'state', u'pop', u'debt'], dtype='object')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop = {'Nevada': {2001:2.4, 2002:2.9}, 'Ohio': {2000:1.5, 2001:1.7, 2002:3.6}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame3 = DataFrame(pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2000     NaN   1.5\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2000  2001  2002\n",
       "Nevada   NaN   2.4   2.9\n",
       "Ohio     1.5   1.7   3.6"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6\n",
       "2003     NaN   NaN"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(pop, index=[2001, 2002, 2003])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pdata = {'Ohio': frame3['Ohio'][:-1],\n",
    "        'Nevada': frame3['Nevada'][:2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nevada  Ohio\n",
       "2000     NaN   1.5\n",
       "2001     2.4   1.7"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(pdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame3.index.name = 'year'; frame3.columns.name = 'state'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  Nevada  Ohio\n",
       "year               \n",
       "2000      NaN   1.5\n",
       "2001      2.4   1.7\n",
       "2002      2.9   3.6"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ nan,  1.5],\n",
       "       [ 2.4,  1.7],\n",
       "       [ 2.9,  3.6]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2000, 'Ohio', 1.5, nan],\n",
       "       [2001, 'Ohio', 1.7, -1.2],\n",
       "       [2002, 'Ohio', 3.6, nan],\n",
       "       [2001, 'Nevada', 2.4, -1.5],\n",
       "       [2002, 'Nevada', 2.9, -1.7]], dtype=object)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5   NaN\n",
       "two    2001    Ohio  1.7  -1.2\n",
       "three  2002    Ohio  3.6   NaN\n",
       "four   2001  Nevada  2.4  -1.5\n",
       "five   2002  Nevada  2.9  -1.7"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3)색인 객체"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series(range(3), index=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = obj.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'a', u'b', u'c'], dtype='object')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'b', u'c'], dtype='object')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Indexes does not support mutable operations",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-149-6712534d5852>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'd'\u001b[0m   \u001b[0;31m#색인 객체는 변경이 불가능\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/hyunkyo/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1055\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1056\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1057\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Indexes does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Indexes does not support mutable operations"
     ]
    }
   ],
   "source": [
    "index[1] = 'd'   #색인 객체는 변경이 불가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = pd.Index(np.arange(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj2 = Series([1.5, -2.5, 0], index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2], dtype='int64')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2], dtype='int64')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index is index   #obj2.index의 값과 index의 값이 같은지 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Nevada</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  Nevada  Ohio\n",
       "year               \n",
       "2000      NaN   1.5\n",
       "2001      2.4   1.7\n",
       "2002      2.9   3.6"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Ohio' in frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2003 in frame3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 핵심 기능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) 재색인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4.5\n",
       "b    7.2\n",
       "a   -5.3\n",
       "c    3.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj2 = obj.reindex(['a','b','c','d','e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.reindex(['a', 'b', 'c', 'd', 'e'], fill_value=0)   #비어있는경우 0으로 채움"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj3 = Series(['blue', 'purple', 'yellow'], index=[0,2,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(range(6), method='ffill')   #method의 ffill값을 사용하면 누락된 값을 다음과 같이 채울 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- DataFrame에 대한 reindex는 색인, 칼럼 또는 둘 다 변경이 가능하다. 그냥 순서만 전달하면 로우가 재색인 됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frame = DataFrame(np.arange(9).reshape((3,3)), index=['a', 'c', 'd'], columns=['Ohio', 'Texas', 'California'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame2 = frame.reindex(['a', 'b', 'c', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "b   NaN    NaN         NaN\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2   #인덱스가 추가 되어도 재 색인 됨."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Coloumns 예약어를 사용해서 재색인 할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "states = ['Texas', 'Utah', 'California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a      1   NaN           2\n",
       "c      4   NaN           5\n",
       "d      7   NaN           8"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.reindex(columns=states)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 로우와 칼럼을 모두 한번에 재색인 할 수있지만, 보간은 로우에 대해서만 이루어 진다.(axis 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a      1   NaN           2\n",
       "b      1   NaN           2\n",
       "c      4   NaN           5\n",
       "d      7   NaN           8"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.reindex(index=['a', 'b', 'c', 'd'], method='ffill', columns=states)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 재색인은 ix를 이용해서 라벨로 색인하면 좀 더 간단하게 해결 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a      1   NaN           2\n",
       "b    NaN   NaN         NaN\n",
       "c      4   NaN           5\n",
       "d      7   NaN           8"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.ix[['a', 'b', 'c', 'd'], states]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) 하나의 로우 또는 칼럼 삭제하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_obj = obj.drop('c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "d    3\n",
       "e    4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "e    4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.drop(['d', 'c'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- DataFrame에서는 로우와 칼럼 모두에서 값을 삭제할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = DataFrame(np.arange(16).reshape((4, 4)), index=['Ohio', 'Colorado', 'Utah', 'New York'], columns=['one', 'two', 'three', 'four'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Colorado', 'Ohio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three  four\n",
       "Ohio        0      2     3\n",
       "Colorado    4      6     7\n",
       "Utah        8     10    11\n",
       "New York   12     14    15"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('two', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three\n",
       "Ohio        0      2\n",
       "Colorado    4      6\n",
       "Utah        8     10\n",
       "New York   12     14"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['two', 'four'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) 색인하기, 선택하기, 자르기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series(np.arange(4.), index=['a', 'b', 'c', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    2\n",
       "d    3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1\n",
       "a    0\n",
       "d    3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[['b', 'a', 'd']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1\n",
       "d    3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[[1,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[obj<2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1\n",
       "c    2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['b':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj['b':'c'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    5\n",
       "c    5\n",
       "d    3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- DataFrame에서 칼럼의 값을 하나 이상 가져올 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = DataFrame(np.arange(16).reshape((4,4)), index=['Ohio', 'Colorado', 'Utah', 'New York'], columns=['one', 'two', 'three', 'four'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</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",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio         1\n",
       "Colorado     5\n",
       "Utah         9\n",
       "New York    13\n",
       "Name: two, dtype: int64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>three</th>\n",
       "      <th>one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          three  one\n",
       "Ohio          2    0\n",
       "Colorado      6    4\n",
       "Utah         10    8\n",
       "New York     14   12"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['three', 'one']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['three']>5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one    two  three   four\n",
       "Ohio       True   True   True   True\n",
       "Colorado   True  False  False  False\n",
       "Utah      False  False  False  False\n",
       "New York  False  False  False  False"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data<5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[data<5] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    0      0     0\n",
       "Colorado    0    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "two      5\n",
       "three    6\n",
       "Name: Colorado, dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix['Colorado', ['two', 'three']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>four</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          four  one  two\n",
       "Colorado     7    0    5\n",
       "Utah        11    8    9"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[['Colorado', 'Utah'], [3,0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one       8\n",
       "two       9\n",
       "three    10\n",
       "four     11\n",
       "Name: Utah, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio        0\n",
       "Colorado    5\n",
       "Utah        9\n",
       "Name: two, dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[:'Utah', 'two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three\n",
       "Colorado    0    5      6\n",
       "Utah        8    9     10\n",
       "New York   12   13     14"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[data.three>5, :3]   #칼럼의 three의 값에서 5보다 큰 index중에서 컬럼 3번째 까지만 출력 되도록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) 산술연산과 데이터 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s1 = Series([7.3, -2.5, 3.4, 1.5], index=['a','c','d','e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s2 = Series([-2.1, 3.6, -1.5, 4, 3.1], index=['a','c','e','f', 'g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.2\n",
       "c    1.1\n",
       "d    NaN\n",
       "e    0.0\n",
       "f    NaN\n",
       "g    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 + s2   #겹치는 색인이 없는 경우에는 다음과 같이 NA로 표시 된다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = DataFrame(np.arange(9.).reshape((3,3)), columns=list('bcd'), index=['Ohio', 'Texas', 'Colorado'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = DataFrame(np.arange(12.).reshape((4,3)), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          b  c  d\n",
       "Ohio      0  1  2\n",
       "Texas     3  4  5\n",
       "Colorado  6  7  8"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "        b   d   e\n",
       "Utah    0   1   2\n",
       "Ohio    3   4   5\n",
       "Texas   6   7   8\n",
       "Oregon  9  10  11"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           b   c   d   e\n",
       "Colorado NaN NaN NaN NaN\n",
       "Ohio       3 NaN   6 NaN\n",
       "Oregon   NaN NaN NaN NaN\n",
       "Texas      9 NaN  12 NaN\n",
       "Utah     NaN NaN NaN NaN"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 + df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**산술연산 메서드에 채워 넣을 값 지정하기**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = DataFrame(np.arange(12.).reshape((3,4)), columns=list('abcd'))\n",
    "df2 = DataFrame(np.arange(20.).reshape((4,5)), columns=list('abcde'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "   a  b   c   d\n",
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       "2  8  9  10  11"
      ]
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     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1"
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  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [
    {
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       "      <th>3</th>\n",
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      "text/plain": [
       "    a   b   c   d   e\n",
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       "3  15  16  17  18  19"
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     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "    a   b   c   d   e\n",
       "0   0   2   4   6 NaN\n",
       "1   9  11  13  15 NaN\n",
       "2  18  20  22  24 NaN\n",
       "3 NaN NaN NaN NaN NaN"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 + df2   #겹치지 않는 부분의 값은 NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>11</td>\n",
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       "      <td>15</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    a   b   c   d   e\n",
       "0   0   2   4   6   4\n",
       "1   9  11  13  15   9\n",
