books/python_data_analysis/ch05_Pandas.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "5. Pandas \uc2dc\uc791\ud558\uae30"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src='files/images/panda.jpeg' width=\"300px\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uac00\uc7a5 \uc790\uc8fc \uc0b4\ud3b4\ubcfc \ub77c\uc774\ube0c\ub7ec\ub9ac\n",
      "- \uace0\uc218\uc900\uc758 \uc790\ub8cc\uad6c\uc870\uc640 \ud30c\uc774\uc36c\uc744 \ud1b5\ud55c \ube60\ub974\uace0 \uc26c\uc6b4 \ub370\uc774\ud130 \ubd84\uc11d \ub3c4\uad6c \ud3ec\ud568\n",
      "- NumPy \uae30\ubc18\uc5d0\uc11c \uac1c\ubc1c\ub418\uc5b4 NumPy\ub97c \uc0ac\uc6a9\ud558\ub294 \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c \uc27d\uac8c \uc0ac\uc6a9\n",
      "\n",
      "## Pandas \uac1c\ubc1c \ub3d9\uae30\n",
      "\n",
      "- \uc790\ub3d9\uc801\uc73c\ub85c \ud639\uc740 \uba85\uc2dc\uc801\uc73c\ub85c \ucd95\uc758 \uc774\ub984\uc5d0 \ub530\ub77c \ub370\uc774\ud130\ub97c \uc815\ub82c\ud560 \uc218 \uc788\ub294 \uc790\ub8cc \uad6c\uc870. \uc798\ubabb \uc815\ub82c\ub41c \ub370\uc774\ud130\uc5d0 \uc758\ud55c \uc77c\ubc18\uc801\uc778 \uc624\ub958\ub97c \uc5d0\ubc29\ud558\uace0 \ub2e4\uc591\ud55c \uc18c\uc2a4\uc5d0\uc11c \uac00\uc838\uc628 \ub2e4\uc591\ud55c \ubc29\uc2dd\uc73c\ub85c \uc0c9\uc778\ub418\uc5b4 \uc788\ub294 \ub370\uc774\ud130\ub97c \ub2e4\ub8f0 \uc218 \uc788\ub294 \uae30\ub2a5\n",
      "- \ud1b5\ud569\ub41c \uc2dc\uacc4\uc5f4 \uae30\ub2a5\n",
      "- \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc640 \ube44\uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ud568\uaed8 \ub2e4\ub8f0 \uc218 \uc788\ub294 \ud1b5\ud569 \uc790\ub8cc \uad6c\uc870\n",
      "- \uc0b0\uc220\uc5f0\uc0b0\uacfc \ud55c \ucd95\uc758 \ubaa8\ub4e0 \uac12\uc744 \ub354\ud558\ub294 \ub4f1\uc758 \ub370\uc774\ud130 \ucd95\uc57d\uc5f0\uc0b0\uc740 \ucd95\uc758 \uc774\ub984 \uac19\uc740 \uba54\ud0c0\ub370\uc774\ud130\ub85c \uc804\ub2ec\ub420 \uc218 \uc788\uc5b4\uc57c \ud568\n",
      "- \ub204\ub77d\ub41c \ub370\uc774\ud130\ub97c \uc720\uc5f0\ud558\uac8c \ucc98\ub9ac\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\n",
      "- SQL \uac19\uc740 \uc77c\ubc18 \ub370\uc774\ud130\ubca0\uc774\uc2a4\ucc98\ub7fc \ub370\uc774\ud130\ub97c \ud569\uce58\uace0 \uad00\uacc4\uc5f0\uc0b0\uc744 \uc218\ud589\ud558\ub294 \uae30\ub2a5\n",
      "- \uac04\ud3b8\ud568, \ub2e8\uc228\ud568\uc5d0 \ub300\ud55c \uae30\ubcf8 \ubc29\uce68\n",
      "\n",
      "###Pandas import \ucee8\ubca4\uc158\n",
      "\n",
      "from pandas import Series, DataFrame\n",
      "\n",
      "import pandas as pd\n",
      "\n",
      "- pd. \uc73c\ub85c \uc2dc\uc791\ud558\ub294\uac8c \uc88b\uc9c0\ub9cc Series\uc640 DataFrame\uc740 \ub9ce\uc774 \uc0ac\uc6a9\ud558\uae30 \ub54c\ubb38\uc5d0 local namespace\ub85c import \ud558\ub294 \uac83\uc774 \ud6e8\uc52c \ud3b8\ud568"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5.1 pandas \uc790\ub8cc \uad6c\uc870 \uc18c\uac1c"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.1.1. Series"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Series\ub294 \uc77c\ub828\uc758 \uac1d\uccb4\ub97c \ub2f4\uc744 \uc218 \uc788\ub294 1\ucc28\uc6d0 \ubc30\uc5f4 \uac19\uc740 \uc790\ub8cc \uad6c\uc870(\uc5b4\ub5a4 NumPy \uc790\ub8cc\ud615\uc774\ub77c\ub3c4 \ub2f4\uc744 \uc218 \uc788\ub2e4)\n",
      "- \uc0c9\uc778\uc774\ub77c\uace0 \ud558\ub294 \ubc30\uc5f4\uc758 \ub370\uc774\ud130\uc5d0 \uc5f0\uad00\ub41c \uc774\ub984\uc744 \uac00\uc9c0\uace0 \uc788\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import Series, DataFrame\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series([4, 7, -5, 3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Error! \ubc30\uc5f4\uc774\ub098 \uc0ac\uc804\ud615\uc73c\ub85c \ub118\uaca8\uc57c \ub428\n",
      "obj2 = Series(4,7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "Index(...) must be called with a collection of some kind, 7 was passed",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-4-30f352d974a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Error! \ubc30\uc5f4\uc774\ub098 \uc0ac\uc804\ud615\uc73c\ub85c \ub118\uaca8\uc57c \ub428\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mobj2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m                 \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc\u001b[0m in \u001b[0;36m_ensure_index\u001b[0;34m(index_like, copy)\u001b[0m\n\u001b[1;32m   2995\u001b[0m             \u001b[0mindex_like\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_like\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2996\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2997\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_like\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2998\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2999\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, dtype, copy, name, fastpath, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m             raise TypeError('Index(...) must be called with a collection '\n\u001b[0;32m--> 131\u001b[0;31m                              'of some kind, %s was passed' % repr(data))\n\u001b[0m\u001b[1;32m    132\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m             \u001b[0;31m# other iterable of some kind\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mTypeError\u001b[0m: Index(...) must be called with a collection of some kind, 7 was passed"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "0    4\n",
        "1    7\n",
        "2   -5\n",
        "3    3\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc67c\ucabd\uc5d0 \uc0c9\uc778\n",
      "- \uc624\ub978\ucabd\uc5d0 \uc0c9\uc778\uc758 \uac12"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([ 4,  7, -5,  3])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "Int64Index([0, 1, 2, 3], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2 = Series([4, 7, -5, 3], index=['d', 'b', 'a', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "d    4\n",
        "b    7\n",
        "a   -5\n",
        "c    3\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "Index([u'd', u'b', u'a', u'c'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2['a']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "-5"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2['d'] = 6"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2[['c', 'a', 'd']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "c    3\n",
        "a   -5\n",
        "d    6\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "d    6\n",
        "b    7\n",
        "a   -5\n",
        "c    3\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc744 \uc0ac\uc6a9\ud574\uc11c \uac12\uc744 \uac78\ub7ec\ub0b4\uac70\ub098 \uc0b0\uc220 \uacf1\uc148\uc744 \uc218\ud589\ud558\uac70\ub098 \ub610\ub294 \uc218\ud559 \ud568\uc218\ub97c \uc801\uc6a9\ud558\ub294 \ub4f1 NumPy \ubc30\uc5f4\uc5f0\uc0b0\uc744 \uc218\ud589\ud574\ub3c4 \uc0c9\uc778-\uac12 \uc5f0\uacb0\uc740 \uc720\uc9c0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2[obj2 > 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "d    6\n",
        "b    7\n",
        "c    3\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2 * 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "d    12\n",
        "b    14\n",
        "a   -10\n",
        "c     6\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.exp(obj2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "d     403.428793\n",
        "b    1096.633158\n",
        "a       0.006738\n",
        "c      20.085537\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Series == \uace0\uc815 \uae38\uc774\uc758 \uc815\ub82c\ub41c \uc0ac\uc804\ud615"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'b' in obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'e' in obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sdata = {'Ohio': 35000,\n",
      "         'Texas': 71000,\n",
      "         'Oregon': 16000,\n",
      "         'Utah': 5000}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj3 = Series(sdata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "Ohio      35000\n",
        "Oregon    16000\n",
        "Texas     71000\n",
        "Utah       5000\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "states = ['California', 'Ohio', 'Oregon', 'Texas']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj4 = Series(sdata, index=states)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "California      NaN\n",
        "Ohio          35000\n",
        "Oregon        16000\n",
        "Texas         71000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####NaN(not a number)\n",
      "\n",
      "- pandas\uc5d0\uc11c\ub294 \ub204\ub77d\ub41c \uac12 \ud639\uc740 NA \uac12\uc73c\ub85c \ucde8\uae09\n",
      "- '\ub204\ub77d\ub41c'\uacfc 'NA'\ub97c \ub204\ub77d\ub41c \ub370\uc774\ud130\ub85c \uc9c0\uce6d\n",
      "- pandas\uc758 isnull\uacfc notnull \ud568\uc218\ub294 \ub204\ub77d\ub41c \ud568\uc218\ub97c \ucc3e\uc744 \ub54c \uc0ac\uc6a9\n",
      "- \uc77c\ubc18\uc801\uc778 \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4\uc5d0\uc11c\ub294 NULL \uc774\ub77c\uace0\ub3c4 \ud55c\ub2e4.\n",
      "- \uac01 \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4\ub9c8\ub2e4 \ub2e4\ub974\uc9c0\ub9cc \uac70\uc758 \ube44\uc2b7\ud55c \uac1c\ub150\uc774 \uc874\uc7ac\ud55c\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.isnull(obj4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "California     True\n",
        "Ohio          False\n",
        "Oregon        False\n",
        "Texas         False\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.notnull(obj4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "California    False\n",
        "Ohio           True\n",
        "Oregon         True\n",
        "Texas          True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Series\uc758 \uc778\uc2a4\ud134\uc2a4 \uba54\uc11c\ub4dc\n",
      "\n",
      "- 'hi'.isalpha()\uc640 \uac19\uc774 \uc778\uc2a4\ud134\uc2a4\uc5d0\uc11c \ubc14\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud3b8\ub9ac\ud568"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj4.isnull()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "California     True\n",
        "Ohio          False\n",
        "Oregon        False\n",
        "Texas         False\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj4.notnull()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "California    False\n",
        "Ohio           True\n",
        "Oregon         True\n",
        "Texas          True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Seriese\uc758 \uac00\uc7a5 \uc911\uc694\ud55c \uae30\ub2a5 - \ub2e4\ub974\uac8c \uc0c9\uc778\ub41c \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc0b0\uc220\uc5f0\uc0b0\n",
      "\n",
      "1. \ub2e4\ub974\uac8c \uc0c9\uc778\ub41c \ub370\uc774\ud130\uc5d0 \uac01\uac01 \uac12\uc774 \uc788\uc5b4\uc57c \ud558\uba70\n",
      "2. intersection\uc774 \ub418\uc9c0 \uc54a\ub294\ub2e4\uba74 NaN \ud45c\uc2dc\n",
      "3. intersecion\uc774 \ub41c\ub2e4\uba74 \ud574\ub2f9 operator \uc5f0\uc0b0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "Ohio      35000\n",
        "Oregon    16000\n",
        "Texas     71000\n",
        "Utah       5000\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "California      NaN\n",
        "Ohio          35000\n",
        "Oregon        16000\n",
        "Texas         71000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj3 + obj4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "California       NaN\n",
        "Ohio           70000\n",
        "Oregon         32000\n",
        "Texas         142000\n",
        "Utah             NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Series \uac1d\uccb4\uc640 Series\uc758 \uc0c9\uc778\uc740 \ubaa8\ub450 name \uc18d\uc131 \uc874\uc7ac"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj4.name = 'population'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj4.index.name = 'state'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "state\n",
        "California      NaN\n",
        "Ohio          35000\n",
        "Oregon        16000\n",
        "Texas         71000\n",
        "Name: population, dtype: float64"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "0    4\n",
        "1    7\n",
        "2   -5\n",
        "3    3\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.index = ['Bob', 'Steve', 'Jeff', 'Ryan']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "Bob      4\n",
        "Steve    7\n",
        "Jeff    -5\n",
        "Ryan     3\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc0c9\uc778\uc758 \uac2f\uc218\ub97c \ub9de\ucdb0\uc918\uc57c \ud55c\ub2e4. \ub2f9\uc5f0\ud558\uc9c0.\n",
      "obj.index = ['Bob', 'Steve', 'Jeff']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "Exception",
       "evalue": "Length mismatch (3 vs 4)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-39-f453eb87918d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# \uc0c9\uc778\uc758 \uac2f\uc218\ub97c \ub9de\ucdb0\uc918\uc57c \ud55c\ub2e4. \ub2f9\uc5f0\ud558\uc9c0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Bob'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Steve'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Jeff'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m   1271\u001b[0m                 \u001b[0mexisting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1272\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexisting\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1273\u001b[0;31m                     \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1274\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1275\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.AxisProperty.__set__ (pandas/lib.c:30028)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_set_axis\u001b[0;34m(self, axis, labels, fastpath)\u001b[0m\n\u001b[1;32m    707\u001b[0m         \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_index'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_subtyp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_all_dates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mset_axis\u001b[0;34m(self, axis, value)\u001b[0m\n\u001b[1;32m   3031\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3032\u001b[0m             raise Exception('Length mismatch (%d vs %d)'\n\u001b[0;32m-> 3033\u001b[0;31m                             % (len(value), len(cur_axis)))\n\u001b[0m\u001b[1;32m   3034\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3035\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mException\u001b[0m: Length mismatch (3 vs 4)"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.1.2. DataFrame"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ud45c \uac19\uc740 \uc2a4\ud504\ub808\ub4dc\uc2dc\ud2b8 \ud615\uc2dd\uc758 \uc790\ub8cc \uad6c\uc870\ub85c \uc5ec\ub7ec \uac1c\uc758 \uceec\ub7fc\n",
      "- \uac01 \uceec\ub7fc\uc740 \uc11c\ub85c \ub2e4\ub978 \uc885\ub958\uc758 \uac12(\uc22b\uc790, \ubb38\uc790\uc5f4, \ubd88\ub9ac\uc5b8) \ub2f4\uc744 \uc218 \uc788\ub2e4\n",
      "- DataFrame\uc740 \ub85c\uc6b0\uc640 \uceec\ub7fc\uc5d0 \ub300\ud55c \uc0c9\uc778 \uc874\uc7ac.\n",
      "- \uc774 DataFrame\uc740 \uc0c9\uc778\uc758 \ubaa8\uc591\uc774 \uac19\uc740 Series \uac1d\uccb4\ub97c \ub2f4\uace0 \uc788\ub294 \ud30c\uc774\uc36c \uc0ac\uc804\uc73c\ub85c \uc0dd\uac01\ud558\uba74 \ud3b8\ud558\ub2e4.\n",
      "- R\uc758 data.frame \uac19\uc740 \ub2e4\ub978 DataFrame\uacfc \ube44\uc2b7\ud55c \uc790\ub8cc \uad6c\uc870\uc640 \ube44\uad50\ud588\uc744 \ub54c, DataFrame\uc5d0\uc11c\uc758 \ub85c\uc6b0 \uc5f0\uc0b0\uacfc \uceec\ub7fc \uc5f0\uc0b0\uc740 \uac70\uc758 \ub300\uce6d\uc801\uc73c\ub85c \ucde8\uae09\n",
      "- \ub0b4\ubd80\uc801\uc73c\ub85c \ub370\uc774\ud130\ub294 \ud558\ub098 \uc774\uc0c1\uc758 2\ucc28\uc6d0 \ubc30\uc5f4\uc5d0 \uc800\uc7a5\n",
      "- \uace0\ucc28\uc6d0\uc758 \ud45c \ud615\uc2dd \ub370\uc774\ud130\ub97c \ub098\uc911\uc5d0 \uc0b4\ud3b4\ubcfc \uacc4\uce35\uc801 \uc0c9\uc778(**Hierachical indexing**)\uc744 \ud1b5\ud574 \uc27d\uac8c \ud45c\ud604(\uace0\uae09 \uae30\ub2a5\uc5d0 \ud544\uc218\uc801\uc778 \uc694\uc18c)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####DataFrame \uac1d\uccb4 \uc0dd\uc131\n",
      "\n",
      "1. \uac19\uc740 \uae38\uc774\uc758 \ub9ac\uc2a4\ud2b8\uc5d0 \ub2f4\uae34 \uc0ac\uc804 \uc774\uc6a9\n",
      "1. NumPy \ubc30\uc5f4 \uc774\uc6a9"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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]}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "   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"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc6d0\ud558\ub294 \uc21c\uc11c\ub300\ub85c Column \uc9c0\uc815 \uac00\ub2a5\n",
      "# SQL\uc5d0\uc11c SELECT year, state pop FROM data \uc640 \ube44\uc2b7\ud558\uac8c \uceec\ub7fc\uba85 \uc21c\uc11c\ub97c \uc9c0\uc815\ud560 \uc218 \uc788\ub2e4.\n",
      "DataFrame(data, columns=['year', 'state', 'pop'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "   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"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2 = DataFrame(data, columns=['year', 'state', 'pop', 'debt'],\n",
      "                   index=['one', 'two', 'three', 'four', 'five'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "Index([u'year', u'state', u'pop', u'debt'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(frame2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####DataFrame \uceec\ub7fc \uc811\uadfc \ubc29\ubc95\n",
      "\n",
      "1. \uc0ac\uc804 \ud615\uc2dd\uc758 \ud45c\uae30\ubc95\uc73c\ub85c \uc811\uadfc(frame2['state'])\n",
      "1. \uc18d\uc131 \ud615\uc2dd\uc73c\ub85c \uc811\uadfc(frame2.state)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2['state']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "one        Ohio\n",
        "two        Ohio\n",
        "three      Ohio\n",
        "four     Nevada\n",
        "five     Nevada\n",
        "Name: state, dtype: object"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2.state"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "one        Ohio\n",
        "two        Ohio\n",
        "three      Ohio\n",
        "four     Nevada\n",
        "five     Nevada\n",
        "Name: state, dtype: object"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2.year"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "one      2000\n",
        "two      2001\n",
        "three    2002\n",
        "four     2001\n",
        "five     2002\n",
        "Name: year, dtype: int64"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\ub85c\uc6b0\ub294 \uc704\uce58\ub098 ix\uac19\uc740 \uba87 \uac00\uc9c0 \uba54\uc11c\ub4dc\ub97c \ud1b5\ud574 \uc811\uadfc \uac00\ub2a5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2.ix['three']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "year     2002\n",
        "state    Ohio\n",
        "pop       3.6\n",
        "debt      NaN\n",
        "Name: three, dtype: object"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# error \uceec\ub7fc\uac12\uc778 year\ub97c \ub123\uc5c8\uc744 \uc2dc\n",
      "frame2.ix['year']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "'year'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-52-d889787286f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# error \uceec\ub7fc\uac12\uc778 year\ub97c \ub123\uc5c8\uc744 \uc2dc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mframe2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m    582\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_iterable\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[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m     64\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_loc\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[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, copy, drop_level)\u001b[0m\n\u001b[1;32m   2171\u001b[0m                                     drop_level=drop_level)\n\u001b[1;32m   2172\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2173\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2175\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    824\u001b[0m         \u001b[0mloc\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0munique\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly\u001b[0m \u001b[0mslice\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    825\u001b[0m         \"\"\"\n\u001b[0;32m--> 826\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\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[0m\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    828\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\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;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3330)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3210)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:10484)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:10438)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: 'year'"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# row name\uc73c\ub85c\ub294 \uc0ac\uc804\ud615\uc2dd\uc73c\ub85c \uc811\uadfc \ubd88\uac00\n",
      "frame2['three']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "u'no item named three'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-53-c83454b4dc1c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# row name\uc73c\ub85c\ub294 \uc0ac\uc804\ud615\uc2dd\uc73c\ub85c \uc811\uadfc \ubd88\uac00\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mframe2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'three'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1827\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1828\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1829\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1831\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\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;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1834\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1835\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1836\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1838\u001b[0m         \u001b[0;31m# duplicate columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m    782\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    785\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2349\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2350\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2351\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_find_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2352\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2353\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_find_block\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_find_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2640\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_have\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2641\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2642\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_check_have\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2645\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_have\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2646\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2647\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'no item named %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpprint_thing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2649\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: u'no item named three'"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "u'no item named 0'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-54-d9f68b2221a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mframe2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1827\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1828\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1829\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1831\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\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;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1834\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1835\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1836\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1838\u001b[0m         \u001b[0;31m# duplicate columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m    782\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    785\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2349\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2350\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2351\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_find_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2352\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2353\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_find_block\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_find_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2640\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_have\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2641\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2642\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_check_have\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2645\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_have\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2646\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2647\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'no item named %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpprint_thing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2649\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: u'no item named 0'"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uceec\ub7fc \ub300\uc785 \uac00\ub2a5\n",
      "\n",
      "- \uc2a4\uce7c\ub77c \uac12\uc774\ub098 \ubc30\uc5f4\uc758 \uac12 \ub300\uc785 \uac00\ub2a5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2['debt'] = 16.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2['debt'] = np.arange(5.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Length of values does not match length of index\n",
