{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 8. \ub3c4\uc2dd\ud654\uc640 \uc2dc\uac01\ud654\n",
      "\n",
      "- \ub3c4\ud45c\ub97c \ub9cc\ub4e4\uac70\ub098 \ub2e4\ub978 \uc5ec\ub7ec \uac00\uc9c0 \ubc29\uc2dd\uc73c\ub85c \uc2dc\uac01\ud654\ud558\ub294 \uc791\uc5c5\uc740 \ub370\uc774\ud130 \ubd84\uc11d\uc5d0\uc11c \ubb34\ucc99 \uc911\uc694\ud55c \uc77c \uc911 \ud558\ub098\n",
      "- \uc2dc\uac01\ud654\ub294 \ud2b9\uc774\uac12\uc744 \ucc3e\uc544\ub0b4\uac70\ub098 \ub370\uc774\ud130 \ubcc0\ud615\uc774 \ud544\uc694\ud55c\uc9c0 \uc54c\uc544\ubcf4\uac70\ub098 \ubaa8\ub378\uc5d0 \ub300\ud55c \uc544\uc774\ub514\uc5b4\ub97c \ucc3e\uae30 \uc704\ud55c \uacfc\uc815\uc758 \uc77c\ubd80\n",
      "- [d3.js](http://d3js.org) \uac19\uc740 \ud234\ud0b7\uc744 \uc0ac\uc6a9\ud574 \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \uc6f9\uc5d0 \uc62c\ub9ac\ub294 \uac8c \ucd5c\uc885 \ubaa9\ud45c\uc77c \uc218 \uc788\ub2e4.\n",
      "- \ud30c\uc774\uc36c\uc740 \ub2e4\uc591\ud55c \uc2dc\uac01\ud654 \ub3c4\uad6c\ub97c \uad6c\ube44\ud558\uace0 \uc788\uc9c0\ub9cc \uc774 \ucc45\uc5d0\uc11c\ub294 [matplotlib](http://matplotlib.sourceforge.net)\uc5d0 \uc911\uc810\uc744 \ub450\uace0 \uc124\uba85\n",
      "\n",
      "- matplotlib\uc740 \uc8fc\ub85c 2D \ub3c4\ud45c\ub97c \uc704\ud55c \ub370\uc2a4\ud06c\ud1b1 \ud328\ud0a4\uc9c0\ub85c, \ucd9c\ud310\ubb3c \uc218\uc900\uc758 \ub3c4\ud45c\ub97c \ub9cc\ub4e4 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\n",
      "- IPython\uacfc matplotlib\uc744 \ud1b5\ud569\n",
      "- IPython\uc5d0\uc11c GUI \ud234\ud0b7\uacfc \ud568\uaed8 matplotlib\uc744 \uc0ac\uc6a9\ud558\uba74 \ub3c4\ud45c\uc758 \ud655\ub300\uc640 \ud68c\uc804 \uac19\uc740 \uc778\ud130\ub809\ud2f0\ube0c\ud55c \uae30\ub2a5 \uc0ac\uc6a9\n",
      "- \ub610\ud55c \ubaa8\ub4e0 \uc6b4\uc601\uccb4\uc81c\uc758 \ub2e4\uc591\ud55c GUI \ubc31\uc5d4\ub4dc\ub97c \uc9c0\uc6d0\n",
      "- PDF, SVG, JPG, PNG, GMP, GIF \ub4f1 \uc77c\ubc18\uc801\uc73c\ub85c \ub110\ub9ac \uc0ac\uc694\uc624\ub514\ub294 \ubca1\ud130 \ud3ec\ub9f7\uacfc \ub798\uc2a4\ud130 \ud3ec\ub9f7\uc73c\ub85c \ub3c4\ud45c\ub97c \uc800\uc7a5\ud560 \uc218 \uc788\ub2e4.\n",
      "- \uc774 \ucc45\uc5d0\uc11c\ubfd0\ub9cc \uc544\ub2c8\ub77c \ub2e4\ub978 \uacf3\uc5d0\uc11c\ub3c4 \ub300\ubd80\ubd84 matplotlib \uc744 \uc774\uc6a9\ud574\uc11c \ub2e4\uc774\uc5b4\uadf8\ub7a8\uc744 \ub9cc\ub4e4\uace0 \uc788\ub2e4.\n",
      "- matplotlib\uc740 3D \ub3c4\uc2dd\uc744 \uc704\ud55c matplot3d \ubc0f \uc9c0\ub3c4\uc640 \ud22c\uc601\uc744 \uc704\ud55c basemap \uac19\uc740 \ub2e4\uc591\ud55c \ud655\uc7a5\ud234\ud0b7\uc774 \uc788\ub294\ub370, \uc774 \uc7a5\uc758 \ub05d\ubd80\ubd84\uc5d0\uc11c .shp(shapefile)\uc744 \uc77d\uc5b4 \uc9c0\ub3c4 \uc704\uc5d0 \ub3c4\uc2dd\uc744 \uadf8\ub9ac\ub294 \uc608\uc81c \uc18c\uac1c \uc608\uc815\n",
      "- \uc774 \uc7a5\uc5d0\uc11c \ub098\uc624\ub294 \uc608\uc81c \ucf54\ub4dc\ub97c \ub3cc\ub824\ubcf4\ub824\uba74 **IPython\uc740 pylab \ubaa8\ub4dc(ipython --pylab)\ub85c \uc2e4\ud589**\ud558\uac70\ub098 **%gui \ub9e4\uc9c1\uc744 \uc774\uc6a9\ud574\uc11c GUI \uc774\ubca4\ud2b8 \ub8e8\ud504 \ud1b5\ud569 \uae30\ub2a5**\uc744 \ubc18\ub4dc\uc2dc \ucf1c\uc57c \ud55c\ub2e4."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 8.1 matplotlib API \uac04\ub7b5\ud558\uac8c \uc0b4\ud3b4\ubcf4\uae30\n",
      "\n",
      "- ipython --pylab \uba85\ub839\uc73c\ub85c, IPython\uc744 pylab \ubaa8\ub4dc\ub85c \uc2e4\ud589\n",
      "- \uc774\ub807\uac8c \uc2e4\ud589\uc2dc\ud0a4\uba74 IPython\uc740 \uc0ac\uc6a9\uc790\uc758 \uc120\ud0dd\uc5d0 \ub530\ub77c Tk, wxPython, PyQt, Mac OS X \ub124\uc774\ud2f0\ube0c, GTK \ub4f1\uacfc \uac19\uc740 \ub2e4\uc591\ud55c GUI \ubc31\uc5d4\ub4dc\ub97c \uc9c0\uc6d0\ud560 \uc218 \uc788\uac8c \ub41c\ub2e4.\n",
      "- \ubcf4\ud1b5\uc758 \uc0ac\uc6a9\uc790\ub77c\uba74 \uae30\ubcf8 \ubca1\uc5d4\ub4dc\ub85c \ucda9\ubd84\n",
      "- pylab \ubaa8\ub4dc\ub294 IPython\uc5d0\uc11c MATLAB \uc2a4\ub7ec\uc6b4 \uc778\ud130\ud398\uc774\uc2a4 \uc81c\uacf5\ud560 \uc218 \uc788\ub3c4\ub85d \ud544\uc694\ud55c \uc5ec\ub7ec\uac00\uc9c0 \ubaa8\ub4c8 \ubc0f \ud568\uc218\uc640 \ud568\uaed8 \uc81c\uacf5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(np.arange(10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1091663d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEACAYAAACeQuziAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBgCHYoEGAIdigQYAh2KBBgCH+h/RqVRygHK4xwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108bab7d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- IPython\uc774 pylab \ubaa8\ub4dc\ub85c \uc81c\ub300\ub85c \uc2e4\ud589\uc774 \ub418\uc5c8\ub2e4\uba74 \uc120 \uadf8\ub798\ud504\uac00 \uadf8\ub824\uc9c8 \uac83\uc774\ub2e4.\n",
      "- \ub9cc\uc57d \uc81c\ub300\ub85c \ub098\ud0c0\ub098\uc9c0 \uc54a\ub294 \ubd84\uc740 Canopy\ub97c \uc124\uce58\ud558\uc2dc\ub77c. \ucd08\ubcf4\ub294 \uadf8\ub0e5 [Canopy](https://www.enthought.com/canopy-express/) \ud558\ub098\ub9cc \uc124\uce58\ud558\uba74 \ub41c\ub2e4. \ub098\uc911\uc5d0\ub294 \ub2e4\ub978 \uc880 \ub354 \uc88b\uc740 \ubc29\ubc95\uc774 \uc788\uaca0\uc9c0\ub9cc \ucd08\ubcf4\ub54c\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc2e4\ud5d8\ud574\ubcf4\uace0 \ud14c\uc2a4\ud2b8 \ud574\ubcfc \uc218 \uc788\ub294 \ud658\uacbd \uad6c\uc131\uc774 \ub9e4\uc6b0 \uc911\uc694\ud558\ub2e4! \uc548 \uadf8\ub7fc \uc5ec\uae30\uc5d0\uc11c \uadf8\ub0e5 \uc9dc\uc99d\ub098\uc11c \ucc45\uc744 \ub0b4\ud33d\uaca8\uce58\uac8c \ub418\ub2c8\uae4c!\n",
      "- plot, close \uac19\uc740 matplotlib API \ud568\uc218\ub294 matplotlib.pyplot \ubaa8\ub4c8\uc5d0 \ud3ec\ud568"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pyplot import convention\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "from pandas import DataFrame, Series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ub098\uc911\uc5d0 pandas\ub85c \uadf8\ub798\ud504\ub97c \uadf8\ub9ac\ub294 \ubc29\ubc95\uacfc \uadf8\ub798\ud504\uac00 \ub9cc\ub4e4\uc5b4\uc9c0\ub294 \uc138\ubd80 \uc0ac\ud56d\uc5d0 \uad00\ud55c \uc7ac\ubbf8\uc5c6\ub294 \ub0b4\uc6a9\uc744 \ub2e4\ub974\uac8c \ub428\n",
      "- \ud568\uc218\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \uc635\uc158\ub9cc \uc0ac\uc6a9\ud558\ub294 \ub370 \ub9cc\uc871\ud558\uc9c0 \uc54a\uace0 \uadf8 \uc774\uc0c1\uc758 \ucd5c\uc801\ud654\ub97c \uc6d0\ud55c\ub2e4\uba74 matplotlib APi\uc5d0 \ub300\ud574\uc11c\ub3c4 \uc5b4\ub290 \uc815\ub3c4 \uc54c\uace0 \uc788\uc5b4\uc57c \ub428\n",
      "- \uc774 \ucc45\uc5d0\uc11c\ub294 \uc608\uc2dc\ub97c \ud1b5\ud574 matplotlib\uc758 \uac04\ub2e8\ud55c \uc0ac\uc6a9\ubc95\ub9cc \uc54c\ub824\uc90c. \uc2ec\ub3c4 \uae4a\uc740 \ub0b4\uc6a9\uc740 \uad6c\uae00\ub9c1!\n",
      "- matplotlib \uac24\ub7ec\ub9ac\uc640 \ubb38\uc11c\uc5d0 \uc788\ub294 \ub0b4\uc6a9\uc744 \ucc38\uace0\ud558\uba74 \uace0\uae09 \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uace0 \uadf8\ub798\ud504\ub97c \uc791\uc131\ud558\ub294 \ub370 \uace0\uc218\uac00 \ub420 \uc218 \uc788\ub294 \uae38\uc7a1\uc774 \uc5ed\ud560 \ucda9\ubd84"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.1.1 Figure\uc640 \uc11c\ube0c\ud50c\ub86f\n",
      "\n",
      "- matplotlib\uc5d0\uc11c \uadf8\ub798\ud504\ub294 Figure \uac1d\uccb4 \ub0b4\uc5d0 \uc874\uc7ac\n",
      "- \uadf8\ub798\ud504\ub97c \uc704\ud55c \uc0c8\ub85c\uc6b4 Figure\ub294 plt.figure\ub97c \uc0ac\uc6a9\ud574\uc11c \uc0dd\uc131 \uac00\ub2a5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10884eb50>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- plt.figure\uc5d0\ub294 \ub2e4\uc591\ud55c \uc635\uc158\n",
      "- figsize: \uadf8\ub798\ud504\uac00 \ub514\uc2a4\ud06c\uc5d0 \uc800\uc7a5\ub420 \uacbd\uc6b0\uc758 \ud06c\uae30\ub098 \uac00\ub85c-\uc138\ub85c \ube44\uc728 \uc815\ud568\n",