       "2  18  20  22  24  14\n",
       "3  15  16  17  18  19"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.add(df2, fill_value=0)   #df1에 df2의 값을 채워 넣으며, 같이 가지고 있는 값의 경우에는 덧셈을 수행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b   c   d  e\n",
       "0  0  1   2   3  0\n",
       "1  4  5   6   7  0\n",
       "2  8  9  10  11  0"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.reindex(columns=df2.columns, fill_value=0)   #df1의 인덱스에 column은 df2의 칼럼으로 채우기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame과 Series 간의 연산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr = np.arange(12.).reshape((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   1.,   2.,   3.],\n",
       "       [  4.,   5.,   6.,   7.],\n",
       "       [  8.,   9.,  10.,  11.]])"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.])"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.],\n",
       "       [ 4.,  4.,  4.,  4.],\n",
       "       [ 8.,  8.,  8.,  8.]])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr - arr[0]   #arr의 각 행마다 arr[0]의 값만큼 빼준다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame = DataFrame(np.arange(12.).reshape((4,3)), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "series = frame.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b   d   e\n",
       "Utah    0   1   2\n",
       "Ohio    3   4   5\n",
       "Texas   6   7   8\n",
       "Oregon  9  10  11"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0\n",
       "d    1\n",
       "e    2\n",
       "Name: Utah, dtype: float64"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series   #series에 Utah인덱스의 값들이 들어간다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b  d  e\n",
       "Utah    0  0  0\n",
       "Ohio    3  3  3\n",
       "Texas   6  6  6\n",
       "Oregon  9  9  9"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame - series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 재색인도 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "series2 = Series(range(3), index=['b', 'e', 'f'])   #d의 컬럼의 값들은 나오지 않는것을 볼 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b   d   e   f\n",
       "Utah    0 NaN   3 NaN\n",
       "Ohio    3 NaN   6 NaN\n",
       "Texas   6 NaN   9 NaN\n",
       "Oregon  9 NaN  12 NaN"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame + series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "series3 = frame['d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b   d   e\n",
       "Utah    0   1   2\n",
       "Ohio    3   4   5\n",
       "Texas   6   7   8\n",
       "Oregon  9  10  11"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah       1\n",
       "Ohio       4\n",
       "Texas      7\n",
       "Oregon    10\n",
       "Name: d, dtype: float64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series3   #frame의 'd'칼럼만 들어있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b  d  e\n",
       "Utah   -1  0  1\n",
       "Ohio   -1  0  1\n",
       "Texas  -1  0  1\n",
       "Oregon -1  0  1"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sub(series3, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) 함수 적용과 매핑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame = DataFrame(np.random.randn(4,3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-1.122857</td>\n",
       "      <td>0.358371</td>\n",
       "      <td>0.125622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-0.175752</td>\n",
       "      <td>-0.141204</td>\n",
       "      <td>-0.946044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-0.762050</td>\n",
       "      <td>-1.341581</td>\n",
       "      <td>-0.045666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>0.572648</td>\n",
       "      <td>-0.798411</td>\n",
       "      <td>-1.129367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b         d         e\n",
       "Utah   -1.122857  0.358371  0.125622\n",
       "Ohio   -0.175752 -0.141204 -0.946044\n",
       "Texas  -0.762050 -1.341581 -0.045666\n",
       "Oregon  0.572648 -0.798411 -1.129367"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>1.122857</td>\n",
       "      <td>0.358371</td>\n",
       "      <td>0.125622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.175752</td>\n",
       "      <td>0.141204</td>\n",
       "      <td>0.946044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.762050</td>\n",
       "      <td>1.341581</td>\n",
       "      <td>0.045666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>0.572648</td>\n",
       "      <td>0.798411</td>\n",
       "      <td>1.129367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b         d         e\n",
       "Utah    1.122857  0.358371  0.125622\n",
       "Ohio    0.175752  0.141204  0.946044\n",
       "Texas   0.762050  1.341581  0.045666\n",
       "Oregon  0.572648  0.798411  1.129367"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(frame)   #절대값을 취한 데이터프레임값이 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 자주 사용되는 또 다른 연산은 각 로우나 칼럼의 1차원 배열에 함수를 적용하는것. apply()함수를 사용"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 배열의 합계, 평균 같은 일반적인 통계는 DataFrame의 메서드로 있으므로 apply() 메소드를 사용해야만 하는 것은 아니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = lambda x: x.max() - x.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.695506\n",
       "d    1.699951\n",
       "e    1.254989\n",
       "dtype: float64"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f)   #b의 칼럼 값에서 가장 큰 값과 가장 작은 값의 차를 나타냄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah      1.481228\n",
       "Ohio      0.804840\n",
       "Texas     1.295914\n",
       "Oregon    1.702015\n",
       "dtype: float64"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f, axis=1)   #각 인덱스의 칼럼 값에서 가장 큰 값과 가장 작은 값의 차를 나타냄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return Series([x.min(), x.max()], index=['min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.122857</td>\n",
       "      <td>-1.341581</td>\n",
       "      <td>-1.129367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.572648</td>\n",
       "      <td>0.358371</td>\n",
       "      <td>0.125622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b         d         e\n",
       "min -1.122857 -1.341581 -1.129367\n",
       "max  0.572648  0.358371  0.125622"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f)   #위아 같이 min과 max로 인덱싱 후에 각 칼럼에서의 최대, 최소값을 구함"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- applymap함수를 사용 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "format = lambda x: '%.2f' % x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-1.12</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-0.76</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>0.57</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-1.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b      d      e\n",
       "Utah    -1.12   0.36   0.13\n",
       "Ohio    -0.18  -0.14  -0.95\n",
       "Texas   -0.76  -1.34  -0.05\n",
       "Oregon   0.57  -0.80  -1.13"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.applymap(format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah       0.13\n",
       "Ohio      -0.95\n",
       "Texas     -0.05\n",
       "Oregon    -1.13\n",
       "Name: e, dtype: object"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['e'].map(format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) 정렬과 순위"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series(range(4), index=['d', 'a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.sort_index()   #sort_index 메소드를 사용하면 알파벳 순으로 정렬 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 로우나 칼럼 중 하나의 축을 기준으로 정렬 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame = DataFrame(np.arange(8).reshape((2,4)), index=['three', 'one'], columns=['d','a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       d  a  b  c\n",
       "one    4  5  6  7\n",
       "three  0  1  2  3"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sort_index()   #로우 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b  c  d\n",
       "three  1  2  3  0\n",
       "one    5  6  7  4"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sort_index(axis=1)   #축이 1이므로 칼럼을 정렬"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 값에 따라 정렬하는 경우에는 order메서드를 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series([4,7,-3, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -3\n",
       "3    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2   -3\n",
       "3    2\n",
       "0    4\n",
       "1    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.order()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Series객체에서 비어있는 값은 기본적으로 가장 마지막에 위치하도록 정렬한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series([4,np.nan, 7,np.nan,-3, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    -3\n",
       "5     2\n",
       "0     4\n",
       "2     7\n",
       "1   NaN\n",
       "3   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.order()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- DataFrame에서는 하나 이상의 칼럼에 있는 값으로 정렬이 필요\n",
    "- by 옵션에 필요한 칼럼의 이름을 남김"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frame = DataFrame({'b': [4,7,-3,2], 'a':[0,1,0,1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  0  4\n",
       "1  1  7\n",
       "2  0 -3\n",
       "3  1  2"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "2  0 -3\n",
       "3  1  2\n",
       "0  0  4\n",
       "1  1  7"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sort_index(by='b')   #b 칼럼만 정렬을 하면 다음과 같이 나옴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "2  0 -3\n",
       "0  0  4\n",
       "3  1  2\n",
       "1  1  7"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sort_index(by=['a', 'b'])   #여러 개의 칼럼을 정렬하려면 칼럼의 이름이 담긴 리스트를 전달"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 순위는 정렬과 거의 흡사, 1부터 배열의 유효한 데이터 개수까지 순위를 매김\n",
    "- 순위는 numpy.argsort에서 반환하는 간접 정렬 색인과 유사\n",
    "- 동률인 순위를 처리하는 방식이 다름\n",
    "- 동점인 항목에 대해서는 평균 순위를 매김"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series([7, -5, 7, 4, 2, 0, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.5\n",
       "1    1.0\n",
       "2    6.5\n",
       "3    4.5\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    4.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.rank()   #어떻게 이렇게 나오는지??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    1\n",
       "2    7\n",
       "3    4\n",
       "4    3\n",
       "5    2\n",
       "6    5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.rank(method='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    7\n",
       "2    2\n",
       "3    4\n",
       "4    5\n",
       "5    6\n",
       "6    4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.rank(ascending=False, method='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) 중복 색인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series(range(5), index=['a', 'a', 'b', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    1\n",
       "b    2\n",
       "b    3\n",
       "c    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- is_unique 속성은 해당 값이 유일한지 아닌지 알려준다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.index.is_unique   #인덱스 값들이 중복 된다는것을 알 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.random.randn(4,3), index=['a','a','b','b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-1.073101</td>\n",
       "      <td>0.519821</td>\n",
       "      <td>0.355883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.442147</td>\n",
       "      <td>-0.153018</td>\n",
       "      <td>1.445654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.049392</td>\n",
       "      <td>1.175752</td>\n",
       "      <td>-1.297503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.843570</td>\n",
       "      <td>0.944608</td>\n",
       "      <td>-1.985873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "a -1.073101  0.519821  0.355883\n",
       "a  0.442147 -0.153018  1.445654\n",
       "b  0.049392  1.175752 -1.297503\n",
       "b  0.843570  0.944608 -1.985873"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.049392</td>\n",
       "      <td>1.175752</td>\n",
       "      <td>-1.297503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.843570</td>\n",
       "      <td>0.944608</td>\n",
       "      <td>-1.985873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "b  0.049392  1.175752 -1.297503\n",
       "b  0.843570  0.944608 -1.985873"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix['b']   #데이터 프레임에서 로우를 선택해서 보는 것도 동일"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 기술통계 계산과 요약"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame([[1.4, np.nan], [7.1, -4.5], [np.nan, np.nan], [0.75, -1.3]], index=['a','b','c','d'], columns=['one','two'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.10</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  7.10 -4.5\n",
       "c   NaN  NaN\n",
       "d  0.75 -1.3"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    9.25\n",
       "two   -5.80\n",
       "dtype: float64"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()   #칼럼의 합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.40\n",
       "b    2.60\n",
       "c     NaN\n",
       "d   -0.55\n",
       "dtype: float64"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis=1)   #인덱스 기준의 합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      NaN\n",
       "b    1.300\n",
       "c      NaN\n",
       "d   -0.275\n",
       "dtype: float64"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=1, skipna=False)   #skipna를 이용하여 NA값은 제외시키고 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    b\n",
       "two    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.idxmax()   #각 칼럼에서 가장 큰 값을 가지고 있는 인덱스를 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>8.50</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>9.25</td>\n",