      "frame2['debt'] = np.arange(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "AssertionError",
       "evalue": "Length of values does not match length of index",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-59-0ba39660d42a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Length of values does not match length of index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mframe2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'debt'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1922\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1923\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1924\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\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[0m\n\u001b[0m\u001b[1;32m   1925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1926\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\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/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1969\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1970\u001b[0m         \"\"\"\n\u001b[0;32m-> 1971\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\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[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\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[0m\n\u001b[1;32m   1973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2010\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2011\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2012\u001b[0;31m                     raise AssertionError('Length of values does not match '\n\u001b[0m\u001b[1;32m   2013\u001b[0m                                          'length of index')\n\u001b[1;32m   2014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mAssertionError\u001b[0m: Length of values does not match length of index"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ub9ac\uc2a4\ud2b8\ub098 \ubc30\uc5f4\uc744 \uce7c\ub7fc\uc5d0 \ub300\uc785\ud560 \ub54c\ub294 \ub300\uc785\ud558\ub824\ub294 \uac12\uc758 \uae38\uc774\uac00 DataFrame\uc758 \ud06c\uae30\uc640 \uac19\uc544\uc57c \ud55c\ub2e4.\n",
      "- Series\ub97c \ub300\uc785\ud558\uba74 DataFrame\uc758 \uc0c9\uc778\uc5d0 \ub530\ub77c \uac12\uc774 \ub300\uc785\ub418\uba70 \uc5c6\ub294 \uc0c9\uc778\uc5d0\ub294 \uac12\uc774 \ub300\uc785\ub418\uc9c0 \uc54a\ub294\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "val = Series([-1.2, -1.5, -1.7], index=['two', 'four', 'five'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "val"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "two    -1.2\n",
        "four   -1.5\n",
        "five   -1.7\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(val)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "pandas.core.series.Series"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2['debt'] = val"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2['eastern'] = frame2.state == 'Ohio'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del frame2['eastern']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "Index([u'year', u'state', u'pop', u'debt'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- DataFrame\uc758 \uc0c9\uc778\uc744 \uc774\uc6a9\ud574\uc11c \uc0dd\uc131\ub41c \uce7c\ub7fc\uc740 \ub0b4\ubd80 \ub370\uc774\ud130\uc5d0 \ub300\ud55c view\uc774\uba70 \ubcf5\uc0ac\uac00 \uc774\ub8e8\uc5b4\uc9c0\uc9c0 \uc54a\ub294\ub2e4.\n",
      "- \ub530\ub77c\uc11c \uc774\ub807\uac8c \uc5bb\uc740 Series \uac1d\uccb4\uc5d0 \ub300\ud55c \ubcc0\uacbd\uc740 \uc2e4\uc81c DataFrame\uc5d0 \ubc18\uc601\ub41c\ub2e4.\n",
      "- \ubcf5\uc0ac\ubcf8\uc774 \ud544\uc694\ud560 \ub54c\ub294 Series\uc758 copy \uba54\uc11c\ub4dc\ub97c \uc774\uc6a9\ud558\uc790"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uc911\ucca9\ub41c \uc0ac\uc804\uc744 \uc774\uc6a9\ud574\uc11c \ub370\uc774\ud130 \uc0dd\uc131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pop = {'Nevada': {2001: 2.4,\n",
      "                  2002: 2.9},\n",
      "       'Ohio': {2000: 1.5,\n",
      "                2001: 1.7,\n",
      "                2002: 3.6}}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pop"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "{'Nevada': {2001: 2.4, 2002: 2.9}, 'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(pop)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "dict"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3 = DataFrame(pop)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "      Nevada  Ohio\n",
        "2000     NaN   1.5\n",
        "2001     2.4   1.7\n",
        "2002     2.9   3.6"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(frame3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3['Ohio'][:-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "2000    1.5\n",
        "2001    1.7\n",
        "Name: Ohio, dtype: float64"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3['Nevada'][:2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "2000    NaN\n",
        "2001    2.4\n",
        "Name: Nevada, dtype: float64"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\ub85c\uc6b0, \uceec\ub7fc \ubcc0\uacbd"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "        2000  2001  2002\n",
        "Nevada   NaN   2.4   2.9\n",
        "Ohio     1.5   1.7   3.6"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####index\ub97c \uc9c1\uc811 \uc9c0\uc815\ud55c\ub2e4\uba74 \uc9c0\uc815\ub41c \uc0c9\uc778\uc73c\ub85c DataFrame \uc0dd\uc131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DataFrame(pop, index=[2001, 2002, 2003])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "      Nevada  Ohio\n",
        "2001     2.4   1.7\n",
        "2002     2.9   3.6\n",
        "2003     NaN   NaN"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DataFrame(pop)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "      Nevada  Ohio\n",
        "2000     NaN   1.5\n",
        "2001     2.4   1.7\n",
        "2002     2.9   3.6"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Series \uac1d\uccb4\ub97c \ub2f4\uace0 \uc788\ub294 \uc0ac\uc804 \ub370\uc774\ud130\ub3c4 \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \ucde8\uae09"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata = {'Ohio': frame3['Ohio'][:-1],\n",
      "         'Nevada': frame3['Nevada'][:2]}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "{'Nevada': 2000    NaN\n",
        " 2001    2.4\n",
        " Name: Nevada, dtype: float64, 'Ohio': 2000    1.5\n",
        " 2001    1.7\n",
        " Name: Ohio, dtype: float64}"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(pdata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "dict"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DataFrame(pdata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "      Nevada  Ohio\n",
        "2000     NaN   1.5\n",
        "2001     2.4   1.7"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "      Nevada  Ohio\n",
        "2000     NaN   1.5\n",
        "2001     2.4   1.7\n",
        "2002     2.9   3.6"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3.index.name = 'year'; frame3.columns.name = 'state'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "state  Nevada  Ohio\n",
        "year               \n",
        "2000      NaN   1.5\n",
        "2001      2.4   1.7\n",
        "2002      2.9   3.6"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uc0c8\ub85c\uc6b4 index\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud588\ub294\ub370 \uacc4\uc18d \ubc14\ub00c\uae30\ub9cc \ud558\ub124..?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3.index.name = 'year3';"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>state</th>\n",
        "      <th>Nevada</th>\n",
        "      <th>Ohio</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year3</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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "state  Nevada  Ohio\n",
        "year3              \n",
        "2000      NaN   1.5\n",
        "2001      2.4   1.7\n",
        "2002      2.9   3.6"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " frame3.columns.name = 'state3'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>state3</th>\n",
        "      <th>Nevada</th>\n",
        "      <th>Ohio</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year3</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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "state3  Nevada  Ohio\n",
        "year3               \n",
        "2000       NaN   1.5\n",
        "2001       2.4   1.7\n",
        "2002       2.9   3.6"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "array([[ nan,  1.5],\n",
        "       [ 2.4,  1.7],\n",
        "       [ 2.9,  3.6]])"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####DataFrame\uc758 \uce7c\ub7fc\uc5d0 \uc11c\ub85c \ub2e4\ub978 dtype\uc774 \uc788\ub2e4\uba74 \ubaa8\ub4e0 \uce7c\ub7fc\uc744 \uc218\uc6a9\ud558\uae30 \uc704\ud574 \uadf8 \uce7c\ub7fc \ubc30\uc5f4\uc758 dtype\uc774 \uc120\ud0dd\ub41c\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "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)"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### DataFrame \uc0dd\uc131\uc790\uc5d0\uc11c \uc0ac\uc6a9 \uac00\ub2a5\ud55c \uc785\ub825 \ub370\uc774\ud130\n",
      "\n",
      "\ud615 | \uc124\uba85\n",
      "--- | ---\n",
      "2\ucc28\uc6d0 ndarray | \ub370\uc774\ud130\ub97c \ub2f4\uace0 \uc788\ub294 \ud589\ub82c. \uc120\ud0dd\uc801\uc73c\ub85c \ub85c\uc6b0\uc640 \uce7c\ub7fc\uc758 \uc774\ub984\uc744 \uc804\ub2ec\ud560 \uc218 \uc788\ub2e4.\n",
      "\ubc30\uc5f4, \ub9ac\uc2a4\ud2b8, \ud29c\ud50c\uc758 \uc0ac\uc804 | \uc0ac\uc804\uc758 \ubaa8\ub4e0 \ud56d\ubaa9\uc740 \uac19\uc740 \uae38\uc774\ub97c \uac00\uc838\uc57c \ud558\uba70, \uac01 \ud56d\ubaa9\uc758 \ub0b4\uc6a9\uc774 DataFrame\uc758 \uce7c\ub7fc\uc774 \ub41c\ub2e4.\n",
      "NumPy\uc758 \uad6c\uc870\ud654 \ubc30\uc5f4 | \ubc30\uc5f4\uc758 \uc0ac\uc804\uacfc \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \ucde8\uae09\ub41c\ub2e4.\n",
      "Series \uc0ac\uc804 | Series\uc758 \uac01 \uac12\uc774 \uceec\ub7fc\uc774 \ub41c\ub2e4. \uba85\uc2dc\uc801\uc73c\ub85c \uc0c9\uc778\uc744 \ub118\uaca8\uc8fc\uc9c0 \uc54a\uc73c\uba74 \uac01 Series\uc758 \uc0c9\uc778\uc774 \ud558\ub098\ub85c \ud569\uccd0\uc838\uc11c \ud615\uc758 \uc0c9\uc778\uc774 \ub41c\ub2e4.\n",
      "\uc0ac\uc804\uc758 \uc0ac\uc804 | \ub0b4\ubd80\uc5d0 \uc788\ub294 \uc0ac\uc804\uc774 \uce7c\ub7fc\uc774 \ub41c\ub2e4. \ud0a4 \uac12\uc740 'Series\uc758 \uc0ac\uc804'\uacfc \ub9c8\ucc2c\uac00\uc9c0\ub85c \ud569\uccd0\uc838 \ub85c\uc6b0\uc758 \uc0c9\uc778\uc774 \ub41c\ub2e4.\n",
      "\uc0ac\uc804\uc774\ub098 Series\uc758 \ub9ac\uc2a4\ud2b8 | \ub9ac\uc2a4\ud2b8\uc758 \uac01 \ud56d\ubaa9\uc774 DataFrame\uc758 \ub85c\uc6b0\uac00 \ub41c\ub2e4. \ud569\uccd0\uc9c4 \uc0ac\uc804\uc758 \ud0a4 \uac12\uc774\ub098 Series\uc758 \uc0c9\uc778\uc774 DataFrame \uce7c\ub7fc\uc758 \uc774\ub984\uc774 \ub41c\ub2e4.\n",
      "\ub9ac\uc2a4\ud2b8\ub098 \ud29c\ud50c\uc758 \ub9ac\uc2a4\ud2b8 | '2\ucc28\uc6d0 ndarray'\uc640 \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \ucde8\uae09\ub41c\ub2e4.\n",
      "\ub2e4\ub978 DataFrame | \uc0c9\uc778\uc774 \ub530\ub85c \uc9c0\uc815\ub418\uc9c0 \uc54a\ub294\ub2e4\uba74 DataFrame\uc758 \uc0c9\uc778\uc774 \uadf8\ub300\ub85c \uc0ac\uc6a9\ub41c\ub2e4.\n",
      "NumPy MaskedArray | '2\ucc28\uc6d0 ndarray'\uc640 \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \ucde8\uae09\ub418\uc9c0\ub9cc \ub9c8\uc2a4\ud06c \uac12\uc740 \ubc18\ud658\ub418\ub294 DataFrame\uc5d0\uc11c NA \uac12\uc774 \ub41c\ub2e4."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.1.3 \uc0c9\uc778 \uac1d\uccb4"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- pandas\uc758 \uc0c9\uc778 \uac1d\uccb4\ub294 \ud45c \ud615\uc2dd\uc758 \ub370\uc774\ud130\uc5d0\uc11c \uac01 \ub85c\uc6b0\uc640 \uce7c\ub7fc\uc5d0 \ub300\ud55c \uc774\ub984\uacfc \ub2e4\ub978 \uba54\ud0c0\ub370\uc774\ud130(\ucd95\uc758 \uc774\ub984 \ub4f1)\ub97c \uc800\uc7a5\ud558\ub294 \uac1d\uccb4\n",
      "- Series\ub098 DataFrame \uac1d\uccb4\ub97c \uc0dd\uc131\ud560 \ub54c \uc0ac\uc6a9\ud558\ub294 \ubc30\uc5f4\uc774\ub098 \ud639\uc740 \ub2e4\ub978 \uc21c\ucc28\uc801\uc778 \uc774\ub984\uc740 \ub0b4\ubd80\uc801\uc73c\ub85c \uc0c9\uc778\uc73c\ub85c \ubcc0\ud658"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series(range(3), index=['a', 'b', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index = obj.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "Index([u'a', u'b', u'c'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index[1:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "Index([u'b', u'c'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc0c9\uc778 \uac1d\uccb4 \ubcc0\uacbd \ubd88\uac00\n",
      "index[1] = 'd'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "'<class 'pandas.core.index.Index'>' does not support mutable operations.",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-98-cc123079b99c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# \uc0c9\uc778 \uac1d\uccb4 \ubcc0\uacbd \ubd88\uac00\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\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;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/base.pyc\u001b[0m in \u001b[0;36m_disabled\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;34m\"\"\"This method will not function because object is immutable.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         raise TypeError(\"'%s' does not support mutable operations.\" %\n\u001b[0;32m--> 141\u001b[0;31m                         self.__class__)\n\u001b[0m\u001b[1;32m    142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0m__setitem__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__setslice__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__delitem__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__delslice__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_disabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mTypeError\u001b[0m: '<class 'pandas.core.index.Index'>' does not support mutable operations."
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index = pd.Index(np.arange(3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "Int64Index([0, 1, 2], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# index=\ub294 \ud568\uc218\uc758 \ud0a4\uc6cc\ub4dc, \ub4a4\uc758 index\ub294 \ubcc0\uc218\n",
      "obj2 = Series([1.5, -2.5, 0], index=index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "0    1.5\n",
        "1   -2.5\n",
        "2    0.0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2.index is index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "Int64Index([0, 1, 2], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "Int64Index([0, 1, 2], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### pandas\uc758 \uc8fc\uc694 Index \uac1d\uccb4\n",
      "\n",
      "\ud074\ub798\uc2a4 | \uc124\uba85\n",
      "--- | ---\n",
      "Index | \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 Index \uac1d\uccb4\uc774\uba70, \ud30c\uc774\uc36c \uac1d\uccb4\uc758 NumPy \ubc30\uc5f4 \ud615\uc2dd\uc73c\ub85c \ucd95\uc758 \uc774\ub984\uc744 \ud45c\ud604\ud55c\ub2e4.\n",
      "Int64Index | \uc815\uc218 \uac12\uc744 \uc704\ud55c \ud2b9\uc218\ud55c Index\n",
      "MultiIndex | \ub2e8\uc77c \ucd95\uc5d0 \uc5ec\ub7ec \ub2e8\uacc4\uc758 \uc0c9\uc778\uc744 \ud45c\ud604\ud558\ub294 \uacc4\uce35\uc801 \uc0c9\uc778 \uac1d\uccb4. \ud29c\ud50c\uc758 \ubc30\uc5f4\uacfc \uc720\uc0ac\ud558\ub2e4\uace0 \ubcfc \uc218 \uc788\ub2e4.\n",
      "DatetimeIndex | \ub098\ub178\ucd08 \ud0c0\uc784\uc2a4\ud0ec\ud504\ub97c \uc800\uc7a5\ud55c\ub2e4(NumPy\uc758 datetime64 dtype\uc73c\ub85c \ud45c\ud604\ub41c\ub2e4).\n",
      "PeriodIndex | \uae30\uac04 \ub370\uc774\ud130\ub97c \uc704\ud55c \ud2b9\uc218\ud55c Index"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>state3</th>\n",
        "      <th>Nevada</th>\n",
        "      <th>Ohio</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year3</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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "state3  Nevada  Ohio\n",
        "year3               \n",
        "2000       NaN   1.5\n",
        "2001       2.4   1.7\n",
        "2002       2.9   3.6"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'Ohio' in frame3.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "2003 in frame3.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "--------\n",
      "\n",
      "#### \uc0c9\uc778 \uba54\uc11c\ub4dc append \uc5f0\uc2b5\n",
      "\n",
      "- \uc5f0\uc2b5\ud574\ubcf4\uace0 \uc2f6\uc73c\uc2e0 \ubd84\ub4e4\uc740 index.tab \ub20c\ub7ec\uc11c \uc5b4\ub5a4 \uba54\uc18c\ub4dc\uac00 \uc788\ub294\uc9c0 \ud655\uc778\ud558\uace0 \ud14c\uc2a4\ud2b8 \ud574\ubcf4\uba74 \uc88b\uaca0\uc2b5\ub2c8\ub2e4.\n",
      "- \ub300\ubd80\ubd84 \uc0c1\uc2dd\uc801\uc73c\ub85c \uc788\uc744\ubc95\ud55c \uba54\uc18c\ub4dc\ub4e4\uc774 \uc788\ub124\uc694.\n",
      "- \uadf8\ub0e5 \uc774\ub7f0 \uac83\ub4e4\uc774 \uc788\uad6c\ub098 \ud558\uace0 \uba38\ub9ac\uc18d\uc5d0 Indexing\ub9cc \ud558\uace0 \ub098\uc911\uc5d0 \ud544\uc694\ud560 \ub54c \uc9c1\uc811 \uc368\ubcf4\ub294 \uac83\uc744 \ucd94\ucc9c\ud569\ub2c8\ub2e4.\n",
      "- \ubaa8\ub4e0 \uba54\uc18c\ub4dc\ub4e4\uc744 \ud14c\uc2a4\ud2b8 \ud574\ubcf4\uace0 \uc775\ud788\ub294\uac8c \uac00\uc7a5 \uc88b\uaca0\uc9c0\ub9cc \uc2dc\uac04\uc774 \ubd80\uc871\ud558\ub2c8\uae4c\uc694.\n",
      "\n",
      "-------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "Int64Index([0, 1, 2], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index2 = pd.Index(np.arange(5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "Int64Index([0, 1, 2, 3, 4], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum_index = index.append(index2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "Int64Index([0, 1, 2], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum_index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "Int64Index([0, 1, 2, 0, 1, 2, 3, 4], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uc0c9\uc778 \uba54\uc11c\ub4dc\uc640 \uc18d\uc131\n",
      "\n",
      "\uba54\uc11c\ub4dc | \uc124\uba85\n",
      "--- | ---\n",
      "append | \ucd94\uac00\uc801\uc778 Index \uac1d\uccb4\ub97c \ub367\ubd99\uc5ec \uc0c8\ub85c\uc6b4 \uc0c9\uc778\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "diff | \uc0c9\uc778\uc758 \ucc28\uc9d1\ud569\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "intersection | \uc0c9\uc778\uc758 \uad50\uc9d1\ud569\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "union | \uc0c9\uc778\uc758 \ud569\uc9d1\ud569\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "isin | \ub118\uaca8\ubc1b\uc740 \uac12\uc774 \ud574\ub2f9 \uc0c9\uc778 \uc704\uce58\uc5d0 \uc874\uc7ac\ud558\ub294\uc9c0 \uc54c\ub824\uc8fc\ub294 \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "delete | i \uc704\uce58\uc758 \uc0c9\uc778\uc774 \uc0ad\uc81c\ub41c \uc0c8\ub85c\uc6b4 \uc0c9\uc778\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "drop | \ub118\uaca8\ubc1b\uc740 \uac12\uc774 \uc0ad\uc81c\ub41c \uc0c8\ub85c\uc6b4 \uc0c9\uc778\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "insert | i \uc704\uce58\uc5d0 \uac12\uc774 \ucd94\uac00\ub41c \uc0c8\ub85c\uc6b4 \uc0c9\uc778\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "is_monotonic | \uc0c9\uc778\uc774 \ub2e8\uc870\uc131\uc744 \uac00\uc9c4\ub2e4\uba74 True\ub97c \ubc18\ud658\ud55c\ub2e4.\n",
      "is_unique | \uc911\ubcf5\ub418\ub294 \uc0c9\uc778\uc774 \uc5c6\ub2e4\uba74 True\ub97c \ubc18\ud658\ud55c\ub2e4.\n",
      "unique | \uc0c9\uc778\uc5d0\uc11c \uc911\ubcf5\ub418\ub294 \uc694\uc18c\ub97c \uc81c\uac70\ud558\uace0 \uc720\uc77c\ud55c \uac12\ub9cc\uc744 \ubc18\ud658\ud55c\ub2e4."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5.2 \ud575\uc2ec \uae30\ub2a5"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Series\ub098 DataFrame\uc5d0 \uc800\uc7a5\ub41c \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \uae30\ubcf8 \ubc29\ubc95 \uc124\uba85\n",
      "- \uc911\uc694\ud55c \uae30\ub2a5\uc5d0\ub9cc \ucd08\uc810"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 5.2.1 \uc7ac\uc0c9\uc778\n",
      "\n",
      "- \uc800\uc790 \ub9d0\ub85c\ub294 \uae30\ub9c9\ud78c \uae30\ub2a5\uc911 \ud558\ub098\uac00 reindex \ub77c\uace0 \ud558\ub294\ub370 \uc65c \uc911\uc694\ud55c\uc9c0 \ubaa8\ub974\uaca0\ub2e4. \ub2f9\uc5f0\ud55c\uac70 \uc544\ub2cc\uac00? \uc544\uc9c1 \uc2e4\uc81c\uc801\uc73c\ub85c \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud574 \ubcf4\ub294 \uc77c\uc744 \uc548\ud574\uc11c \ubabb \ub290\ub07c\ub294 \uac78 \uc218\ub3c4 \uc788\ub2e4.\n",
      "- reindex: \uc0c8\ub85c\uc6b4 \uc0c9\uc778\uc5d0 \ub9de\ub3c4\ub85d \uac1d\uccb4\ub97c \uc0c8\ub85c \uc0dd\uc131\ud558\ub294 \uae30\ub2a5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "d    4.5\n",
        "b    7.2\n",
        "a   -5.3\n",
        "c    3.6\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Series \uac1d\uccb4\uc5d0 \ub300\ud574 reindex\ub97c \ud638\ucd9c\ud558\uba74 \ub370\uc774\ud130\ub97c \uc0c8\ub85c\uc6b4 \uc0c9\uc778\uc5d0 \ub9de\uac8c \uc7ac\ubc30\uc5f4\ud558\uace0, \uc5c6\ub294 \uc0c9\uc778 \uac12\uc774 \uc788\ub2e4\uba74 \ube44\uc5b4\uc788\ub294 \uac12\uc744 \uc0c8\ub85c \ucd94\uac00"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "a   -5.3\n",
        "b    7.2\n",
        "c    3.6\n",
        "d    4.5\n",
        "e    NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fill_value\ub294 \ub370\uc774\ud130 \uc774\uac00 \ube60\uc9c4\uac78 \ucc44\uc6cc\ub123\uc744 \uc218 \uc788\uae30 \ub54c\ubb38\uc5d0 \ub9e4\uc6b0 \uc88b\uc740 \uae30\ub2a5\uc774\ub2e4.\n",
      "obj.reindex(['a', 'b', 'c', 'd', 'e'], fill_value=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "a   -5.3\n",
        "b    7.2\n",
        "c    3.6\n",
        "d    4.5\n",
        "e    0.0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uac1d\uccb4\uac00 \uc6d0\ub798 \ubdf0\uc5d0 \ub300\ud55c \uc218\uc815\uc774 \uc774\ub8e8\uc5b4\uc9c0\ub294 \uac83\uc778\uc9c0? \uc544\ub2c8\uba74 \ubcf5\uc0ac\ud55c \uac1d\uccb4\uc5d0 \ub300\ud574 \uc218\uc815\uc774 \uc774\ub8e8\uc5b4\uc9c0\ub294 \uac83\uc778\uc9c0? \uba85\ud655\ud558\uc9c0 \uc54a\ub2e4.."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "d    4.5\n",
        "b    7.2\n",
        "a   -5.3\n",
        "c    3.6\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uc2dc\uacc4\uc5f4 \uac19\uc740 \uc21c\ucc28\uc801\uc778 \ub370\uc774\ud130\ub97c \uc7ac\uc0c9\uc778\ud560 \ub54c \uac12\uc744 \ubcf4\uac04\ud558\uac70\ub098 \ucc44\uc6cc \ub123\uc5b4\uc57c \ud560 \uacbd\uc6b0\n",
      "\n",
      "- ffill\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc55e\uc758 \uac12\uc73c\ub85c \ub204\ub77d\ub41c \uac12\uc744 \ucc44\uc6cc \ub123\uc744 \uc218 \uc788\ub2e4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj3 = Series(['blue', 'purple', 'yellow'], index=[0, 2, 3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "0      blue\n",
        "2    purple\n",
        "3    yellow\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj3.reindex(range(6), method='ffill')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 123,
       "text": [
        "0      blue\n",
        "1      blue\n",
        "2    purple\n",
        "3    yellow\n",
        "4    yellow\n",
        "5    yellow\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### reindex \uba54\uc11c\ub4dc(\ubcf4\uac04) \uc635\uc158\n",
      "\n",
      "\uc778\uc790 | \uc124\uba85\n",
      "--- | ---\n",
      "ffill \ub610\ub294 pad | \uc55e\uc758 \uac12\uc73c\ub85c \ucc44\uc6cc \ub123\ub294\ub2e4.\n",
      "bfill \ub610\ub294 backfill | \ub4a4\uc758 \uac12\uc73c\ub85c \ucc44\uc6cc \ub123\ub294\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame(np.arange(9).reshape((3, 3)), index=['a', 'c', 'd'],\n",
      "                  columns=['Ohio', 'Texas', 'California'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "   Ohio  Texas  California\n",
        "a     0      1           2\n",
        "c     3      4           5\n",
        "d     6      7           8"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2 = frame.reindex(['a', 'b', 'c', 'd'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "   Ohio  Texas  California\n",
        "a     0      1           2\n",
        "b   NaN    NaN         NaN\n",
        "c     3      4           5\n",
        "d     6      7           8"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "states = ['Texas', 'Utah', 'California']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.reindex(columns=states)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "   Texas  Utah  California\n",
        "a      1   NaN           2\n",
        "c      4   NaN           5\n",
        "d      7   NaN           8"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\ub85c\uc6b0\uc640 \uce7c\ub7fc\uc744 \ubaa8\ub450 \ud55c \ubc88\uc5d0 \uc7ac\uc0c9\uc778\ud560 \uc218 \uc788\uc9c0\ub9cc \ubcf4\uac04\uc740 \ub85c\uc6b0\uc5d0 \ub300\ud574\uc11c\ub9cc \uc774\ub8e8\uc5b4\uc9c4\ub2e4(axis 0)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "   Ohio  Texas  California\n",
        "a     0      1           2\n",
        "c     3      4           5\n",
        "d     6      7           8"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1. \ub85c\uc6b0\ub97c a,b,c,d\ub85c \uc7ac\uc0c9\uc778\n",
      "# 2. \uceec\ub7fc\uc744 states\ub85c \uc7ac\uc0c9\uc778\n",
      "# 3. \ub85c\uc6b0\ub97c ffill\ub85c \ubcf4\uac04 \uc801\uc6a9\n",
      "frame.reindex(index=['a', 'b', 'c', 'd'], method='ffill',\n",
      "              columns=states)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 131,
       "text": [
        "   Texas  Utah  California\n",
        "a      1   NaN           2\n",
        "b      1   NaN           2\n",
        "c      4   NaN           5\n",
        "d      7   NaN           8"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.ix[['a', 'b', 'c', 'd'], states]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 132,
       "text": [
        "   Texas  Utah  California\n",
        "a      1   NaN           2\n",
        "b    NaN   NaN         NaN\n",