      "- matplotlib\uc5d0\uc11c Figure \uac1d\uccb4\ub294 MATLAB\ucc98\ub7fc \uc22b\uc790\ub97c \ubc1b\ub294\ub2e4(plt.figure(2)\uac19\uc740)\n",
      "- \ud604\uc7ac \ud65c\uc131\ud654\ub41c Figure\uc5d0 \ub300\ud55c \ucc38\uc870\ub294 plt.gcf() \ud568\uc218\ub97c \ud1b5\ud574 \uc5bb\uc744 \uc218 \uc788\ub2e4.\n",
      "- \ube48 Figure \uac1d\uccb4\ub85c\ub294 \uadf8\ub798\ud504\ub97c \ub9cc\ub4e4 \uc218 \uc5c6\uc73c\ubbc0\ub85c add_subplot\uc744 \uc0ac\uc6a9\ud574 \ucd5c\uc18c\ud55c \ud558\ub098 \uc774\uc0c1\uc758 \uc11c\ube0c\ud50c\ub86f\uc744 \uc0dd\uc131\ud574\uc57c \ud55c\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax1 = fig.add_subplot(2, 2, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uac1d\uccb4 \ud06c\uae30\uac00 2 * 2\uc774\uace0, 4\uac1c\uc758 \uc11c\ube0c\ud50c\ub86f \uc911\uc5d0\uc11c \uccab\ubc88\uc9f8 \uc120\ud0dd(\uc11c\ube0c\ud50c\ub86f\uc740 1\ubd80\ud130 \uc22b\uc790\uac00 \ub9e4\uaca8\uc9d0)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax2 = fig.add_subplot(2, 2, 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax3 = fig.add_subplot(2, 2, 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- plt.plot([1.5, 3.5, -2, 1.6]) \uba85\ub839\uc73c\ub85c \uadf8\ub798\ud504\ub97c \ub098\ud0c0\ub0b4\uba74 matplotlib\uc740 \uac00\uc7a5 \ucd5c\uadfc\uc758 Figure \uac1d\uccb4\uc640 \uadf8 \uc11c\ube0c\ud50c\ub86f\uc744 \uadf8\ub9b0\ub2e4.\n",
      "- \uc11c\ube0c\ud50c\ub86f\uc774 \uc5c6\ub2e4\uba74 \uc11c\ube0c\ud50c\ub86f \ud558\ub098\ub97c \uc0dd\uc131\ud55c\ub2e4.\n",
      "- \uc774\ub807\uac8c \ud574\uc11c Figure\uc640 \uc11c\ube0c\ud50c\ub86f\uc774 \uc0dd\uc131\ub418\ub294 \uacfc\uc815\uc744 \uc228\uaca8\uc900\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy.random import randn"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# k \uc635\uc158\uc740 \uac80\uc740 \uc810\uc120\uc744 \uadf8\ub9ac\uae30 \uc704\ud55c \uc2a4\ud0c0\uc77c \uc635\uc158(black)\n",
      "plt.plot(randn(50).cumsum(), 'k--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[<matplotlib.lines.Line2D at 0x108b78150>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8TOf+B/DPTBZbVWKJhKhE0oTIMol9q1gilkQoKmq7\ntFrXVn3httyXn2qL69Kq4rYolZZWS5WK7SImImiQWDKCWEKEbDQhJCGZ8/vDFU2zzXJmziyf9+uV\n1+tm5pzn+d7T+OTkmed5jkwQBAFERGTW5FIXQERE+mOYExFZAIY5EZEFYJgTEVkAhjkRkQVgmBMR\nWQBRwry0tBSBgYEIDw8XozkiItKSKGG+cuVK+Pj4QCaTidEcERFpSe8wv337Nvbu3Yu3334bXH9E\nRCQNvcP8/fffx7JlyyCXc/idiEgqeiVwdHQ0nJycEBgYyLtyIiIpCXqYO3eu4OrqKri5uQnOzs5C\n3bp1hbFjx5Y7xsPDQwDAL37xi1/80uLLw8NDqzzWK8z/TKlUCmFhYRU7gGhdmL0FCxZIXYLJ4LV4\ngdfiBV6LF7TNTlEHujmbhYhIGrZiNdSzZ0/07NlTrOaIiEgLnIJiRMHBwVKXYDJ4LV7gtXiB10J3\nsv+NzRiuA5mMM12IiLSkbXbyzpyIyAIwzImILADDnIjIAjDMiYgsAMOciMgCMMyJiCwAw5yIyAIw\nzImILIBkYb5u3TqMGTMGO3fulKoEIiKLIVmY//zzz2jQoAFu3LghVQlERBZDsuX8Li4u+P333/HK\nK68YsnsiIrNkFsv57927h0ePHqFFixaVvn/gwAEkJCQYuSoiIvMl2ha42sjNzcWQIUOq3P88KSkJ\nmZmZ6Nixo5ErIyIyTya5a2J8fDxmzJiBM2fOGKgqIiLTpm12mmSYFxcXo1GjRrhz5w5efvllA1VG\nRGS6zGLMvCa1atVCu3btcOLECalLISIyCyYZ5gDQo0cPxMXFSV0GEZFZkOQDUE2MHTsW9+7dk7oM\nIiKzYPQx81u3biE7Oxvt27c3ZLdERGZN2zFzo9+Z79q1CxcvXmSYExGJyOhj5iqVCm3btjV2t0RE\nFs3oYZ6cnMwwJyISmVHHzAVBQMOGDXH58mU4OTkZslsiIrNm0vPM7969Czs7O62CfPTo0Th//rwB\nqyIiMn9GDfPi4mJMnz5dq3Pq1KmDo0ePGqgiIiLLYJLL+f8sKioKe/bswc8//yxiVUREps2kh1l0\n8XwlqIF/5xARmTWTD3N3d3fI5XJcu3ZN6lKIiEyWyYe5TCZDjx49cPz4calLISIyWSY/Zg4ADx8+\nxEsvvVTlwyyIiCyNyY6ZX7hwAb/++qtO59avX59BTkRUDaOF+f79+xEbG2us7oiIrIrRwlylUsHX\n19dg7ScmJuLSpUsGa5+IyJQZNcwNuSfL+PHjMXv2bIO1T0RkyvQO8/T0dPTq1Qtt27ZFcHAwNm3a\nVOEYtVqNlJQUvcJcEATcuHGjyvd37tyJM2fOcD46EVklvfczt7Ozw4oVK6BQKJCbmwtfX1906tQJ\nbdq0KTsmLS0NDRs21OvhzE+fPoWfnx8yMjLQoEGDCu+3atUKtWrVQkpKCnx8fHTuh4jIHOl9Z+7s\n7AyFQgEAaNy4MTp06IA7d+6UO6Zu3bpYunSpXv3Y29ujQ4cOVc43l8lk6NOnDw4fPqxXP0TmrqCg\nAFevXkV8fDx++eUXrFmzBikpKVKXRQYm6pj51atXoVKp0Llz53KvOzs7Y9SoUXq336NHD0RFRWHQ\noEEoKiqq8H7fvn0Z5mTV5s6dCycnJ/Tr1w9z5szB5s2bkZycjEePHkldGhmYaIuGCgoKEBwcjPnz\n5yMiIuJFBzIZFixYUPZ9cHAwgoODderj0KFDGDhwINauXYsJEyZUeP/x48d48uQJHBwcdGqfyNzl\n5ubCwcEBtrYm+6x2qoJSqYRSqSz7fuHChVp9BihKmD99+hRhYWEYMGAAZs6cWb4DEVaAPicIAjIy\nMuDq6ipKe0Tm6uTJkxX+AibLom126h3mgiBg/PjxaNy4MT7//HO9CyKi6t24cQMdOnRAdnY25HKT\n316JdGT05fzx8fHYvHkzYmJiEBgYiMDAQOzfv1/fZomoCvv27cOgQYN0CvJp06bxw1ALpffAWvfu\n3aFWq6s9ZvXq1Zg2bZq+XRERnoX56NGjdTo3OzsbSUlJ5aYOk2Uwyt9oGRkZxuimzOPHj3H//n2j\n9klkDEVFRYiNjUW/fv10Ot/X1xfJyckiV0WmwChhbshl/JVZvHgxli9fbtQ+iYzh6NGj8PX1RcOG\nDXU6n2FuuSwyzLl4iCxVvXr1KswY0wbD3HIZ5eEUjx8/Rp06dQzZTTlFRUVo0qQJ0tPTOeec6E9K\nS0tRv359ZGdn46WXXpK6HKqGST6cwphBDgC1a9dG586duX860V/Y2Njg5MmTqFWrltSlkMgsdpIq\nh1qIKufv7w87OzupyyCRWWyY9+/fH/b29lKXQURkFGbxQGciImtjkmPmRKSfuLg4LFq0SOoyyIQx\nzInMwK+//lrjSmuybgxzIjOwb98+DBgwQLT2hg8fjri4ONHaI+kxzIm0EB8fb/RFN2lpabh//z6C\ngoJEa7Nhw4ZcPGRhLD7MlUolpyiSKDIyMjBy5EjUrl3bqP3u27cPoaGhom53y5Wglsfiw/zmzZtY\nu3at1GWQmVOr1Rg3bhzeffddeHp6GrXv/fv3izrEAjDMLZHFT028ffs2FAoFN/Invfz73//G7t27\noVQqYWNjY9S+8/PzYWdnh7p164rWZnZ2Ntq0aYPc3FzIZDLR2iXxcGriX7i6uqJZs2YYPnw4du7c\nieLiYqlLIjNz5swZLF++HJs3bzZ6kANAgwYNRA1yAHBycoKtrS2ysrJEbZekY/FhDjwbN+/fvz++\n+OIL/PLLL1KXQyampl/w3377Lb788ku0bNnSSBUZR1paGpydnaUug0Ri8cMsRDXp1asXBg0ahFmz\nZlU65CAIQrnXCwsLkZeXBxcXF2OWSVaGwyxEWoqKisKPP/6IsWPHorCwsML7fw346OhoTJkyxVjl\nEWnE6sNcEASMHDkSly9flroUMpK8vDwsXbq07PtXXnkFcXFxUKvV6NGjB9LT06s938/PD+fPnzd0\nmbh16xYePXpk8H7IMlh9mMtkMgQHB2P06NF48uSJ1OWQEezduxfHjh0r91rdunWxZcsWjBw5Ep06\ndar2g0FPT09kZmaioKDAoHXOnDkTO3fuNGgfZDmsPswBYPLkyXBxccFHH30kdSlkBLt27cKQIUMq\nvC6TyTBnzhwcOnQITZs2rfJ8W1tbtG7dWuN52qWlpTh06JDWdZ49exYdOnTQ+jxtPHr0CEVFRQbt\ng4yDYY5n/4g3bNiATZs28elEFq64uBgHDhxAWFhYlcf4+PjU2I6/vz8uXLigUZ85OTkICQlBSUmJ\nxnU+fPgQWVlZ8PDw0PgcXYwYMQIHDx40aB9kHAzz/3FycsI333yDcePGIT8/X+pyyECOHDkCX1/f\nau+8NdGjRw88ffpUo2OdnZ3h6emJK1euaNy+SqVC69atDT6vnStBLYet1AWYkoEDB+Lrr79GvXr1\npC6FDGTnzp2IiIjQu52JEydqdby/vz/Onz+v0V0/ACQnJ8PPz0+X0rTi6+uLAwcOGLwfMjzemf/F\ngAEDYGvL33GWaubMmRg3bpzR+w0ICNBqBoxcLkdwcLDhCvof3plbDi4aIjKCnTt3Yv369dizZ4/U\npZRTWFiIRo0ale3/QqZD2+zkLSiREXTs2BG3bt2SuowK6tSpg06dOiEzMxMtWrSQuhzSA+/Ma1BY\nWIg6depIXQaZqYULF2L48OFo27at1KWQmeFyfhGp1Wp07dqV0xWpUrm5uTh69GiV76vVaqxatQoN\nGjQwYlVkrRjm1ZDL5ViyZAnGjBmDe/fuSV0O6SEvL0/0vxBv3ryJadOmVfn+xYsX0aBBA7i6uora\nL1FlGOY16N+/P0aOHImJEyea9XCRtQsPDxf98YE+Pj5ITU2tchuIo0ePomfPnlq3q1QqcefOHX3L\nIyvDMNfA4sWLkZGRgTVr1khdCukgOzsbFy5cQI8ePURtt06dOmjZsmWVm7QdPXoUr732mtbtfvjh\nh7h+/bq+5ZGVYZhrwN7eHlu3bsWnn36KnJwcqcshLUVHR6Nfv36oVauW6G1XtaxfEATExsaWuzNX\nq9WYMWMGSktLq2xPrVZDpVIZ/QPTK1euIC0tzah9krgY5hry9PSESqVCkyZNpC6FtCTWqs/KVLcd\n7p49e+Dm5lb2vVwuR3R0NK5du1Zle2lpaXBwcICjo6PYpVbr+++/54PPzZzeYX706FEEBQXB398f\nq1atEqMmk9WoUSOpSyAtPXr0CEqlEgMHDjRI+3369IGXl1eF12UyGYKCgio82CIgIADnzp2rsr3k\n5GT4+vqKXmdN+vbtq9POjmQ69Arz0tJSTJw4ETt27MCZM2ewYcMGpKSkiFUbkd4yMjIwfvx4g93p\ndu3aVat9Wp7v0VKVCxcuGGVPlr/q0qULLl++jPv37xu9bxKHXmGekJAAT09PuLm5wc7ODpGRkdi1\na5dYtRHpzcvLy6T+YqwpzL28vAz2V0R17O3t0b17dxw5csTofZM49ArzjIyMckuAXV1dkZGRoXdR\nRJaqpg23RowYYZQNtirTt29f0advkvHoFeaVPcncWvztb39DYmKi1GWQCaru8YOtWrXCsmXLTHLN\nQlhYmCTj9SQOvTbaat68ebmH36anp1e62u3Pj2MLDg6W7M5DTE2aNMHOnTsRFBQkdSlkYqZOnYou\nXbpUOpYul8sxfPhwCaqqmZeXV6Uf5pJxKJVKKJVKnc/Xa6OtkpISeHt74/Dhw2jWrBk6duyIH3/8\nEW3atHnRgZlvtFWVY8eOYdq0aTh79qzUpZDEcnJysHXrVkyfPh3As1Dcvn07/P39Ja6MzJm22an3\nromxsbGYOXMmSkpKMGnSJMyYMUOvgsxFaWkpnJ2dcerUqXJzicl07N69G15eXvD29jZoP3/88Qde\neeUV5OfnIysrC23btkVubi7kci7jIN0ZfdfEnj17IikpCRcuXKgQ5JbMxsYGYWFh+O2336Quhaqw\ndOlSo3wg7+joCAcHB6SlpSEuLg49evTQKcjXrl2LGzduGKBCsga8ddBDREQEt8c1UWq1GufOnUNg\nYKBR+nu+rD82Nlan/VgA4F//+hdKSkpEroysBcNcD2FhYdi2bZvUZVAlUlNT0bhxY6Mti3++rD8z\nM1OjMB81alS5zbQePnyI7OxstGrVypBlamT79u3YvHmz1GWQlhjmerC1teW4qIlKSkoy6kyj53fm\nv/zyCzp06FDj8Y8ePSr34blKpUKbNm1gY2NjyDI1IpfLsWXLFqnLIC0xicgiJSYmGm2IBQBee+01\njBo1SuPj/f39y+3RItWeLJXp1asX4uPjUVxcLHUppAWGOVmkXr16GWynxMq4urpi6NChGh//12X9\nUu3JUhlHR0e0bt0aJ0+elLoU0gLD3Aiq27+aDGPAgAEmE46V+euy/qFDhyI8PFzCisrjLormh2Eu\ngtzc3CpntajVaoSGhuLYsWPlXn/06JExSiMT5enpiczMTDx8+BDAs5XRprT6kmFufhjmIsjOzsbY\nsWMrneC/atUqFBUVoUuXLmWvlZSUoGPHjti4caMxyyQTYmNjgwsXLqBu3bpSl1Kp7t27Y+vWrVKX\nQVrQewVojR1Y6ArQPxMEAa+++iq2b98OhUJR9vrly5fRvXt3nDhxAp6enuXOuXz5MgYOHIjIyEh8\n8sknnBVDROUYfQUoPbvoERER5fZyLykpwfjx47Fw4cIKQQ4A3t7eOHnyJI4cOYLRo0ejqKjImCUT\nkYVhmItk8ODB5Zb2r1ixAvXr18fkyZOrPKdJkyY4fPgw7t+/j9WrVxujTKswYcKEsrFoImvBYRaR\nlJSUwNnZGUlJSWjRogXu3r0LtVqN5s2b13ju2bNnkZWVhdDQUCNUatmys7Ph7e2N+/fvm81++1Om\nTMGcOXPg7u4udSlkQjjMIhFbW1usWrWqLEBcXFw0CnIAUCgUDHKRPF/5aS5BrlarERUVZbRtB7RV\nWlqKP/74Q+oySAMMcxGNGjWq0odzWKtr165V+9QdQzD2yk99derUCY8fP4aDg4PUpVRq06ZNZfu0\nk2ljmJPBDB06tNrnXRqCsfdk0dfjx4+lLqFa3bp1w4kTJ6QugzTAMCeDadeundGfk2pud+bfffcd\nduzYIXUZVfLy8kJOTg6HWswAw9zEWNKHxUFBQUYP86ioKJNaSVmTdu3aabWni7HJ5XL4+/vz8Yhm\ngGFuQi5evIiePXtKXYZeUlNTy34hSRHm3bp1M4ltZC2JFP8dSXsMcxPi7e2NK1euIDU1VepSdJKd\nnY0uXbogPT0dwLPNpFQqFZ4+fSpxZaSPbt26mfzYPnGeucmZNm0amjVrhnnz5klditamT58OuVyO\nlStXlr02atQofPbZZ2jWrJmElRGZH22zk2FuYo4ePYoZM2aY3RhlamoqunTpgpSUFDRp0kTqcojM\nHhcNmblu3bohOzsbly9flroUrcybNw+zZs1ikBNJhGFuYmxsbDB27Fijz8/WR0JCAk6ePIn33ntP\nshpOnTqFIUOGSNY/kdRspS6AKlq6dKnUJWhFoVDgwIEDku7NfebMGTRq1Eiy/omkxjtz0pu9vT18\nfHwkrcHcFguZm6KiIuzfv1/qMqgaDHMyuNOnT5d7Er0m1Go14uLiND7e3Jbxm6PXX38dxcXFUpdB\nVWCYk8EdPnwYUVFRWp2TkZGBt956C6GhoTXO7Hn69ClUKhUCAgL0KZOqUbt2bXh6eiI5OVnqUqgK\nDHMyOF32aGnRogVUKhUiIiLQv39/jB07Fjdu3EBpaWmFY1NTU+Hm5oZ69eqJVTJVgitBTRvD3IQl\nJiZi27ZtUpdRpRMnTuD+/fs1HhcYGIikpCSo1epqj5s0aRJu3rxZ9r2dnR2mTJmC1NRUuLu7Q6FQ\nICYmpsJ5Pj4+OHPmjPb/B0grDHPTxjA3YcXFxfjoo4+kLqNKEyZMQFpaWo3HNWrUCI6Ojrh+/XqV\nx2RlZWHbtm1wcXGp8F79+vXx8ccfIz8/HyEhIZWeX6dOHY3rJt0EBgYyzE0Yw9yEderUCbm5ubh1\n65bUpVSQkZGBnJwcKBQKjY4PCgqq9u553759CAkJgb29vVglksgUCgW6d+8udRlUBYa5CZPL5QgK\nCjLJpf2HDx9G7969IZdr9iM0ceJEtGjRosr3o6OjERYWJlZ5ZAD169fHZ599JnUZVAWGuYlTKBQm\nG+Z9+vTR+PiwsDB07dq10veePHmCQ4cOYcCAAWKVR2R1GOYmzhTDXBAEHDp0CH379hWlvWPHjsHH\nxwdOTk6itEdkjbic38T16tULjRs3lrqMcoqKijBx4kR4eHiI0l6vXr3w22+/idIWkbXiFrhERCbI\nqFvgzpkzB23atEFQUBBmzpyJ/Px8fZojIjOwevXqsqdJkenQK8z79esHlUqF06dP49GjR1iyZIlY\ndZEFunr1Kn9GLEBMTAyOHz+u07nXr1+Ho6Mjxo0bh5iYmBoXkpHm9ArzkJAQyOVyyOVyhIaG4vbt\n22LVRRaodu3a+OKLLzjsZub0WQmal5eHCRMmICgoCLNmzYK7uzs+/vhj/kyIQLTZLOvXr0dERIRY\nzZEFat68OQRBwJ07dwAADx8+xJUrVySuirSlT5gHBQXh888/x8yZM5GUlIRdu3bBxcUFMplM5Cqt\nT41hHhISAj8/vwpfu3fvLjtm0aJFqF+/PkaMGGHQYq1VaWkpevbsaRJPuZ81axZSU1N1Olcmk5UL\ngt27d2POnDlilkdG8Py/oRh30wqFApMmTRKhKqpxauLBgwerfX/Tpk3Yu3cvDh8+XOUxf95fJDg4\nGMHBwRoXSM8eJZednY1Lly7Bz89P53ZUKhUcHBzQvHlznc4vLCzEunXrsGDBAp1reB4E4eHhXPVp\nppydnWFvb49bt26hZcuWUpdjMZRKJZRKpe4NCHrYt2+f4OPjI+Tm5lZ5jJ5d0P9ERkYK3333nV5t\nhIaGCt9//73O5x86dEjo0qWLXjVs27ZNGDx4sPD06VPB0dFRuH37tl7tkTR27dol5OTkSF2GRdM2\nO/WaZ/7qq6/iyZMnaNiwIQCgS5cu+M9//lPuGM4zF8fSpUuRnZ2t894YN2/eRFBQEG7fvq3zDoPz\n5s2DjY0NPvnkE53OB4B79+7h8uXLKCkpwfvvv8+ta6mcn376CR07doS7u7vUpUjOqPPMU1NTcfPm\nTSQlJSEpKalCkJN49F3Wv3HjRrz55ptlQb5y5Upcu3ZNqzbEWMLfqFEjdO3alUMsVujIkSM4f/58\ntcfs3r1bv6EGK8a9WczE8zDX5a+c0tJSbNy4EW+//XbZazk5OVi+fLnGbeTl5eHSpUvo3Lmz1v1X\nxs3NDcOHDxelLTIPq1evxsWLF6s9xt/fv8bAp8oxzM1E06ZNcerUKZ3OPXDgAFxcXMo9I3PGjBn4\n6aefkJWVpVEbL7/8Ms6ePYtatWrpVMNfTZkyRa8Pc8n8JCUlITAwsNpjAgICGOY64t4sVuDatWvI\nysqqsAXtlClT4OjoiEWLFklUGVmLvLw8uLq6Ij8/HzY2NlUed/fuXfj5+SEnJ8fq554bdcyczIOH\nh0ele4nPnj0ba9euxYMHDySoiszdDz/8gDVr1mh07Llz5+Dv719tkAPPpj3KZDJkZmaKUaJVYZhb\nsVatWqFv377Ys2eP1KWQGWrSpAl++OEHjY5NSkrS6BGDMpkMy5cvrzH0qSIOs1i54uJi0cbByboU\nFRXByckJt27dgoODQ7XHKpVK2Nra8hmiWuAwi4VTq9Wi7jRXXZD//vvv6NatG1JSUkTrjyxH7dq1\n0a1bN8TExNR4bHBwMIPcwBjmZqZv374abz/6fEMrbWVkZGDcuHF4/fXX8c4778Db21undsjy9evX\nDwcOHJC6DALD3Ox4eXlptHjo/v378PHx0eqBIUVFRVi8eDH8/f3h6uqKS5cuYfz48ZDL+WNClQsN\nDa12XyYyHj4D1MwoFAqcPn26xuM2b96MQYMGoUGDBhq3nZeXB5VKhVOnTqFVq1b6lElWok2bNhr9\nPJLh8ZbLzGiyrF8QBKxfv77cik9NODs7Y8uWLQxy0phMJqvxw09dbNy4kcM3WmKYmxk/Pz9cvHix\n2r3Nf//9dxQVFXGrYTIJc+fOxc2bN7U6JzMzk8M3WmKYm5l69erB29u7yn8cpaWlmD9/PiZPnmz1\nK+hIeqWlpVi9ejVefvllrc7jHi3a45i5GUpMTKwyqAVBwLBhw7QeYiEyhGvXrqFRo0ZwdHTU6jzu\n0aI93pmboeruuG1tbTF58mTY2vL3NBlPYWFhpY8T1GRzrcq4urqisLAQOTk5YpRnFRjmRKS3xMRE\nvPHGGxVe1zXMZTIZh1q0xDAnIr116tQJaWlpFbZUPnv2rE5hDjx7gMqft22m6nFvFjMnCAIePHig\n1XxyIkMYNmwYhg4dijFjxpS9durUKbz66qsGmb5o6bg3i5XIzMxEVlYW1q1bh5EjR0pdDlGlS/s7\ndOjAIDcS3pmbqQ8++ACpqamIi4tDfHw8vLy8pC6JrFxaWho6deqEu3fvcgsIEfDO3EooFAr8+uuv\nWLt2LYOcTIKbmxveeOMNPuxEIrwzN1P5+fk4cOBApTMIiKzdF198gbp16+Kdd94pe00QBLNaSKdt\ndjLMichkffvtt7h79y7mzZun8TmPHz9Gq1atcPDgwbKHhkdFRSE5ORnLli0zVKmi4zALEUnuwYMH\nCAoK0vtGztHREfHx8Vqds27dOnTt2rUsyAEgPDwc0dHRWL16tV71mDIuEyQi0Z07dw729vZ6D2to\nu3CoqKgIy5YtQ3R0dLnXGzZsiL1796J79+5o0aIFIiIi9KrLFPHOnIhEp+kDnGvi5uaG/Px83L9/\nX6PjN2zYgKCgoEoXKrm7u2PXrl2YNGkSEhIS9K7N1DDMiUhUeXl5eO+993Re+flncrkcfn5+uHDh\ngkbHZ2VlYf78+VW+3759e2zcuBHvvfeexX2Wxw9AiUhUarUaNjY2iI2NxWuvvaZ3e3//+9/Rpk0b\nzJgxQ4TqnikuLq72YeamgLNZiEhymZmZaNq0qShTAfPz81GvXj2r2wmUYU5EZAE4NZGIyAoxzInI\nbBQWFuLJkyflXisoKJCoGtPCMCcis7Fjxw7Ur18fbm5u6Nu3L959910EBAQgMTFRp/YSExPxj3/8\nQ+QqpcExcyIyK0+fPsXNmzdx7do1XLt2DYIgYMqUKTp92JqXlwd3d3ekpKTA2dnZANXqjh+AEhFp\n4Z133kHLli3xz3/+U+pSymGYExFp4ezZsxg8eDCuX79uUtMfjT6b5bPPPoNcLtd4uS0RkSlRKBRw\ndXXFnj17pC5FL3qFeXp6Og4ePIiWLVuKVQ8RkdFNnToV//3vf6UuQy96DbOMGDEC8+fPR0REBM6c\nOYOGDRtW7IDDLERk4tRqNWQymUk9vELb7NR5gGjXrl1wdXWFv7+/rk0QEZkES3hmabVhHhISgszM\nzAqvL1q0CEuWLCn3Z0l1v0E++uijsv8dHByM4OBg7SslIrJgSqUSSqVS5/N1GmZJTk5Gnz59ULdu\nXQDA7du30bx5cyQkJMDJyal8BxxmISLSmiRTE93d3TlmTkQkIkk22jKlDw2IiPSxZs0avYY7pCJK\nmF+/fr3Su3IiInPj4OCA+fPnm92Igvl/hEtEJKLIyEhkZ2cjJiZG6lK0wjAnIvoTGxsb/N///R8W\nLFhgVnfnDHMior+IjIxETk4ODh8+LHUpGmOYExH9hY2NDRYsWIDY2FipS9EYd00kIjJBfAYoEZEV\nYpgTEVkAhjkRkQVgmBMRaaC0tFTqEqrFMCciqkFCQgL69Olj0pM5GOZERDVo164dsrOzcfDgQalL\nqRLDnIioBjY2Nli4cCEmT56Ms2fPSl1OpRjmREQaGDFiBD799FOEhIRg9erVJjfkwjAnItLQm2++\niRMnTiB48nCPAAAGsklEQVQuLg6PHz/Wqy21Wo3i4mKRKuMKUCIik8QVoEREVohhTkRkARjmRER6\nKi4uxvjx4/HkyRPJamCYExHpqVatWrh16xZ27NhR47EFBQUoKCgQvQaGORGRCGbMmIEvv/yyxuM2\nbNiAGTNmiN4/w5yISATh4eHIyMjA6dOnqz1uy5YtiIyMFL1/hjkRkQhsbW0xdepUrFq1qspjUlNT\nkZ6ejt69e4veP8OciEgkb731FuLi4lBUVFTp+1u2bMHIkSNha2sret9cNEREJKKSkpJKw1oQBHh5\neWHLli3o2LFjje1w0RARkYSquusuKCjAgAED0KFDB4P0yztzIiITxDtzIiIrxDAnIrIADHMiIgNQ\nqVRYsmSJ0frjmDkRkQHcu3cPnp6euHz5MpycnLQ+n2PmREQmoFGjRhg2bBiGDBmCjz/+2OD9McyJ\niAxk+vTpOHHiBNzc3AzeF4dZiIgM6KuvvsK4ceNQr149rc7TNjsZ5kREJohj5kREVkivMP/222/R\nvn17+Pr64oMPPhCrJiIi0pLOYX7kyBH88MMPOHHiBJKTkzF79mwx67JISqVS6hJMBq/FC7wWL/Ba\n6E7nMP/qq68wd+5c2NnZAQCaNGkiWlGWij+oL/BavMBr8QKvhe50DvPU1FQcPXoUQUFB6NmzJxIT\nE8Wsi4iItFDtDukhISHIzMys8PqiRYtQUlKC69evIz4+HocOHcLs2bMRExNjsEKJiKgago769+8v\n7Nmzp+x7FxcXobCwsMJxHh4eAgB+8Ytf/OKXFl8eHh5aZbLOzy4aMmQI9u7diwEDBiAhIQEeHh6o\nXbt2heOuXr2qaxdERKQhnRcNlZaWYurUqThy5Ajs7Ozw9ddfo3v37mLXR0REGjD4ClAiIjI8g64A\nfT7bxd/fH6tWrTJkVyZn4sSJaNq0Kfz8/Mpee/jwIYYMGQJ/f38MHToUBQUFElZoPOnp6ejVqxfa\ntm2L4OBgbNq0CYD1XY+ioiJ06tQJCoUCnTt3xooVKwBY33X4s9LSUgQGBiI8PByA9V4LNzc3+Pv7\nIzAwsOxhz9peC4OFeWlpKSZOnIgdO3bgzJkz2LBhA1JSUgzVncmZMGEC9u/fX+61Tz75BF27dsX5\n8+fRuXNnfPrppxJVZ1x2dnZYsWIFVCoVtm/fjg8//BApKSlWdz1q166NI0eO4OzZs4iNjcWGDRuQ\nmppqddfhz1auXAkfHx/IZDIA1vtvRCaTQalUIikpCQkJCQB0uBa6zmapyfHjx4XQ0NCy75csWSIs\nWbLEUN2ZpBs3bgi+vr5l33t7ewuZmZmCIAjC3bt3BW9vb6lKk1RYWJhw8OBBq74eubm5QuvWrYWb\nN29a7XVIT08X+vTpI8TExAhhYWGCIFjvvxE3NzchNze33GvaXguD3ZlnZGSgRYsWZd+7uroiIyPD\nUN2ZhaysLDRt2hQA0LRpU2RlZUlckfFdvXoVKpUKnTt3tsrroVarERAQgKZNm2Lq1Kl45ZVXrPI6\nAMD777+PZcuWQS5/EUPWei1kMhl69+6NwMBArF+/HoD210LnqYmaFEdVk8lkVneNCgoKEBkZiRUr\nVuCll14q9561XA+5XI5z584hLS0NAwcORLdu3cq9by3XITo6Gk5OTggMDKxyCb+1XAsAiI+Ph4uL\nC1JSUjBw4EC0bt263PuaXAuD3Zk3b94c6enpZd+np6fD1dXVUN2ZhaZNm5atqL17965OzwU0V0+f\nPsWwYcMwZswYREREALDu6+Hm5oaBAwciNjbWKq/D8ePH8dtvv8Hd3R2jRo1CTEwMxo4da5XXAgBc\nXFwAAG3atMHQoUORkJCg9bUwWJi3b98eqampSEtLw5MnT/DTTz9h8ODBhurOLAwePBhRUVEAgKio\nKAwZMkTiioxDEAS89dZbaNu2LWbOnFn2urVdj9zcXOTl5QF49rDfffv2wc/Pz+quAwAsXrwY6enp\nuHHjBrZu3YrevXvj+++/t8pr8fjxYzx8+BAAkJOTg7179+r2c2GoAX1BEASlUikoFArB19dXWLly\npSG7MjmRkZGCi4uLYG9vL7i6ugobN24UHjx4IERERAh+fn7CkCFDhIcPH0pdplHExcUJMplMCAgI\nEBQKhaBQKIR9+/ZZ3fU4f/68EBgYKPj7+wv9+vUTvvnmG0EQBKu7Dn+lVCqF8PBwQRCs81pcv35d\nCAgIEAICAoTevXsLX3/9tSAI2l8LLhoiIrIAfGwcEZEFYJgTEVkAhjkRkQVgmBMRWQCGORGRBWCY\nExFZAIY5EZEFYJgTEVmA/wd5wNrX+i0i4wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108b2fd10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### IPython Notebook\uc5d0\uc11c\ub294 \uc81c\ub300\ub85c \ud45c\uc2dc\uac00 \ub418\uc9c0 \uc54a\uc544 ipython console \uc5d0\uc11c \uc791\uc5c5\uc911\n",