       "      <td>-5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  8.50 -4.5\n",
       "c   NaN  NaN\n",
       "d  9.25 -5.8"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.083333</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.493685</td>\n",
       "      <td>2.262742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>-4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.075000</td>\n",
       "      <td>-3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.250000</td>\n",
       "      <td>-2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>-1.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one       two\n",
       "count  3.000000  2.000000\n",
       "mean   3.083333 -2.900000\n",
       "std    3.493685  2.262742\n",
       "min    0.750000 -4.500000\n",
       "25%    1.075000 -3.700000\n",
       "50%    1.400000 -2.900000\n",
       "75%    4.250000 -2.100000\n",
       "max    7.100000 -1.300000"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()   #한번에 통계 결과를 여러 개 만들어낸다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series(['a', 'a', 'b', 'c'] * 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     a\n",
       "1     a\n",
       "2     b\n",
       "3     c\n",
       "4     a\n",
       "5     a\n",
       "6     b\n",
       "7     c\n",
       "8     a\n",
       "9     a\n",
       "10    b\n",
       "11    c\n",
       "12    a\n",
       "13    a\n",
       "14    b\n",
       "15    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     16\n",
       "unique     3\n",
       "top        a\n",
       "freq       8\n",
       "dtype: object"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.describe()   #통계가 다음과 같이 나옴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) 상관관계와 공분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas.io.data as web\n",
    "\n",
    "all_data = {}\n",
    "for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']:\n",
    "    all_data[ticker] = web.get_data_yahoo(ticker)\n",
    "    \n",
    "price = DataFrame({tic: data['Adj Close'] for tic, data in all_data.iteritems()})\n",
    "volume = DataFrame({tic: data['Volume'] for tic, data in all_data.iteritems()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "returns = price.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>-0.042294</td>\n",
       "      <td>-0.000302</td>\n",
       "      <td>-0.016680</td>\n",
       "      <td>-0.036802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>-0.000479</td>\n",
       "      <td>-0.026914</td>\n",
       "      <td>0.008606</td>\n",
       "      <td>0.012516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>-0.005273</td>\n",
       "      <td>-0.032160</td>\n",
       "      <td>-0.012243</td>\n",
       "      <td>-0.003687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>-0.013896</td>\n",
       "      <td>0.005934</td>\n",
       "      <td>-0.004257</td>\n",
       "      <td>-0.012843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0.004969</td>\n",
       "      <td>0.001180</td>\n",
       "      <td>0.006161</td>\n",
       "      <td>-0.000220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      GOOG       IBM      MSFT\n",
       "Date                                              \n",
       "2015-07-22 -0.042294 -0.000302 -0.016680 -0.036802\n",
       "2015-07-23 -0.000479 -0.026914  0.008606  0.012516\n",
       "2015-07-24 -0.005273 -0.032160 -0.012243 -0.003687\n",
       "2015-07-27 -0.013896  0.005934 -0.004257 -0.012843\n",
       "2015-07-28  0.004969  0.001180  0.006161 -0.000220"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- corr은 메서드는 NA가 아니고 정렬된 색인에서 연속하는 두 Series에 대해 상관관계를 계산\n",
    "- cov메서드는 공분산을 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4951826267525804"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.MSFT.corr(returns.IBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.3240782420226681e-05"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.MSFT.cov(returns.IBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277287</td>\n",
       "      <td>0.381394</td>\n",
       "      <td>0.351797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.277287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.319138</td>\n",
       "      <td>0.375100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.381394</td>\n",
       "      <td>0.319138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.351797</td>\n",
       "      <td>0.375100</td>\n",
       "      <td>0.495183</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL      GOOG       IBM      MSFT\n",
       "AAPL  1.000000  0.277287  0.381394  0.351797\n",
       "GOOG  0.277287  1.000000  0.319138  0.375100\n",
       "IBM   0.381394  0.319138  1.000000  0.495183\n",
       "MSFT  0.351797  0.375100  0.495183  1.000000"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL      GOOG       IBM      MSFT\n",
       "AAPL  0.000275  0.000061  0.000074  0.000083\n",
       "GOOG  0.000061  0.000252  0.000058  0.000084\n",
       "IBM   0.000074  0.000058  0.000138  0.000083\n",
       "MSFT  0.000083  0.000084  0.000083  0.000204"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.cov()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- corrwith메서드는 다른 Series나 DataFrame과의 상관관계를 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.381394\n",
       "GOOG    0.319138\n",
       "IBM     1.000000\n",
       "MSFT    0.495183\n",
       "dtype: float64"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corrwith(returns.IBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.092666\n",
       "GOOG    0.226701\n",
       "IBM    -0.183754\n",
       "MSFT   -0.097645\n",
       "dtype: float64"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corrwith(volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) 유일 값, 값 세기, 멤버십"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj = Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "uniques = obj.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['c', 'a', 'd', 'b'], dtype=object)"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a    3\n",
       "b    2\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.value_counts()   #도수를 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "c    3\n",
       "b    2\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(obj.values, sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- isin메서드는 어떤 값이 Series에 있는지 나타내는 불리언 벡터를 반환\n",
    "- 칼럼에서 값을 골라내고 싶을 때 유용하게 사용 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = obj.isin(['b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    c\n",
       "5    b\n",
       "6    b\n",
       "7    c\n",
       "8    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[mask]   #true값만 출력 되도록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qu1</th>\n",
       "      <th>Qu2</th>\n",
       "      <th>Qu3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Qu1  Qu2  Qu3\n",
       "0    1    2    1\n",
       "1    3    3    5\n",
       "2    4    1    2\n",
       "3    3    2    4\n",
       "4    4    3    4"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataFrame({'Qu1': [1, 3, 4, 3, 4],\n",
    "                  'Qu2': [2, 3, 1, 2, 3],\n",
    "                  'Qu3': [1, 5, 2, 4, 4]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qu1</th>\n",
       "      <th>Qu2</th>\n",
       "      <th>Qu3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Qu1  Qu2  Qu3\n",
       "1    1    1    1\n",
       "2    0    2    1\n",
       "3    2    2    0\n",
       "4    2    0    2\n",
       "5    0    0    1"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = data.apply(pd.value_counts).fillna(0)   #비어있는 값은 0으로 채운다.\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 누락된 데이터 처리하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     aardvark\n",
       "1    artichoke\n",
       "2          NaN\n",
       "3      avocado\n",
       "dtype: object"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data = Series(['aardvark', 'artichoke', np.nan, 'avocado'])\n",
    "string_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isnull()   #null값이 있는경우 true가 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "string_data[0] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_data.isnull()   #NaN과 None값들과 같이 실수든, 누락이든 null과 모두 NA값으로 취급된다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) 누락된 데이터 골라내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import nan as NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = Series([1, NA, 3.5, NA, 7])\n",
    "data.dropna()   #NA의 값은 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "2    3.5\n",
       "4    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.notnull()]   #NA값이 아닌 값들만 출력 되도록 위와 같은 결과를 갖음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1   2\n",
       "0   1  6.5   3\n",
       "1   1  NaN NaN\n",
       "2 NaN  NaN NaN\n",
       "3 NaN  6.5   3"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataFrame([[1., 6.5, 3.], [1., NA, NA],\n",
    "                  [NA, NA, NA], [NA, 6.5, 3.]])\n",
    "cleaned = data.dropna()   #NA의 값들을 drop시킴\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1  2\n",
       "0  1  6.5  3"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned   #NA가 없는 인덱스의 값만 출력되게 된다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1   2\n",
       "0   1  6.5   3\n",
       "1   1  NaN NaN\n",
       "3 NaN  6.5   3"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(how='all')   #인덱스에 해당하는 칼럼의 값들이 모두 NA인 경우만 제외하고 출력 하도록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1   2   4\n",
       "0   1  6.5   3 NaN\n",
       "1   1  NaN NaN NaN\n",
       "2 NaN  NaN NaN NaN\n",
       "3 NaN  6.5   3 NaN"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[4] = NA\n",
    "data   #새로운 칼럼을 추가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1   2\n",
       "0   1  6.5   3\n",
       "1   1  NaN NaN\n",
       "2 NaN  NaN NaN\n",
       "3 NaN  6.5   3"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(axis=1, how='all')   #칼럼을 축으로, NA인 경우 제외시키고 출력되도록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.833422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.573418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.805062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.715176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.456606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.763459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.188040</td>\n",
       "      <td>-0.718948</td>\n",
       "      <td>-2.953361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.248404</td>\n",
       "      <td>2.732638</td>\n",
       "      <td>1.212794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.833422       NaN       NaN\n",
       "1  0.000499       NaN       NaN\n",
       "2 -1.573418       NaN       NaN\n",
       "3 -0.805062       NaN  0.715176\n",
       "4  1.456606       NaN -0.763459\n",
       "5 -0.188040 -0.718948 -2.953361\n",
       "6 -0.248404  2.732638  1.212794"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(np.random.randn(7, 3))\n",
    "df.ix[:4, 1] = NA; df.ix[:2, 2] = NA\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>5</th>\n",
       "      <td>-0.188040</td>\n",
       "      <td>-0.718948</td>\n",
       "      <td>-2.953361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.248404</td>\n",
       "      <td>2.732638</td>\n",
       "      <td>1.212794</td>\n",
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       "          0         1         2\n",
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       "6 -0.248404  2.732638  1.212794"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(thresh=3)   #몇개 이상의 값이 들어있는 인덱스만 살펴보고 싶은 경우 thresh에 값을 적용하면 된다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) 누락된 값 채우기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>-1.573418</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.805062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.715176</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>-0.763459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.188040</td>\n",
       "      <td>-0.718948</td>\n",
       "      <td>-2.953361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.248404</td>\n",
       "      <td>2.732638</td>\n",
       "      <td>1.212794</td>\n",
       "    </tr>\n",
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       "          0         1         2\n",
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       "5 -0.188040 -0.718948 -2.953361\n",
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      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.833422</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>-1.573418</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.805062</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.715176</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.456606</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.763459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.188040</td>\n",
       "      <td>-0.718948</td>\n",
       "      <td>-2.953361</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.248404</td>\n",
       "      <td>2.732638</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.833422  0.500000       NaN\n",
       "1  0.000499  0.500000       NaN\n",
       "2 -1.573418  0.500000       NaN\n",
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       "6 -0.248404  2.732638  1.212794"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({1: 0.5, 3: -1})   #1번 칼럼에서 NA는 0.5로 채우고, 3번 칼럼의 경우에는 NaN으로 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.805062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.715176</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.456606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.763459</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.188040</td>\n",