        "c      4   NaN           5\n",
        "d      7   NaN           8"
       ]
      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.reindex?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 133
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uc7ac\uc0c9\uc778 \ud568\uc218 \uc778\uc790\n",
      "\n",
      "\uc778\uc790 | \uc124\uba85\n",
      "--- | ---\n",
      "index| \uc0c9\uc778\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc0c8\ub85c\uc6b4 \uc21c\uc11c. Index \uc778\uc2a4\ud134\uc2a4\ub098 \ub2e4\ub978 \uc21c\ucc28\uc801\uc778 \uc790\ub8cc \uad6c\uc870\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub2e4. \uc0c9\uc778\uc740 \ubcf5\uc0ac\uac00 \uc774\ub8e8\uc5b4\uc9c0\uc9c0 \uc54a\uace0 \uadf8\ub300\ub85c \uc0ac\uc6a9\ub41c\ub2e4.\n",
      "method | \ubcf4\uac04 \uba54\uc11c\ub4dc\n",
      "fill_value | \uc7ac\uc0c9\uc778 \uacfc\uc815 \uc911\uc5d0 \uc0c8\ub86d\uac8c \ub098\ud0c0\ub098\ub294 \ube44\uc5b4\uc788\ub294 \ub370\uc774\ud130\ub97c \ucc44\uc6b0\uae30 \uc704\ud55c \uac12\n",
      "limit | \uc804/\ud6c4 \ubcf4\uac04 \uc2dc\uc5d0 \uc0ac\uc6a9\ud560 \ucd5c\ub300 \uac2d \ud06c\uae30\n",
      "level | MultiIndex \ub2e8\uacc4(level)\uc5d0 \ub2e8\uc21c \uc0c9\uc778\uc744 \ub9de\ucd98\ub2e4. \uadf8\ub807\uc9c0 \uc54a\uc73c\uba74 MultiIndex\uc758 \ud558\uc704 \ubd80\ubd84\uc9d1\ud569\uc5d0 \ub9de\ucd98\ub2e4.\n",
      "copy | True\uc778 \uacbd\uc6b0 \uc0c8\ub85c\uc6b4 \uc0c9\uc778\uc774 \uc774\uc804 \uc0c9\uc778\uacfc \uac19\ub354\ub77c\ub3c4 \ub370\uc774\ud130\ub97c \ubcf5\uc0ac\ud55c\ub2e4. False\ub77c\uba74 \ub450 \uc0c9\uc778\uc774 \uac19\uc740 \uacbd\uc6b0 \ub370\uc774\ud130\ub97c \ubcf5\uc0ac\ud558\uc9c0 \uc54a\ub294\ub2e4."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.2.2 \ud558\ub098\uc758 \ub85c\uc6b0 \ub610\ub294 \uce7c\ub7fc \uc0ad\uc81c\ud558\uae30"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 135,
       "text": [
        "a    0\n",
        "b    1\n",
        "c    2\n",
        "d    3\n",
        "e    4\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_obj = obj.drop('c')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 137,
       "text": [
        "a    0\n",
        "b    1\n",
        "d    3\n",
        "e    4\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.drop(['d', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 138,
       "text": [
        "a    0\n",
        "b    1\n",
        "e    4\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = DataFrame(np.arange(16).reshape((4, 4)),\n",
      "                 index=['Ohio', 'Colorado', 'Utah', 'New York'],\n",
      "                 columns=['one', 'two', 'three', 'four'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "      <th>three</th>\n",
        "      <th>four</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "          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"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.drop(['Colorado', 'Ohio'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "      <th>three</th>\n",
        "      <th>four</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "          one  two  three  four\n",
        "Utah        8    9     10    11\n",
        "New York   12   13     14    15"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.drop('two', axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 142,
       "text": [
        "          one  three  four\n",
        "Ohio        0      2     3\n",
        "Colorado    4      6     7\n",
        "Utah        8     10    11\n",
        "New York   12     14    15"
       ]
      }
     ],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# list\ub85c drop \ud560 \ubaa9\ub85d \ub118\uae38 \uc218\ub3c4 \uc788\uace0\n",
      "# \uceec\ub7fc\uc778\uc9c0 \ub85c\uc6b0\uc778\uc9c0 \uc120\ud0dd \uac00\ub2a5\n",
      "data.drop(['two', 'four'], axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 143,
       "text": [
        "          one  three\n",
        "Ohio        0      2\n",
        "Colorado    4      6\n",
        "Utah        8     10\n",
        "New York   12     14"
       ]
      }
     ],
     "prompt_number": 143
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.2.3 \uc0c9\uc778\ud558\uae30, \uc120\ud0dd\ud558\uae30, \uac70\ub974\uae30"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Series\uc758 \uc0c9\uc778\uc740 NumPy \ubc30\uc5f4\uc758 \uc0c9\uc778\uacfc \uc720\uc0ac\ud558\uac8c \ub3d9\uc791\ud558\ub294\ub370, Seriese\uc758 \uc0c9\uc778\uc740 \uc815\uc218\uac00 \uc544\ub2c8\uc5b4\ub3c4 \ub41c\ub2e4\ub294 \uc810\uc774 \ub2e4\ub974\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series(np.arange(4.), index=['a', 'b', 'c', 'd'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#obj = Series([1, 1.1, 2, 3], index=['a', 'b', 'c', 'd'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 146,
       "text": [
        "a    0\n",
        "b    1\n",
        "c    2\n",
        "d    3\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj['b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 147,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 148,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj[2:4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 149,
       "text": [
        "c    2\n",
        "d    3\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj[['b', 'a', 'd']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 150,
       "text": [
        "b    1\n",
        "a    0\n",
        "d    3\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj[[1, 3]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 151,
       "text": [
        "b    1\n",
        "d    3\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj[obj < 2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 152,
       "text": [
        "a    0\n",
        "b    1\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\ub77c\ubca8 \uc774\ub984\uc73c\ub85c \uc2ac\ub77c\uc774\uc2f1\ud558\ub294 \uac83\uc740 \uc2dc\uc791\uc810\uacfc \ub05d\uc810\uc744 \ud3ec\ud568\ud55c\ub2e4\ub294 \uc810\uc774 \uc77c\ubc18 \ud30c\uc774\uc36c\uc5d0\uc11c\uc758 \uc2ac\ub77c\uc774\uc2f1\uacfc \ub2e4\ub978 \uc810\uc774\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj['b':'c']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 153,
       "text": [
        "b    1\n",
        "c    2\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj['b':'c'] = 5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 154
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 155,
       "text": [
        "a    0\n",
        "b    5\n",
        "c    5\n",
        "d    3\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = DataFrame(np.arange(16).reshape((4, 4)),\n",
      "                 index=['Ohio', 'Colorado', 'Utah', 'New York'],\n",
      "                 columns=['one', 'two', 'three', 'four'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 156
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "      <th>three</th>\n",
        "      <th>four</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 157,
       "text": [
        "          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"
       ]
      }
     ],
     "prompt_number": 157
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['two']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 158,
       "text": [
        "Ohio         1\n",
        "Colorado     5\n",
        "Utah         9\n",
        "New York    13\n",
        "Name: two, dtype: int64"
       ]
      }
     ],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[['three', 'one']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 159,
       "text": [
        "          three  one\n",
        "Ohio          2    0\n",
        "Colorado      6    4\n",
        "Utah         10    8\n",
        "New York     14   12"
       ]
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     ],
     "prompt_number": 159
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uc2ac\ub77c\uc774\uc2f1\uc73c\ub85c \ub85c\uc6b0 \uc120\ud0dd\n",
      "#### \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\ub85c \uceec\ub7fc \uc120\ud0dd"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[:2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "      <th>three</th>\n",
        "      <th>four</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "      <td> 6</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 160,
       "text": [
        "          one  two  three  four\n",
        "Ohio        0    1      2     3\n",
        "Colorado    4    5      6     7"
       ]
      }
     ],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[data['three'] > 5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 161,
       "text": [
        "          one  two  three  four\n",
        "Colorado    4    5      6     7\n",
        "Utah        8    9     10    11\n",
        "New York   12   13     14    15"
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     ],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "      <th>three</th>\n",
        "      <th>four</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 162,
       "text": [
        "          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"
       ]
      }
     ],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc2e4\uc6a9\uc131\uc5d0 \uae30\uc778\ud55c \ubb38\ubc95\n",
      "data < 5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 163,
       "text": [
        "            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"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[data < 5] = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 164
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "      <th>three</th>\n",
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        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 165,
       "text": [
        "          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"
       ]
      }
     ],
     "prompt_number": 165
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### ix \uba54\uc18c\ub4dc\uc5d0 \ub300\ud55c \uac1c\uc778\uc801\uc778 \uc0dd\uac01\n",
      "\n",
      "- ix\uac00 \uc88b\uc740 \uac83 \uac19\ub2e4. \uc65c\ub0d0\ud558\uba74 \uc55e\uc740 \ud589, \ub4a4\ub294 \uc5f4\ub85c \ub531 \ud615\uc2dd\uc774 \uc815\ud574\uc838 \uc788\uae30 \ub54c\ubb38\uc774\ub2e4.\n",
      "- \ub2e4\ub978 \uba54\uc18c\ub4dc\ub4e4 \uac19\uc740 \uacbd\uc6b0 axis\uac00 0\uc77c\ub54c \ud589\uc77c\ub54c\ub3c4 \uc788\uace0 \uc5f4\uc77c \ub54c\ub3c4 \uc788\ub2e4. \uc5b4\ub5a4 \uae30\uc900\uc744 \uac00\uc9c0\uace0 \ud589\uc774 \ub418\uace0, \uc5f4\uc774 \ub418\ub294\uc9c0 \ubd88\uba85\ud655\ud558\ub2e4.\n",
      "- \uc989, \uc4f8\ub824\uba74 \ud55c \ubc88 \uac80\uc99d\uc774 \ud544\uc694\ud558\uae30 \ub54c\ubb38\uc5d0 \uadc0\ucc2e\ub2e4.\n",
      "- \uadf8\ub0e5 ix \uba54\uc18c\ub4dc\ub97c \uc0ac\uc6a9\ud574\uc11c \uc55e\uc740 \ud589, \ub4a4\ub294 \uc5f4\ub85c \uc0dd\uac01\ud560 \ud544\uc694\uc5c6\uc774 \ubc14\ub85c \uc0ac\uc6a9\ud558\ub294\uac8c \ud6e8\uc52c \ub098\uc5d0\uac8c \ub9de\ub294 \ubc29\ubc95 \uac19\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix['Colorado', ['two', 'three']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 166,
       "text": [
        "two      5\n",
        "three    6\n",
        "Name: Colorado, dtype: int64"
       ]
      }
     ],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[['Colorado', 'Utah'], ['two', 'three']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>two</th>\n",
        "      <th>three</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <td> 5</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Utah</th>\n",
        "      <td> 9</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 167,
       "text": [
        "          two  three\n",
        "Colorado    5      6\n",
        "Utah        9     10"
       ]
      }
     ],
     "prompt_number": 167
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[['Colorado', 'Utah'], [3, 0, 1]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 168,
       "text": [
        "          four  one  two\n",
        "Colorado     7    0    5\n",
        "Utah        11    8    9"
       ]
      }
     ],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 169,
       "text": [
        "one       8\n",
        "two       9\n",
        "three    10\n",
        "four     11\n",
        "Name: Utah, dtype: int64"
       ]
      }
     ],
     "prompt_number": 169
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[:'Utah', 'two']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 170,
       "text": [
        "Ohio        0\n",
        "Colorado    5\n",
        "Utah        9\n",
        "Name: two, dtype: int64"
       ]
      }
     ],
     "prompt_number": 170
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[data.three > 5, :3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "      <th>three</th>\n",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 171,
       "text": [
        "          one  two  three\n",
        "Colorado    0    5      6\n",
        "Utah        8    9     10\n",
        "New York   12   13     14"
       ]
      }
     ],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# ,\ub97c \uae30\uc900\uc73c\ub85c \uc55e\uc740 \ud589. \ub4a4\ub85c\ub294 \uc5f4\uc744 \ub098\ud0c0\ub0b8\ub2e4.\n",
      "data.ix[data.three > 5, :2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Colorado</th>\n",
        "      <td>  0</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Utah</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New York</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 172,
       "text": [
        "          one  two\n",
        "Colorado    0    5\n",
        "Utah        8    9\n",
        "New York   12   13"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\ube48\ubc88\ud558\uac8c \uc77c\uc5b4\ub098\ub294 \uce7c\ub7fc \uc120\ud0dd \uc791\uc5c5\uc744 \ud560 \ub54c\ub9c8\ub2e4 \uce7c\ub7fc\uc744 \uc120\ud0dd\ud558\uae30 \uc704\ud574 frame[:, col]\uc774\ub77c\uace0 \uc785\ub825\ud574\uc57c \ud558\ub294 \uac83\uc774 \ub108\ubb34\uacfc\ud558\ub2e4\uace0 \uc0dd\uac01\n",
      "####[\uae40\uc815\uc8fc] - DataFrame\uc5d0\uc11c\ub294 \uae30\ubcf8\uc801\uc73c\ub85c \uceec\ub7fc\uc744 \uae30\uc900\uc73c\ub85c \uacc4\uc0b0\ud55c\ub2e4. \uc65c\ub0d0\ud558\uba74 \ub85c\uc6b0\ubcf4\ub2e4 \uceec\ub7fc\uc744 \uae30\uc900\uc73c\ub85c \ub370\uc774\ud130\ub97c \ucd94\ucd9c\ud558\ub294\uac8c \ud6e8\uc52c \ub9ce\uae30 \ub54c\ubb38\uc774\ub2e4.\n",
      "- \ub77c\ubca8 \uc0c9\uc778 \uae30\ub2a5\uc744 \ubaa8\ub450 ix\uc5d0 \ub123\uc5c8\ub2e4."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### DataFrame\uc758 \uac12 \uc120\ud0dd\ud558\uae30\n",
      "\n",
      "\ubc29\uc2dd | \uc124\uba85\n",
      "--- | ---\n",
      "obj[val] | DataFrame\uc5d0\uc11c \ud558\ub098\uc758 \uce7c\ub7fc \ub610\ub294 \uc5ec\ub7ec \uce7c\ub7fc\uc744 \uc120\ud0dd\ud55c\ub2e4. \ud3b8\uc758\ub97c \uc704\ud574 \ubd88\ub9ac\uc5b8 \ubc30\uc5f4, \uc2ac\ub77c\uc774\uc2a4, \ubd88\ub9ac\uc5b8 DataFrame(\uc5b4\ub5a4 \uae30\uc900\uc5d0 \uadfc\uac70\ud574\uc11c \uac12\uc744 \ub300\uc785\ud574\uc57c \ud560 \ub54c)\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub2e4.\n",
      "obj.ix[val] | DataFrame\uc5d0\uc11c \ub85c\uc6b0\uc758 \ubd80\ubd84\uc9d1\ud569\uc744 \uc120\ud0dd\ud55c\ub2e4.\n",
      "obj.ix[:, val] | DataFrame\uc5d0\uc11c \uce7c\ub7fc\uc758 \ubd80\ubd84\uc9d1\ud569\uc744 \uc120\ud0dd\ud55c\ub2e4.\n",
      "obj.ix[val1, val2] | DataFrame\uc5d0\uc11c \ub85c\uc6b0\uc640 \uce7c\ub7fc\uc758 \ubd80\ubd84\uc9d1\ud569\uc744 \uc120\ud0dd\ud55c\ub2e4.\n",
      "reindex \uba54\uc11c\ub4dc | \ud558\ub098 \uc774\uc0c1\uc758 \ucd95\uc744 \uc0c8\ub85c\uc6b4 \uc0c9\uc778\uc73c\ub85c \ub9de\ucd98\ub2e4.\n",
      "xs \uba54\uc11c\ub4dc | \ub77c\ubca8 \uc774\ub984\uc73c\ub85c \ub2e8\uc77c \ub85c\uc6b0\ub098 \uce7c\ub7fc\uc744 Series \ud615\uc2dd\uc73c\ub85c \uc120\ud0dd\ud55c\ub2e4.\n",
      "icol, irow \uba54\uc11c\ub4dc | \uac01\uac01 \uc815\uc218 \uc0c9\uc778\uc73c\ub85c \ub2e8\uc77c \ub85c\uc6b0\ub098 \uce7c\ub7fc\uc744 Series \ud615\uc2dd\uc73c\ub85c \uc120\ud0dd\ud55c\ub2e4.\n",
      "get_value, set_value \uba54\uc11c\ub4dc | \ub85c\uc6b0\uc640 \uce7c\ub7fc \uc774\ub984\uc73c\ub85c DataFrame\uc758 \uac12\uc744 \uc120\ud0dd\ud55c\ub2e4."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.2.4 \uc0b0\uc220\uc5f0\uc0b0\uacfc \ub370\uc774\ud130 \uc815\ub82c"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 = Series([7.3, -2.5, 3.4, 1.5], index=['a', 'c', 'd', 'e'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 173
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2 = Series([-2.1, 3.6, -1.5, 4, 3.1], index=['a', 'c', 'e', 'f', 'g'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 174
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 175,
       "text": [
        "a    7.3\n",
        "c   -2.5\n",
        "d    3.4\n",
        "e    1.5\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 176,
       "text": [
        "a   -2.1\n",
        "c    3.6\n",
        "e   -1.5\n",
        "f    4.0\n",
        "g    3.1\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1 + s2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 177,
       "text": [
        "a    5.2\n",
        "c    1.1\n",
        "d    NaN\n",
        "e    0.0\n",
        "f    NaN\n",
        "g    NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uc11c\ub85c \uacb9\uce58\ub294 \uc0c9\uc778\uc774 \uc5c6\ub2e4\uba74 \ub370\uc774\ud130\ub294 NA \uac12\uc774 \ub41c\ub2e4.\n",
      "\n",
      "- \uc0b0\uc220\uc5f0\uc0b0 \uc2dc \ub204\ub77d\ub41c \uac12\uc740 \uc804\ud30c\n",
      "- DataFrame\uc5d0\uc11c\ub294 \ub85c\uc6b0\uc640 \uce7c\ub7fc \ubaa8\ub450\uc5d0 \uc801\uc6a9"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "DataFrame\uacfc Series\uc758 \ucc28\uc774\uc810\uc740??"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list('bcd')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 178,
       "text": [
        "['b', 'c', 'd']"
       ]
      }
     ],
     "prompt_number": 178
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = DataFrame(np.arange(9.).reshape((3, 3)), columns=list('bcd'),\n",
      "                index=['Ohio', 'Texas', 'Colorado'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 179
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'),\n",
      "                index=['Utah', 'Ohio', 'Texas', 'Oregon'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 181,
       "text": [
        "          b  c  d\n",
        "Ohio      0  1  2\n",
        "Texas     3  4  5\n",
        "Colorado  6  7  8"
       ]
      }
     ],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 182,
       "text": [
        "        b   d   e\n",
        "Utah    0   1   2\n",
        "Ohio    3   4   5\n",
        "Texas   6   7   8\n",
        "Oregon  9  10  11"
       ]
      }
     ],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 + df2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 183,
       "text": [
        "           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"
       ]
      }
     ],
     "prompt_number": 183
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uc0b0\uc220\uc5f0\uc0b0 \uba54\uc11c\ub4dc\uc5d0 \ucc44\uc6cc \ub123\uc744 \uac12 \uc9c0\uc815\ud558\uae30"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = DataFrame(np.arange(12.).reshape((3, 4)), columns=list('abcd'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = DataFrame(np.arange(20.).reshape((4, 5)), columns=list('abcde'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\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",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 8</td>\n",
        "      <td> 9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 186,
       "text": [
        "   a  b   c   d\n",
        "0  0  1   2   3\n",
        "1  4  5   6   7\n",
        "2  8  9  10  11"
       ]
      }
     ],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </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>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 187,
       "text": [
        "    a   b   c   d   e\n",
        "0   0   1   2   3   4\n",
        "1   5   6   7   8   9\n",
        "2  10  11  12  13  14\n",
        "3  15  16  17  18  19"
       ]
      }
     ],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 + df2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  0</td>\n",
        "      <td>  2</td>\n",
        "      <td>  4</td>\n",
        "      <td>  6</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 13</td>\n",
        "      <td> 15</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 18</td>\n",
        "      <td> 20</td>\n",
        "      <td> 22</td>\n",
        "      <td> 24</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <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",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 188,
       "text": [
        "    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"
       ]
      }
     ],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fill value=0\uc778\ub370 \uc65c 4,9,14,19\ub85c \ucc44\uc6cc\uc9c0\uc9c0??\n",
      "df1.add(df2, fill_value=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  0</td>\n",
        "      <td>  2</td>\n",
        "      <td>  4</td>\n",
        "      <td>  6</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 13</td>\n",
        "      <td> 15</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <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",
        "    </tr>\n",
        "    <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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 189,
       "text": [
        "    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"
       ]
      }
     ],
     "prompt_number": 189
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1.add(df2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  0</td>\n",
        "      <td>  2</td>\n",
        "      <td>  4</td>\n",
        "      <td>  6</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 13</td>\n",
        "      <td> 15</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 18</td>\n",
        "      <td> 20</td>\n",
        "      <td> 22</td>\n",
        "      <td> 24</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <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",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 190,
       "text": [
        "    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"
       ]
      }
     ],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc544\ud558! \uc6d0\ub798\uc758 df2 \uac12\uc5d0 fill_value\uc758 \uac12\uc744 \ub354\ud558\ub294\uad70!!\n",
      "df1.add(df2, fill_value=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  0</td>\n",
        "      <td>  2</td>\n",
        "      <td>  4</td>\n",
        "      <td>  6</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 13</td>\n",
        "      <td> 15</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 18</td>\n",
        "      <td> 20</td>\n",
        "      <td> 22</td>\n",
        "      <td> 24</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 16</td>\n",
        "      <td> 17</td>\n",
        "      <td> 18</td>\n",
        "      <td> 19</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 191,
       "text": [
        "    a   b   c   d   e\n",
        "0   0   2   4   6   5\n",
        "1   9  11  13  15  10\n",
        "2  18  20  22  24  15\n",
        "3  16  17  18  19  20"
       ]
      }
     ],
     "prompt_number": 191
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1.add(df2, fill_value=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  0</td>\n",
        "      <td>  2</td>\n",
        "      <td>  4</td>\n",
        "      <td>  6</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 13</td>\n",
        "      <td> 15</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 18</td>\n",
        "      <td> 20</td>\n",
        "      <td> 22</td>\n",
        "      <td> 24</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 17</td>\n",
        "      <td> 18</td>\n",
        "      <td> 19</td>\n",
        "      <td> 20</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 192,
       "text": [
        "    a   b   c   d   e\n",
        "0   0   2   4   6   6\n",
        "1   9  11  13  15  11\n",
        "2  18  20  22  24  16\n",
        "3  17  18  19  20  21"
       ]
      }
     ],
     "prompt_number": 192
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc6d0\ub798 \ub0b4\uac00 \uc0dd\uac01\ud588\ub358 \ud568\uc218\uc758 \uc5ed\ud560\uc774\uc5c8\uc9c0\ub9cc \uc798\ubabb\ub41c \uc0dd\uac01\uc778\ub4ef.\n",
      "df1.reindex(columns=df2.columns, fill_value=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </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",
        "    </tr>\n",
        "    <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",
        "    </tr>\n",
        "    <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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 193,
       "text": [
        "   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"
       ]
      }
     ],
     "prompt_number": 193
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uc0b0\uc220\uc5f0\uc0b0 \uba54\uc11c\ub4dc\n",
      "\n",
      "\uba54\uc11c\ub4dc | \uc124\uba85\n",
      "--- | ---\n",
      "add | \ub367\uc148(+)\uc744 \uc704\ud55c \uba54\uc11c\ub4dc\n",
      "sub | \ube84\uc148(-)\uc744 \uc704\ud55c \uba54\uc11c\ub4dc\n",
      "div | \ub098\ub217\uc148(/)\uc744 \uc704\ud55c \uba54\uc11c\ub4dc\n",
      "mul | \uacf1\uc148(*)\uc744 \uc704\ud55c \uba54\uc11c\ub4dc"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####DataFrame\uacfc Series \uac04\uc758 \uc5f0\uc0b0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr = np.arange(12.).reshape((3, 4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 194
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 195,
       "text": [
        "array([[  0.,   1.,   2.,   3.],\n",
        "       [  4.,   5.,   6.,   7.],\n",
        "       [  8.,   9.,  10.,  11.]])"