      "\n",
      "- ipython --pylab \uc73c\ub85c \uc2e4\ud589 \uc2dc\ud0a4\uba74\n",
      "- PyQt\ub098 PySlide\ub97c \uc124\uce58\ud558\ub77c\uace0 \ud55c\ub2e4. \uc774\uac78 \uc124\uce58\ud574\uc57c console\uc5d0\uc11c \uadf8\ub798\ud504\ub97c \uadf8\ub824\ubcf4\uace0 \ud14c\uc2a4\ud2b8 \ud560 \uc218 \uc788\ub2e4.\n",
      "\n",
      "#### PyQt, PySide \uc124\uce58\ubc29\ubc95(Mac \uae30\uc900)\n",
      "\n",
      "- pip install PyQt\n",
      "- pip install -U PySide\n",
      "\n",
      "<img src=\"files/images/subplot1.png\" width=\"500px\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- fig.add_subplot\uc5d0\uc11c \ubc18\ud658\ub418\ub294 \uac1d\uccb4\ub294 AxesSubplot \uac1d\uccb4\n",
      "- \uac01\uac01\uc758 \uc778\uc2a4\ud134\uc2a4 \uba54\uc11c\ub4dc\ub97c \ud638\ucd9c\ud574\uc11c \ub2e4\ub978 \ube48 \uc11c\ube0c\ud50c\ub86f\uc5d0 \uc9c1\uc811 \uadf8\ub798\ud504\ub97c \uadf8\ub9b4 \uc218 \uc788\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.collections.PathCollection at 0x108b5f6d0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10884bb10>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10841cd90>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x108b15450>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \ub3c4\uc2dd\uc744 \ucd94\uac00\ud55c Figure\n",
      "\n",
      "<img src=\"files/images/fig2.png\" width=\"500px\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ud2b9\uc815\ud55c \ubc30\uce58\uc5d0 \ub9de\ucd94\uc5b4 \uc5ec\ub7ec \uac1c\uc758 \uc11c\ube0c\ud50c\ub86f\uc744 \ud3ec\ud568\ud558\ub294 Figure\ub97c \uc0dd\uc131\ud558\ub294 \uc77c\uc740 \ud754\ud788 \uc811\ud558\uac8c \ub418\ub294 \uc5c5\ubb34\n",
      "- plt.subplots \ub77c\ub294 \ud3b8\ub9ac\ud55c \uba54\uc11c\ub4dc\n",
      "- NumPy \ubc30\uc5f4\uacfc \uc11c\ube0c\ud50c\ub86f \uac1d\uccb4\ub97c \uc0c8\ub85c \uc0dd\uc131\ud558\uc5ec \ubc18\ud658"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(2, 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10915fe50>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "axes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1090df410>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x1090d4290>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x108bb5a50>],\n",
        "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1090cdd10>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x1093c8bd0>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x1093e0a90>]], dtype=object)"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- axes \ubc30\uc5f4\uc740 axes[0, 1]\ucc98\ub7fc 2\ucc28\uc6d0 \ubc30\uc5f4\ub85c \uc27d\uac8c \uc0c9\uc778\n",
      "- \uc11c\ube0c\ud50c\ub86f\uc774 \uac19\uc740 X \ud639\uc740 Y \ucd95\uc744 \uac00\uc838\uc57c \ud55c\ub2e4\uba74 \uac01\uac01 sharex\uc640 sharey\ub97c \uc0ac\uc6a9\ud574\uc11c \uc9c0\uc815\n",
      "- \uac19\uc740 \ubc94\uc704 \ub0b4\uc5d0\uc11c \ub370\uc774\ud130\ub97c \ube44\uad50\ud574\uc57c \ud560 \uacbd\uc6b0 \ud2b9\ud788 \uc720\uc6a9\n",
      "- \uadf8\ub807\uc9c0 \uc54a\uc73c\uba74 matplotlib\uc740 \uac01 \uadf8\ub798\ud504\uc758 \ubc94\uc704\ub97c \ub3c5\ub9bd\uc801\uc73c\ub85c \uc870\uc815\n",
      "\n",
      "#### pyplot.subplots \uc635\uc158\n",
      "\n",
      "\uc778\uc790 | \uc124\uba85\n",
      "--- | ---\n",
      "nrows | \uc11c\ube0c\ud50c\ub86f\uc758 \ub85c\uc6b0 \uc218\n",
      "ncols | \uc11c\ube0c\ud50c\ub86f\uc758 \uce7c\ub7fc \uc218\n",
      "sharex | \ubaa8\ub4e0 \uc11c\ube0c\ud50c\ub86f\uc774 \uac19\uc740 x\ucd95 \ub208\uae08\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ud55c\ub2e4(xlim \uac12\uc744 \uc870\uc808\ud558\uba74 \ubaa8\ub4e0 \uc11c\ube0c\ud50c\ub86f\uc5d0 \uc801\uc6a9\ub41c\ub2e4).\n",
      "sharey | \ubaa8\ub4e0 \uc11c\ube0c\ud50c\ub86f\uc774 \uac19\uc740 y\ucd95 \ub208\uae08\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ud55c\ub2e4(ylim \uac12\uc744 \uc870\uc808\ud558\uba74 \ubaa8\ub4e0 \uc11c\ube0c\ud50c\ub86f\uc5d0 \uc801\uc6a9\ub41c\ub2e4).\n",
      "subplot_kw | add_subplot()\uc744 \uc0ac\uc6a9\ud574 \uac01 \uc11c\ube0c\ud50c\ub86f\uc744 \uc0dd\uc131\ud560 \ub54c \uc0ac\uc6a9\ud560 \ud0a4\uc6cc\ub4dc\ub97c \ub2f4\uace0 \uc788\ub294 \uc0ac\uc804\n",
      "\\*\\*fig_kw | Figure\ub97c \uc0dd\uc131\ud560 \ub54c \uc0ac\uc6a9\ud560 \ucd94\uac00\uc801\uc778 \ud0a4\uc6cc\ub4dc \uc778\uc790 <br /> \uc608) plt.subplots(2, 2, figsize=(8, 6))"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.1.2 \uc0c9\uc0c1, \ub9c8\ucee4, \uc120 \uc2a4\ud0c0\uc77c\n",
      "\n",
      "- \uac00\uc7a5 \uc911\uc694\ud55c plot \ud568\uc218: X\uc640 Y \uc88c\ud45c \uac12\uc774 \ub2f4\uae34 \ubc30\uc5f4\uacfc \ucd94\uac00\uc801\uc73c\ub85c \uc0c9\uc0c1\uacfc \uc120 \uc2a4\ud0c0\uc77c\uc744 \ub098\ud0c0\ub0b4\ub294 \ucd95\uc57d \ubb38\uc790\uc5f4\uc744 \uc778\uc790\ub85c \ubc1b\ub294\ub2e4.\n",
      "- \ub179\uc0c9 \uc810\uc120\uc73c\ub85c \uadf8\ub824\uc9c4 x \ub300 y \uadf8\ub798\ud504\ub294 \ub2e4\uc74c\ucc98\ub7fc\n",
      "\n",
      "> ax.plot(x, y, 'g--')\n",
      "\n",
      "- \ubb38\uc790\uc5f4\ub85c \uc0c9\uc0c1\uacfc \uc120 \uc2a4\ud0c0\uc77c\uc744 \uc9c0\uc815\ud558\ub294 \ud3b8\uc758\ub97c \uc704\ud574 \uc81c\uacf5\n",
      "- \uc2e4\ubb34\uc5d0\uc11c \ud504\ub85c\uadf8\ub7a8\uc744 \uc0ac\uc6a9\ud574 \uadf8\ub798\ud504\ub97c \uc0dd\uc131\ud55c\ub2e4\uba74 \uadf8\ub798\ud504\ub97c \uc6d0\ud558\ub294 \ud615\uc2dd\uc73c\ub85c \uc0dd\uc131\ud558\uae30 \uc704\ud574 \ubb38\uc790\uc5f4\uc744 \uc9c0\uc800\ubd84\ud558\uac8c \uc11e\uc5b4 \uc4f0\uace0 \uc2f6\uc9c0 \uc54a\uc744 \uac83\n",
      "- \uba85\uc2dc\uc801\uc778 \ubc29\ubc95\n",
      "\n",
      ">ax.plot(x, y, linestyle='--', color='g')\n",
      "\n",
      "- \ud754\ud788 \uc0ac\uc6a9\ub418\ub294 \uc0c9\uc0c1\uc744 \uc704\ud574 \uba87 \uac00\uc9c0 \uc0c9\uc0c1 \ubb38\uc790\uc5f4 \uc874\uc7ac\n",
      "- RGB \uac12(#CECECE)\uc744 \uc9c1\uc811 \uc9c0\uc815\n",
      "- \uc120 \uc2a4\ud0c0\uc77c\uc5d0 \ub300\ud55c \uc804\uccb4 \ubaa9\ub85d\uc740 plot \uba54\uc11c\ub4dc\uc758 \ub3c4\uc6c0\ub9d0 \ucc38\uace0\n",
      "\n",
      "- \ud2b9\uc815 \uc9c0\uc810\uc758 \uc2e4\uc81c \ub370\uc774\ud130\ub97c \ub3cb\ubcf4\uc774\uac8c \ud558\uae30 \uc704\ud574 \ub9c8\ucee4 \ucd94\uac00 \uac00\ub2a5\n",
      "- \ub9c8\ucee4\ub3c4 \uc2a4\ud0c0\uc77c \ubb38\uc790\uc5f4\uc5d0 \ud3ec\ud568 \uac00\ub2a5, \uc0c9\uc0c1 \ub9c8\ucee4 \ub2e4\uc74c\uc5d0 \ub9c8\ucee4 \uc2a4\ud0c0\uc77c\uc774 \uc624\uace0 \uadf8 \ub4a4\uc5d0 \uc120 \uc2a4\ud0c0\uc77c \uc9c0\uc815"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'ko--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1091ab910>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10915b5d0>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uba85\uc2dc\uc801 \ubc29\ubc95\n",