       "      <td>-0.718948</td>\n",
       "      <td>-2.953361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.248404</td>\n",
       "      <td>2.732638</td>\n",
       "      <td>1.212794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.833422  0.000000  0.000000\n",
       "1  0.000499  0.000000  0.000000\n",
       "2 -1.573418  0.000000  0.000000\n",
       "3 -0.805062  0.000000  0.715176\n",
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       "5 -0.188040 -0.718948 -2.953361\n",
       "6 -0.248404  2.732638  1.212794"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fillna는 값을 채워 넣은 객체의 참조를 반환한다.\n",
    "_ = df.fillna(0, inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.random.randn(6,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.ix[2:, 1] = NA; df.ix[4:, 2] = NA   #인덱스 2부터 1번 칼럼에 NA를 넣고, 인덱스 4부터 2번 칼럼에 NA를 넣는다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.400409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.517182</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.102480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.844858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.426016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.342911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "          0         1         2\n",
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       "4 -0.426016       NaN       NaN\n",
       "5 -0.342911       NaN       NaN"
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "          0         1         2\n",
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       "4 -0.426016  0.205214  0.844858\n",
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     "execution_count": 366,
     "metadata": {},
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   ],
   "source": [
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>5</th>\n",
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       "          0         1         2\n",
       "0  0.223863 -0.030594 -0.980556\n",
       "1  0.625663  0.205214  0.294257\n",
       "2  1.400409  0.205214  0.517182\n",
       "3 -0.102480  0.205214  0.844858\n",
       "4 -0.426016       NaN  0.844858\n",
       "5 -0.342911       NaN  0.844858"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill', limit=2)   #limit를 두어서 위의 값을 가져오되, 2번까지만 가져오도록 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = Series([1., NA, 3.5, NA, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.000000\n",
       "1    3.833333\n",
       "2    3.500000\n",
       "3    3.833333\n",
       "4    7.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(data.mean())   #빈 공간을 바로 위의 값으로 채우면서, 각 인덱싱의 평균값을 구함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 계층적 색인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 계층적 색인은 pandas의 중요한 기능\n",
    "- 축에 대해 다중 색인 단계를 지정할 수 있도록 해준다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1   -0.104324\n",
       "   2   -1.925584\n",
       "   3   -1.026046\n",
       "b  1   -0.925487\n",
       "   2    0.903981\n",
       "   3   -1.509898\n",
       "c  1    1.129553\n",
       "   2    1.916166\n",
       "d  2   -0.405995\n",
       "   3    0.223241\n",
       "dtype: float64"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = Series(np.random.randn(10),\n",
    "              index=[['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd', 'd'],\n",
    "                     [1, 2, 3, 1, 2, 3, 1, 2, 2, 3]])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- MultiIndex를 색인으로 하는 Series로, 색인의 계층을 보여주고 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[[u'a', u'b', u'c', u'd'], [1, 2, 3]],\n",
       "           labels=[[0, 0, 0, 1, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 1, 2, 0, 1, 1, 2]])"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   -0.925487\n",
       "2    0.903981\n",
       "3   -1.509898\n",
       "dtype: float64"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b  1   -0.925487\n",
       "   2    0.903981\n",
       "   3   -1.509898\n",
       "c  1    1.129553\n",
       "   2    1.916166\n",
       "dtype: float64"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['b':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b  1   -0.925487\n",
       "   2    0.903981\n",
       "   3   -1.509898\n",
       "d  2   -0.405995\n",
       "   3    0.223241\n",
       "dtype: float64"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[['b', 'd']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -1.925584\n",
       "b    0.903981\n",
       "c    1.916166\n",
       "d   -0.405995\n",
       "dtype: float64"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- unstack 메서드를 사용해서 데이터를 새롭게 배열이 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-0.104324</td>\n",
       "      <td>-1.925584</td>\n",
       "      <td>-1.026046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.925487</td>\n",
       "      <td>0.903981</td>\n",
       "      <td>-1.509898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1.129553</td>\n",
       "      <td>1.916166</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.405995</td>\n",
       "      <td>0.223241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2         3\n",
       "a -0.104324 -1.925584 -1.026046\n",
       "b -0.925487  0.903981 -1.509898\n",
       "c  1.129553  1.916166       NaN\n",
       "d       NaN -0.405995  0.223241"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1   -0.104324\n",
       "   2   -1.925584\n",
       "   3   -1.026046\n",
       "b  1   -0.925487\n",
       "   2    0.903981\n",
       "   3   -1.509898\n",
       "c  1    1.129553\n",
       "   2    1.916166\n",
       "d  2   -0.405995\n",
       "   3    0.223241\n",
       "dtype: float64"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.unstack().stack()   #unstack에 반대되는 stack 메서드를 수행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ohio     Colorado\n",
       "    Green Red    Green\n",
       "a 1     0   1        2\n",
       "  2     3   4        5\n",
       "b 1     6   7        8\n",
       "  2     9  10       11"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = DataFrame(np.arange(12).reshape((4, 3)),\n",
    "                  index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],\n",
    "                  columns=[['Ohio', 'Ohio', 'Colorado'],\n",
    "                           ['Green', 'Red', 'Green']])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key1 key2                   \n",
       "a    1        0   1        2\n",
       "     2        3   4        5\n",
       "b    1        6   7        8\n",
       "     2        9  10       11"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.index.names = ['key1', 'key2']        #각 인덱스를 key1, key2로 나눔\n",
    "frame.columns.names = ['state', 'color']    #각 칼럼을 다음과 같이 state와 color로 나눔\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color      Green  Red\n",
       "key1 key2            \n",
       "a    1         0    1\n",
       "     2         3    4\n",
       "b    1         6    7\n",
       "     2         9   10"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['Ohio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) 계층 순서 바꾸고 정렬하기기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key2</th>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>b</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>b</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key2 key1                   \n",
       "1    a        0   1        2\n",
       "2    a        3   4        5\n",
       "1    b        6   7        8\n",
       "2    b        9  10       11"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.swaplevel('key1', 'key2')   #key의 순서를 바꿈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key1 key2                   \n",
       "a    1        0   1        2\n",
       "b    1        6   7        8\n",
       "a    2        3   4        5\n",
       "b    2        9  10       11"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sortlevel(1)   #sortlevel 메서드를 사용하여 사전식 정렬을 하도록 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key2</th>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key2 key1                   \n",
       "1    a        0   1        2\n",
       "     b        6   7        8\n",
       "2    a        3   4        5\n",
       "     b        9  10       11"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.swaplevel(0,1).sortlevel(0)   #key2를 정렬, key1을 다시 사전식으로 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) 단계별 요약통계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  Ohio     Colorado\n",
       "color Green Red    Green\n",
       "key2                    \n",
       "1         6   8       10\n",
       "2        12  14       16"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sum(level='key2')   #key2의 값 별로 합을 구함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color      Green  Red\n",
       "key1 key2            \n",
       "a    1         2    1\n",
       "     2         8    4\n",
       "b    1        14    7\n",
       "     2        20   10"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.sum(level='color', axis=1)   #color에 따라 합을 구함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) DataFrame의 칼럼 사용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>two</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b    c  d\n",
       "0  0  7  one  0\n",
       "1  1  6  one  1\n",
       "2  2  5  one  2\n",
       "3  3  4  two  0\n",
       "4  4  3  two  1\n",
       "5  5  2  two  2\n",
       "6  6  1  two  3"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = DataFrame({'a': range(7), 'b': range(7, 0, -1),\n",
    "                   'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],\n",
    "                   'd': [0, 1, 2, 0, 1, 2, 3]})\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">two</th>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b\n",
       "c   d      \n",
       "one 0  0  7\n",
       "    1  1  6\n",
       "    2  2  5\n",
       "two 0  3  4\n",
       "    1  4  3\n",
       "    2  5  2\n",
       "    3  6  1"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2 = frame.set_index(['c', 'd'])   #'c'와 'd'를 인덱스로 하여 재색인\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">two</th>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>two</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b    c  d\n",
       "c   d              \n",
       "one 0  0  7  one  0\n",
       "    1  1  6  one  1\n",
       "    2  2  5  one  2\n",
       "two 0  3  4  two  0\n",
       "    1  4  3  two  1\n",
       "    2  5  2  two  2\n",
       "    3  6  1  two  3"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.set_index(['c', 'd'], drop=False)   #drop을 써줌으로써 c와 d 칼럼이 나타나는 것을 알 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     c  d  a  b\n",
       "0  one  0  0  7\n",
       "1  one  1  1  6\n",
       "2  one  2  2  5\n",
       "3  two  0  3  4\n",
       "4  two  1  4  3\n",
       "5  two  2  5  2\n",
       "6  two  3  6  1"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.reset_index()   #reset_index는 set_index와 반대되는 개념으로, 계층적 색인 단계가 칼럼으로 이동"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. pandas와 관련된 기타 주제"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) 정수 색인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ser = Series(np.arange(3.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ser2 = Series(np.arange(3.), index=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.ix[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5    0\n",
       " 1    1\n",
       " 3    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser3 = Series(range(3), index=[-5, 1, 3])\n",
    "ser3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser3.iget_value(2)   #해당 번째의 값을 불러온다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.iget_value(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1\n",
       "2  0  1\n",
       "0  2  3\n",
       "1  4  5"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = DataFrame(np.arange(6).reshape((3,2)), index=[2,0,1])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "Name: 2, dtype: int64"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.irow(0)   #0번째 인덱스의 칼럼 값들을 불러온다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "0    3\n",
       "1    5\n",
       "Name: 1, dtype: int64"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.icol(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Panel 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas.io.data as web\n",
    "\n",
    "pdata = pd.Panel(dict((stk, web.get_data_yahoo(stk))\n",
    "                       for stk in ['AAPL', 'GOOG', 'MSFT', 'DELL']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 4 (items) x 1421 (major_axis) x 6 (minor_axis)\n",
       "Items axis: AAPL to MSFT\n",
       "Major_axis axis: 2010-01-04 00:00:00 to 2015-07-28 00:00:00\n",
       "Minor_axis axis: Open to Adj Close"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata   #pdata의 정보"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pdata = pdata.swapaxes('items', 'minor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>DELL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>28.718094</td>\n",
       "      <td>14.06528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.769336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>28.767737</td>\n",
       "      <td>14.38450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.777984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>28.310151</td>\n",
       "      <td>14.10397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.613650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>28.257821</td>\n",