       ]
      }
     ],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 196,
       "text": [
        "array([ 0.,  1.,  2.,  3.])"
       ]
      }
     ],
     "prompt_number": 196
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr - arr[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 197,
       "text": [
        "array([[ 0.,  0.,  0.,  0.],\n",
        "       [ 4.,  4.,  4.,  4.],\n",
        "       [ 8.,  8.,  8.,  8.]])"
       ]
      }
     ],
     "prompt_number": 197
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr - arr[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 198,
       "text": [
        "array([[-4., -4., -4., -4.],\n",
        "       [ 0.,  0.,  0.,  0.],\n",
        "       [ 4.,  4.,  4.,  4.]])"
       ]
      }
     ],
     "prompt_number": 198
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\ube0c\ub85c\ub4dc\uce90\uc2a4\ud305"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame(np.arange(12.).reshape((4, 3)), columns=list('bde'),\n",
      "                  index=['Utah', 'Ohio', 'Texas', 'Oregon'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series = frame.ix[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 200
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 201,
       "text": [
        "        b   d   e\n",
        "Utah    0   1   2\n",
        "Ohio    3   4   5\n",
        "Texas   6   7   8\n",
        "Oregon  9  10  11"
       ]
      }
     ],
     "prompt_number": 201
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 202,
       "text": [
        "b    0\n",
        "d    1\n",
        "e    2\n",
        "Name: Utah, dtype: float64"
       ]
      }
     ],
     "prompt_number": 202
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame - series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 203,
       "text": [
        "        b  d  e\n",
        "Utah    0  0  0\n",
        "Ohio    3  3  3\n",
        "Texas   6  6  6\n",
        "Oregon  9  9  9"
       ]
      }
     ],
     "prompt_number": 203
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uae30\ubcf8\uc801\uc73c\ub85c DataFrame \uacfc Series \uac04\uc758 \uc0b0\uc220\uc5f0\uc0b0\uc740 Series\uc758 \uc0c9\uc778\uc744 DataFrame\uc758 \uce7c\ub7fc\uc5d0 \ub9de\ucd94\uace0 \uc544\ub798 \ub85c\uc6b0\ub85c \uc804\ud30c\n",
      "\n",
      "####\ub9cc\uc57d \uc0c9\uc778 \uac12\uc744 DataFrame\uc758 \uce7c\ub7fc\uc774\ub098 Series\uc758 \uc0c9\uc778\uc5d0\uc11c \ucc3e\uc744 \uc218 \uc5c6\ub2e4\uba74 \uadf8 \uac1d\uccb4\ub294 \ud615\uc2dd\uc744 \ub9de\ucd94\uae30 \uc704\ud574 \uc7ac\uc0c9\uc778"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series2 = Series(range(3), index=['b', 'e', 'f']) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame + series2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 205,
       "text": [
        "        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"
       ]
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series2, type(series2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 206,
       "text": [
        "(b    0\n",
        " e    1\n",
        " f    2\n",
        " dtype: int64, pandas.core.series.Series)"
       ]
      }
     ],
     "prompt_number": 206
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 207,
       "text": [
        "        b   d   e\n",
        "Utah    0   1   2\n",
        "Ohio    3   4   5\n",
        "Texas   6   7   8\n",
        "Oregon  9  10  11"
       ]
      }
     ],
     "prompt_number": 207
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uac01 \ub85c\uc6b0\uc5d0 \ub300\ud574 \uc5f0\uc0b0\uc744 \uc218\ud589\ud558\uace0 \uc2f6\ub2e4\uba74 \uc0b0\uc220\uc5f0\uc0b0 \uba54\uc11c\ub4dc \uc0ac\uc6a9"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series3 = frame['d']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 208
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 209,
       "text": [
        "        b   d   e\n",
        "Utah    0   1   2\n",
        "Ohio    3   4   5\n",
        "Texas   6   7   8\n",
        "Oregon  9  10  11"
       ]
      }
     ],
     "prompt_number": 209
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 210,
       "text": [
        "Utah       1\n",
        "Ohio       4\n",
        "Texas      7\n",
        "Oregon    10\n",
        "Name: d, dtype: float64"
       ]
      }
     ],
     "prompt_number": 210
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc778\uc790\ub85c \ub118\uae30\ub294 axis \uac12\uc740 \uc5f0\uc0b0\uc744 \uc801\uc6a9\ud560 \ucd95 \ubc88\ud638\n",
      "# axis=0\uc740 DataFrame\uc758 \ub85c\uc6b0\ub97c \ub530\ub77c \uc5f0\uc0b0\uc744 \uc218\ud589\n",
      "frame.sub(series3, axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 211,
       "text": [
        "        b  d  e\n",
        "Utah   -1  0  1\n",
        "Ohio   -1  0  1\n",
        "Texas  -1  0  1\n",
        "Oregon -1  0  1"
       ]
      }
     ],
     "prompt_number": 211
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.2.5 \ud568\uc218 \uc801\uc6a9\uacfc \ub9e4\ud551"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- pandas \uac1d\uccb4\uc5d0\ub3c4 NumPy\uc758 \uc720\ub2c8\ubc84\uc15c \ud568\uc218(\ubc30\uc5f4\uc758 \uac01 \uc6d0\uc18c\uc5d0 \uc801\uc6a9\ub418\ub294 \uba54\uc11c\ub4dc)\ub97c \uc801\uc6a9 \uac00\ub2a5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame(np.random.randn(4, 3), columns=list('bde'),\n",
      "                  index=['Utah', 'Ohio', 'Texas', 'Oregon'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.abs(frame)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>b</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Utah</th>\n",
        "      <td> 1.908651</td>\n",
        "      <td> 0.943723</td>\n",
        "      <td> 0.250739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td> 1.207835</td>\n",
        "      <td> 1.298138</td>\n",
        "      <td> 0.690437</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
        "      <td> 1.227648</td>\n",
        "      <td> 0.427507</td>\n",
        "      <td> 0.235306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oregon</th>\n",
        "      <td> 0.430131</td>\n",
        "      <td> 0.873474</td>\n",
        "      <td> 0.594105</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 213,
       "text": [
        "               b         d         e\n",
        "Utah    1.908651  0.943723  0.250739\n",
        "Ohio    1.207835  1.298138  0.690437\n",
        "Texas   1.227648  0.427507  0.235306\n",
        "Oregon  0.430131  0.873474  0.594105"
       ]
      }
     ],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>b</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Utah</th>\n",
        "      <td>-1.908651</td>\n",
        "      <td> 0.943723</td>\n",
        "      <td> 0.250739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td> 1.207835</td>\n",
        "      <td>-1.298138</td>\n",
        "      <td>-0.690437</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
        "      <td>-1.227648</td>\n",
        "      <td>-0.427507</td>\n",
        "      <td>-0.235306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oregon</th>\n",
        "      <td>-0.430131</td>\n",
        "      <td>-0.873474</td>\n",
        "      <td> 0.594105</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 214,
       "text": [
        "               b         d         e\n",
        "Utah   -1.908651  0.943723  0.250739\n",
        "Ohio    1.207835 -1.298138 -0.690437\n",
        "Texas  -1.227648 -0.427507 -0.235306\n",
        "Oregon -0.430131 -0.873474  0.594105"
       ]
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc790\uc8fc \uc0ac\uc6a9\ub418\ub294 \ub610 \ub2e4\ub978 \uc5f0\uc0b0\uc740 \uac01 \ub85c\uc6b0\ub098 \uce7c\ub7fc\uc758 1\ucc28\uc6d0 \ubc30\uc5f4\uc5d0 \ud568\uc218\ub97c \uc801\uc6a9\ud558\ub294 \uac83\n",
      "- DataFrame\uc758 apply \uba54\uc11c\ub4dc\ub97c \ud1b5\ud574 \uc218\ud589"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = lambda x: x.max() - x.min()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 215
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Applies function along input axis of DataFrame. \n",
      "# Objects passed to functions are Series objects having index either the DataFrame's index(axis=0)\n",
      "# or the columns (axis=1).\n",
      "# Return type depends on whether passed function aggregates\n",
      "\n",
      "frame.apply?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 216
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1. b \uceec\ub7fc \uae30\uc900\uc73c\ub85c row \uac12\ub4e4\uc744 \ub300\uc0c1\uc73c\ub85c\n",
      "# 2. max\uac12\uacfc min\uac12\uc744 \uad6c\ud55c\ub2e4.\n",
      "# 3. max - min\uc744 \ud55c \ud6c4 \uacb0\uacfc\uac12 \ub3cc\ub824\uc900\ub2e4.\n",
      "# 1.107814 - (-0.026992) = 1.134806\n",
      "frame.apply(f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 217,
       "text": [
        "b    3.116486\n",
        "d    2.241861\n",
        "e    1.284542\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 217
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 0.328717 - 0.121724 = 0.206992\n",
      "frame.apply(f, axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 218,
       "text": [
        "Utah      2.852374\n",
        "Ohio      2.505973\n",
        "Texas     0.992342\n",
        "Oregon    1.467579\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>b</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Utah</th>\n",
        "      <td>-1.908651</td>\n",
        "      <td> 0.943723</td>\n",
        "      <td> 0.250739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td> 1.207835</td>\n",
        "      <td>-1.298138</td>\n",
        "      <td>-0.690437</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
        "      <td>-1.227648</td>\n",
        "      <td>-0.427507</td>\n",
        "      <td>-0.235306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oregon</th>\n",
        "      <td>-0.430131</td>\n",
        "      <td>-0.873474</td>\n",
        "      <td> 0.594105</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 219,
       "text": [
        "               b         d         e\n",
        "Utah   -1.908651  0.943723  0.250739\n",
        "Ohio    1.207835 -1.298138 -0.690437\n",
        "Texas  -1.227648 -0.427507 -0.235306\n",
        "Oregon -0.430131 -0.873474  0.594105"
       ]
      }
     ],
     "prompt_number": 219
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###axis\uc758 \ub85c\uc6b0, \uceec\ub7fc \uad6c\ubd84\n",
      "\n",
      "####\ub85c\uc6b0 \n",
      "- frame.sub(series3, **axis=0**) \n",
      "- frame.apply(f, **aixs=1**)\n",
      "- df.sum(**axis=1**)\n",
      "\n",
      "####\uceec\ub7fc\n",
      "- frame.sort_index(**axis=1**)\n",
      "\n",
      "\n",
      "#### DataFrame\uc5d0\uc11c axis=\n",
      "\n",
      "- 0: row\n",
      "- 1: column"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ubc30\uc5f4\uc758 \ud569\uacc4\ub098 \ud3c9\uade0\uac19\uc740 \uc77c\ubc18\uc801\uc778 \ud1b5\uacc4\ub294 DataFrame\uc758 \uba54\uc11c\ub4dc\ub85c \uc788\uc73c\ubbc0\ub85c apply \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud574\uc57c\ub9cc \ud558\ub294 \uac83\uc740 \uc544\ub2c8\ub2e4.\n",
      "- apply \uba54\uc11c\ub4dc\uc5d0 \uc804\ub2ec\ub41c \ud568\uc218\ub294 \uc2a4\uce7c\ub77c \uac12\uc744 \ubc18\ud658\ud560 \ud544\uc694 \uc5c6\uc73c\uba70, Series \ub610\ub294 \uc5ec\ub7ec \uac12\uc744 \ubc18\ud658\ud574\ub3c4 \ub41c\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    return Series([x.min(), x.max()], index=['min', 'max'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.apply(f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>b</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>-1.908651</td>\n",
        "      <td>-1.298138</td>\n",
        "      <td>-0.690437</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1.207835</td>\n",
        "      <td> 0.943723</td>\n",
        "      <td> 0.594105</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 221,
       "text": [
        "            b         d         e\n",
        "min -1.908651 -1.298138 -0.690437\n",
        "max  1.207835  0.943723  0.594105"
       ]
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(frame.apply(f))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 222,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 222
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "format = lambda x: '%.2f' % x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 223
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.applymap(format)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>b</th>\n",
        "      <th>d</th>\n",
        "      <th>e</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Utah</th>\n",
        "      <td> -1.91</td>\n",
        "      <td>  0.94</td>\n",
        "      <td>  0.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ohio</th>\n",
        "      <td>  1.21</td>\n",
        "      <td> -1.30</td>\n",
        "      <td> -0.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
        "      <td> -1.23</td>\n",
        "      <td> -0.43</td>\n",
        "      <td> -0.24</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oregon</th>\n",
        "      <td> -0.43</td>\n",
        "      <td> -0.87</td>\n",
        "      <td>  0.59</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 224,
       "text": [
        "            b      d      e\n",
        "Utah    -1.91   0.94   0.25\n",
        "Ohio     1.21  -1.30  -0.69\n",
        "Texas   -1.23  -0.43  -0.24\n",
        "Oregon  -0.43  -0.87   0.59"
       ]
      }
     ],
     "prompt_number": 224
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['e'].map(format)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 225,
       "text": [
        "Utah       0.25\n",
        "Ohio      -0.69\n",
        "Texas     -0.24\n",
        "Oregon     0.59\n",
        "Name: e, dtype: object"
       ]
      }
     ],
     "prompt_number": 225
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc6d0\ub798 float\uac12\uc778\ub370 \uc704\uc5d0\uc11c string \ud615\uc2dd\uc73c\ub85c \ubcc0\uacbd\ud588\uae30 \ub54c\ubb38\uc5d0 dtype\uc774 object\uac00 \ub410\ub2e4.\n",
      "frame['e']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 226,
       "text": [
        "Utah      0.250739\n",
        "Ohio     -0.690437\n",
        "Texas    -0.235306\n",
        "Oregon    0.594105\n",
        "Name: e, dtype: float64"
       ]
      }
     ],
     "prompt_number": 226
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.2.6 \uc815\ub82c\uacfc \uc21c\uc704"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series(range(4), index=['d', 'a', 'b', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.sort_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 228,
       "text": [
        "a    1\n",
        "b    2\n",
        "c    3\n",
        "d    0\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 228
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame(np.arange(8).reshape((2, 4)), index=['three', 'one'],\n",
      "                  columns=['d', 'a', 'b', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 229
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>d</th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\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",
        "    <tr>\n",
        "      <th>one</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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 230,
       "text": [
        "       d  a  b  c\n",
        "three  0  1  2  3\n",
        "one    4  5  6  7"
       ]
      }
     ],
     "prompt_number": 230
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sort_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 231,
       "text": [
        "       d  a  b  c\n",
        "one    4  5  6  7\n",
        "three  0  1  2  3"
       ]
      }
     ],
     "prompt_number": 231
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sort_index(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 232,
       "text": [
        "       a  b  c  d\n",
        "three  1  2  3  0\n",
        "one    5  6  7  4"
       ]
      }
     ],
     "prompt_number": 232
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### index, column \ubaa8\ub450 \ub9cc\uc871\uc2dc\ud0a4\ub294 \uc815\ub82c\uc740 \uc5b4\ub5bb\uac8c \ud574\uc57c\ub3fc\uc9c0???"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sort_index(axis=1, ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>d</th>\n",
        "      <th>c</th>\n",
        "      <th>b</th>\n",
        "      <th>a</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>three</th>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>one</th>\n",
        "      <td> 4</td>\n",
        "      <td> 7</td>\n",
        "      <td> 6</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 233,
       "text": [
        "       d  c  b  a\n",
        "three  0  3  2  1\n",
        "one    4  7  6  5"
       ]
      }
     ],
     "prompt_number": 233
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series([4, 7, -3, 2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 234
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uac12\uc5d0 \ub530\ub77c \uc815\ub82c\n",
      "obj.order()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 235,
       "text": [
        "2   -3\n",
        "3    2\n",
        "0    4\n",
        "1    7\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 235
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series([4, np.nan, 7, np.nan, -3, 2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 236
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.order()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 237,
       "text": [
        "4    -3\n",
        "5     2\n",
        "0     4\n",
        "2     7\n",
        "1   NaN\n",
        "3   NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 237
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\uc815\ub82c\uc2dc NaN\uc740 \uac00\uc7a5 \ub9c8\uc9c0\ub9c9\uc5d0 \uc704\uce58\n",
      "<pre>\n",
      "4    -3\n",
      "5     2\n",
      "0     4\n",
      "2     7\n",
      "1   NaN\n",
      "3   NaN\n",
      "</pre>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame({'b': [4, 7, -3, 2],\n",
      "                   'a': [0, 1, 0, 1]})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 238
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 239,
       "text": [
        "   a  b\n",
        "0  0  4\n",
        "1  1  7\n",
        "2  0 -3\n",
        "3  1  2"
       ]
      }
     ],
     "prompt_number": 239
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sort_index(by='b')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 240,
       "text": [
        "   a  b\n",
        "2  0 -3\n",
        "3  1  2\n",
        "0  0  4\n",
        "1  1  7"
       ]
      }
     ],
     "prompt_number": 240
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sort_index(by=['a', 'b'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 241,
       "text": [
        "   a  b\n",
        "2  0 -3\n",
        "0  0  4\n",
        "3  1  2\n",
        "1  1  7"
       ]
      }
     ],
     "prompt_number": 241
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### \ubb58 \uae30\uc900\uc73c\ub85c rank \ud558\ub294\uc9c0 \ub3c4\uc800\ud788 \ubaa8\ub974\uaca0\ub2e4..!! -> \uc2a4\ud130\ub514 \ud6c4 \uc54c\uac8c \ub428\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series([7, -5, 7, 4, 2, 0, 4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 242
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.rank()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 243,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 243
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Rank \ud574\uc11d\n",
      "\n",
      "1. -5\uac00 1.0\n",
      "2. 0\uc774 2.0\n",
      "3. 2\uac00 3.0\n",
      "4. 4\uac00 4.5, \uc65c\ub0d0\ud558\uba74 \ub3d9\ub960\uc774\uae30 \ub54c\ubb38\uc5d0 4.0\uc774 \uc548\ub418\uace0 4.0\uacfc 5.0\uc758 \uc911\uac04\uc778 4.5\uac00 \ub428\n",
      "5. 7\ub3c4 \uc911\ubcf5\uc774\uae30 \ub54c\ubb38\uc5d0 6.0\uacfc 7.0 \uc911\uac04\uc778 6.5\uac00 \ub428"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ub370\uc774\ud130 \uc0c1\uc5d0\uc11c \ub098\ud0c0\ub098\ub294 \uc21c\uc11c\uc5d0 \ub530\ub77c \uc21c\uc704\n",
      "obj.rank(method='first')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 244,
       "text": [
        "0    6\n",
        "1    1\n",
        "2    7\n",
        "3    4\n",
        "4    3\n",
        "5    2\n",
        "6    5\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 244
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ub0b4\ub9bc\ucc28\uc21c\uc73c\ub85c \uc21c\uc704\n",
      "obj.rank(ascending=False, method='max')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 245,
       "text": [
        "0    2\n",
        "1    7\n",
        "2    2\n",
        "3    4\n",
        "4    5\n",
        "5    6\n",
        "6    4\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 245
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame({'b': [4.3, 7, -3, 2],\n",
      "                   'a':[0, 1, 0, 1],\n",
      "                   'c':[-2, 5, 8, -2.5]})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 246
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 4.3</td>\n",
        "      <td>-2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 7.0</td>\n",
        "      <td> 5.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "      <td>-3.0</td>\n",
        "      <td> 8.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2.0</td>\n",
        "      <td>-2.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 247,
       "text": [
        "   a    b    c\n",
        "0  0  4.3 -2.0\n",
        "1  1  7.0  5.0\n",
        "2  0 -3.0  8.0\n",
        "3  1  2.0 -2.5"
       ]
      }
     ],
     "prompt_number": 247
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 0, 4.3, -2.0 \uc5d0\uc11c rank \uc815\ud568\n",
      "frame.rank(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 248,
       "text": [
        "   a  b  c\n",
        "0  2  3  1\n",
        "1  1  3  2\n",
        "2  2  1  3\n",
        "3  2  3  1"
       ]
      }
     ],
     "prompt_number": 248
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 0, 1, 0, 1 \uc5d0\uc11c rank \uc815\ud568\n",
      "frame.rank()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.5</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 3.5</td>\n",
        "      <td> 4</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1.5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3.5</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 249,
       "text": [
        "     a  b  c\n",
        "0  1.5  3  2\n",
        "1  3.5  4  3\n",
        "2  1.5  1  4\n",
        "3  3.5  2  1"
       ]
      }
     ],
     "prompt_number": 249
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uc21c\uc704\uc758 \ub3d9\ub960\uc744 \ucc98\ub9ac\ud558\ub294 \uba54\uc11c\ub4dc\n",
      "\n",
      "\uba54\uc11c\ub4dc | \uc124\uba85\n",
      "--- | ---\n",
      "'average' | \uae30\ubcf8 \uac12: \uac19\uc740 \uac12\uc744 \uac00\uc9c0\ub294 \ud56d\ubaa9\uc758 \ud3c9\uade0 \uac12\uc744 \uc21c\uc704\ub85c \uc0bc\ub294\ub2e4.\n",
      "'min' | \uac19\uc740 \uac12\uc744 \uac00\uc9c0\ub294 \uadf8\ub8f9\uc744 \ub0ae\uc740 \uc21c\uc704\ub85c \ub9e4\uae34\ub2e4.\n",
      "'max' | \uac19\uc740 \uac12\uc744 \uac00\uc9c0\ub294 \uadf8\ub8f9\uc744 \ub192\uc740 \uc21c\uc704\ub85c \ub9e4\uae34\ub2e4.\n",
      "'first' | \ub370\uc774\ud130 \ub0b4\uc5d0\uc11c \uc704\uce58\uc5d0 \ub530\ub77c \uc21c\uc704\ub97c \ub9e4\uae34\ub2e4."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.2.7 \uc911\ubcf5 \uc0c9\uc778"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series(range(5), index=['a', 'a', 'b', 'b', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 250
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 251,
       "text": [
        "a    0\n",
        "a    1\n",
        "b    2\n",
        "b    3\n",
        "c    4\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 251
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.index.is_unique"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 252,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 252
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc911\ubcf5\ub418\ub294 \uc0c9\uc778 \uac12\uc774 \uc788\uc73c\uba74 \uc0c9\uc778\uc744 \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc120\ud0dd\uc740 \ub2e4\ub974\uac8c \ub3d9\uc791\ud558\uace0 \ud558\ub098\uc758 Series \uac1d\uccb4 \ubc18\ud658\n",
      "- \uc911\ubcf5\ub418\ub294 \uc0c9\uc778 \uac12\uc774 \uc5c6\uc73c\uba74 \uc0c9\uc778\uc744 \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc120\ud0dd\uc740 \uc2a4\uce7c\ub77c \uac12\uc744 \ubc18\ud658"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj['a']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 253,
       "text": [
        "array([0, 1])"
       ]
      }
     ],
     "prompt_number": 253
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ucc45\uc5d0\uc11c\ub294 \ud558\ub098\uc758 Series \uac1d\uccb4 \ubc18\ud658 \ud55c\ub2e4\uace0 \ud588\ub290\ub370 numpy.ndarray\ub124?!\n",
      "type(obj['a'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 254,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 254
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj['c']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 255,
       "text": [
        "4"
       ]
      }
     ],
     "prompt_number": 255
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(np.random.randn(4, 3), index=['a', 'a', 'b', 'b'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 256
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td> 0.284454</td>\n",
        "      <td> 1.189586</td>\n",
        "      <td> 0.394071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td> 0.754665</td>\n",