      "plot(randn(30).cumsum(), color='k', linestyle='dashed', marker='o')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c374690>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBjCZExFpAJM5EZEGMJkTEWnA/wOnSsYjNKmuGAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b786350>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "------\n",
      "\n",
      "#### plt.plot \ud568\uc218 \uc5f0\uc2b5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "    ================    ===============================\n",
      "    character           description\n",
      "    ================    ===============================\n",
      "    ``'-'``             solid line style\n",
      "    ``'--'``            dashed line style\n",
      "    ``'-.'``            dash-dot line style\n",
      "    ``':'``             dotted line style\n",
      "    ``'.'``             point marker\n",
      "    ``','``             pixel marker\n",
      "    ``'o'``             circle marker\n",
      "    ``'v'``             triangle_down marker\n",
      "    ``'^'``             triangle_up marker\n",
      "    ``'<'``             triangle_left marker\n",
      "    ``'>'``             triangle_right marker\n",
      "    ``'1'``             tri_down marker\n",
      "    ``'2'``             tri_up marker\n",
      "    ``'3'``             tri_left marker\n",
      "    ``'4'``             tri_right marker\n",
      "    ``'s'``             square marker\n",
      "    ``'p'``             pentagon marker\n",
      "    ``'*'``             star marker\n",
      "    ``'h'``             hexagon1 marker\n",
      "    ``'H'``             hexagon2 marker\n",
      "    ``'+'``             plus marker\n",
      "    ``'x'``             x marker\n",
      "    ``'D'``             diamond marker\n",
      "    ``'d'``             thin_diamond marker\n",
      "    ``'|'``             vline marker\n",
      "    ``'_'``             hline marker\n",
      "    ================    ===============================\n",
      "\n",
      "\n",
      "    The following color abbreviations are supported:\n",
      "\n",
      "    ==========  ========\n",
      "    character   color\n",
      "    ==========  ========\n",
      "    'b'         blue\n",
      "    'g'         green\n",
      "    'r'         red\n",
      "    'c'         cyan\n",
      "    'm'         magenta\n",
      "    'y'         yellow\n",
      "    'k'         black\n",
      "    'w'         white\n",
      "    ==========  ========"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'kv--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1095b22d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naFoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10915ff90>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k.--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "[<matplotlib.lines.Line2D at 0x109a0abd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1091abe90>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k,--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "[<matplotlib.lines.Line2D at 0x109a3b4d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nseiJiFSORU9EpHL/D4osxfF0G4fTAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109a1dc10>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k^--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "[<matplotlib.lines.Line2D at 0x109d855d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109d66b10>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k<--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a01de50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a001d10>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k>--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a210190>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wGUSPIknTSXgwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1095b8a50>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k1--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a29e090>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD7CAYAAACYLnSTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109d9bad0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k2--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a2c8c10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a02e110>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k3--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a477cd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niEgCWMyJiCTgf4clGrYjFtwlAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a22dc50>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k4--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a561dd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niEgDGOZERBrAMCci0oD/B+1u1GN4y4D8AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a028790>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'ks--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a58fed0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1W5X85dU6AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a56d490>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'kp--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a61e7d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a5f6610>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'kH--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a6ae110>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMicikgEmcyIiGWAyJyKSgf8Da+UD7ne+NBcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a629950>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k*--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a8c8250>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJyJSgP8H3kZr6AuudxcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a58cc90>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k+--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a8f8b50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwGJORKQCLOZERCrAYk5EpAL/D8a7tPN6qiUxAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a2d3550>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'kx--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10ac8a450>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwDAnIlIBhjkRkQowzImIVIBhTkSkAv8HM2CffHc4O+0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ac6ea50>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'kD--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10af1b550>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmzUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ac99890>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'kd--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10b20cdd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niYhU4P8BuPjATHjWtM4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ac7c050>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k|--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10b23e6d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10af0c150>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'k_--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10b2cdf50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noaRPCCF6hJI+IYToEUr6hBCiR/4fffE9QeNrU5oAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b2aeb90>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'ko--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10b582810>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b55f5d0>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'ko-')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10b777110>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b214d10>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(randn(30).cumsum(), 'ko:')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c3dcb10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c10ed50>"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "-----"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc120 \uadf8\ub798\ud504\ub97c \ubcf4\uba74 \uc77c\uc815\ud55c \uac04\uaca9\uc73c\ub85c \uc5f0\uc18d\ub41c \uc9c0\uc810\uc774 \uc5f0\uacb0\n",