       "      <td>14.23940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.336875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>28.445687</td>\n",
       "      <td>14.36516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.518508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>28.194745</td>\n",
       "      <td>14.37483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.181189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>27.874037</td>\n",
       "      <td>14.56830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.008204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>28.267213</td>\n",
       "      <td>14.57797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.250383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14</th>\n",
       "      <td>28.103497</td>\n",
       "      <td>14.22005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.777984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-15</th>\n",
       "      <td>27.633830</td>\n",
       "      <td>13.92985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.691493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-19</th>\n",
       "      <td>28.856306</td>\n",
       "      <td>14.32646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.899074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-20</th>\n",
       "      <td>28.412135</td>\n",
       "      <td>14.03626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.457964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-21</th>\n",
       "      <td>27.921004</td>\n",
       "      <td>13.92017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.956309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-22</th>\n",
       "      <td>26.536154</td>\n",
       "      <td>13.18982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.048140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-25</th>\n",
       "      <td>27.250048</td>\n",
       "      <td>13.43650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.359512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-26</th>\n",
       "      <td>27.635174</td>\n",
       "      <td>13.13662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.515199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-27</th>\n",
       "      <td>27.895500</td>\n",
       "      <td>13.08825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.662235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-28</th>\n",
       "      <td>26.742807</td>\n",
       "      <td>12.84641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.221125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-29</th>\n",
       "      <td>25.772608</td>\n",
       "      <td>12.47882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.373502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>26.130897</td>\n",
       "      <td>12.78837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.572434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-02</th>\n",
       "      <td>26.282534</td>\n",
       "      <td>12.86576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.615679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-03</th>\n",
       "      <td>26.734757</td>\n",
       "      <td>12.92380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.762716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-04</th>\n",
       "      <td>25.771266</td>\n",
       "      <td>12.58523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.079428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-05</th>\n",
       "      <td>26.228860</td>\n",
       "      <td>12.80772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.235114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-08</th>\n",
       "      <td>26.049045</td>\n",
       "      <td>12.95282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.975637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-09</th>\n",
       "      <td>26.326814</td>\n",
       "      <td>13.10760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.226465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-10</th>\n",
       "      <td>26.183238</td>\n",
       "      <td>13.30107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.209166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-11</th>\n",
       "      <td>26.659611</td>\n",
       "      <td>13.49454</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.321607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-12</th>\n",
       "      <td>26.889071</td>\n",
       "      <td>13.38813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.157271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-16</th>\n",
       "      <td>27.294328</td>\n",
       "      <td>13.67834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.635203</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>2015-06-16</th>\n",
       "      <td>127.599998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>528.150024</td>\n",
       "      <td>45.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>127.300003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>529.260010</td>\n",
       "      <td>45.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>127.879997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>536.729980</td>\n",
       "      <td>46.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>126.599998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>536.690002</td>\n",
       "      <td>46.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-22</th>\n",
       "      <td>127.610001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538.190002</td>\n",
       "      <td>46.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-23</th>\n",
       "      <td>127.029999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>540.479980</td>\n",
       "      <td>45.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-24</th>\n",
       "      <td>128.110001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>537.840027</td>\n",
       "      <td>45.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-25</th>\n",
       "      <td>127.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>535.229980</td>\n",
       "      <td>45.650002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-26</th>\n",
       "      <td>126.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>531.690002</td>\n",
       "      <td>45.259998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-29</th>\n",
       "      <td>124.529999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>521.520020</td>\n",
       "      <td>44.369999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>125.430000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520.510010</td>\n",
       "      <td>44.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>126.599998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>521.840027</td>\n",
       "      <td>44.450001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>126.440002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>523.400024</td>\n",
       "      <td>44.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>522.859985</td>\n",
       "      <td>44.389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-07</th>\n",
       "      <td>125.690002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>525.020020</td>\n",
       "      <td>44.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>122.570000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>516.830017</td>\n",
       "      <td>44.240002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>120.070000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520.679993</td>\n",
       "      <td>44.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>123.279999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>530.130005</td>\n",
       "      <td>44.610001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>125.660004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>546.549988</td>\n",
       "      <td>45.540001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>125.610001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>561.099976</td>\n",
       "      <td>45.619999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>126.820000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>560.219971</td>\n",
       "      <td>45.759998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>128.509995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>579.849976</td>\n",
       "      <td>46.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>129.619995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>672.929993</td>\n",
       "      <td>46.619999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>132.070007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>663.020020</td>\n",
       "      <td>46.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>130.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>662.299988</td>\n",
       "      <td>47.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>125.220001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>662.099976</td>\n",
       "      <td>45.540001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>125.160004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>644.280029</td>\n",
       "      <td>46.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>124.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623.559998</td>\n",
       "      <td>45.939999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>122.769997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>627.260010</td>\n",
       "      <td>45.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>123.379997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>45.340000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1421 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL      DELL        GOOG       MSFT\n",
       "Date                                                   \n",
       "2010-01-04   28.718094  14.06528         NaN  26.769336\n",
       "2010-01-05   28.767737  14.38450         NaN  26.777984\n",
       "2010-01-06   28.310151  14.10397         NaN  26.613650\n",
       "2010-01-07   28.257821  14.23940         NaN  26.336875\n",
       "2010-01-08   28.445687  14.36516         NaN  26.518508\n",
       "2010-01-11   28.194745  14.37483         NaN  26.181189\n",
       "2010-01-12   27.874037  14.56830         NaN  26.008204\n",
       "2010-01-13   28.267213  14.57797         NaN  26.250383\n",
       "2010-01-14   28.103497  14.22005         NaN  26.777984\n",
       "2010-01-15   27.633830  13.92985         NaN  26.691493\n",
       "2010-01-19   28.856306  14.32646         NaN  26.899074\n",
       "2010-01-20   28.412135  14.03626         NaN  26.457964\n",
       "2010-01-21   27.921004  13.92017         NaN  25.956309\n",
       "2010-01-22   26.536154  13.18982         NaN  25.048140\n",
       "2010-01-25   27.250048  13.43650         NaN  25.359512\n",
       "2010-01-26   27.635174  13.13662         NaN  25.515199\n",
       "2010-01-27   27.895500  13.08825         NaN  25.662235\n",
       "2010-01-28   26.742807  12.84641         NaN  25.221125\n",
       "2010-01-29   25.772608  12.47882         NaN  24.373502\n",
       "2010-02-01   26.130897  12.78837         NaN  24.572434\n",
       "2010-02-02   26.282534  12.86576         NaN  24.615679\n",
       "2010-02-03   26.734757  12.92380         NaN  24.762716\n",
       "2010-02-04   25.771266  12.58523         NaN  24.079428\n",
       "2010-02-05   26.228860  12.80772         NaN  24.235114\n",
       "2010-02-08   26.049045  12.95282         NaN  23.975637\n",
       "2010-02-09   26.326814  13.10760         NaN  24.226465\n",
       "2010-02-10   26.183238  13.30107         NaN  24.209166\n",
       "2010-02-11   26.659611  13.49454         NaN  24.321607\n",
       "2010-02-12   26.889071  13.38813         NaN  24.157271\n",
       "2010-02-16   27.294328  13.67834         NaN  24.635203\n",
       "...                ...       ...         ...        ...\n",
       "2015-06-16  127.599998       NaN  528.150024  45.830002\n",
       "2015-06-17  127.300003       NaN  529.260010  45.970001\n",
       "2015-06-18  127.879997       NaN  536.729980  46.720001\n",
       "2015-06-19  126.599998       NaN  536.690002  46.099998\n",
       "2015-06-22  127.610001       NaN  538.190002  46.230000\n",
       "2015-06-23  127.029999       NaN  540.479980  45.910000\n",
       "2015-06-24  128.110001       NaN  537.840027  45.639999\n",
       "2015-06-25  127.500000       NaN  535.229980  45.650002\n",
       "2015-06-26  126.750000       NaN  531.690002  45.259998\n",
       "2015-06-29  124.529999       NaN  521.520020  44.369999\n",
       "2015-06-30  125.430000       NaN  520.510010  44.150002\n",
       "2015-07-01  126.599998       NaN  521.840027  44.450001\n",
       "2015-07-02  126.440002       NaN  523.400024  44.400002\n",
       "2015-07-06  126.000000       NaN  522.859985  44.389999\n",
       "2015-07-07  125.690002       NaN  525.020020  44.299999\n",
       "2015-07-08  122.570000       NaN  516.830017  44.240002\n",
       "2015-07-09  120.070000       NaN  520.679993  44.520000\n",
       "2015-07-10  123.279999       NaN  530.130005  44.610001\n",
       "2015-07-13  125.660004       NaN  546.549988  45.540001\n",
       "2015-07-14  125.610001       NaN  561.099976  45.619999\n",
       "2015-07-15  126.820000       NaN  560.219971  45.759998\n",
       "2015-07-16  128.509995       NaN  579.849976  46.660000\n",
       "2015-07-17  129.619995       NaN  672.929993  46.619999\n",
       "2015-07-20  132.070007       NaN  663.020020  46.919998\n",
       "2015-07-21  130.750000       NaN  662.299988  47.279999\n",
       "2015-07-22  125.220001       NaN  662.099976  45.540001\n",
       "2015-07-23  125.160004       NaN  644.280029  46.110001\n",
       "2015-07-24  124.500000       NaN  623.559998  45.939999\n",