        "      <td>-1.483700</td>\n",
        "      <td> 1.019817</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 0.246102</td>\n",
        "      <td>-1.525030</td>\n",
        "      <td>-0.418855</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 0.575552</td>\n",
        "      <td> 0.920265</td>\n",
        "      <td>-0.012548</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 257,
       "text": [
        "          0         1         2\n",
        "a  0.284454  1.189586  0.394071\n",
        "a  0.754665 -1.483700  1.019817\n",
        "b  0.246102 -1.525030 -0.418855\n",
        "b  0.575552  0.920265 -0.012548"
       ]
      }
     ],
     "prompt_number": 257
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix['b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 0.246102</td>\n",
        "      <td>-1.525030</td>\n",
        "      <td>-0.418855</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 0.575552</td>\n",
        "      <td> 0.920265</td>\n",
        "      <td>-0.012548</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 258,
       "text": [
        "          0         1         2\n",
        "b  0.246102 -1.525030 -0.418855\n",
        "b  0.575552  0.920265 -0.012548"
       ]
      }
     ],
     "prompt_number": 258
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src='files/images/panda2.jpg' width='300px' />"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5.3 \uae30\uc220\ud1b5\uacc4 \uacc4\uc0b0\uacfc \uc694\uc57d"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- pandas \uac1d\uccb4\ub294 \uc77c\ubc18\uc801\uc778 \uc218\ud559 \uba54\uc11c\ub4dc\uc640 \ud1b5\uacc4 \uba54\uc11c\ub4dc \uc874\uc7ac\n",
      "- \uc774 \uba54\uc11c\ub4dc\ub294 \ub300\ubd80\ubd84 Series\ub098 DataFrame \ud558\ub098\uc758 \uce7c\ub7fc\uc774\ub098 \ub85c\uc6b0\uc5d0\uc11c \ub2e8\uc77c \uac12(\ud569\uc774\ub098 \ud3c9\uade0 \uac19\uc740)\uc744 \uad6c\ud558\ub294 \ucd95\uc18c \ud639\uc740 \uc694\uc57d\ud1b5\uacc4 \ubc94\uc8fc\uc5d0 \uc18d\ud568\n",
      "- \ucc98\uc74c\ubd80\ud130 \ub204\ub77d\ub41c \ub370\uc774\ud130\ub97c \uc81c\uc678\ud558\ub3c4\ub85d \uc124\uacc4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame([[1.4, np.nan], [7.1, -4.5],\n",
      "                [np.nan, np.nan], [0.75, -1.3]],\n",
      "               index=['a', 'b', 'c', 'd'],\n",
      "               columns=['one', 'two'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 259
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 260,
       "text": [
        "    one  two\n",
        "a  1.40  NaN\n",
        "b  7.10 -4.5\n",
        "c   NaN  NaN\n",
        "d  0.75 -1.3"
       ]
      }
     ],
     "prompt_number": 260
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# NaN\uc740 \uacc4\uc0b0 \uc548\ub428\n",
      "df.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 261,
       "text": [
        "one    9.25\n",
        "two   -5.80\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 261
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uac01 \ub85c\uc6b0\uc758 \ud569 \ubc18\ud658\n",
      "df.sum(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 262,
       "text": [
        "a    1.40\n",
        "b    2.60\n",
        "c     NaN\n",
        "d   -0.55\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 262
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.mean(axis=1, skipna=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 263,
       "text": [
        "a      NaN\n",
        "b    1.300\n",
        "c      NaN\n",
        "d   -0.275\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 263
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \ucd95\uc18c \uba54\uc11c\ub4dc \uc635\uc158\n",
      "\n",
      "\uc635\uc158 | \uc124\uba85\n",
      "--- | ---\n",
      "axis | \uc5f0\uc0b0\uc744 \uc218\ud589\ud560 \ucd95. DataFrame\uc5d0\uc11c 0\uc740 \ub85c\uc6b0\uace0 1\uc740 \uce7c\ub7fc\uc774\ub2e4.\n",
      "skipna | \ub204\ub77d\ub41c \uac12\uc744 \uc81c\uc678\ud560 \uac83\uc778\uc9c0 \uc815\ud558\ub294 \uc635\uc158. \uae30\ubcf8\uac12\uc740 True\ub2e4.\n",
      "level | \uacc4\uc0b0\ud558\ub824\ub294 \ucd95\uc774 \uacc4\uce35\uc801 \uc0c9\uc778(\ub2e4\uc911 \uc0c9\uc778)\uc774\ub77c\uba74 \ub808\ubca8\uc5d0 \ub530\ub77c \ubb36\uc5b4\uc11c \uacc4\uc0b0\ud55c\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Return index of first occurrence of maximum over requested axis.\n",
      "# NA/null values are excluded.\n",
      "df.idxmax?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 264
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.idxmax()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 265,
       "text": [
        "one    b\n",
        "two    d\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 265
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# cumulative. \uc544\ub798\ub85c \uac08\uc218\ub85d \ub204\uc0b0 \ub428\n",
      "df.cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 266,
       "text": [
        "    one  two\n",
        "a  1.40  NaN\n",
        "b  8.50 -4.5\n",
        "c   NaN  NaN\n",
        "d  9.25 -5.8"
       ]
      }
     ],
     "prompt_number": 266
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 267,
       "text": [
        "    one  two\n",
        "a  1.40  NaN\n",
        "b  7.10 -4.5\n",
        "c   NaN  NaN\n",
        "d  0.75 -1.3"
       ]
      }
     ],
     "prompt_number": 267
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>one</th>\n",
        "      <th>two</th>\n",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 268,
       "text": [
        "            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"
       ]
      }
     ],
     "prompt_number": 268
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series(['a', 'a', 'b', 'c'] * 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 269
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 270,
       "text": [
        "count     16\n",
        "unique     3\n",
        "top        a\n",
        "freq       8\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 270
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 271,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 271
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uae30\uc220\ud1b5\uacc4\uc640 \uc694\uc57d\ud1b5\uacc4\n",
      "\n",
      "\uba54\uc11c\ub4dc | \uc124\uba85\n",
      "--- | ---\n",
      "count | NA \uac12\uc744 \uc81c\uc678\ud55c \uac12\uc758 \uc218\ub97c \ubc18\ud658\ud55c\ub2e4.\n",
      "describe | Series\ub098 DataFrame\uc758 \uac01 \uce7c\ub7fc\uc5d0 \ub300\ud55c \uc694\uc57d\ud1b5\uacc4\ub97c \uacc4\uc0b0\ud55c\ub2e4.\n",
      "min, max | \ucd5c\uc18c, \ucd5c\ub300\uac12\uc744 \uacc4\uc0b0\ud55c\ub2e4.\n",
      "argmin, argmax | \uac01\uac01 \ucd5c\uc18c, \ucd5c\ub300\uac12\uc744 \uac16\uace0 \uc788\ub294 \uc0c9\uc778\uc758 \uc704\uce58(\uc815\uc218)\ub97c \ubc18\ud658\ud55c\ub2e4.\n",
      "idxmin, idxmax | \uac01\uac01 \ucd5c\uc18c, \ucd5c\ub300 \uac12\uc744 \uac16\uace0 \uc788\ub294 \uc0c9\uc778\uc758 \uac12\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "quantile | 0\ubd80\ud130 1\uae4c\uc9c0\uc758 \ubd84\uc704\uc218\ub97c \uacc4\uc0b0\ud55c\ub2e4.\n",
      "sum | \ud569\uc744 \uacc4\uc0b0\ud55c\ub2e4.\n",
      "mean | \ud3c9\uade0\uc744 \uacc4\uc0b0\ud55c\ub2e4.\n",
      "median | \uc911\uac04 \uac12(50% \ubd84\uc704)\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "mad | \ud3c9\uade0 \uac12\uc5d0\uc11c \uc808\ub300 \ud3c9\uade0\ud3b8\ucc28\ub97c \uad6c\ud55c\ub2e4.\n",
      "var | \ud45c\ubcf8 \ubd84\uc0b0\uc758 \uac12\uc744 \uad6c\ud55c\ub2e4.\n",
      "std | \ud45c\ubcf8 \uc815\uaddc \ubd84\uc0b0\uc758 \uac12\uc744 \uad6c\ud55c\ub2e4.\n",
      "skew | \ud45c\ubcf8 \ube44\ub300\uce6d\ub3c4(3\ucc28 \uc801\ub960)\uc758 \uac12\uc744 \uad6c\ud55c\ub2e4.\n",
      "cumsum | \ub204\uc801 \ud569\uc744 \uad6c\ud55c\ub2e4.\n",
      "cummin, cummax | \uac01\uac01 \ub204\uc801 \ucd5c\uc18c \uac12\uacfc \ub204\uc801 \ucd5c\ub300 \uac12\uc744 \uacc4\uc0b0\ud55c\ub2e4.\n",
      "cumprod | \ub204\uc801 \uacf1\uc744 \uad6c\ud55c\ub2e4.\n",
      "diff | 1\ucc28 \uc0b0\uc220 \ucc28\ub97c \uad6c\ud55c\ub2e4(\uc2dc\uac8c\uc5f4 \ub370\uc774\ud130 \ucc98\ub9ac\uc2dc \uc720\uc6a9\ud558\ub2e4).\n",
      "pct_change | \ud37c\uc13c\ud2b8 \ubcc0\ud654\uc728\uc744 \uacc4\uc0b0\ud55c\ub2e4.\n",
      "\n",
      "#### \ud655\ub960, \ud1b5\uacc4 \ubaa8\ub974\uba74 \ud798\ub4e4\uc5b4\uc9c4\ub2e4.. \uc6a9\uc5b4\ubd80\ud130 \uc774\ud574\uac00 \uac00\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0..."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.3.1 \uc0c1\uad00\uad00\uacc4\uc640 \uacf5\ubd84\uc0b0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas.io.data as web\n",
      "\n",
      "all_data = {}\n",
      "\n",
      "# GOOG -> GOOGL\n",
      "for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOGL']:\n",
      "    all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2000', '5/12/2014')\n",
      "\n",
      "price = DataFrame({tic: data['Adj Close']\n",
      "                   for tic, data in all_data.iteritems()})\n",
      "volume = DataFrame({tic: data['Volume']\n",
      "                    for tic, data in all_data.iteritems()})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 272
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Percent change over given number of periods\n",
      "returns = price.pct_change()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 273
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>AAPL</th>\n",
        "      <th>GOOGL</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>2014-05-06</th>\n",
        "      <td>-0.010893</td>\n",
        "      <td>-0.023836</td>\n",
        "      <td>-0.006416</td>\n",
        "      <td>-0.009195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-05-07</th>\n",
        "      <td>-0.003502</td>\n",
        "      <td>-0.008745</td>\n",
        "      <td> 0.001958</td>\n",
        "      <td> 0.009023</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-05-08</th>\n",
        "      <td>-0.001783</td>\n",
        "      <td> 0.004189</td>\n",
        "      <td>-0.002060</td>\n",
        "      <td> 0.005621</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-05-09</th>\n",
        "      <td>-0.004167</td>\n",
        "      <td> 0.012400</td>\n",
        "      <td> 0.006193</td>\n",
        "      <td>-0.002541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-05-12</th>\n",
        "      <td> 0.012450</td>\n",
        "      <td> 0.022426</td>\n",
        "      <td> 0.013100</td>\n",
        "      <td> 0.010953</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 274,
       "text": [
        "                AAPL     GOOGL       IBM      MSFT\n",
        "Date                                              \n",
        "2014-05-06 -0.010893 -0.023836 -0.006416 -0.009195\n",
        "2014-05-07 -0.003502 -0.008745  0.001958  0.009023\n",
        "2014-05-08 -0.001783  0.004189 -0.002060  0.005621\n",
        "2014-05-09 -0.004167  0.012400  0.006193 -0.002541\n",
        "2014-05-12  0.012450  0.022426  0.013100  0.010953"
       ]
      }
     ],
     "prompt_number": 274
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "price"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "DatetimeIndex: 3611 entries, 2000-01-03 00:00:00 to 2014-05-12 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "AAPL     3611  non-null values\n",
        "GOOGL    2449  non-null values\n",
        "IBM      3611  non-null values\n",
        "MSFT     3611  non-null values\n",
        "dtypes: float64(4)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 275,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 3611 entries, 2000-01-03 00:00:00 to 2014-05-12 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "AAPL     3611  non-null values\n",
        "GOOGL    2449  non-null values\n",
        "IBM      3611  non-null values\n",
        "MSFT     3611  non-null values\n",
        "dtypes: float64(4)"
       ]
      }
     ],
     "prompt_number": 275
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "volume"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "DatetimeIndex: 3611 entries, 2000-01-03 00:00:00 to 2014-05-12 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "AAPL     3611  non-null values\n",
        "GOOGL    2449  non-null values\n",
        "IBM      3611  non-null values\n",
        "MSFT     3611  non-null values\n",
        "dtypes: float64(1), int64(3)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 276,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 3611 entries, 2000-01-03 00:00:00 to 2014-05-12 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "AAPL     3611  non-null values\n",
        "GOOGL    2449  non-null values\n",
        "IBM      3611  non-null values\n",
        "MSFT     3611  non-null values\n",
        "dtypes: float64(1), int64(3)"
       ]
      }
     ],
     "prompt_number": 276
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>AAPL</th>\n",
        "      <th>GOOGL</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>2000-01-03</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-04</th>\n",
        "      <td>-0.084486</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-0.033944</td>\n",
        "      <td>-0.033814</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-05</th>\n",
        "      <td> 0.014700</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0.035137</td>\n",
        "      <td> 0.010524</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-06</th>\n",
        "      <td>-0.086519</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-0.017284</td>\n",
        "      <td>-0.033422</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000-01-07</th>\n",
        "      <td> 0.047577</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-0.004344</td>\n",
        "      <td> 0.013029</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 277,
       "text": [
        "                AAPL  GOOGL       IBM      MSFT\n",
        "Date                                           \n",
        "2000-01-03       NaN    NaN       NaN       NaN\n",
        "2000-01-04 -0.084486    NaN -0.033944 -0.033814\n",
        "2000-01-05  0.014700    NaN  0.035137  0.010524\n",
        "2000-01-06 -0.086519    NaN -0.017284 -0.033422\n",
        "2000-01-07  0.047577    NaN -0.004344  0.013029"
       ]
      }
     ],
     "prompt_number": 277
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.MSFT"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 278,
       "text": [
        "Date\n",
        "2000-01-03         NaN\n",
        "2000-01-04   -0.033814\n",
        "2000-01-05    0.010524\n",
        "2000-01-06   -0.033422\n",
        "2000-01-07    0.013029\n",
        "2000-01-10    0.007420\n",
        "2000-01-11   -0.025780\n",
        "2000-01-12   -0.032510\n",
        "2000-01-13    0.019015\n",
        "2000-01-14    0.041155\n",
        "2000-01-18    0.027253\n",
        "2000-01-19   -0.072180\n",
        "2000-01-20   -0.009274\n",
        "2000-01-21   -0.021321\n",
        "2000-01-24   -0.023911\n",
        "...\n",
        "2014-04-22    0.001261\n",
        "2014-04-23   -0.007555\n",
        "2014-04-24    0.004314\n",
        "2014-04-25    0.001263\n",
        "2014-04-28    0.023972\n",
        "2014-04-29   -0.008625\n",
        "2014-04-30   -0.002734\n",
        "2014-05-01   -0.009970\n",
        "2014-05-02   -0.007805\n",
        "2014-05-05   -0.006597\n",
        "2014-05-06   -0.009195\n",
        "2014-05-07    0.009023\n",
        "2014-05-08    0.005621\n",
        "2014-05-09   -0.002541\n",
        "2014-05-12    0.010953\n",
        "Name: MSFT, Length: 3611"
       ]
      }
     ],
     "prompt_number": 278
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uc0c1\uad00\uad00\uacc4\uc640 \uacf5\ubd84\uc0b0 \uac1c\ub150 \uc774\ud574\n",
      "\n",
      "- [\uc0c1\uad00\uad00\uacc4](http://ezstat.snu.ac.kr/textbook_sources/chapter_05.pdf)\n",
      "- [\uacf5\ubd84\uc0b0\uacfc \uc0c1\uad00\uacc4\uc218](http://carstart.tistory.com/132)\n",
      "- [\uc0c1\uad00\ubd84\uc11d](http://carstart.tistory.com/111)\n",
      "- [\uacf5\ubd84\uc0b0(Covariance)\uc774\ub780 \ubb34\uc5c7\uc778\uac00?](http://blog.naver.com/PostView.nhn?blogId=jindog2929&logNo=10161293384)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.MSFT.corr(returns.IBM)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 279,
       "text": [
        "0.4984519862052475"
       ]
      }
     ],
     "prompt_number": 279
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.MSFT.cov(returns.IBM)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 280,
       "text": [
        "0.00017666857300561051"
       ]
      }
     ],
     "prompt_number": 280
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>AAPL</th>\n",
        "      <th>GOOGL</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> 0.456068</td>\n",
        "      <td> 0.406758</td>\n",
        "      <td> 0.414118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GOOGL</th>\n",
        "      <td> 0.456068</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.397134</td>\n",
        "      <td> 0.435762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IBM</th>\n",
        "      <td> 0.406758</td>\n",
        "      <td> 0.397134</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.498452</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MSFT</th>\n",
        "      <td> 0.414118</td>\n",
        "      <td> 0.435762</td>\n",
        "      <td> 0.498452</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 281,
       "text": [
        "           AAPL     GOOGL       IBM      MSFT\n",
        "AAPL   1.000000  0.456068  0.406758  0.414118\n",
        "GOOGL  0.456068  1.000000  0.397134  0.435762\n",
        "IBM    0.406758  0.397134  1.000000  0.498452\n",
        "MSFT   0.414118  0.435762  0.498452  1.000000"
       ]
      }
     ],
     "prompt_number": 281
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.cov()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>AAPL</th>\n",
        "      <th>GOOGL</th>\n",
        "      <th>IBM</th>\n",
        "      <th>MSFT</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AAPL</th>\n",
        "      <td> 0.000807</td>\n",
        "      <td> 0.000220</td>\n",
        "      <td> 0.000199</td>\n",
        "      <td> 0.000241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GOOGL</th>\n",
        "      <td> 0.000220</td>\n",
        "      <td> 0.000438</td>\n",
        "      <td> 0.000114</td>\n",
        "      <td> 0.000156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IBM</th>\n",
        "      <td> 0.000199</td>\n",
        "      <td> 0.000114</td>\n",
        "      <td> 0.000298</td>\n",
        "      <td> 0.000177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MSFT</th>\n",
        "      <td> 0.000241</td>\n",
        "      <td> 0.000156</td>\n",
        "      <td> 0.000177</td>\n",
        "      <td> 0.000421</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 282,
       "text": [
        "           AAPL     GOOGL       IBM      MSFT\n",
        "AAPL   0.000807  0.000220  0.000199  0.000241\n",
        "GOOGL  0.000220  0.000438  0.000114  0.000156\n",
        "IBM    0.000199  0.000114  0.000298  0.000177\n",
        "MSFT   0.000241  0.000156  0.000177  0.000421"
       ]
      }
     ],
     "prompt_number": 282
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Compute pairwise correlation of columns, excluding NA/null values\n",
      "returns.corr?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 283
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Compute pairwise covariance of columns, excluding NA/null values\n",
      "returns.cov?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 284
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.corrwith(returns.IBM)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 285,
       "text": [
        "AAPL     0.406758\n",
        "GOOGL    0.397134\n",
        "IBM      1.000000\n",
        "MSFT     0.498452\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 285
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "returns.corrwith(volume)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 286,
       "text": [
        "AAPL    -0.062011\n",
        "GOOGL    0.053223\n",
        "IBM     -0.032225\n",
        "MSFT    -0.034290\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 286
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.3.2 \uc720\uc77c \uac12, \uac12 \uc138\uae30, \uba64\ubc84\uc2ed"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj = Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 287
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 288,
       "text": [
        "0    c\n",
        "1    a\n",
        "2    d\n",
        "3    a\n",
        "4    a\n",
        "5    b\n",
        "6    b\n",
        "7    c\n",
        "8    c\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 288
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uniques = obj.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 289
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uniques"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 290,
       "text": [
        "array(['c', 'a', 'd', 'b'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 290
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 291,
       "text": [
        "c    3\n",
        "a    3\n",
        "b    2\n",
        "d    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 291
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.value_counts(sort=False, ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 292,
       "text": [
        "a    3\n",
        "c    3\n",
        "b    2\n",
        "d    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 292
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 293,
       "text": [
        "array(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 293
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.value_counts(obj.values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 294,
       "text": [
        "c    3\n",
        "a    3\n",
        "b    2\n",
        "d    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 294
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.value_counts(obj.values, sort=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 295,
       "text": [
        "a    3\n",
        "c    3\n",
        "b    2\n",
        "d    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 295
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# sort\uac00 True\uc778\ub370 \uc65c c, a \uc21c\uc73c\ub85c \ub098\uc624\uc9c0..? \uc774\ud574 \ubd88\uac00..\n",
      "# \ucc45\uc774\ub791 \ubc18\ub300\ub77c\uc11c \uac1c\ub150 \ud63c\ub780\n",
      "pd.value_counts(obj.values, sort=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 296,
       "text": [
        "c    3\n",
        "a    3\n",
        "b    2\n",
        "d    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 296
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2 = Series(['c', 'c', 'd', 'd', 'c', 'd'])\n",
      "pd.value_counts(obj2.values, sort=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 297,
       "text": [
        "c    3\n",
        "d    3\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 297
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj2 = Series(['d', 'c', 'c', 'd', 'c', 'd'])\n",
      "pd.value_counts(obj2.values, sort=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 298,
       "text": [
        "d    3\n",
        "c    3\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 298
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mask = obj.isin(['b', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 299
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mask"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 300,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 300
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj[mask]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 301,
       "text": [
        "0    c\n",
        "5    b\n",
        "6    b\n",
        "7    c\n",
        "8    c\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 301
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "obj[True]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "True",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-302-8d9c188ba330>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    903\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[1;32m    904\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\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[0m\n\u001b[0m\u001b[1;32m    906\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m    834\u001b[0m         \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 836\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    837\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'integer'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2658)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2473)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3177)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.Int64Engine._check_type (pandas/index.c:6304)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: True"
       ]
      }
     ],
     "prompt_number": 302
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uc720\uc77c \uac12, \uac12 \uc138\uae30, \ubc84\ub9ac\uae30 \uba54\uc11c\ub4dc\n",
      "\n",
      "\uba54\uc11c\ub4dc | \uc124\uba85\n",
      "--- | ---\n",
      "isin | Series\uc758 \uac01 \uc6d0\uc18c\uac00 \ub118\uaca8\ubc1b\uc740 \uc5f0\uc18d\ub41c \uac12\uc5d0 \uc18d\ud558\ub294\uc9c0\ub97c \ub098\ud0c0\ub0b4\ub294 \ubd88\ub9ac\uc5b8 \ubc30\uc5f4\uc744 \ubc18\ud658\ud55c\ub2e4.\n",
      "unique | Series\uc5d0\uc11c \uc911\ubcf5\ub418\ub294 \uac12\uc744 \uc81c\uac70\ud558\uace0 \uc720\uc77c\ud55c \uac12\ub9cc \ud3ec\ud568\ud558\ub294 \ubc30\uc5f4\uc744 \ubc18\ud658\ud55c\ub2e4. \uacb0\uacfc\ub294 Series\uc5d0\uc11c \ubc1c\uacac\ub41c \uc21c\uc11c\ub300\ub85c \ubc18\ud658\ub41c\ub2e4.\n",