      "- drawstyle \uc635\uc158\uc744 \uc774\uc6a9\ud574\uc11c \ubcc0\uacbd"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d3eaa10>"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = randn(30).cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(data, 'k--', label='Default')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10d572d90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d5690d0>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10d713850>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d56fc90>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niLgDgEHEHQAMIu4AYBBxBwCD/g+4Ur6Z0cN9pQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e01ead0>"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"files/images/legend.png\" width=\"500px\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "-----\n",
      "\n",
      "#### plt.plot \uc5f0\uc2b5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(data, 'k--', drawstyle='steps-post', label='steps-post')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c613150>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEACAYAAACXqUyYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c65fb90>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(data, 'k--', drawstyle='steps-post')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c9ebd10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEACAYAAACXqUyYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c3dde90>"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(data, 'k--', label='steps-post')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10ccdae10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEACAYAAACXqUyYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nukbGMlQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c9c78d0>"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "-----"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.1.3 \ub208\uae08, \ub77c\ubca8, \ubc94\ub840\n",
      "\n",
      "#### \uadf8\ub798\ud504\ub97c \uafb8\ubbf8\ub294 \ubc29\ubc95\n",
      "\n",
      "1. pyplot \uc778\ud130\ud398\uc774\uc2a4\ub97c \uc0ac\uc6a9\ud574\uc11c \uc21c\ucc28\uc801\uc73c\ub85c \uafb8\ubbf8\ub358\uac00(MATLAB \uc0ac\uc6a9\uc790\uc5d0\uac8c \uce5c\uc219)\n",
      "2. matplotlib\uc774 \uc81c\uacf5\ud558\ub294 API\ub97c \uc0ac\uc6a9\ud574\uc11c \uc880 \ub354 \uac1d\uccb4\uc9c0\ud5a5\uc801\uc778 \ubc29\ubc95\uc73c\ub85c \uafb8\ubbf8\ub294 \ubc29\ubc95\n",
      "\n",
      "\n",
      "- pyplot \uc778\ud130\ud398\uc774\uc2a4\ub294 \uc778\ud130\ub799\ud2f0\ube0c \uc0ac\uc6a9\uc5d0 \ub9de\ucd94\uc5b4 \uc124\uacc4\n",
      "- xlim, xticks, xticklabels \uac19\uc740 \uba54\uc11c\ub4dc\n",
      "- \ud45c\uc758 \ubc94\uc704\ub97c \uc9c0\uc815, \ub208\uae08\uc758 \uc704\uce58, \ub208\uae08\uc758 \uc774\ub984\uc744 \uac01\uac01 \uc870\uc808\n",
      "\n",
      "\n",
      "- \uc544\ubb34\ub7f0 \uc778\uc790\ub3c4 \uc5c6\uc774 \ud638\ucd9c\ud558\uba74 \ud604\uc7ac \uc124\uc815\ub418\uc5b4 \uc788\ub294 \ub9e4\uac1c\ubcc0\uc218\uc758 \uac12 \ubc18\ud658. plt.xlim() \uba54\uc11c\ub4dc\ub294 \ud604\uc7ac X\ucd95\uc758 \ubc94\uc704\ub97c \ubc18\ud658\n",
      "- \uc778\uc790\ub97c \uc804\ub2ec\ud558\uba74 \ub9e4\uac1c\ubcc0\uc218\uc758 \uac12 \uc124\uc815. \uadf8\ub798\uc11c plt.xlim([0, 10])\uc744 \ud638\ucd9c\ud558\uba74 X\ucd95\uc758 \ubc94\uc704\uac00 0\ubd80\ud130 10\uae4c\uc9c0\ub85c \uc124\uc815\n",
      "\n",
      "- \uc774 \ubaa8\ub4e0 \uba54\uc11c\ub4dc\ub294 \ud604\uc7ac \ud65c\uc131\ud654\ub41c, \ud639\uc740 \uac00\uc7a5 \ucd5c\uadfc\uc5d0 \uc0dd\uc131\ub41c AxesSubplot \uac1d\ucc44\uc5d0 \ub300\ud574 \ub3d9\uc791\n",
      "- \uc55e\uc11c \uc18c\uac1c\ud55c \ubaa8\ub4e0 \uba54\uc11c\ub4dc\ub294 \uc11c\ube0c\ud50c\ub86f \uac1d\uccb4\uc758 set/get \uba54\uc11c\ub4dc\ub85c\ub3c4 \uc874\uc7ac\n",
      "- xlim\uc774\ub77c\uba74 ax.get_xlim\uacfc ax.set_xlim \uba54\uc11c\ub4dc\uac00 \uc874\uc7ac\n",
      "- \uba85\uc2dc\uc801\uc778 \uac83 \uc120\ud638\ud558\uae30 \ub54c\ubb38\uc5d0 \ud2b9\ud788 \uc5ec\ub7ec \uac1c\uc758 \uc11c\ube0c\ud50c\ub86f\uc744 \ub2e4\ub8f0 \ub54c\ub294 \uc11c\ube0c\ud50c\ub86f \uc778\uc2a4\ud134\uc2a4 \uba54\uc11c\ub4dc \uc0ac\uc6a9\ud55c\ub2e4. \ud558\uc9c0\ub9cc \ub3c5\uc790\ub4e4\uc740 \uac01\uc790\uc5d0\uac8c \ud3b8\ub9ac\ud55c \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud574\ub3c4 \uc0c1\uad00\uc5c6\ub2e4."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### \uc81c\ubaa9, \ucd95 \uc774\ub984, \ub208\uae08, \ub208\uae08 \uc774\ub984 \uc124\uc815\ud558\uae30"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEH1JREFUeJzt3VFI1ff/x/HXGedcZEEFheE5ByTPKMdUFMtiRWdb0Upm\nWF0cu1hpM4vddBGMrqZddRsGm0G2jSyCLbCx2sDodC6aGDayqMhatuMhYs4JsWp57Pu/+P2nc9Y5\nRz3n2N49HyB4+H76ft/7IM99OcdvuRzHcQQAMOWNmR4AAJB+xB0ADCLuAGAQcQcAg4g7ABhE3AHA\noKRxr6urU25uroqKil66Zv/+/SouLtaKFSt069attA4IAJi8pHGvra3VDz/88NLjZ8+e1dWrV9XT\n06NDhw5px44d6ZwPADAFSeO+evVqzZ8//6XHz5w5o+3bt0uSKioqNDQ0pIcPH6ZvQgDApE37PfdY\nLCa/3z/62ufzqb+/f7qnBQBMQ1o+UP3332DgcrnScVoAwBS5p3sCr9eraDQ6+rq/v19er3fCukAg\noLt37073cgDwWikoKNCdO3cm/eemfedeVVWlr7/+WpLU2dmpefPmKTc3d8K6u3fvynEcvhxHn332\n2YzP8Kp8sRfsBXuR+GuqN8VJ79xramp08eJFDQwMyO/3q6mpScPDw5KkhoYGbdy4UZFIREVFRZo9\ne7aOHTs2pUEAAOmTNO4nT55MepKDBw/q4MGDaRkIADB9PKE6A4LB4EyP8MpgL8awF2PYi+lzOY6T\nlX+sw+VyKUuXAgAzptpO7twBwCDiDgAGEXcAMIi4A4BBxB0ADCLuAGAQcQcAg4g7ABhE3AHAIOIO\nAAYRdwAwiLgDgEHEHQAMIu4AYBBxBwCDiDsAGETcAcAg4g4ABhF3ADCIuAOAQcQdAAwi7gBgEHEH\nAIOIOwAYRNwBwCDiDgAGEXcAMIi4A4BBxB0ADCLuAGAQcQcAg4g7ABhE3AHAIOIOAAYljXskElFZ\nWZmKi4vV3Nw84fiTJ0+0fft2lZaWas2aNWpvb8/IoACA1LkTHRwZGVFdXZ06Ojrk9Xq1bNkyrV27\nVoWFhaNrvvrqK82ePVs///yz7t+/r/fee09VVVVyuVwZHx4A8GIJ79y7uroUCASUn58vj8ejUCg0\n4c587ty5evTokYaHhzU4OKicnBzCDgAzLGHcY7GY/H7/6Gufz6dYLDZuTU1NjUZGRrRgwQKtWrVK\nbW1tmZkUAJCyhG/LpHIHfvjwYbndbj148EDXrl1TZWWl7t+/rzfemPj/jcbGxtHvg8GggsHgpAcG\nAMvC4bDC4fC0z5Mw7l6vV9FodPR1NBqVz+cbtyYSiWjnzp3KyclRRUWF8vLydPv2bS1dunTC+f4Z\ndwDARP++8W1qaprSeRK+LVNeXq7e3l719fXp2bNnOnXqlKqqqsatef/99/Xdd9/p+fPn+uWXXzQ4\nOPjCsAMAsifhnbvb7VZra6uqq6sVj8dVX1+vwsJCtbS0SJIaGhoUCoV048YNlZeXa+HChTp06FBW\nBgcAvJzLcRwnKxdyuZSlSwGAGVNtJ0+oAoBBxB0ADCLuAGAQcQcAg4g7ABhE3AHAIOIOAAYRdwAw\niLgDgEHEHQAMIu4AYBBxBwCDiDsAGETcAcAg4g4ABhF3ADCIuAOAQcQdAAwi7gBgEHEHAIOIOwAY\nRNwBwCDiDgAGEXcAMIi4A4BBxB0ADCLuAGAQcQcAg4g7ABhE3AHAIOIOAAYRdwAwiLgDgEHEHQAM\nIu4AYFDSuEciEZWVlam4uFjNzc0vXHP58mWtWrVKJSUlCgaD6Z4RADBJLsdxnJcdHBkZ0ZIlS9TR\n0SGv16tly5bp5MmTKiwsHF0zNDSkd955Rz/++KN8Pp8GBga0YMGCiRdyuZTgUgCAF5hqOxPeuXd1\ndSkQCCg/P18ej0ehUEjt7e3j1pw4cUJbtmyRz+eTpBeGHQCQXQnjHovF5Pf7R1/7fD7FYrFxa3p7\nezU4OKjVq1ertLRUbW1tmZkUAJAyd6KDLpcr6QmGh4cVDofV0dGhx48fa926ddq8ebNmzZqVtiEB\nAJOTMO5er1fRaHT0dTQaHX375W9+v18bNmzQokWLJEnl5eWKRCJav379hPM1NjaOfh8MBvnwFQD+\nJRwOKxwOT/s8CT9QjcfjWrJkic6fP6+8vDwtX758wgeqt27d0kcffaRwOKynT59qxYoVunLliubM\nmTP+QnygCgCTNtV2Jrxzd7vdam1tVXV1teLxuOrr61VYWKiWlhZJUkNDg5YuXara2lqVl5fr6dOn\n2rdv34SwAwCyK+Gde1ovxJ07AExaRn4VEgDw30TcAcAg4g4ABhF3ADCIuAOAQcQdAAwi7gBgEHEH\nAIOIOwAYRNwBwCDiDgAGEXcAMIi4A4BBxB0ADCLuAGAQcQcAg4g7ABhE3AHAIOIOAAYRdwAwiLgD\ngEHEHQAMIu4AYBBxBwCDiDsAGETcAcAg4g4ABhF3ADCIuAOAQcQdAAwi7gBgEHEHAIOIOwAYRNwB\nwCDiDgAGEXcAMChp3CORiMrKylRcXKzm5uaXrrt8+bLcbrdOnz6d1gEBAJPnTnRwZGREdXV16ujo\nkNfr1bJly7R27VoVFhZOWPfpp5/qgw8+kOM4GR0YAJBcwjv3rq4uBQIB5efny+PxKBQKqb29fcK6\n5uZmbd26VQsXLszYoACA1CWMeywWk9/vH33t8/kUi8UmrGlvb9eePXskSS6XKwNjAgAmI2HcUwn1\n3r17dfDgQblcLjmOw9syAPAKSPieu9frVTQaHX0djUbl8/nGrenu7lYoFJIkDQwM6Ny5c/J4PKqq\nqppwvsbGxtHvg8GggsHgNEYHAHvC4bDC4fC0z+NyEtxqx+NxLVmyROfPn1deXp6WL1+ukydPTvhA\n9W+1tbX68MMPtXnz5okX+v87ewBA6qbazoR37m63W62traqurlY8Hld9fb0KCwvV0tIiSWpoaJja\ntACAjEp4557WC3HnDgCTNtV28oQqABhE3AHAIOIOAAYRdwAwiLgDgEHEHQAMIu4AYBBxBwCDiDsA\nGETcAcAg4g4ABhF3ADCIuAOAQcQdAAwi7gBgEHEHAIOIOwAYRNwBwCDiDgAGEXcAMIi4A4BBxB0A\nDCLuAGAQcQcAg4g7ABhE3AHAIOIOAAYRdwAwiLgDgEHEHQAMIu4AYBBxBwCDiDsAGETcAcAg4g4A\nBhF3ADAopbhHIhGVlZWpuLhYzc3NE463tbWppKREJSUl2rZtm65fv572QQEAqUsa95GREdXV1en0\n6dPq7u7W0aNHdfPmzXFrFi9erEgkoqtXr2r9+vX6+OOPMzYwACC5pHHv6upSIBBQfn6+PB6PQqGQ\n2tvbx61ZuXKl5s6dK0mqrKxUf39/ZqYFAKQkadxjsZj8fv/oa5/Pp1gs9tL1R44c0aZNm9IzHQBg\nStzJFrhcrpRPduHCBR0/flyXLl164fHGxsbR74PBoILBYMrnBoDXQTgcVjgcnvZ5ksbd6/UqGo2O\nvo5Go/L5fBPW9fT0aNeuXTp37pzmzZv3wnP9M+4AgIn+fePb1NQ0pfMkfVumvLxcvb296uvr07Nn\nz3Tq1ClVVVWNW/Prr79qy5YtOn78uAKBwJQGAQCkT9I7d7fbrdbWVlVXVysej6u+vl6FhYVqaWmR\nJDU0NOjAgQMaHBzU7t27JUkej0ddXV2ZnRwA8FIux3GcrFzI5VKWLgUAZky1nTyhCgAGEXcAMIi4\nA4BBxB0ADCLuAGAQcQcAg4g7ABhE3AHAIOIOAAYRdwAwiLgDgEHEHQAMIu4AYBBxBwCDiDsAGETc\nAcAg4g4ABhF3ADCIuAOAQcQdAAwi7gBgEHEHAIOIOwAYRNwBwCDiDgAGEXcAMIi4A4BBxB0ADCLu\nAGAQcQcAg4g7ABhE3AHAIOIOAAYRdwAwiLgDgEFJ4x6JRFRWVqbi4mI1Nze/cM3+/ftVXFysFStW\n6NatW2kfEgAwOQnjPjIyorq6Op0+fVrd3d06evSobt68OW7N2bNndfXqVfX09OjQoUPasWNHJuc1\nIRwOz/QIrwz2Ygx7MYa9mL6Ece/q6lIgEFB+fr48Ho9CoZDa29vHrTlz5oy2b98uSaqoqNDQ0JAe\nPnyYuYkN4Ad3DHsxhr0Yw15MX8K4x2Ix+f3+0dc+n0+xWCzpmv7+/jSPCQCYjIRxd7lcKZ3EcZwp\n/TkAQGa4Ex30er2KRqOjr6PRqHw+X8I1/f398nq9E85VUFBA9P+hqalppkd4ZbAXY9iLMezF/xQU\nFEzpzyWMe3l5uXp7e9XX16e8vDydOnVKJ0+eHLemqqpKhw8fVigUUmdnp+bNm6fc3NwJ57pz586U\nBgQATF7CuLvdbrW2tqq6ulrxeFz19fUqLCxUS0uLJKmhoUEbN25UJBJRUVGRZs+erWPHjmVlcADA\ny7mcf79hDgD4z0v7E6o89DQm2V60tbWppKREJSUl2rZtm65fvz4DU2ZHKj8XknT58mW53W6dPn06\ni9NlTyr7cPnyZa1atUolJSUKBoPZHTCLku3FkydPtH37dpWWlmrNmjUTfg3bkrq6OuXm5qqoqOil\naybdTSeN4vG4U1BQ4Ny7d8959uyZU1JS4ty4cWPcmu+//97ZsGGD4ziO09nZ6VRUVKRzhFdGKntx\n6dIlZ2hoyHEcx/nyyy9f6734e927777rVFZWOt98880MTJpZqezDH3/84bz11ltONBp1HMdxfvvt\nt5kYNeNS2YvPP//c2bNnj+M4jtPX1+csXrzYef78+UyMm3GRSMS5cuWK8/bbb7/w+FS6mdY7dx56\nGpPKXqxcuVJz586VJFVWVpp9PiCVvZCk5uZmbd26VQsXLpyBKTMvlX04ceKEtmzZMvpbaQsWLJiJ\nUTMulb2YO3euHj16pOHhYQ0ODionJ8fsb9ytXr1a8+fPf+nxqXQzrXHnoacxqezFPx05ckSbNm3K\nxmhZl+rPRXt7u/bs2SPJ5rMSqexDb2+vBgcHtXr1apWWlqqtrS3bY2ZFKntRU1OjkZERLViwQKtW\nrTK7F6mYSjcT/rbMZPHQ05jJ/DdduHBBx48f16VLlzI40cxJZS/27t2rgwcPyuVyyXGcCT8jFqSy\nD8PDwwqHw+ro6NDjx4+1bt06bd68WbNmzcrChNmTyl4cPnxYbrdbDx480LVr11RZWan79+/rjTde\nz7/MdrLdTGvc0/nQ039dKnshST09Pdq1a5fOnTunefPmZXPErEllL7q7uxUKhSRJAwMDOnfunDwe\nj6qqqrI6ayalsg9+v18bNmzQokWLJP3vWZNIJKL169dnddZMS2UvIpGIdu7cqZycHFVUVCgvL0+3\nb9/W0qVLsz3ujJtSN9P2iYDjOMPDw87ixYude/fuOX/99VfSD1R/+uknsx8iprIX9+/fdwKBgNPZ\n2TlDU2ZHKnvxTzt27HC+/fbbLE6YHansw82bN51ly5Y5f/75p/P77787b775pvPo0aMZmjhzUtmL\nL774wvnkk0+ckZER5+7du04gEJihabPj3r17KX2gmmo303rnzkNPY1LZiwMHDmhwcFC7d++WJHk8\nHnV1dc3k2BmRyl68DlLZh6VLl6q2tlbl5eV6+vSp9u3bpzlz5szw5OmXyl6EQiHduHFD5eXlWrhw\noQ4dOjTDU2dOTU2NLl68qIGBAfn9fjU1NWl4eFjS1LvJQ0wAYNDr+ckEABhH3AHAIOIOAAYRdwAw\niLgDgEHEHQAMIu4AYBBxBwCD/g+4Ur6Z0cN9pQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d71af50>"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.plot(randn(1000).cumsum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10e0a4490>]"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"files/images/plt1.png\" width=\"500px\" />"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticks = ax.set_xticks([0, 250, 500, 750, 1000])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],\n",
      "                            rotation=30, fontsize='small')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.set_title('My first matplotlib plot')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "<matplotlib.text.Text at 0x10c38fbd0>"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.set_xlabel('Stages')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "<matplotlib.text.Text at 0x10c36e950>"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"files/images/plt2.png\" width=\"500px\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### \ubc94\ub840 \ucd94\uac00\ud558\uae30\n",
      "\n",
      "- \ubc94\ub840\ub294 \uadf8\ub798\ud504\uc758 \uc694\uc18c\ub97c \ud655\uc778\ud558\uae30 \uc704\ud574 \uc911\uc694\ud55c \uc694\uc18c"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niLgDgEHEHQAMIu4AYBBxBwCD/g+4Ur6Z0cN9pQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c1a9750>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.plot(randn(1000).cumsum(), 'k', label='one')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c36e590>]"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.plot(randn(1000).cumsum(), 'k--', label='two')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c39be10>]"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.plot(randn(1000).cumsum(), 'k.', label='three')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c398490>]"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uadf8\ub0e5\uc740 \uc548 \ubcf4\uc774\uace0 pan zoom\uc744 \uac74\ub4dc\ub9ac\ub2c8 \ubcf4\uc774\ub124\n",
      "ax.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "<matplotlib.legend.Legend at 0x10b2aecd0>"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uae68\ub2ec\uc558\ub2e4.\n",
      "# \ud55c \uce78\uc5d0 \ubaa8\ub450 \ub123\uc5b4\uc57c plot\uc744 \ubcf4\uc5ec\uc8fc\ub294\uad6c\ub098.\n",
      "\n",
      "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)\n",
      "\n",
      "ax.plot(randn(1000).cumsum(), 'k', label='one')\n",
      "ax.plot(randn(1000).cumsum(), 'k--', label='two')\n",
      "ax.plot(randn(1000).cumsum(), 'k.', label='three')\n",
      "\n",
      "ax.legend(loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "<matplotlib.legend.Legend at 0x110793690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYDAYUQpTAAwGgxGlMAXAYDAYUcr/A+4iIjf7UQnkAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111337210>"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- loc(location) \uc778\uc790\ub294 \ubc94\ub840\ub97c \uadf8\ub798\ud504\uc5d0\uc11c \uc5b4\ub514\uc5d0 \uc704\uce58\uc2dc\ud0ac\uc9c0 \uc9c0\uc815\ud574\uc8fc\ub294 \uc778\uc790\n",
      "- \ucd5c\ub300\ud55c \ubc29\ud574\uac00 \ub418\uc9c0 \uc54a\ub294 \uacf3\uc5d0 \ub450\ub294 'best' \uc635\uc158\ub9cc\uc73c\ub85c\ub3c4 \ucda9\ubd84\n",
      "- \ubc94\ub840\uc5d0\uc11c \uc81c\uc678\ud558\uace0 \uc2f6\uc740 \uc694\uc18c\uac00 \uc788\ub2e4\uba74 label \uc778\uc790\ub97c \ub118\uae30\uc9c0 \uc54a\uac70\ub098 label='_nolegend_' \uc635\uc158 \uc0ac\uc6a9\n",
      "\n",
      "#### \uadf8\ub9bc\ub4e4\uc740 IPython Notebook\uc5d0\uc11c \uc0dd\uc131 \uc548\ub418\uc11c IPython console\uc5d0\uc11c \uc9c4\ud589\ud558\uc5ec \uadf8\ub9bc\ub9cc \ubd99\uc5ec\ub123\ub294\uc911 -> \ud55c \uce78\uc5d0 \uc18c\uc2a4\ub97c \ubaa8\ub450 \ub123\uc5b4\uc57c \ub428\n",
      "\n",
      "<img src=\"files/images/plt3.png\" width=\"500px\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.1.4 \uc8fc\uc11d\uacfc \uadf8\ub9bc \ucd94\uac00\n",
      "\n",
      "- \uc77c\ubc18\uc801\uc778 \ub3c4\ud45c\uc5d0 \ucd94\uac00\uc801\uc73c\ub85c \uae00\uc790\ub098 \ud654\uc0b4\ud45c \ud639\uc740 \ub2e4\ub978 \ub3c4\ud615\uc73c\ub85c \uc790\uae30\ub9cc\uc758 \uc8fc\uc11d\uc744 \uadf8\ub9ac\uace0 \uc2f6\uc744 \ub54c\n",
      "- \uc8fc\uc11d\uacfc \uae00\uc790\ub294 text, arrow, annotate \ud568\uc218\ub97c \uc0ac\uc6a9\ud574\uc11c \ucd94\uac00\n",
      "- text \ud568\uc218\ub294 \ub3c4\ud45c \ub0b4\uc758 \uc8fc\uc5b4\uc9c4 \uc88c\ud45c(x, y)\uc5d0 \ubd80\uac00\uc801\uc778 \uc2a4\ud0c0\uc77c\uc758 \uae00\uc790\ub97c \uadf8\ub824\uc900\ub2e4."