       "2015-07-27  122.769997       NaN  627.260010  45.349998\n",
       "2015-07-28  123.379997       NaN  628.000000  45.340000\n",
       "\n",
       "[1421 rows x 4 columns]"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata['Adj Close']   #야후에서 받아오는 주식 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>569.159996</td>\n",
       "      <td>572.649956</td>\n",
       "      <td>560.520012</td>\n",
       "      <td>560.990036</td>\n",
       "      <td>130246900</td>\n",
       "      <td>75.279486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.150000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>12.045000</td>\n",
       "      <td>12.070000</td>\n",
       "      <td>19397600</td>\n",
       "      <td>11.675920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>28.760000</td>\n",
       "      <td>28.959999</td>\n",
       "      <td>28.440001</td>\n",
       "      <td>28.450001</td>\n",
       "      <td>56634300</td>\n",
       "      <td>26.126961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open        High         Low       Close     Volume  Adj Close\n",
       "AAPL  569.159996  572.649956  560.520012  560.990036  130246900  75.279486\n",
       "DELL   12.150000   12.300000   12.045000   12.070000   19397600  11.675920\n",
       "GOOG         NaN         NaN         NaN         NaN        NaN        NaN\n",
       "MSFT   28.760000   28.959999   28.440001   28.450001   56634300  26.126961"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata.ix[:, '6/1/2012', :]   #날짜를 지정하여 받아오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>DELL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-05-22</th>\n",
       "      <td>74.740031</td>\n",
       "      <td>14.58765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.329995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-23</th>\n",
       "      <td>76.563680</td>\n",
       "      <td>12.08221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.733070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-24</th>\n",
       "      <td>75.860527</td>\n",
       "      <td>12.04351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.696335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-25</th>\n",
       "      <td>75.453926</td>\n",
       "      <td>12.05319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.687151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.05319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-29</th>\n",
       "      <td>76.793152</td>\n",
       "      <td>12.24666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.146325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-30</th>\n",
       "      <td>77.719062</td>\n",
       "      <td>12.14992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.944289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-31</th>\n",
       "      <td>77.525831</td>\n",
       "      <td>11.92743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.806537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-01</th>\n",
       "      <td>75.279486</td>\n",
       "      <td>11.67592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.126961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-04</th>\n",
       "      <td>75.722313</td>\n",
       "      <td>11.60821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.218794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-05</th>\n",
       "      <td>75.526394</td>\n",
       "      <td>11.76298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.182061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-06</th>\n",
       "      <td>76.684450</td>\n",
       "      <td>11.81619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.953473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-07</th>\n",
       "      <td>76.719351</td>\n",
       "      <td>11.73396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.843270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-08</th>\n",
       "      <td>77.873386</td>\n",
       "      <td>11.72429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.228976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-11</th>\n",
       "      <td>76.645536</td>\n",
       "      <td>11.47278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.540216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-12</th>\n",
       "      <td>77.315149</td>\n",
       "      <td>11.57919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.898372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-13</th>\n",
       "      <td>76.778382</td>\n",
       "      <td>11.87423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.751435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-14</th>\n",
       "      <td>76.693845</td>\n",
       "      <td>11.93711</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.944289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-15</th>\n",
       "      <td>77.042741</td>\n",
       "      <td>11.89841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.568765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-18</th>\n",
       "      <td>78.606065</td>\n",
       "      <td>12.01449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.403462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-19</th>\n",
       "      <td>78.824788</td>\n",
       "      <td>11.78233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.193241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-20</th>\n",
       "      <td>78.600690</td>\n",
       "      <td>11.89841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.404460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-21</th>\n",
       "      <td>77.517783</td>\n",
       "      <td>11.60821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.678965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-22</th>\n",
       "      <td>78.112240</td>\n",
       "      <td>11.80168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.193241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-25</th>\n",
       "      <td>76.591858</td>\n",
       "      <td>11.55500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.431013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-26</th>\n",
       "      <td>76.760939</td>\n",
       "      <td>11.53565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.568765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-27</th>\n",
       "      <td>77.092391</td>\n",
       "      <td>11.92743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.706517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-28</th>\n",
       "      <td>76.361060</td>\n",
       "      <td>11.55984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.467746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-29</th>\n",
       "      <td>78.367204</td>\n",
       "      <td>12.10155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.092222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-02</th>\n",
       "      <td>79.510504</td>\n",
       "      <td>11.98064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.064671</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>2015-06-16</th>\n",
       "      <td>127.599998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>528.150024</td>\n",
       "      <td>45.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>127.300003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>529.260010</td>\n",
       "      <td>45.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>127.879997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>536.729980</td>\n",
       "      <td>46.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>126.599998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>536.690002</td>\n",
       "      <td>46.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-22</th>\n",
       "      <td>127.610001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>538.190002</td>\n",
       "      <td>46.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-23</th>\n",
       "      <td>127.029999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>540.479980</td>\n",
       "      <td>45.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-24</th>\n",
       "      <td>128.110001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>537.840027</td>\n",
       "      <td>45.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-25</th>\n",
       "      <td>127.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>535.229980</td>\n",
       "      <td>45.650002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-26</th>\n",
       "      <td>126.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>531.690002</td>\n",
       "      <td>45.259998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-29</th>\n",
       "      <td>124.529999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>521.520020</td>\n",
       "      <td>44.369999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>125.430000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520.510010</td>\n",
       "      <td>44.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>126.599998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>521.840027</td>\n",
       "      <td>44.450001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>126.440002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>523.400024</td>\n",
       "      <td>44.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>522.859985</td>\n",
       "      <td>44.389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-07</th>\n",
       "      <td>125.690002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>525.020020</td>\n",
       "      <td>44.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>122.570000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>516.830017</td>\n",
       "      <td>44.240002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>120.070000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>520.679993</td>\n",
       "      <td>44.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>123.279999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>530.130005</td>\n",
       "      <td>44.610001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>125.660004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>546.549988</td>\n",
       "      <td>45.540001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>125.610001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>561.099976</td>\n",
       "      <td>45.619999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>126.820000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>560.219971</td>\n",
       "      <td>45.759998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>128.509995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>579.849976</td>\n",
       "      <td>46.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>129.619995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>672.929993</td>\n",
       "      <td>46.619999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>132.070007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>663.020020</td>\n",
       "      <td>46.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>130.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>662.299988</td>\n",
       "      <td>47.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>125.220001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>662.099976</td>\n",
       "      <td>45.540001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>125.160004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>644.280029</td>\n",
       "      <td>46.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>124.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623.559998</td>\n",
       "      <td>45.939999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>122.769997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>627.260010</td>\n",
       "      <td>45.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>123.379997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>45.340000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>814 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL      DELL        GOOG       MSFT\n",
       "Date                                                   \n",
       "2012-05-22   74.740031  14.58765         NaN  27.329995\n",
       "2012-05-23   76.563680  12.08221         NaN  26.733070\n",
       "2012-05-24   75.860527  12.04351         NaN  26.696335\n",
       "2012-05-25   75.453926  12.05319         NaN  26.687151\n",
       "2012-05-28         NaN  12.05319         NaN        NaN\n",
       "2012-05-29   76.793152  12.24666         NaN  27.146325\n",
       "2012-05-30   77.719062  12.14992         NaN  26.944289\n",
       "2012-05-31   77.525831  11.92743         NaN  26.806537\n",
       "2012-06-01   75.279486  11.67592         NaN  26.126961\n",
       "2012-06-04   75.722313  11.60821         NaN  26.218794\n",
       "2012-06-05   75.526394  11.76298         NaN  26.182061\n",
       "2012-06-06   76.684450  11.81619         NaN  26.953473\n",
       "2012-06-07   76.719351  11.73396         NaN  26.843270\n",
       "2012-06-08   77.873386  11.72429         NaN  27.228976\n",
       "2012-06-11   76.645536  11.47278         NaN  26.540216\n",
       "2012-06-12   77.315149  11.57919         NaN  26.898372\n",
       "2012-06-13   76.778382  11.87423         NaN  26.751435\n",
       "2012-06-14   76.693845  11.93711         NaN  26.944289\n",
       "2012-06-15   77.042741  11.89841         NaN  27.568765\n",
       "2012-06-18   78.606065  12.01449         NaN  27.403462\n",
       "2012-06-19   78.824788  11.78233         NaN  28.193241\n",
       "2012-06-20   78.600690  11.89841         NaN  28.404460\n",
       "2012-06-21   77.517783  11.60821         NaN  27.678965\n",