      "value_counts | Series\uc5d0\uc11c \uc720\uc77c \uac12\uc5d0 \ub300\ud55c \uc0c9\uc778 \uac12\uacfc \ub3c4\uc218\ub97c \uacc4\uc0b0\ud55c\ub2e4. \uacb0\uacfc\ub294 \ub3c4\uc218 \uac12\uc758 \ub0b4\ub9bc\ucc28\uc21c\uc73c\ub85c \uc815\ub82c\ub41c\ub2e4."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### DataFrame\uc758 \uc5ec\ub7ec \ub85c\uc6b0\uc5d0 \ub300\ud574 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \uad6c\ud574\uc57c \ud558\ub294 \uacbd\uc6b0\n",
      "\n",
      "- DataFrame\uc758 apply \ud568\uc218\uc5d0 pandas.value_counts\ub97c \ub118\uae30\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\ub2e4.\n",
      "- value_counts \uba54\uc11c\ub4dc\uc758 \uacb0\uacfc\uac00 DataFrame\uc758 \uce7c\ub7fc \ud06c\uae30\ubcf4\ub2e4 \uc791\uc744 \uc218 \uc788\uae30 \ub54c\ubb38\uc5d0 fillna(0) \ud568\uc218\ub97c \uc774\uc6a9\ud574\uc11c \ube44\uc5b4\uc788\ub294 \uac12\uc740 0\uc73c\ub85c \ucc44\uc6cc\uc900\ub2e4.\n",
      "\n",
      "#### \uc774\uac8c \ub3c4\uc800\ud788 \uc774\ud574\uac00 \uc548\ub41c\ub2e4... \ubb58 \ub9d0\ud558\ub294\uac70\uc9c0..?? \uc660 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \uad6c\ud558\ub098? -> \uc774\ud574\ub410\ub2e4. counts \uc138\ub294\uac70\ub2c8\uae4c \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc774\uc9c0\n",
      "\n",
      "- Qu1\uc5d0 1\uc774 1\uac1c \uc788\uace0 2\ub294 \uc5c6\uace0 3\uc740 2\uac1c, 4\ub3c4 2\uac1c\n",
      "- Qu2\uc5d0 1\uc740 1\uac1c, 2\uac00 2\uac1c, 3\uc774 2\uac1c, 4\uac00 0\uac1c\n",
      "- Qu3\uc5d0 1\uc774 1\uac1c, 2\uac00 1\uac1c, 4\uac00 2\uac1c, 5\uac00 1\uac1c"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = DataFrame({'Qu1': [1, 3, 4, 3, 4],\n",
      "                  'Qu2': [2, 3, 1, 2, 3],\n",
      "                  'Qu3': [1, 5, 2, 4, 4]})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 303
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 304,
       "text": [
        "   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"
       ]
      }
     ],
     "prompt_number": 304
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = data.apply(pd.value_counts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 305
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>NaN</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>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  2</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 306,
       "text": [
        "   Qu1  Qu2  Qu3\n",
        "1    1    1    1\n",
        "2  NaN    2    1\n",
        "3    2    2  NaN\n",
        "4    2  NaN    2\n",
        "5  NaN  NaN    1"
       ]
      }
     ],
     "prompt_number": 306
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = data.apply(pd.value_counts).fillna(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 307
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 308,
       "text": [
        "   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"
       ]
      }
     ],
     "prompt_number": 308
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5.4 \ub204\ub77d\ub41c \ub370\uc774\ud130 \ucc98\ub9ac\ud558\uae30"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ub204\ub77d\ub41c \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \uc77c\uc740 \ub370\uc774\ud130 \ubd84\uc11d \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c \ud754\uc774 \uc788\ub294 \uc77c\n",
      "- \ub204\ub77d \ub370\uc774\ud130\ub97c \uac00\ub2a5\ud55c \uc27d\uac8c \ucc98\ub9ac\n",
      "- \ubaa8\ub4e0 \uae30\uc220\ud1b5\uacc4\ub294 \ub204\ub77d\ub41c \ub370\uc774\ud130\ub97c \ubc30\uc81c\ud558\uace0 \ucc98\ub9ac\n",
      "- \ub204\ub77d\ub41c \ub370\uc774\ud130\ub97c \uc2e4\uc218\ub4e0 \uc544\ub2c8\ub4e0 \ubaa8\ub450 NaN(Not a Number)\uc73c\ub85c \ucde8\uae09\n",
      "- \ub204\ub77d\ub41c \uac12\uc744 \uc27d\uac8c \ucc3e\uc744 \uc218 \uc788\ub294 \ud30c\uc218\ubcd1 \uc5ed\ud560"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "string_data = Series(['aardvark', 'artichoke', np.nan, 'avocado'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 309
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "string_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 310,
       "text": [
        "0     aardvark\n",
        "1    artichoke\n",
        "2          NaN\n",
        "3      avocado\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 310
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "string_data.isnull()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 311,
       "text": [
        "0    False\n",
        "1    False\n",
        "2     True\n",
        "3    False\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 311
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Python \ub0b4\uc7a5 None \ub610\ud55c null \ucde8\uae09\n",
      "string_data[0] = None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 312
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "string_data.isnull()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 313,
       "text": [
        "0     True\n",
        "1    False\n",
        "2     True\n",
        "3    False\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 313
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### NA \ucc98\ub9ac \uba54\uc11c\ub4dc\n",
      "\n",
      "\uc778\uc790 | \uc124\uba85\n",
      "--- | ---\n",
      "dropna | \ub204\ub77d\ub41c \ub370\uc774\ud130\uac00 \uc788\ub294 \ucd95(\ub85c\uc6b0, \uce7c\ub7fc)\uc744 \uc81c\uc678\uc2dc\ud0a8\ub2e4. \uc5b4\ub290 \uc815\ub3c4\uc758 \ub204\ub77d \ub370\uc774\ud130\uae4c\uc9c0 \uc6a9\uc778\ud560 \uac83\uc778\uc9c0 \uc9c0\uc815\ud560 \uc218 \uc788\ub2e4.\n",
      "fillna | \ub204\ub77d\ub41c \ub370\uc774\ud130\ub97c \ub300\uc2e0\ud560 \uac12\uc744 \ucc44\uc6b0\uac70\ub098 'ffill' \ub610\ub294 'bfill' \uac19\uc740 \ubcf4\uac04 \uba54\uc11c\ub4dc\ub97c \uc801\uc6a9\ud55c\ub2e4.\n",
      "isnull | \ub204\ub77d\ub418\uac70\ub098 NA\uc778 \uac12\uc744 \uc54c\ub824\uc8fc\ub294 \ubd88\ub9ac\uc5b8 \uac12\uc774 \uc800\uc7a5\ub41c, \uac19\uc740 \ud615\uc758 \uac1d\uccb4\ub97c \ubc18\ud658\ud55c\ub2e4.\n",
      "notnull | isnull\uacfc \ubc18\ub300\ub418\ub294 \uba54\uc11c\ub4dc\ub2e4."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "--------\n",
      "\n",
      "####\uc790\uafb8 \ub4dc\ub294 \uc758\ubb38\uc810\n",
      "\n",
      "1. \uc774\uac78 \ubc30\uc6cc\uc11c \uc5b4\ub514\uc5d0 \uc368\uba39\uc744 \uc218 \uc788\uc744\uae4c?\n",
      "1. \uc9c0\uae08 \ub0b4\uac8c \ub2f9\uc7a5 \ud544\uc694\ud55c \uac83\uc778\uac00? \ud604\uc5c5\uc5d0\uc11c \uc774 \uae30\uc220\uc744 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\uc73c\uba74 \uc9c4\ud589\ud560 \uc218 \uc5c6\ub294\uac00?\n",
      "1. \ubbf8\ub798\ub97c \uc704\ud55c \ud22c\uc790\ub85c \ubd10\uc57c \ub418\ub098?\n",
      "1. \uc9c1\uc811 \uc801\uc6a9\ud574 \ubcfc \uc608\uc81c\ub4e4\uc774 \uc5c6\uc73c\ub2c8 \uc2ec\uc2ec\ud558\ub2e4. \uc608\uc81c\ub77c\ub3c4 \uc788\uc73c\uba74 \uc2e4\ud5d8\ud574 \ubcf4\uba74\uc11c \ud560 \uc218 \uc788\uc744\ud150\ub370. \ucc45\uc774 \ub108\ubb34 \ub2e8\uc870\ub86d\ub2e4. \uadf8\ub0e5 \ubb38\ubc95 \uacf5\ubd80\ud558\ub294 \ub290\ub08c?\n",
      "\n",
      "--------"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.4.1 \ub204\ub77d\ub41c \ub370\uc774\ud130 \uace8\ub77c\ub0b4\uae30"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import nan as NA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 314
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = Series([1, NA, 3.5, NA, 7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 315
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 316,
       "text": [
        "0    1.0\n",
        "2    3.5\n",
        "4    7.0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 316
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[data.notnull()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 317,
       "text": [
        "0    1.0\n",
        "2    3.5\n",
        "4    7.0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 317
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Series\uc640 DataFrame\uc758 \ucc28\uc774\uc810\uc740??\n",
      "\n",
      "- \uc774\uac78 \uc774\ud574\ud574\uc57c\uc9c0 \uc9c4\ub3c4\ub97c \ub098\uac08 \uc218 \uc788\uc744 \uac83 \uac19\ub2e4.\n",
      "- \uadf8\ub0e5 \ud30c\uc774\uc36c \ubb38\ubc95\uc774\uace0 \uc57d\uac04 \uc5b4\ub824\uc6b4 \uc815\ub3c4\ub77c \ub530\ub77c\uce58\uae30\ub294 \ud558\uc9c0\ub9cc \uc544\uc9c1 \uc5b4\ub5a4 \uc810\uc774 \ucc28\uc774\uac00 \ub098\ub294\uc9c0 \ubaa8\ub974\uaca0\ub2e4.\n",
      "- \uc774\uac78 \uc5b4\ub5bb\uac8c \ud558\uba74 \uc27d\uac8c \uc774\ud574\ud560 \uc218 \uc788\uc744\uae4c?\n",
      "\n",
      "### 2\ubc88\uc9f8 \ubcf4\ub2c8 \uc774\ud574\uac00 \ub41c\ub2e4.\n",
      "\n",
      "- Series\ub294 \uadf8\ub0e5 1\ucc28\uc6d0 \ubc30\uc5f4\ub85c \uc0dd\uac01\ud558\uba74 \ub418\uace0\n",
      "- DataFrame\uc740 \uc5d1\uc140 \uac19\uc774 \uc2a4\ud504\ub808\ub4dc\uc2dc\ud2b8\ub77c\uace0 \uc0dd\uac01\ud558\uba74 \ub420\ub4ef\ud558\ub2e4.\n",
      "- \uc138\uc138\ud558\uac8c \ucc28\uc774\uc810\uc744 \ud655\uc778\ud558\uace0 \uc2f6\ub2e4\uba74 \ucc45\uc744 \ucc38\uace0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = DataFrame([[1., 6.5, 3.], [1., NA, NA],\n",
      "                  [NA, NA, NA], [NA, 6.5, 3]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 318
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cleaned = data.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 319
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 320,
       "text": [
        "    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"
       ]
      }
     ],
     "prompt_number": 320
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# NA\uac00 \ud558\ub098\ub77c\ub3c4 \uc788\uc73c\uba74 \uae30\ubcf8\uc801\uc73c\ub85c \uc81c\uc678\ud574\uc11c \ubcf4\uc5ec\uc90c\n",
      "cleaned"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 321,
       "text": [
        "   0    1  2\n",
        "0  1  6.5  3"
       ]
      }
     ],
     "prompt_number": 321
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.dropna(how='all')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 322,
       "text": [
        "    0    1   2\n",
        "0   1  6.5   3\n",
        "1   1  NaN NaN\n",
        "3 NaN  6.5   3"
       ]
      }
     ],
     "prompt_number": 322
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.dropna?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 323
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Type:        instancemethod\n",
      "String form:\n",
      "<bound method DataFrame.dropna of    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>\n",
      "File:        /Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.py\n",
      "Definition:  data.dropna(self, axis=0, how='any', thresh=None, subset=None)\n",
      "Docstring:\n",
      "Return object with labels on given axis omitted where alternately any\n",
      "or all of the data are missing\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "axis : {0, 1}, or tuple/list thereof\n",
      "    Pass tuple or list to drop on multiple axes\n",
      "how : {'any', 'all'}\n",
      "    any : if any NA values are present, drop that label\n",
      "    all : if all values are NA, drop that label\n",
      "thresh : int, default None\n",
      "    int value : require that many non-NA values\n",
      "subset : array-like\n",
      "    Labels along other axis to consider, e.g. if you are dropping rows\n",
      "    these would be a list of columns to include\n",
      "\n",
      "Returns\n",
      "-------\n",
      "dropped : DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Failed. how\uc5d0 \ub4e4\uc5b4\uac00\ub294 \uc778\uc790\uac12\uc774 \uc5b4\ub5a4 \uac83\ub4e4\uc774 \uc788\ub294\uc9c0 \uc5b4\ub5bb\uac8c \uc54c\uc9c0? -> any, all 2\uac1c\ub9cc \uc788\ub124. \n",
      "# \uba85\ub839\uc5b4 \ub4a4\uc5d0 ? \ubd99\uc774\uba74 \uc124\uba85 \ub098\uc634\n",
      "data.dropna(how='one')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "do not recognize one",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-324-649caaa8dea3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Failed. how\uc5d0 \ub4e4\uc5b4\uac00\ub294 \uc778\uc790\uac12\uc774 \uc5b4\ub5a4 \uac83\ub4e4\uc774 \uc788\ub294\uc9c0 \uc5b4\ub5bb\uac8c \uc54c\uc9c0? -> any, all 2\uac1c\ub9cc \uc788\ub124.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# \uba85\ub839\uc5b4 \ub4a4\uc5d0 ? \ubd99\uc774\uba74 \uc124\uba85 \ub098\uc634\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'one'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mdropna\u001b[0;34m(self, axis, how, thresh, subset)\u001b[0m\n\u001b[1;32m   2616\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2617\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2618\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'do not recognize %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2619\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2620\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'must specify how or thresh'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: do not recognize one"
       ]
      }
     ],
     "prompt_number": 324
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[4] = NA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 325
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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",
        "    </tr>\n",
        "    <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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 326,
       "text": [
        "    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"
       ]
      }
     ],
     "prompt_number": 326
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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",
        "    </tr>\n",
        "    <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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 327,
       "text": [
        "    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"
       ]
      }
     ],
     "prompt_number": 327
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# axis=0 is row.\n",
      "data.dropna(axis=0, how='all')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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",
        "    </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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 328,
       "text": [
        "    0    1   2   4\n",
        "0   1  6.5   3 NaN\n",
        "1   1  NaN NaN NaN\n",
        "3 NaN  6.5   3 NaN"
       ]
      }
     ],
     "prompt_number": 328
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.dropna(axis=1, how='all')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 329,
       "text": [
        "    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"
       ]
      }
     ],
     "prompt_number": 329
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.dropna(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>Int64Index([0, 1, 2, 3], dtype=int64)</td>\n",
        "      <td>Empty DataFrame</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 330,
       "text": [
        "Empty DataFrame\n",
        "Columns: []\n",
        "Index: [0, 1, 2, 3]"
       ]
      }
     ],
     "prompt_number": 330
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(np.random.randn(7, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 331
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.105290</td>\n",
        "      <td> 0.865731</td>\n",
        "      <td>-0.336369</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.955932</td>\n",
        "      <td> 0.748322</td>\n",
        "      <td> 1.036024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.679063</td>\n",
        "      <td> 0.568578</td>\n",
        "      <td> 0.380878</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td> 0.511421</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td>-2.473332</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 332,
       "text": [
        "          0         1         2\n",
        "0  1.105290  0.865731 -0.336369\n",
        "1 -0.955932  0.748322  1.036024\n",
        "2 -1.679063  0.568578  0.380878\n",
        "3 -1.082717  0.511421 -0.244171\n",
        "4 -0.014430 -2.473332 -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 332
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc5ec\uae30\uc5d0\uc11c\ub294 :4\ub77c\uace0 \ud588\uc73c\uba74 \uc815\uc0c1\uc801\uc73c\ub85c\ub294 0,1,2,3\ub9cc \ud574\ub2f9\uc774 \ub418\uc57c \ub418\ub294\ub370 4\uae4c\uc9c0 \uc801\uc6a9\uc774 \ub418\ub124???\n",
      "# ix\ub294 slicing \ub9c8\uc9c0\ub9c9 \ubb38\uc790\uae4c\uc9c0 \ud3ec\ud568 \ub428\n",
      "df.ix[:4, 1] = NA; df.ix[:2, 2] = NA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 333
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.105290</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.955932</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.679063</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 334,
       "text": [
        "          0         1         2\n",
        "0  1.105290       NaN       NaN\n",
        "1 -0.955932       NaN       NaN\n",
        "2 -1.679063       NaN       NaN\n",
        "3 -1.082717       NaN -0.244171\n",
        "4 -0.014430       NaN -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 334
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####thresh\n",
      "\n",
      "- \uba87 \uac1c \uc774\uc0c1\uc758 \uac12\uc774 \ub4e4\uc5b4\uc788\ub294 \ub85c\uc6b0\ub9cc \uc0b4\ud3b4\ubcf4\uace0 \uc2f6\uc744 \ub54c"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.dropna(thresh=3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 335,
       "text": [
        "          0         1         2\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 335
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.dropna(thresh=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 336,
       "text": [
        "          0         1         2\n",
        "3 -1.082717       NaN -0.244171\n",
        "4 -0.014430       NaN -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 336
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.4.2 \ub204\ub77d\ub41c \uac12 \ucc44\uc6b0\uae30"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ub204\ub77d\ub41c \uac12\uc744 \uc81c\uc678\uc2dc\ud0a4\uc9c0 \uc54a\uace0(\uc7a0\uc7ac\uc801\uc73c\ub85c \ub2e4\ub978 \ub370\uc774\ud130\ub3c4 \ud568\uaed8 \ubc84\ub824\uc9c8 \uac00\ub2a5\uc131\uc774 \uc788\ub2e4) \ub370\uc774\ud130\uc0c1\uc758 '\uad6c\uba4d'\uc744 \uc5b4\ub5bb\uac8c\ub4e0 \uba54\uc6b0\uace0 \uc2f6\uc740 \uacbd\uc6b0 ffillna \uba54\uc11c\ub4dc \ud65c\uc6a9"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.105290</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.955932</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.679063</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 337,
       "text": [
        "          0         1         2\n",
        "0  1.105290  0.000000  0.000000\n",
        "1 -0.955932  0.000000  0.000000\n",
        "2 -1.679063  0.000000  0.000000\n",
        "3 -1.082717  0.000000 -0.244171\n",
        "4 -0.014430  0.000000 -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 337
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# dictionary \ud615\uc2dd\uc73c\ub85c \ubc1b\uc558\ub294\ub370 \uc55e\uc758 key\uac00 \uceec\ub7fc\uc744 \ub098\ud0c0\ub0c4\n",
      "df.fillna({1: 0.5, 3: -1})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.105290</td>\n",
        "      <td> 0.500000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.955932</td>\n",
        "      <td> 0.500000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.679063</td>\n",
        "      <td> 0.500000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td> 0.500000</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td> 0.500000</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 338,
       "text": [
        "          0         1         2\n",
        "0  1.105290  0.500000       NaN\n",
        "1 -0.955932  0.500000       NaN\n",
        "2 -1.679063  0.500000       NaN\n",
        "3 -1.082717  0.500000 -0.244171\n",
        "4 -0.014430  0.500000 -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 338
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna({2:0.5, 1:-1})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.105290</td>\n",
        "      <td>-1.000000</td>\n",
        "      <td> 0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.955932</td>\n",
        "      <td>-1.000000</td>\n",
        "      <td> 0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.679063</td>\n",
        "      <td>-1.000000</td>\n",
        "      <td> 0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td>-1.000000</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td>-1.000000</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 339,
       "text": [
        "          0         1         2\n",
        "0  1.105290 -1.000000  0.500000\n",
        "1 -0.955932 -1.000000  0.500000\n",
        "2 -1.679063 -1.000000  0.500000\n",
        "3 -1.082717 -1.000000 -0.244171\n",
        "4 -0.014430 -1.000000 -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 339
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 340
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "inplace : boolean, default False\n",
      "    If True, fill the DataFrame in place. Note: this will modify any\n",
      "    other views on this DataFrame, like if you took a no-copy slice of\n",
      "    an existing DataFrame, for example a column in a DataFrame. Returns\n",
      "    a reference to the filled object, which is self if inplace=True"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fillna\ub294 \uac12\uc744 \ucc44\uc6cc \ub123\uc740 \uac1d\uccb4\uc758 \ucc38\uc870\ub97c \ubc18\ud658\n",
      "_ = df.fillna(0, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 341
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.105290</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.955932</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.679063</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 342,
       "text": [
        "          0         1         2\n",
        "0  1.105290  0.000000  0.000000\n",
        "1 -0.955932  0.000000  0.000000\n",
        "2 -1.679063  0.000000  0.000000\n",
        "3 -1.082717  0.000000 -0.244171\n",
        "4 -0.014430  0.000000 -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 342
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = df.fillna(1, inplace=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 343
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.105290</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.955932</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.679063</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 344,
       "text": [
        "          0         1         2\n",
        "0  1.105290  0.000000  0.000000\n",
        "1 -0.955932  0.000000  0.000000\n",
        "2 -1.679063  0.000000  0.000000\n",
        "3 -1.082717  0.000000 -0.244171\n",
        "4 -0.014430  0.000000 -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 344
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna(1, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 345
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc774\ubbf8 NA\uac12\uc774 0.0 \uc73c\ub85c \ucc44\uc6cc\uc838\uc788\uae30 \ub54c\ubb38\uc5d0 1\ub85c \ubc14\ub00c\uc9c0 \uc54a\ub294\ub2e4.\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1.105290</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>-0.955932</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-1.679063</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.082717</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.244171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>-0.014430</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-1.772991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-0.384078</td>\n",
        "      <td> 0.365353</td>\n",
        "      <td>-0.252302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>-0.016087</td>\n",
        "      <td>-0.632474</td>\n",
        "      <td>-0.628749</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 346,
       "text": [
        "          0         1         2\n",
        "0  1.105290  0.000000  0.000000\n",
        "1 -0.955932  0.000000  0.000000\n",
        "2 -1.679063  0.000000  0.000000\n",
        "3 -1.082717  0.000000 -0.244171\n",
        "4 -0.014430  0.000000 -1.772991\n",
        "5 -0.384078  0.365353 -0.252302\n",
        "6 -0.016087 -0.632474 -0.628749"
       ]
      }
     ],
     "prompt_number": 346
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(np.random.randn(6, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 347
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[2:, 1] = NA\n",
      "df.ix[4:, 2] = NA"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 348
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.031714</td>\n",
        "      <td> 0.522461</td>\n",
        "      <td> 1.543997</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1.084614</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td>-0.680466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1.387762</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.723371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.152981</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.283652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1.282276</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-1.048720</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 349,
       "text": [
        "          0         1         2\n",
        "0  0.031714  0.522461  1.543997\n",
        "1  1.084614 -1.225614 -0.680466\n",
        "2  1.387762       NaN  0.723371\n",
        "3 -1.152981       NaN  0.283652\n",
        "4  1.282276       NaN       NaN\n",
        "5 -1.048720       NaN       NaN"
       ]
      }
     ],
     "prompt_number": 349
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna(method='ffill')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.031714</td>\n",
        "      <td> 0.522461</td>\n",
        "      <td> 1.543997</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1.084614</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td>-0.680466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1.387762</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td> 0.723371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.152981</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td> 0.283652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1.282276</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td> 0.283652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-1.048720</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td> 0.283652</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 350,
       "text": [
        "          0         1         2\n",
        "0  0.031714  0.522461  1.543997\n",
        "1  1.084614 -1.225614 -0.680466\n",
        "2  1.387762 -1.225614  0.723371\n",
        "3 -1.152981 -1.225614  0.283652\n",
        "4  1.282276 -1.225614  0.283652\n",
        "5 -1.048720 -1.225614  0.283652"
       ]
      }
     ],
     "prompt_number": 350
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna(method='ffill', limit=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.031714</td>\n",
        "      <td> 0.522461</td>\n",
        "      <td> 1.543997</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1.084614</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td>-0.680466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1.387762</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td> 0.723371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>-1.152981</td>\n",
        "      <td>-1.225614</td>\n",
        "      <td> 0.283652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1.282276</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.283652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>-1.048720</td>\n",
        "      <td>      NaN</td>\n",
        "      <td> 0.283652</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 351,
       "text": [
        "          0         1         2\n",
        "0  0.031714  0.522461  1.543997\n",
        "1  1.084614 -1.225614 -0.680466\n",
        "2  1.387762 -1.225614  0.723371\n",
        "3 -1.152981 -1.225614  0.283652\n",
        "4  1.282276       NaN  0.283652\n",
        "5 -1.048720       NaN  0.283652"
       ]
      }
     ],
     "prompt_number": 351
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = Series([1., NA, 3.5, NA, 7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 352
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- fillna\uc5d0 \ud3c9\uade0\uac12\uc774\ub098 \uc911\uac04\uac12\uc744 \uc804\ub2ec\ud574\uc11c \ub370\uc774\ud130\uc758 \ub192\ub0ae\uc774\ub97c \uc904\uc77c \uc218 \uc788\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.fillna(data.mean())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 353,
       "text": [
        "0    1.000000\n",
        "1    3.833333\n",
        "2    3.500000\n",
        "3    3.833333\n",
        "4    7.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 353
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 354,
       "text": [
        "0    1.0\n",
        "1    NaN\n",
        "2    3.5\n",
        "3    NaN\n",
        "4    7.0\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 354
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### fillna \ud568\uc218 \uc778\uc790\n",
      "\n",
      "\uc778\uc790 | \uc124\uba85\n",
      "--- | ---\n",
      "value | \ube44\uc5b4\uc788\ub294 \uac12\uc744 \ucc44\uc6b8 \uc2a4\uce7c\ub77c \uac12\uc774\ub098 \uc0ac\uc804 \ud615\uc2dd\uc758 \uac1d\uccb4\n",
      "method | \ubcf4\uac04 \ubc29\uc2dd. \uae30\ubcf8\uc801\uc73c\ub85c 'ffill'\uc744 \uc0ac\uc6a9\ud55c\ub2e4.\n",
      "axis | \uac12\uc744 \ucc44\uc6cc \ub123\uc744 \ucd95. \uae30\ubcf8 \uac12\uc740 0\n",
      "inace | \ubcf5\uc0ac\ubcf8\uc744 \uc0dd\uc131\ud558\uc9c0 \uc54a\uace0 \ud638\ucd9c\ud55c \uac1d\uccb4\ub97c \ubcc0\uacbd\ud55c\ub2e4. \uae30\ubcf8\uac12\uc740 False\n",
      "limit | \uac12\uc744 \uc55e \ud639\uc740 \ub4a4\uc5d0\uc11c \uba87 \uac1c\uae4c\uc9c0 \ucc44\uc6b8\uc9c0\ub97c \uc9c0\uc815\ud55c\ub2e4."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5.5 \uacc4\uce35\uc801 \uc0c9\uc778"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- **\uacc4\uce35\uc801 \uc0c9\uc778**\uc740 pandas\uc758 \uc911\uc694\ud55c \uae30\ub2a5\n",
      "- \ucd95\uc5d0 \ub300\ud574 \ub2e4\uc911(\ub458 \uc774\uc0c1) \uc0c9\uc778 \ub2e8\uacc4\ub97c \uc9c0\uc815\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc900\ub2e4.\n",
      "- \uc57d\uac04 \ucd94\uc0c1\uc801\uc73c\ub85c \ub9d0\ud558\uba74 \ucc28\uc6d0\uc774 \ub192\uc740(\uace0\ucc28\uc6d0) \ub370\uc774\ud130\ub97c \ub0ae\uc740 \ucc28\uc6d0\uc758 \ud615\uc2dd\uc73c\ub85c \ub2e4\ub8f0 \uc218 \uc788\uac8c \ud574\uc8fc\ub294 \uae30\ub2a5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 355
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 356,
       "text": [
        "a  1   -0.041546\n",
        "   2   -1.894623\n",
        "   3    1.326466\n",
        "b  1   -0.210593\n",
        "   2    1.410247\n",
        "   3    2.717005\n",
        "c  1    1.639305\n",
        "   2   -2.528040\n",
        "d  2   -0.947952\n",
        "   3   -0.001164\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 356
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 357,
       "text": [
        "MultiIndex\n",
        "[(u'a', 1), (u'a', 2), (u'a', 3), (u'b', 1), (u'b', 2), (u'b', 3), (u'c', 1), (u'c', 2), (u'd', 2), (u'd', 3)]"
       ]
      }
     ],
     "prompt_number": 357
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 358,
       "text": [
        "1   -0.210593\n",
        "2    1.410247\n",
        "3    2.717005\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 358
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['b':'c']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 359,
       "text": [
        "b  1   -0.210593\n",
        "   2    1.410247\n",
        "   3    2.717005\n",
        "c  1    1.639305\n",
        "   2   -2.528040\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 359
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[['b', 'd']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 360,
       "text": [
        "b  1   -0.210593\n",
        "   2    1.410247\n",
        "   3    2.717005\n",
        "d  2   -0.947952\n",
        "   3   -0.001164\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 360
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['b':'d']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 361,
       "text": [
        "b  1   -0.210593\n",
        "   2    1.410247\n",
        "   3    2.717005\n",
        "c  1    1.639305\n",
        "   2   -2.528040\n",
        "d  2   -0.947952\n",
        "   3   -0.001164\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 361
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Failed!\n",
      "data['b', 'c']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "('b', 'c')",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-362-c6d18aac3c30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Failed!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    925\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 927\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\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;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    942\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    943\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 944\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    945\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_get_values_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    991\u001b[0m         \u001b[0;31m# If key is contained, would have returned by now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 992\u001b[0;31m         \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    993\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    994\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc_level\u001b[0;34m(self, key, level, drop_level)\u001b[0m\n\u001b[1;32m   2638\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2639\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2640\u001b[0;31m                         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\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[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2641\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2642\u001b[0m                         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3330)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3210)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:10484)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:10438)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: ('b', 'c')"
       ]
      }
     ],
     "prompt_number": 362
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[:, 2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 363,
       "text": [
        "a   -1.894623\n",
        "b    1.410247\n",
        "c   -2.528040\n",
        "d   -0.947952\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 363
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ud604\uc7ac index\uac00 multi index\ub2c8\uae4c\n",
      "# \uc55e\uc740 a,b,c,d \uc911\uc5d0 \ud558\ub098\n",
      "# \ub4a4\ub294 1,2,3 \uc911\uc5d0 \ud558\ub098\n",
      "data['a',2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 364,
       "text": [
        "-1.8946225480802827"
       ]
      }
     ],
     "prompt_number": 364
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc5c6\ub294 \ud0a4\ub2c8\uae4c \ub2f9\uc5f0\ud788 \uc5d0\ub7ec. c\ub294 2\ubc88\uc9f8 index\uac00 1,2 \ubc16\uc5d0 \uc5c6\uc74c\n",
      "data['c',3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "('c', 3)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-365-668c176dd129>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# \uc5c6\ub294 \ud0a4\ub2c8\uae4c \ub2f9\uc5f0\ud788 \uc5d0\ub7ec. c\ub294 2\ubc88\uc9f8 index\uac00 1,2 \ubc16\uc5d0 \uc5c6\uc74c\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    925\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 927\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\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;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    942\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    943\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 944\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    945\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_get_values_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    991\u001b[0m         \u001b[0;31m# If key is contained, would have returned by now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 992\u001b[0;31m         \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    993\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    994\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc_level\u001b[0;34m(self, key, level, drop_level)\u001b[0m\n\u001b[1;32m   2638\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2639\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2640\u001b[0;31m                         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\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[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2641\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2642\u001b[0m                         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3330)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3210)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:10484)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:10438)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: ('c', 3)"
       ]
      }
     ],
     "prompt_number": 365
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['c', 2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 366,
       "text": [
        "-2.5280397543435429"
       ]
      }
     ],
     "prompt_number": 366
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 367,
       "text": [
        "a  1   -0.041546\n",
        "   2   -1.894623\n",
        "   3    1.326466\n",
        "b  1   -0.210593\n",
        "   2    1.410247\n",
        "   3    2.717005\n",
        "c  1    1.639305\n",
        "   2   -2.528040\n",
        "d  2   -0.947952\n",
        "   3   -0.001164\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 367
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uacc4\uce35\uc801 \uc0c9\uc778\uc740 \ub370\uc774\ud130\ub97c \uc7ac\ud615\uc131\ud558\uace0 \ud53c\ubc97 \ud14c\uc774\ube14 \uc0dd\uc131\uac19\uc740 \uadf8\ub8f9 \uae30\ubc18\uc758 \uc791\uc5c5\uc744 \ud560 \ub54c \uc911\uc694\ud558\uac8c \uc0ac\uc6a9\n",
      "- unstack \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud574\uc11c \ub370\uc774\ud130\ub97c \uc0c8\ub86d\uac8c \ubc30\uc5f4 \uac00\ub2a5\n",
      "\n",
      "#### stack\uc740 \uc313\ub294\ub2e4. unstack\uc740 \ub2f9\uc5f0\ud788 \uc313\uc5ec\uc838 \uc788\ub294 \uac83\uc744 \ud47c\ub2e4 \ub77c\ub294 \uc758\ubbf8\ub85c \uc0dd\uac01\ub428"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td>-0.041546</td>\n",
        "      <td>-1.894623</td>\n",
        "      <td> 1.326466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td>-0.210593</td>\n",
        "      <td> 1.410247</td>\n",
        "      <td> 2.717005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
        "      <td> 1.639305</td>\n",
        "      <td>-2.528040</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>d</th>\n",
        "      <td>      NaN</td>\n",
        "      <td>-0.947952</td>\n",
        "      <td>-0.001164</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 368,
       "text": [
        "          1         2         3\n",
        "a -0.041546 -1.894623  1.326466\n",
        "b -0.210593  1.410247  2.717005\n",
        "c  1.639305 -2.528040       NaN\n",
        "d       NaN -0.947952 -0.001164"
       ]
      }
     ],
     "prompt_number": 368
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ub2f9\uc5f0\ud788 unstack \ud6c4\uc5d0 \ub2e4\uc2dc stack \ud558\ub2c8 \ub2e4\uc2dc \ub3cc\uc544\uac00\uaca0\uc9c0\n",
      "data.unstack().stack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 369,
       "text": [
        "a  1   -0.041546\n",
        "   2   -1.894623\n",
        "   3    1.326466\n",
        "b  1   -0.210593\n",
        "   2    1.410247\n",
        "   3    2.717005\n",
        "c  1    1.639305\n",
        "   2   -2.528040\n",
        "d  2   -0.947952\n",
        "   3   -0.001164\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 369
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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']])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 370
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 371,
       "text": [
        "      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"
       ]
      }
     ],
     "prompt_number": 371
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.index.names = ['key1', 'key2']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 372
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.columns.names = ['state', 'color']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 373
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 374,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 374
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['Ohio']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 375,
       "text": [
        "color      Green  Red\n",
        "key1 key2            \n",
        "a    1         0    1\n",
        "     2         3    4\n",
        "b    1         6    7\n",
        "     2         9   10"
       ]
      }
     ],
     "prompt_number": 375
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### MultiIndex\ub294 \ub530\ub85c \uc0dd\uc131\ud55c \ub2e4\uc74c\uc5d0 \uc7ac\uc0ac\uc6a9"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.MultiIndex.from_arrays([['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']],\n",
      "                       names=['state', 'color'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 376,
       "text": [
        "MultiIndex\n",
        "[(u'Ohio', u'Green'), (u'Ohio', u'Red'), (u'Colorado', u'Green')]"
       ]
      }
     ],
     "prompt_number": 376
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.5.1 \uacc4\uce35 \uc21c\uc11c \ubc14\uafb8\uace0 \uc815\ub82c\ud558\uae30"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####swallevel\uc740 \ub118\uaca8\ubc1b\uc740 2\uac1c\uc758 \uacc4\uce35 \ubc88\ud638\ub098 \uc774\ub984\uc774 \ub4a4\ubc14\ub010 \uc0c8\ub85c\uc6b4 \uac1d\uccb4\ub97c \ubc18\ud658(\ud558\uc9c0\ub9cc \ub370\uc774\ud130\ub294 \ubcc0\uacbd\ub418\uc9c0 \uc54a\ub294\ub2e4)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.swaplevel('key1', 'key2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 377,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 377
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 378,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 378
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### swaplevel\n",
      "\n",
      "- 0, 1\uc778 index\ub85c\ub3c4 swap \ud560 \uc218 \uc788\uace0\n",
      "- \uc774\ub984\uc73c\ub85c\ub3c4 swap \ud560 \uc218 \uc788\ub2e4.\n",
      "\n",
      "#### sortlevel\n",
      "\n",
      "- 0: key1\n",
      "- 1: kye2\n",
      "- \uc774\ub984\uc73c\ub85c\ub3c4 \ud560 \uc218 \uc788\ub2e4.\n",
      "- \ub97c \uc815\ub82c \uae30\uc900\uc73c\ub85c \ud55c\ub2e4\ub294 \uac83"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sortlevel?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 379
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sortlevel(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 380,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 380
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sortlevel('key2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 381,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 381
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sortlevel(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 382,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 382
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sortlevel('key1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 383,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 383
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.swaplevel(0, 1).sortlevel(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 384,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 384
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.swaplevel(0, 1).sortlevel(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 385,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 385
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 386,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 386
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.swaplevel(1, 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 387,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 387
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.swaplevel('key2', 'key1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 388,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 388
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.swaplevel('key1', 'key2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 389,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 389
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 390,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 390
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.5.2 \ub2e8\uacc4\ubcc4 \uc694\uc57d\ud1b5\uacc4"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uae30\uc220\ud1b5\uacc4\uc640 \uc694\uc57d\ud1b5\uacc4\ub294 level \uc635\uc158\uc744 \uac00\uc9c0\uace0 \uc788\ub2e4.\n",
      "- \uc5b4\ub5a0\ud55c \ucd95\uc5d0 \ub300\ud574 \ud569\uc744 \uad6c\ud558\uace0 \uc2f6\uc740 \ub2e8\uacc4 \uc9c0\uc815"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "-----\n",
      "\n",
      "#### \uac1c\uc778\uc801\uc778 \uc0dd\uac01\uc73c\ub85c \ud504\ub85c\uadf8\ub798\ubc0d \uc798 \ubc30\uc6b0\ub294 \ubc29\ubc95\n",
      "\n",
      "- \ucc45\uc5d0 \uc788\ub294\ub300\ub85c \uba3c\uc800 \uccd0\ubcf8\ub2e4.\n",
      "- \ucc45\uc5d0 \uc788\ub294 \uc608\uc81c\ub97c \ubcc0\uc218\ub098 \uc0c1\uc218\ub4e4\ub9cc \uc0b4\uc9dd \ubc14\uafd4\uc11c \ud14c\uc2a4\ud2b8 \ud574\ubcf8\ub2e4.\n",
      "- \ucc45\uc5d0 \uc788\ub294 \uc18c\uc2a4\ub4e4\uc744 \ub098\ub9cc\uc758 \ubc29\ubc95\uc73c\ub85c \ub2e4\uc2dc \uc7ac\ucc3d\uc870 \ud574\ubcf8\ub2e4.\n",
      "-----"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sum(level='key1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>key1</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>a</th>\n",
        "      <td>  3</td>\n",
        "      <td>  5</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td> 15</td>\n",
        "      <td> 17</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 391,
       "text": [
        "state   Ohio       Colorado\n",
        "color  Green  Red     Green\n",
        "key1                       \n",
        "a          3    5         7\n",
        "b         15   17        19"
       ]
      }
     ],
     "prompt_number": 391
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.sum(level='key2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 392,
       "text": [
        "state   Ohio       Colorado\n",
        "color  Green  Red     Green\n",
        "key2                       \n",
        "1          6    8        10\n",
        "2         12   14        16"
       ]
      }
     ],
     "prompt_number": 392
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  soqnwjrdmfh  pandas\uc758 groupby \uae30\ub2a5 \uc774\uc6a9\ud574\uc11c \uad6c\ud604\n",
      "frame.sum(level='color', axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 393,
       "text": [
        "color      Green  Red\n",
        "key1 key2            \n",
        "a    1         2    1\n",
        "     2         8    4\n",
        "b    1        14    7\n",
        "     2        20   10"
       ]
      }
     ],
     "prompt_number": 393
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 394,
       "text": [
        "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"
       ]
      }
     ],
     "prompt_number": 394
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.5.3 DataFrame\uc758 \uce7c\ub7fc \uc0ac\uc6a9\ud558\uae30"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame({'a': range(7),\n",
      "                   'b': range(7, 0, -1),\n",
      "                   'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],\n",
      "                   'd': [0, 1, 2, 0, 1, 2, 3]})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 395
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "      <th>d</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 396,
       "text": [
        "   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"
       ]
      }
     ],
     "prompt_number": 396
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2 = frame.set_index(['c', 'd'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 397
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 398,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 398
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.set_index(['c', 'd'], drop=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 399,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 399
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uacc4\uce35\uc801 \uc0c9\uc778 \ub2e8\uacc4 -> \uceec\ub7fc\n",
      "frame2.reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 400,
       "text": [
        "     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"
       ]
      }
     ],
     "prompt_number": 400
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 401,
       "text": [
        "       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"
       ]
      }
     ],
     "prompt_number": 401
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5.6 pandas\uc640 \uad00\ub828\ub41c \uae30\ud0c0 \uc8fc\uc81c"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.6.1 \uc815\uc218 \uc0c9\uc778"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc5ec\uae30 ser \uac1d\uccb4\ub294 0, 1, 2 \uc0c9\uc778\uc744 \uac00\uc9c0\uace0 \uc788\uc9c0\ub9cc \uc0ac\uc6a9\uc790\uac00 \uc6d0\ud558\ub294 \uac83\uc774 \uc704\uce58 \uc0c9\uc778\uc778\uc9c0? \uc774\ub984 \uc0c9\uc778\uc778\uc9c0 \uc54c\uc544\ub9de\ud788\ub294 \uac83\uc740 \uc5b4\ub824\uc6b4 \uc77c\n",
      "- \uc77c\uad00\uc131\uc744 \uc720\uc9c0\ud558\uae30 \uc704\ud574 \uc0c9\uc778 \uac12\uc744 \uac00\uc9c4 \ucd95 \uc0c9\uc778\uc774 \uc788\uc744 \uacbd\uc6b0 \uc815\uc218 \ub370\uc774\ud130\ub294 \ud56d\uc0c1 \uc774\ub984\uc744 \uc9c0\ud5a5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser = Series(np.arange(3.))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 425
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc67c\ucabd\uc5d0 \uc788\ub294 \uac83\uc774 \uc774\ub984 \uc0c9\uc778(0, 1, 2)\n",
      "# \uc704\uce58\uc0c9\uc778\uc740 0, 1, 2 \ubc88\uc9f8 \uc21c\uc11c\ub300\ub85c \uc788\ub294 \uac83\n",
      "# \ud604\uc7ac\ub294 \uc704\uce58 \uc0c9\uc778\uacfc, \uc774\ub984 \uc0c9\uc778\uc774 \uac19\uae30 \ub54c\ubb38\uc5d0 \ud504\ub85c\uadf8\ub7a8\uc5d0\uac8c\ub294 \ud63c\ub3d9\uc774 \uc62c \uc218 \ubc16\uc5d0\n",
      "ser"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 426,
       "text": [
        "0    0\n",
        "1    1\n",
        "2    2\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 426