       "2012-06-22   78.112240  11.80168         NaN  28.193241\n",
       "2012-06-25   76.591858  11.55500         NaN  27.431013\n",
       "2012-06-26   76.760939  11.53565         NaN  27.568765\n",
       "2012-06-27   77.092391  11.92743         NaN  27.706517\n",
       "2012-06-28   76.361060  11.55984         NaN  27.467746\n",
       "2012-06-29   78.367204  12.10155         NaN  28.092222\n",
       "2012-07-02   79.510504  11.98064         NaN  28.064671\n",
       "...                ...       ...         ...        ...\n",
       "2015-06-16  127.599998       NaN  528.150024  45.830002\n",
       "2015-06-17  127.300003       NaN  529.260010  45.970001\n",
       "2015-06-18  127.879997       NaN  536.729980  46.720001\n",
       "2015-06-19  126.599998       NaN  536.690002  46.099998\n",
       "2015-06-22  127.610001       NaN  538.190002  46.230000\n",
       "2015-06-23  127.029999       NaN  540.479980  45.910000\n",
       "2015-06-24  128.110001       NaN  537.840027  45.639999\n",
       "2015-06-25  127.500000       NaN  535.229980  45.650002\n",
       "2015-06-26  126.750000       NaN  531.690002  45.259998\n",
       "2015-06-29  124.529999       NaN  521.520020  44.369999\n",
       "2015-06-30  125.430000       NaN  520.510010  44.150002\n",
       "2015-07-01  126.599998       NaN  521.840027  44.450001\n",
       "2015-07-02  126.440002       NaN  523.400024  44.400002\n",
       "2015-07-06  126.000000       NaN  522.859985  44.389999\n",
       "2015-07-07  125.690002       NaN  525.020020  44.299999\n",
       "2015-07-08  122.570000       NaN  516.830017  44.240002\n",
       "2015-07-09  120.070000       NaN  520.679993  44.520000\n",
       "2015-07-10  123.279999       NaN  530.130005  44.610001\n",
       "2015-07-13  125.660004       NaN  546.549988  45.540001\n",
       "2015-07-14  125.610001       NaN  561.099976  45.619999\n",
       "2015-07-15  126.820000       NaN  560.219971  45.759998\n",
       "2015-07-16  128.509995       NaN  579.849976  46.660000\n",
       "2015-07-17  129.619995       NaN  672.929993  46.619999\n",
       "2015-07-20  132.070007       NaN  663.020020  46.919998\n",
       "2015-07-21  130.750000       NaN  662.299988  47.279999\n",
       "2015-07-22  125.220001       NaN  662.099976  45.540001\n",
       "2015-07-23  125.160004       NaN  644.280029  46.110001\n",
       "2015-07-24  124.500000       NaN  623.559998  45.939999\n",
       "2015-07-27  122.769997       NaN  627.260010  45.349998\n",
       "2015-07-28  123.379997       NaN  628.000000  45.340000\n",
       "\n",
       "[814 rows x 4 columns]"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdata.ix['Adj Close', '5/22/2012':, :]   #2012년 5월 22일 부터 자료들 다 받아오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>minor</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 rowspan=\"3\" valign=\"top\">2012-05-30</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>569.200050</td>\n",
       "      <td>579.989990</td>\n",
       "      <td>566.559990</td>\n",
       "      <td>579.169998</td>\n",
       "      <td>132357400</td>\n",
       "      <td>77.719062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.590000</td>\n",
       "      <td>12.700000</td>\n",
       "      <td>12.460000</td>\n",
       "      <td>12.560000</td>\n",
       "      <td>19787800</td>\n",
       "      <td>12.149920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>29.350000</td>\n",
       "      <td>29.480000</td>\n",
       "      <td>29.120001</td>\n",
       "      <td>29.340000</td>\n",
       "      <td>41585500</td>\n",
       "      <td>26.944289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-05-31</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>580.740021</td>\n",
       "      <td>581.499985</td>\n",
       "      <td>571.459969</td>\n",
       "      <td>577.730019</td>\n",
       "      <td>122918600</td>\n",
       "      <td>77.525831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.530000</td>\n",
       "      <td>12.540000</td>\n",
       "      <td>12.330000</td>\n",
       "      <td>12.330000</td>\n",
       "      <td>19955600</td>\n",
       "      <td>11.927430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>29.299999</td>\n",
       "      <td>29.420000</td>\n",
       "      <td>28.940001</td>\n",
       "      <td>29.190001</td>\n",
       "      <td>39134000</td>\n",
       "      <td>26.806537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-06-01</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>569.159996</td>\n",
       "      <td>572.649956</td>\n",
       "      <td>560.520012</td>\n",
       "      <td>560.990036</td>\n",
       "      <td>130246900</td>\n",
       "      <td>75.279486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.150000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>12.045000</td>\n",
       "      <td>12.070000</td>\n",
       "      <td>19397600</td>\n",
       "      <td>11.675920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>28.760000</td>\n",
       "      <td>28.959999</td>\n",
       "      <td>28.440001</td>\n",
       "      <td>28.450001</td>\n",
       "      <td>56634300</td>\n",
       "      <td>26.126961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-06-04</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>561.500008</td>\n",
       "      <td>567.499985</td>\n",
       "      <td>548.499977</td>\n",
       "      <td>564.290031</td>\n",
       "      <td>139248900</td>\n",
       "      <td>75.722313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.110000</td>\n",
       "      <td>12.112500</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>17015700</td>\n",
       "      <td>11.608210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>28.620001</td>\n",
       "      <td>28.780001</td>\n",
       "      <td>28.320000</td>\n",
       "      <td>28.549999</td>\n",
       "      <td>47926300</td>\n",
       "      <td>26.218794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-06-05</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>561.269989</td>\n",
       "      <td>566.470055</td>\n",
       "      <td>558.330002</td>\n",
       "      <td>562.830025</td>\n",
       "      <td>97053600</td>\n",
       "      <td>75.526394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>11.950000</td>\n",
       "      <td>12.240000</td>\n",
       "      <td>11.950000</td>\n",
       "      <td>12.160000</td>\n",
       "      <td>15620900</td>\n",
       "      <td>11.762980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>28.510000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>28.389999</td>\n",
       "      <td>28.510000</td>\n",
       "      <td>45715400</td>\n",
       "      <td>26.182061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-06-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>567.770004</td>\n",
       "      <td>573.849983</td>\n",
       "      <td>565.499992</td>\n",
       "      <td>571.459969</td>\n",
       "      <td>100363900</td>\n",
       "      <td>76.684450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.210000</td>\n",
       "      <td>12.280000</td>\n",
       "      <td>12.090000</td>\n",
       "      <td>12.215000</td>\n",
       "      <td>20779900</td>\n",
       "      <td>11.816190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>28.879999</td>\n",
       "      <td>29.370001</td>\n",
       "      <td>28.809999</td>\n",
       "      <td>29.350000</td>\n",
       "      <td>46860500</td>\n",
       "      <td>26.953473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-06-07</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>577.290009</td>\n",
       "      <td>577.320023</td>\n",
       "      <td>570.500000</td>\n",
       "      <td>571.720055</td>\n",
       "      <td>94941700</td>\n",
       "      <td>76.719351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.320000</td>\n",
       "      <td>12.410000</td>\n",
       "      <td>12.120000</td>\n",
       "      <td>12.130000</td>\n",
       "      <td>20074000</td>\n",
       "      <td>11.733960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>29.639999</td>\n",
       "      <td>29.700001</td>\n",
       "      <td>29.170000</td>\n",
       "      <td>29.230000</td>\n",
       "      <td>37792800</td>\n",
       "      <td>26.843270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-06-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>571.599998</td>\n",
       "      <td>580.580017</td>\n",
       "      <td>568.999992</td>\n",
       "      <td>580.320038</td>\n",
       "      <td>86879100</td>\n",
       "      <td>77.873386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.130000</td>\n",
       "      <td>12.225000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>12.120000</td>\n",
       "      <td>18155600</td>\n",
       "      <td>11.724290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>29.209999</td>\n",
       "      <td>29.680000</td>\n",
       "      <td>29.049999</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>42551100</td>\n",
       "      <td>27.228976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-06-11</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>587.719994</td>\n",
       "      <td>588.499985</td>\n",
       "      <td>570.629990</td>\n",
       "      <td>571.169975</td>\n",
       "      <td>147816200</td>\n",
       "      <td>76.645536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>12.150000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>11.820000</td>\n",
       "      <td>11.860000</td>\n",
       "      <td>16931900</td>\n",
       "      <td>11.472780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>29.730000</td>\n",
       "      <td>29.809999</td>\n",
       "      <td>28.820000</td>\n",
       "      <td>28.900000</td>\n",
       "      <td>46361900</td>\n",
       "      <td>26.540216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012-06-12</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>574.459984</td>\n",
       "      <td>576.620033</td>\n",
       "      <td>566.699966</td>\n",
       "      <td>576.159996</td>\n",
       "      <td>108845100</td>\n",
       "      <td>77.315149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELL</th>\n",
       "      <td>11.910000</td>\n",
       "      <td>12.010000</td>\n",
       "      <td>11.680000</td>\n",
       "      <td>11.970000</td>\n",
       "      <td>17608100</td>\n",
       "      <td>11.579190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>29.100000</td>\n",
       "      <td>29.299999</td>\n",
       "      <td>28.840000</td>\n",
       "      <td>29.290001</td>\n",
       "      <td>35337900</td>\n",
       "      <td>26.898372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-15</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>125.720001</td>\n",
       "      <td>127.150002</td>\n",
       "      <td>125.580002</td>\n",
       "      <td>126.820000</td>\n",
       "      <td>33463100</td>\n",
       "      <td>126.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>560.130005</td>\n",
       "      <td>566.502991</td>\n",
       "      <td>556.789978</td>\n",
       "      <td>560.219971</td>\n",
       "      <td>1776600</td>\n",
       "      <td>560.219971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>45.680000</td>\n",
       "      <td>45.889999</td>\n",
       "      <td>45.430000</td>\n",
       "      <td>45.759998</td>\n",
       "      <td>26482000</td>\n",
       "      <td>45.759998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-16</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>127.739998</td>\n",
       "      <td>128.570007</td>\n",
       "      <td>127.349998</td>\n",
       "      <td>128.509995</td>\n",
       "      <td>35866800</td>\n",
       "      <td>128.509995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>565.119995</td>\n",
       "      <td>580.679993</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>579.849976</td>\n",
       "      <td>4053900</td>\n",
       "      <td>579.849976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>46.009998</td>\n",
       "      <td>46.689999</td>\n",
       "      <td>45.970001</td>\n",
       "      <td>46.660000</td>\n",
       "      <td>25894400</td>\n",
       "      <td>46.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-17</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>129.080002</td>\n",
       "      <td>129.619995</td>\n",
       "      <td>128.309998</td>\n",
       "      <td>129.619995</td>\n",
       "      <td>45693300</td>\n",
       "      <td>129.619995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>649.000000</td>\n",
       "      <td>674.468018</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>672.929993</td>\n",
       "      <td>11134500</td>\n",
       "      <td>672.929993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>46.549999</td>\n",
       "      <td>46.779999</td>\n",
       "      <td>46.259998</td>\n",
       "      <td>46.619999</td>\n",
       "      <td>29262900</td>\n",
       "      <td>46.619999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-20</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>130.970001</td>\n",
       "      <td>132.970001</td>\n",
       "      <td>130.699997</td>\n",
       "      <td>132.070007</td>\n",
       "      <td>54159800</td>\n",
       "      <td>132.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>659.239990</td>\n",
       "      <td>668.880005</td>\n",
       "      <td>653.010010</td>\n",
       "      <td>663.020020</td>\n",
       "      <td>5848800</td>\n",
       "      <td>663.020020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>46.650002</td>\n",
       "      <td>47.130001</td>\n",
       "      <td>46.439999</td>\n",
       "      <td>46.919998</td>\n",
       "      <td>30271200</td>\n",
       "      <td>46.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-21</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>132.850006</td>\n",
       "      <td>132.919998</td>\n",
       "      <td>130.320007</td>\n",
       "      <td>130.750000</td>\n",
       "      <td>58898800</td>\n",
       "      <td>130.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>655.210022</td>\n",
       "      <td>673.000000</td>\n",
       "      <td>654.299988</td>\n",
       "      <td>662.299988</td>\n",
       "      <td>3348400</td>\n",
       "      <td>662.299988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>46.779999</td>\n",
       "      <td>47.330002</td>\n",
       "      <td>46.480000</td>\n",
       "      <td>47.279999</td>\n",
       "      <td>41205000</td>\n",
       "      <td>47.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-22</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>121.989998</td>\n",
       "      <td>125.500000</td>\n",
       "      <td>121.989998</td>\n",
       "      <td>125.220001</td>\n",
       "      <td>114692800</td>\n",
       "      <td>125.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>660.890015</td>\n",
       "      <td>678.640015</td>\n",
       "      <td>659.000000</td>\n",
       "      <td>662.099976</td>\n",
       "      <td>3689600</td>\n",
       "      <td>662.099976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>45.439999</td>\n",
       "      <td>46.930000</td>\n",
       "      <td>45.200001</td>\n",
       "      <td>45.540001</td>\n",
       "      <td>58724600</td>\n",
       "      <td>45.540001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-23</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>126.199997</td>\n",
       "      <td>127.089996</td>\n",
       "      <td>125.059998</td>\n",
       "      <td>125.160004</td>\n",
       "      <td>50688700</td>\n",
       "      <td>125.160004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>661.270020</td>\n",
       "      <td>663.630005</td>\n",
       "      <td>641.000000</td>\n",
       "      <td>644.280029</td>\n",
       "      <td>3001800</td>\n",
       "      <td>644.280029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>45.270000</td>\n",
       "      <td>46.230000</td>\n",
       "      <td>45.099998</td>\n",
       "      <td>46.110001</td>\n",
       "      <td>33465600</td>\n",
       "      <td>46.110001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-24</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>125.320000</td>\n",
       "      <td>125.739998</td>\n",
       "      <td>123.900002</td>\n",
       "      <td>124.500000</td>\n",
       "      <td>41051600</td>\n",
       "      <td>124.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>647.000000</td>\n",
       "      <td>648.169983</td>\n",
       "      <td>622.520020</td>\n",
       "      <td>623.559998</td>\n",
       "      <td>3608400</td>\n",
       "      <td>623.559998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>45.910000</td>\n",
       "      <td>46.320000</td>\n",
       "      <td>45.799999</td>\n",
       "      <td>45.939999</td>\n",
       "      <td>32077000</td>\n",
       "      <td>45.939999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-27</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>123.089996</td>\n",
       "      <td>123.610001</td>\n",
       "      <td>122.120003</td>\n",
       "      <td>122.769997</td>\n",
       "      <td>44274800</td>\n",
       "      <td>122.769997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>621.000000</td>\n",
       "      <td>634.299988</td>\n",
       "      <td>620.500000</td>\n",
       "      <td>627.260010</td>\n",
       "      <td>2668700</td>\n",
       "      <td>627.260010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>45.939999</td>\n",
       "      <td>46.009998</td>\n",
       "      <td>45.250000</td>\n",
       "      <td>45.349998</td>\n",
       "      <td>39189900</td>\n",
       "      <td>45.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015-07-28</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>123.379997</td>\n",
       "      <td>123.910004</td>\n",
       "      <td>122.550003</td>\n",
       "      <td>123.379997</td>\n",
       "      <td>33448900</td>\n",
       "      <td>123.379997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>632.830017</td>\n",
       "      <td>632.830017</td>\n",
       "      <td>623.309998</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>1704300</td>\n",
       "      <td>628.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>45.580002</td>\n",
       "      <td>45.639999</td>\n",
       "      <td>44.790001</td>\n",
       "      <td>45.340000</td>\n",
       "      <td>34177100</td>\n",
       "      <td>45.340000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2297 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open        High         Low       Close     Volume  \\\n",
       "Date       minor                                                              \n",
       "2012-05-30 AAPL   569.200050  579.989990  566.559990  579.169998  132357400   \n",
       "           DELL    12.590000   12.700000   12.460000   12.560000   19787800   \n",
       "           MSFT    29.350000   29.480000   29.120001   29.340000   41585500   \n",
       "2012-05-31 AAPL   580.740021  581.499985  571.459969  577.730019  122918600   \n",
       "           DELL    12.530000   12.540000   12.330000   12.330000   19955600   \n",
       "           MSFT    29.299999   29.420000   28.940001   29.190001   39134000   \n",
       "2012-06-01 AAPL   569.159996  572.649956  560.520012  560.990036  130246900   \n",
       "           DELL    12.150000   12.300000   12.045000   12.070000   19397600   \n",
       "           MSFT    28.760000   28.959999   28.440001   28.450001   56634300   \n",
       "2012-06-04 AAPL   561.500008  567.499985  548.499977  564.290031  139248900   \n",
       "           DELL    12.110000   12.112500   11.800000   12.000000   17015700   \n",
       "           MSFT    28.620001   28.780001   28.320000   28.549999   47926300   \n",
       "2012-06-05 AAPL   561.269989  566.470055  558.330002  562.830025   97053600   \n",
       "           DELL    11.950000   12.240000   11.950000   12.160000   15620900   \n",
       "           MSFT    28.510000   28.750000   28.389999   28.510000   45715400   \n",
       "2012-06-06 AAPL   567.770004  573.849983  565.499992  571.459969  100363900   \n",
       "           DELL    12.210000   12.280000   12.090000   12.215000   20779900   \n",
       "           MSFT    28.879999   29.370001   28.809999   29.350000   46860500   \n",
       "2012-06-07 AAPL   577.290009  577.320023  570.500000  571.720055   94941700   \n",
       "           DELL    12.320000   12.410000   12.120000   12.130000   20074000   \n",
       "           MSFT    29.639999   29.700001   29.170000   29.230000   37792800   \n",
       "2012-06-08 AAPL   571.599998  580.580017  568.999992  580.320038   86879100   \n",
       "           DELL    12.130000   12.225000   12.020000   12.120000   18155600   \n",
       "           MSFT    29.209999   29.680000   29.049999   29.650000   42551100   \n",
       "2012-06-11 AAPL   587.719994  588.499985  570.629990  571.169975  147816200   \n",
       "           DELL    12.150000   12.200000   11.820000   11.860000   16931900   \n",
       "           MSFT    29.730000   29.809999   28.820000   28.900000   46361900   \n",
       "2012-06-12 AAPL   574.459984  576.620033  566.699966  576.159996  108845100   \n",
       "           DELL    11.910000   12.010000   11.680000   11.970000   17608100   \n",
       "           MSFT    29.100000   29.299999   28.840000   29.290001   35337900   \n",
       "...                      ...         ...         ...         ...        ...   \n",
       "2015-07-15 AAPL   125.720001  127.150002  125.580002  126.820000   33463100   \n",
       "           GOOG   560.130005  566.502991  556.789978  560.219971    1776600   \n",
       "           MSFT    45.680000   45.889999   45.430000   45.759998   26482000   \n",
       "2015-07-16 AAPL   127.739998  128.570007  127.349998  128.509995   35866800   \n",
       "           GOOG   565.119995  580.679993  565.000000  579.849976    4053900   \n",
       "           MSFT    46.009998   46.689999   45.970001   46.660000   25894400   \n",
       "2015-07-17 AAPL   129.080002  129.619995  128.309998  129.619995   45693300   \n",
       "           GOOG   649.000000  674.468018  645.000000  672.929993   11134500   \n",
       "           MSFT    46.549999   46.779999   46.259998   46.619999   29262900   \n",
       "2015-07-20 AAPL   130.970001  132.970001  130.699997  132.070007   54159800   \n",
       "           GOOG   659.239990  668.880005  653.010010  663.020020    5848800   \n",
       "           MSFT    46.650002   47.130001   46.439999   46.919998   30271200   \n",
       "2015-07-21 AAPL   132.850006  132.919998  130.320007  130.750000   58898800   \n",
       "           GOOG   655.210022  673.000000  654.299988  662.299988    3348400   \n",
       "           MSFT    46.779999   47.330002   46.480000   47.279999   41205000   \n",
       "2015-07-22 AAPL   121.989998  125.500000  121.989998  125.220001  114692800   \n",
       "           GOOG   660.890015  678.640015  659.000000  662.099976    3689600   \n",
       "           MSFT    45.439999   46.930000   45.200001   45.540001   58724600   \n",
       "2015-07-23 AAPL   126.199997  127.089996  125.059998  125.160004   50688700   \n",
       "           GOOG   661.270020  663.630005  641.000000  644.280029    3001800   \n",
       "           MSFT    45.270000   46.230000   45.099998   46.110001   33465600   \n",
       "2015-07-24 AAPL   125.320000  125.739998  123.900002  124.500000   41051600   \n",
       "           GOOG   647.000000  648.169983  622.520020  623.559998    3608400   \n",
       "           MSFT    45.910000   46.320000   45.799999   45.939999   32077000   \n",
       "2015-07-27 AAPL   123.089996  123.610001  122.120003  122.769997   44274800   \n",
       "           GOOG   621.000000  634.299988  620.500000  627.260010    2668700   \n",
       "           MSFT    45.939999   46.009998   45.250000   45.349998   39189900   \n",
       "2015-07-28 AAPL   123.379997  123.910004  122.550003  123.379997   33448900   \n",
       "           GOOG   632.830017  632.830017  623.309998  628.000000    1704300   \n",
       "           MSFT    45.580002   45.639999   44.790001   45.340000   34177100   \n",
       "\n",
       "                   Adj Close  \n",
       "Date       minor              \n",
       "2012-05-30 AAPL    77.719062  \n",
       "           DELL    12.149920  \n",
       "           MSFT    26.944289  \n",
       "2012-05-31 AAPL    77.525831  \n",
       "           DELL    11.927430  \n",
       "           MSFT    26.806537  \n",
       "2012-06-01 AAPL    75.279486  \n",
       "           DELL    11.675920  \n",
       "           MSFT    26.126961  \n",
       "2012-06-04 AAPL    75.722313  \n",
       "           DELL    11.608210  \n",
       "           MSFT    26.218794  \n",
       "2012-06-05 AAPL    75.526394  \n",
       "           DELL    11.762980  \n",
       "           MSFT    26.182061  \n",
       "2012-06-06 AAPL    76.684450  \n",
       "           DELL    11.816190  \n",
       "           MSFT    26.953473  \n",
       "2012-06-07 AAPL    76.719351  \n",
       "           DELL    11.733960  \n",
       "           MSFT    26.843270  \n",
       "2012-06-08 AAPL    77.873386  \n",
       "           DELL    11.724290  \n",
       "           MSFT    27.228976  \n",
       "2012-06-11 AAPL    76.645536  \n",
       "           DELL    11.472780  \n",
       "           MSFT    26.540216  \n",
       "2012-06-12 AAPL    77.315149  \n",
       "           DELL    11.579190  \n",
       "           MSFT    26.898372  \n",
       "...                      ...  \n",
       "2015-07-15 AAPL   126.820000  \n",
       "           GOOG   560.219971  \n",
       "           MSFT    45.759998  \n",
       "2015-07-16 AAPL   128.509995  \n",
       "           GOOG   579.849976  \n",
       "           MSFT    46.660000  \n",
       "2015-07-17 AAPL   129.619995  \n",
       "           GOOG   672.929993  \n",
       "           MSFT    46.619999  \n",
       "2015-07-20 AAPL   132.070007  \n",
       "           GOOG   663.020020  \n",
       "           MSFT    46.919998  \n",
       "2015-07-21 AAPL   130.750000  \n",
       "           GOOG   662.299988  \n",
       "           MSFT    47.279999  \n",
       "2015-07-22 AAPL   125.220001  \n",
       "           GOOG   662.099976  \n",
       "           MSFT    45.540001  \n",
       "2015-07-23 AAPL   125.160004  \n",
       "           GOOG   644.280029  \n",
       "           MSFT    46.110001  \n",
       "2015-07-24 AAPL   124.500000  \n",
       "           GOOG   623.559998  \n",
       "           MSFT    45.939999  \n",
       "2015-07-27 AAPL   122.769997  \n",
       "           GOOG   627.260010  \n",
       "           MSFT    45.349998  \n",
       "2015-07-28 AAPL   123.379997  \n",
       "           GOOG   628.000000  \n",
       "           MSFT    45.340000  \n",
       "\n",
       "[2297 rows x 6 columns]"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked = pdata.ix[:, '5/30/2012':, :].to_frame()   #DataFrame에 쌓아서 출력 하도록 한다.\n",
    "stacked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 6 (items) x 808 (major_axis) x 4 (minor_axis)\n",
       "Items axis: Open to Adj Close\n",
       "Major_axis axis: 2012-05-30 00:00:00 to 2015-07-28 00:00:00\n",
       "Minor_axis axis: AAPL to MSFT"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.to_panel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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