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 431,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 431
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser[2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 433,
       "text": [
        "2.0"
       ]
      }
     ],
     "prompt_number": 433
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# -1\uc774 0,1,2,...,-2,-1 \uc758 \uc815\uc218\n",
      "ser[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "-1",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-434-e3493e85eec5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# -1\uc774 0,1,2,...,-2,-1 \uc758 \uc815\uc218\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mser\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    903\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[1;32m    904\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\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[0m\n\u001b[0m\u001b[1;32m    906\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m    834\u001b[0m         \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 836\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    837\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'integer'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2658)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2473)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3210)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6422)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6366)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: -1"
       ]
      }
     ],
     "prompt_number": 434
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 404,
       "text": [
        "0    0\n",
        "1    1\n",
        "2    2\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 404
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc815\uc218 \uc0c9\uc778\uc774 \uc544\ub2d0\ub54c"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser5 = Series([0, 0, 1], index=[5, 6, 7])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 443
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 444,
       "text": [
        "5    0\n",
        "6    0\n",
        "7    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 444
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc774\uac78 \ubcf4\uba74 \uc774\ub984 \uc0c9\uc778\uc73c\ub85c \uc811\uadfc\ud558\ub294 \uac83\uc744 \uc54c \uc218 \uc788\ub2e4.\n",
      "ser5[6]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 445,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 445
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc774\ub984 \uc0c9\uc778\uc774 \uc815\uc218\ub77c\uba74 \uc704\uce58 \uc0c9\uc778\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc5c6\ub2e4.\n",
      "ser5[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "-1",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-450-cf70057686cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mser5\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    903\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[1;32m    904\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\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[0m\n\u001b[0m\u001b[1;32m    906\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m    834\u001b[0m         \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 836\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    837\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'integer'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2658)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2473)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3210)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6422)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:6366)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: -1"
       ]
      }
     ],
     "prompt_number": 450
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser2 = Series(np.arange(3.), index=['a', 'b', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 405
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 406,
       "text": [
        "a    0\n",
        "b    1\n",
        "c    2\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 406
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser2[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 451,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 451
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser2[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 453,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 453
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser2[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 407,
       "text": [
        "2.0"
       ]
      }
     ],
     "prompt_number": 407
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# ix\ub294 \ub05d \uc778\ub371\uc2a4\uc778 1\uae4c\uc9c0 \ud3ec\ud568\ud55c\ub2e4.\n",
      "ser.ix[:1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 408,
       "text": [
        "0    0\n",
        "1    1\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 408
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\ub9cc\uc77c \uc0c9\uc778\uc758 \uc885\ub958\uc5d0 \uc0c1\uad00\uc5c6\uc774 \uc704\uce58 \uae30\ubc18\uc758 \uc0c9\uc778\uc774 \ud544\uc694\ud558\ub2e4\uba74 Series\uc758 iget_value \uba54\uc11c\ub4dc\uc640 DataFrame\uc758 irow, icol \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uba74 \ub41c\ub2e4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser3 = Series(range(3), index=[-5, 1, 3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 409
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 410,
       "text": [
        "-5    0\n",
        " 1    1\n",
        " 3    2\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 410
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 0,1\uc758 1\ubc88\uc9f8\uc5d0 \uc788\ub294 \uac12\uc744 \ubcf4\uc5ec\uc900\ub2e4.\n",
      "ser3.iget_value(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 411,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 411
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser3.iget_value(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 412,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 412
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser4 = Series([5,6,7], index=[-5, 1, 3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 435
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc704\uce58\uc778\uc9c0 \ub2e4\uc2dc \ud55c \ubc88 \ud655\uc778\ud588\ub2e4.\n",
      "# \uc704 \uc608\uc81c\uc5d0\uc11c\ub294 \uc774\ub984 \uc0c9\uc778\uc744 \uc9c0\uc815\ud574\uc8fc\uc5c8\uc9c0\ub9cc(-5, 1, 3) \uc544\ubb34\ub798\ub3c4 \ud63c\ub780\uc2a4\ub7ec\uc6cc\uc11c \ub2e4\uc2dc \ud55c \ubc88 \ud14c\uc2a4\ud2b8 \ud574\ubd24\ub2e4.\n",
      "ser4.iget_value(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 436,
       "text": [
        "5"
       ]
      }
     ],
     "prompt_number": 436
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame(np.arange(6).reshape((3, 2)), index=[2, 0, 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 413
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.irow(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 414,
       "text": [
        "0    0\n",
        "1    1\n",
        "Name: 2, dtype: int64"
       ]
      }
     ],
     "prompt_number": 414
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 415,
       "text": [
        "   0  1\n",
        "2  0  1\n",
        "0  2  3\n",
        "1  4  5"
       ]
      }
     ],
     "prompt_number": 415
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.irow(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 416,
       "text": [
        "0    2\n",
        "1    3\n",
        "Name: 0, dtype: int64"
       ]
      }
     ],
     "prompt_number": 416
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.6.2 Panel \ub370\uc774\ud130"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Panel\uc740 DataFrame\uc758 3\ucc28\uc6d0 \ubc84\uc804\n",
      "- pandas \uac1c\ubc1c\uc740 \uc2a4\ud504\ub808\ub4dc\uc2dc\ud2b8 \ud615\uc2dd\uc758 \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \ub370 \ucd08\uc810\n",
      "- \uacc4\uce35\uc801 \uc0c9\uc778\uc744 \uc774\uc6a9\ud558\uba74 \ub300\uac1c\uc758 \uacbd\uc6b0 N\ucc28\uc6d0\uc758 \ubc30\uc5f4\uc740 \ubd88\ud544\uc694"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# GOOG -> GOOGL\n",
      "import pandas.io.data as web\n",
      "pdata = pd.Panel(dict((stk, web.get_data_yahoo(stk, '1/1/2009', '6/1/2012'))\n",
      "                      for stk in ['AAPL', 'GOOGL', 'MSFT', 'DELL']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 454
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 455,
       "text": [
        "<class 'pandas.core.panel.Panel'>\n",
        "Dimensions: 4 (items) x 868 (major_axis) x 6 (minor_axis)\n",
        "Items axis: AAPL to MSFT\n",
        "Major_axis axis: 2009-01-02 00:00:00 to 2012-06-01 00:00:00\n",
        "Minor_axis axis: Open to Adj Close"
       ]
      }
     ],
     "prompt_number": 455
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata = pdata.swapaxes('items', 'minor')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 456
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 457,
       "text": [
        "<class 'pandas.core.panel.Panel'>\n",
        "Dimensions: 6 (items) x 868 (major_axis) x 4 (minor_axis)\n",
        "Items axis: Open to Adj Close\n",
        "Major_axis axis: 2009-01-02 00:00:00 to 2012-06-01 00:00:00\n",
        "Minor_axis axis: AAPL to MSFT"
       ]
      }
     ],
     "prompt_number": 457
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata['Adj Close']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "DatetimeIndex: 868 entries, 2009-01-02 00:00:00 to 2012-06-01 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "AAPL     861  non-null values\n",
        "DELL     868  non-null values\n",
        "GOOGL    861  non-null values\n",
        "MSFT     861  non-null values\n",
        "dtypes: float64(4)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 458,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 868 entries, 2009-01-02 00:00:00 to 2012-06-01 00:00:00\n",
        "Data columns (total 4 columns):\n",
        "AAPL     861  non-null values\n",
        "DELL     868  non-null values\n",
        "GOOGL    861  non-null values\n",
        "MSFT     861  non-null values\n",
        "dtypes: float64(4)"
       ]
      }
     ],
     "prompt_number": 458
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####ix\ub97c \uc774\uc6a9\ud55c \ub77c\ubca8 \uc0c9\uc778\uc744 \ud1b5\ud55c \uc811\uadfc\uc740 3\ucc28\uc6d0\uc5d0\ub3c4 \uc77c\ubc18\ud654\ub418\uc5b4 \ud2b9\uc815 \ub0a0\uc9dc\ub098 \uc5b4\ub5a4 \uae30\uac04 \ub3d9\uc548\uc758 \ubaa8\ub4e0 \ub370\uc774\ud130\ub97c \ub2e4\uc74c\ucc98\ub7fc \uc120\ud0dd\ud560 \uc218 \uc788\ub2e4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1\ubc88\uc9f8 \uc778\uc790: Open, High, Low, Close, Volume, Adj Close\n",
      "# 2\ubc88\uc9f8 \uc778\uc790: \ub0a0\uc9dc\n",
      "# 3\ubc88\uc9f8 \uc778\uc790: \ud68c\uc0ac\n",
      "pdata.ix[:, '6/1/2012', :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>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> 569.16</td>\n",
        "      <td> 572.65</td>\n",
        "      <td> 560.52</td>\n",
        "      <td> 560.99</td>\n",
        "      <td> 18606700</td>\n",
        "      <td> 536.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DELL</th>\n",
        "      <td>  12.15</td>\n",
        "      <td>  12.30</td>\n",
        "      <td>  12.05</td>\n",
        "      <td>  12.07</td>\n",
        "      <td> 19397600</td>\n",
        "      <td>  11.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GOOGL</th>\n",
        "      <td> 571.79</td>\n",
        "      <td> 572.65</td>\n",
        "      <td> 568.35</td>\n",
        "      <td> 570.98</td>\n",
        "      <td>  6109600</td>\n",
        "      <td> 285.78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MSFT</th>\n",
        "      <td>  28.76</td>\n",
        "      <td>  28.96</td>\n",
        "      <td>  28.44</td>\n",
        "      <td>  28.45</td>\n",
        "      <td> 56634300</td>\n",
        "      <td>  26.82</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 459,
       "text": [
        "         Open    High     Low   Close    Volume  Adj Close\n",
        "AAPL   569.16  572.65  560.52  560.99  18606700     536.20\n",
        "DELL    12.15   12.30   12.05   12.07  19397600      11.68\n",
        "GOOGL  571.79  572.65  568.35  570.98   6109600     285.78\n",
        "MSFT    28.76   28.96   28.44   28.45  56634300      26.82"
       ]
      }
     ],
     "prompt_number": 459
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata.ix['High', '5/22/2012':, :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>AAPL</th>\n",
        "      <th>DELL</th>\n",
        "      <th>GOOGL</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> 573.88</td>\n",
        "      <td> 15.29</td>\n",
        "      <td> 613.81</td>\n",
        "      <td> 29.88</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-23</th>\n",
        "      <td> 572.80</td>\n",
        "      <td> 13.10</td>\n",
        "      <td> 609.60</td>\n",
        "      <td> 29.40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-24</th>\n",
        "      <td> 576.50</td>\n",
        "      <td> 12.63</td>\n",
        "      <td> 611.92</td>\n",
        "      <td> 29.30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-25</th>\n",
        "      <td> 565.85</td>\n",
        "      <td> 12.70</td>\n",
        "      <td> 601.73</td>\n",
        "      <td> 29.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-28</th>\n",
        "      <td>    NaN</td>\n",
        "      <td> 12.46</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-29</th>\n",
        "      <td> 574.00</td>\n",
        "      <td> 12.84</td>\n",
        "      <td> 599.13</td>\n",
        "      <td> 29.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-30</th>\n",
        "      <td> 579.99</td>\n",
        "      <td> 12.70</td>\n",
        "      <td> 591.90</td>\n",
        "      <td> 29.48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-31</th>\n",
        "      <td> 581.50</td>\n",
        "      <td> 12.54</td>\n",
        "      <td> 590.00</td>\n",
        "      <td> 29.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-06-01</th>\n",
        "      <td> 572.65</td>\n",
        "      <td> 12.30</td>\n",
        "      <td> 572.65</td>\n",
        "      <td> 28.96</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 468,
       "text": [
        "              AAPL   DELL   GOOGL   MSFT\n",
        "Date                                    \n",
        "2012-05-22  573.88  15.29  613.81  29.88\n",
        "2012-05-23  572.80  13.10  609.60  29.40\n",
        "2012-05-24  576.50  12.63  611.92  29.30\n",
        "2012-05-25  565.85  12.70  601.73  29.36\n",
        "2012-05-28     NaN  12.46     NaN    NaN\n",
        "2012-05-29  574.00  12.84  599.13  29.72\n",
        "2012-05-30  579.99  12.70  591.90  29.48\n",
        "2012-05-31  581.50  12.54  590.00  29.42\n",
        "2012-06-01  572.65  12.30  572.65  28.96"
       ]
      }
     ],
     "prompt_number": 468
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata.ix['High', '5/22/2012', :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 470,
       "text": [
        "AAPL     573.88\n",
        "DELL      15.29\n",
        "GOOGL    613.81\n",
        "MSFT      29.88\n",
        "Name: 2012-05-22 00:00:00, dtype: float64"
       ]
      }
     ],
     "prompt_number": 470
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata.ix['Open', '5/22/2012', 'AAPL']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 477,
       "text": [
        "569.54999999999995"
       ]
      }
     ],
     "prompt_number": 477
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata.ix['High', '5/22/2012':, 'AAPL']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 478,
       "text": [
        "Date\n",
        "2012-05-22    573.88\n",
        "2012-05-23    572.80\n",
        "2012-05-24    576.50\n",
        "2012-05-25    565.85\n",
        "2012-05-28       NaN\n",
        "2012-05-29    574.00\n",
        "2012-05-30    579.99\n",
        "2012-05-31    581.50\n",
        "2012-06-01    572.65\n",
        "Name: AAPL, dtype: float64"
       ]
      }
     ],
     "prompt_number": 478
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pdata.ix['Adj Close', '5/22/2012':, :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>AAPL</th>\n",
        "      <th>DELL</th>\n",
        "      <th>GOOGL</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> 532.36</td>\n",
        "      <td> 14.59</td>\n",
        "      <td> 300.70</td>\n",
        "      <td> 28.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-23</th>\n",
        "      <td> 545.35</td>\n",
        "      <td> 12.08</td>\n",
        "      <td> 305.04</td>\n",
        "      <td> 27.44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-24</th>\n",
        "      <td> 540.34</td>\n",
        "      <td> 12.04</td>\n",
        "      <td> 302.13</td>\n",
        "      <td> 27.40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-25</th>\n",
        "      <td> 537.45</td>\n",
        "      <td> 12.05</td>\n",
        "      <td> 296.06</td>\n",
        "      <td> 27.39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-28</th>\n",
        "      <td>    NaN</td>\n",
        "      <td> 12.05</td>\n",
        "      <td>    NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-29</th>\n",
        "      <td> 546.98</td>\n",
        "      <td> 12.25</td>\n",
        "      <td> 297.47</td>\n",
        "      <td> 27.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-30</th>\n",
        "      <td> 553.58</td>\n",
        "      <td> 12.15</td>\n",
        "      <td> 294.41</td>\n",
        "      <td> 27.66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-05-31</th>\n",
        "      <td> 552.20</td>\n",
        "      <td> 11.93</td>\n",
        "      <td> 290.72</td>\n",
        "      <td> 27.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-06-01</th>\n",
        "      <td> 536.20</td>\n",
        "      <td> 11.68</td>\n",
        "      <td> 285.78</td>\n",
        "      <td> 26.82</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 460,
       "text": [
        "              AAPL   DELL   GOOGL   MSFT\n",
        "Date                                    \n",
        "2012-05-22  532.36  14.59  300.70  28.05\n",
        "2012-05-23  545.35  12.08  305.04  27.44\n",
        "2012-05-24  540.34  12.04  302.13  27.40\n",
        "2012-05-25  537.45  12.05  296.06  27.39\n",
        "2012-05-28     NaN  12.05     NaN    NaN\n",
        "2012-05-29  546.98  12.25  297.47  27.86\n",
        "2012-05-30  553.58  12.15  294.41  27.66\n",
        "2012-05-31  552.20  11.93  290.72  27.51\n",
        "2012-06-01  536.20  11.68  285.78  26.82"
       ]
      }
     ],
     "prompt_number": 460
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####\ud1b5\uacc4 \ubaa8\ub378\uc5d0 \ub9de\uac8c Panel \ub370\uc774\ud130\ub97c \ucd9c\ub825\ud558\ub294 \ub2e4\ub978 \ubc29\ubc95\uc740 DataFrame\uc744 \uc313\uc544 \ub193\ub294 \uac83"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Dimensions: 6, 3, 4 \ub77c\ub294\uac78 \ud655\uc778\ud560 \uc218 \uc788\ub2e4.\n",
      "pdata.ix[:, '5/30/2012':, :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 479,
       "text": [
        "<class 'pandas.core.panel.Panel'>\n",
        "Dimensions: 6 (items) x 3 (major_axis) x 4 (minor_axis)\n",
        "Items axis: Open to Adj Close\n",
        "Major_axis axis: 2012-05-30 00:00:00 to 2012-06-01 00:00:00\n",
        "Minor_axis axis: AAPL to MSFT"
       ]
      }
     ],
     "prompt_number": 479
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stacked = pdata.ix[:, '5/30/2012':, :].to_frame()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 461
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stacked"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></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=\"4\" valign=\"top\">2012-05-30</th>\n",
        "      <th>AAPL</th>\n",
        "      <td> 569.20</td>\n",
        "      <td> 579.99</td>\n",
        "      <td> 566.56</td>\n",
        "      <td> 579.17</td>\n",
        "      <td> 18908200</td>\n",
        "      <td> 553.58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DELL</th>\n",
        "      <td>  12.59</td>\n",
        "      <td>  12.70</td>\n",
        "      <td>  12.46</td>\n",
        "      <td>  12.56</td>\n",
        "      <td> 19787800</td>\n",
        "      <td>  12.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GOOGL</th>\n",
        "      <td> 588.16</td>\n",
        "      <td> 591.90</td>\n",
        "      <td> 583.53</td>\n",
        "      <td> 588.23</td>\n",
        "      <td>  3809500</td>\n",
        "      <td> 294.41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MSFT</th>\n",
        "      <td>  29.35</td>\n",
        "      <td>  29.48</td>\n",
        "      <td>  29.12</td>\n",
        "      <td>  29.34</td>\n",
        "      <td> 41585500</td>\n",
        "      <td>  27.66</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">2012-05-31</th>\n",
        "      <th>AAPL</th>\n",
        "      <td> 580.74</td>\n",
        "      <td> 581.50</td>\n",
        "      <td> 571.46</td>\n",
        "      <td> 577.73</td>\n",
        "      <td> 17559800</td>\n",
        "      <td> 552.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DELL</th>\n",
        "      <td>  12.53</td>\n",
        "      <td>  12.54</td>\n",
        "      <td>  12.33</td>\n",
        "      <td>  12.33</td>\n",
        "      <td> 19955600</td>\n",
        "      <td>  11.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GOOGL</th>\n",
        "      <td> 588.72</td>\n",
        "      <td> 590.00</td>\n",
        "      <td> 579.00</td>\n",
        "      <td> 580.86</td>\n",
        "      <td>  5930600</td>\n",
        "      <td> 290.72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MSFT</th>\n",
        "      <td>  29.30</td>\n",
        "      <td>  29.42</td>\n",
        "      <td>  28.94</td>\n",
        "      <td>  29.19</td>\n",
        "      <td> 39134000</td>\n",
        "      <td>  27.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">2012-06-01</th>\n",
        "      <th>AAPL</th>\n",
        "      <td> 569.16</td>\n",
        "      <td> 572.65</td>\n",
        "      <td> 560.52</td>\n",
        "      <td> 560.99</td>\n",
        "      <td> 18606700</td>\n",
        "      <td> 536.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DELL</th>\n",
        "      <td>  12.15</td>\n",
        "      <td>  12.30</td>\n",
        "      <td>  12.05</td>\n",
        "      <td>  12.07</td>\n",
        "      <td> 19397600</td>\n",
        "      <td>  11.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GOOGL</th>\n",
        "      <td> 571.79</td>\n",
        "      <td> 572.65</td>\n",
        "      <td> 568.35</td>\n",
        "      <td> 570.98</td>\n",
        "      <td>  6109600</td>\n",
        "      <td> 285.78</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MSFT</th>\n",
        "      <td>  28.76</td>\n",
        "      <td>  28.96</td>\n",
        "      <td>  28.44</td>\n",
        "      <td>  28.45</td>\n",
        "      <td> 56634300</td>\n",
        "      <td>  26.82</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 462,
       "text": [
        "                    Open    High     Low   Close    Volume  Adj Close\n",
        "Date       minor                                                     \n",
        "2012-05-30 AAPL   569.20  579.99  566.56  579.17  18908200     553.58\n",
        "           DELL    12.59   12.70   12.46   12.56  19787800      12.15\n",
        "           GOOGL  588.16  591.90  583.53  588.23   3809500     294.41\n",
        "           MSFT    29.35   29.48   29.12   29.34  41585500      27.66\n",
        "2012-05-31 AAPL   580.74  581.50  571.46  577.73  17559800     552.20\n",
        "           DELL    12.53   12.54   12.33   12.33  19955600      11.93\n",
        "           GOOGL  588.72  590.00  579.00  580.86   5930600     290.72\n",
        "           MSFT    29.30   29.42   28.94   29.19  39134000      27.51\n",
        "2012-06-01 AAPL   569.16  572.65  560.52  560.99  18606700     536.20\n",
        "           DELL    12.15   12.30   12.05   12.07  19397600      11.68\n",
        "           GOOGL  571.79  572.65  568.35  570.98   6109600     285.78\n",
        "           MSFT    28.76   28.96   28.44   28.45  56634300      26.82"
       ]
      }
     ],
     "prompt_number": 462
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(stacked), type(pdata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 463,
       "text": [
        "(pandas.core.frame.DataFrame, pandas.core.panel.Panel)"
       ]
      }
     ],
     "prompt_number": 463
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####DataFrame\uc5d0\ub294 to_panel \uba54\uc11c\ub4dc\uc640 \uadf8 \ubc18\ub300\uc778 to_frame \uba54\uc11c\ub4dc\uac00 \uc788\ub2e4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stacked.to_panel()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 464,
       "text": [
        "<class 'pandas.core.panel.Panel'>\n",
        "Dimensions: 6 (items) x 3 (major_axis) x 4 (minor_axis)\n",
        "Items axis: Open to Adj Close\n",
        "Major_axis axis: 2012-05-30 00:00:00 to 2012-06-01 00:00:00\n",
        "Minor_axis axis: AAPL to MSFT"
       ]
      }
     ],
     "prompt_number": 464
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"files/images/level.jpg\" width=\"300px\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## [About my IPython in github](https://github.com/re4lfl0w/ipython)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}