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "ax.text(x, y, 'Hello world!',\n",
      "        family='monospace', fontsize=10)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc8fc\uc11d\uc740 \uae00\uc790\uc640 \ud654\uc0b4\ud45c\ub97c \ud568\uaed8 \uc368\uc11c \uadf8\ub9b4 \uc218 \uc788\ub294\ub370\n",
      "- \uc57c\ud6c4! \ud30c\uc774\ub0b8\uc2a4\uc5d0\uc11c \uc5bb\uc740 2007\ub144\ubd80\ud130 S&P 500 \uc9c0\uc218 \ub370\uc774\ud130\ub85c \uadf8\ub798\ud504\ub97c \uc0dd\uc131\n",
      "- 2008~2009\ub144 \uc0ac\uc774\uc5d0 \uc788\uc5c8\ub358 \uc7ac\uc815\uc704\uae30 \uc911 \uc911\uc694\ud55c \ub0a0\uc9dc\ub97c \uc8fc\uc11d\uc73c\ub85c \ucd94\uac00"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head ch08/spx.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ",SPX\r\n",
        "1990-02-01 00:00:00,328.79\r\n",
        "1990-02-02 00:00:00,330.92\r\n",
        "1990-02-05 00:00:00,331.85\r\n",
        "1990-02-06 00:00:00,329.66\r\n",
        "1990-02-07 00:00:00,333.75\r\n",
        "1990-02-08 00:00:00,332.96\r\n",
        "1990-02-09 00:00:00,333.62\r\n",
        "1990-02-12 00:00:00,330.08\r\n",
        "1990-02-13 00:00:00,331.02\r\n"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime\n",
      "\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "\n",
      "data = pd.read_csv('ch08/spx.csv', index_col=0, parse_dates=True)\n",
      "spx = data['SPX']\n",
      "\n",
      "\n",
      "spx.plot(ax=ax, style='k-')\n",
      "\n",
      "crisis_data = [\n",
      "               (datetime(2007, 10, 11), 'Peak of bull market'),\n",
      "               (datetime(2008, 3, 12), 'Bear Stearns Fails'),\n",
      "               (datetime(2008, 9, 15), 'Lehman Bankruptcy')\n",
      "               ]\n",
      "\n",
      "for date, label in crisis_data:\n",
      "    ax.annotate(label, xy=(date, spx.asof(date) + 50),\n",
      "                xytext=(date, spx.asof(date) + 200),\n",
      "                arrowprops=dict(facecolor='black'),\n",
      "                horizontalalignment='left',\n",
      "                verticalalignment='top')\n",
      "    \n",
      "# 2007-2010 \uad6c\uac04\uc744 \ud655\ub300\n",
      "ax.set_xlim(['1/1/2007', '1/1/2011'])\n",
      "ax.set_ylim([600, 1800])\n",
      "\n",
      "ax.set_title('Important dates in 2008-2009 financial crisis')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "<matplotlib.text.Text at 0x111314450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEJCAYAAAB/pOvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8f9BcmUvGJIXyQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111337450>"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc628\ub77c\uc778\uc5d0\uc11c [matplotlib \uac24\ub7ec\ub9ac](http://matplotlib.org/gallery.html) \ub97c \ub458\ub7ec\ubcf4\uba74 \ubc30\uc6b8\ub9cc\ud55c \uc5ec\ub7ec\uac00\uc9c0 \uc8fc\uc11d \uc608\uc81c\ub97c \ud655\uc778 \uac00\ub2a5\n",
      "- \ub3c4\ud615\uc744 \uadf8\ub9ac\ub824\uba74 \uc880 \ub354 \uc2e0\uacbd\n",
      "- matplotlib\uc740 \uc77c\ubc18\uc801\uc778 \ub3c4\ud615\uc744 \ud45c\ud604\ud558\ub294 patches \uac1d\uccb4\ub97c \uc81c\uacf5\ud558\ub294\ub370, \uc774 \uc911\uc5d0 Rectangle\uacfc Circle \ub4f1\uc740 matplotlib.pyplot\uc5d0\uc11c\ub3c4 \ucc3e\uc744 \uc218 \uc788\uc9c0\ub9cc \uc804\uccb4 \ubaa8\uc74c\uc740 matplotlib.patches\uc5d0 \uc788\ub2e4.\n",
      "\n",
      "\n",
      "- \ub3c4\ud45c\uc5d0 \ub3c4\ud615\uc744 \ucd94\uac00\ud558\ub824\uba74 patches \uac1d\uccb4\uc778 shp\ub97c \ub9cc\ub4e4\uc5b4\uc11c \uc11c\ube0c\ud50c\ub86f\uc5d0 ax.add_patch(shp)\ub97c \ud638\ucd9c\ud558\uc5ec \ucd94\uac00"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "\n",
      "rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)\n",
      "circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)\n",
      "pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],\n",
      "                   color='g', alpha=0.5)\n",
      "\n",
      "ax.add_patch(rect)\n",
      "ax.add_patch(circ)\n",
      "ax.add_patch(pgon)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 121,
       "text": [
        "<matplotlib.patches.Polygon at 0x101ea3110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTMYPLwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1107be550>"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.1.5 \uadf8\ub798\ud504\ub97c \ud30c\uc77c\ub85c \uc800\uc7a5\n",
      "\n",
      "- \ud65c\uc131\ud654\ub41c Figure\ub294 plt.savefig \uba54\uc11c\ub4dc\ub97c \uc774\uc6a9\ud574\uc11c \ud30c\uc77c\ub85c \uc800\uc7a5\n",
      "- Figure \uac1d\uccb4\uc758 \uc778\uc2a4\ud134\uc2a4 \uba54\uc11c\ub4dc\uc778 savefig\uc640 \ub3d9\uc77c\n",
      "- Figure\ub97c SVG \ud3ec\ub9f7\uc73c\ub85c \uc800\uc7a5\ud558\ub824\uba74 \ub2e4\uc74c\ucc98\ub7fc \ud558\uba74 \ub41c\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plt.savefig\n",
      "fig.savefig('ch08/figpath.svg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 127
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ud30c\uc77c\uc758 \uc885\ub958\ub294 \ud655\uc7a5\uc790\ub97c \ud1b5\ud574 \uacb0\uc815\n",
      "- .svg \ub300\uc2e0 .pdf\ub97c \uc785\ub825\ud588\ub2e4\uba74 PDF \ud30c\uc77c\uc744 \uc5bb\uc744 \uc218 \uc788\ub2e4.\n",
      "- \ucd9c\ud310\uc6a9 \uadf8\ub798\ud53d \ud30c\uc77c\uc744 \uc0dd\uc131\ud560 \ub54c \ub0b4\uac00 \uc790\uc8fc \uc0ac\uc6a9\ud558\ub294 \uba87 \uac00\uc9c0 \uc911\uc694\ud55c \uc635\uc158\uc774 \uc788\ub294\ub370, \ubc14\ub85c dpi\uc640 bbox_inches\ub2e4.\n",
      "- dpi: \uc778\uce58\ub2f9 \ub3c4\ud2b8 \ud574\uc0c1\ub3c4\ub97c \uc870\uc808\n",
      "- bbox_inches: \uc2e4\uc81c Figure \ub458\ub808\uc758 \uacf5\ubc31\uc744 \uc798\ub77c\ub0b8\ub2e4.\n",
      "- \uadf8\ub798\ud504 \uac04 \ucd5c\uc18c \uacf5\ubc31\uc744 \uac00\uc9c0\ub294 400 DPI \uc9dc\ub9ac PNG \ud30c\uc77c\uc744 \ub9cc\ub4e4\ub824\uba74 \ub2e4\uc74c\ucc98\ub7fc \uc785\ub825"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig.savefig('ch08/figpath.png', dpi=400, bbox_inches='tight')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig.savefig('ch08/figpath_nobbox.png', dpi=400)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- savefig \uba54\uc11c\ub4dc\ub85c \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud560 \uc218 \uc788\uc744 \ubfd0 \uc544\ub2c8\ub77c StringIO \uac19\uc740 \ud30c\uc77c\uacfc \uc720\uc0ac\ud55c \uac1d\uccb4\uc5d0\ub3c4 \uc800\uc7a5 \uac00\ub2a5\n",
      "- \uc774 \uae30\ub2a5\uc740 \uc6f9\uc744 \ud1b5\ud574 \ub3d9\uc801\uc73c\ub85c \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub97c \ubcf4\uc5ec\uc8fc\uace0\uc790 \ud560 \ub54c \uc720\uc6a9"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "from io import StringIO\n",
      "buffer = StringIO()\n",
      "plt.savefig(buffer)\n",
      "plot_data = buffer.getvalue()"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Figure.savefig \uc635\uc158\n",
      "\n",
      "\uc778\uc790 | \uc124\uba85\n",
      "--- | ---\n",
      "fname | \ud30c\uc77c \uacbd\ub85c\ub098 \ud30c\uc774\uc36c\uc758 \ud30c\uc77c\uacfc \uc720\uc0ac\ud55c \uac1d\uccb4\ub97c \ub098\ud0c0\ub0b4\ub294 \ubb38\uc790\uc5f4. \uc800\uc7a5\ub418\ub294 \ud3ec\ub9f7\uc740 \ud30c\uc77c \ud655\uc7a5\uc790\ub97c \ud1b5\ud574 \uacb0\uc815\ub41c\ub2e4. <br /> \uc608) .pdf\ub294 PDF\ud3ec\ub9f7. .png\ub294 PNG\ud3ec\ub9f7\n",
      "dpi | Figure\uc758 \ud574\uc0c1\ub3c4 dpi. \uae30\ubcf8\uac12\uc740 100\uc774\uba70 \uc124\uc815\uc774 \uac00\ub2a5\n",
      "facecolor, edge color | \uc11c\ube0c\ud50c\ub86f \ubc14\uae65 \ubc30\uacbd \uc0c9\uc0c1. \uae30\ubcf8\uac12\uc740 w(\ud770\uc0c9)\n",
      "format | \uba85\uc2dc\uc801\uc778 \ud30c\uc77c \ud3ec\ub9f7('png', 'pdf', 'svg', 'ps', 'eps', ...)\n",
      "bbox_inches | Figure\uc5d0\uc11c \uc800\uc7a5\ud560 \ubd80\ubd84. \ub9cc\uc57d\uc5d0 'tight'\ub77c\uace0 \uc9c0\uc815\ud558\uba74 Figure \ub458\ub808\uc758 \ube44\uc5b4\uc788\ub294 \uacf5\uac04\uc744 \ubaa8\ub450 \uc81c\uac70\ud558\uac8c \ub41c\ub2e4."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.1.6 matplotlib \uc124\uc815\n",
      "\n",
      "- matplotlib\uc740 \ucd9c\ud310\ubb3c\uc6a9 \ub3c4\ud45c\ub97c \ub9cc\ub4dc\ub294\ub370 \uc190\uc0c9\uc774 \uc5c6\ub294 \uae30\ubcf8 \uc124\uc815\uacfc \uc0c9\uc0c1 \uc2a4\ud0a4\ub9c8\ub97c \ud568\uaed8 \uc81c\uacf5\n",
      "- \uac70\uc758 \ubaa8\ub4e0 \uae30\ubcf8 \ub3d9\uc791\uc740 \uc804\uc5ed \uc778\uc790\ub97c \ud1b5\ud574 \uc124\uc815 \uac00\ub2a5\n",
      "- \ub3c4\ud45c \ud06c\uae30, \uc11c\ube0c\ud50c\ub86f \uac04\uaca9, \uc0c9\uc0c1, \uae00\uc790 \ud06c\uae30, \uaca9\uc790 \uc2a4\ud0c0\uc77c \uac19\uc740 \uac83\ub4e4\uc758 \uc124\uc815 \uac00\ub2a5\n",
      "- matplotlib\uc758 \ud658\uacbd \uc124\uc815 \uc2dc\uc2a4\ud15c\uc740 2\uac00\uc9c0 \ubc29\ubc95\uc73c\ub85c \ub2e4\ub8f8\n",
      "- \uccab \ubc88\uc9f8\ub294 rc \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud574\uc11c \ud504\ub85c\uadf8\ub798\ubc0d\uc801\uc73c\ub85c \uc124\uc815\ud558\ub294 \ubc29\ubc95\n",
      "- \uc608\ub97c \ub4e4\uc5b4 Figure\uc758 \ud06c\uae30\ub97c 10*10\uc73c\ub85c \uc804\uc5ed \uc124\uc815\ud574\ub450\uace0 \uc2f6\ub2e4\uba74 \ub2e4\uc74c \ucf54\ub4dc \uc2e4\ud589\n",
      "\n",
      "> plt.rc('figure', figsize=(10, 10))\n",
      "\n",
      "- rc \uba54\uc11c\ub4dc\uc758 \uccab\ubc88\uc9f8 \uc778\uc790\ub294 \uc124\uc815\ud558\uace0\uc790 \ud558\ub294 'figure', 'axes', 'xtick', 'ytick', 'grid', 'legend' \ubc0f \ub2e4\ub978 \ucef4\ud3ec\ub10c\ud2b8\uc758 \uc774\ub984\n",
      "- \uadf8 \ub2e4\uc74c\uc73c\ub85c \uc124\uc815\ud560 \uac12\uc5d0 \ub300\ud55c \ud0a4\uc6cc\ub4dc \uc778\uc790\ub97c \ub118\uae40\n",
      "- \uc774 \uc635\uc158\uc744 \uc27d\uac8c \uc791\uc131\ud558\ub294 \ubc29\ubc95\uc73c\ub85c\ub294 \ud30c\uc774\uc36c\uc758 \uc0ac\uc804 \ud0c0\uc785\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "font_options = {'family': 'monospace',\n",
      "                    'weight' : 'bold',\n",
      "                    'size': 'small'}\n",
      "plt.rc('font', **font_options)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ub354 \ub9ce\uc740 \uc124\uc815\uacfc \uc635\uc158\uc758 \uc885\ub958\ub294 matplotlib/mpl-data \ub514\ub809\ud1a0\ub9ac \uc548\uc758 matplotlibrc \ud30c\uc77c\uc5d0 \uc800\uc7a5\n",
      "- \ub9cc\uc57d \uc774 \ud30c\uc77c\uc744 \uc801\uc808\ud788 \uc218\uc815\ud574\uc11c \uc0ac\uc6a9\uc790 \ud648 \ub514\ub809\ud1a0\ub9ac\uc5d0 .matplotlibrc\ub77c\ub294 \uc774\ub984\uc73c\ub85c \uc800\uc7a5\ud574\ub450\uba74 matplotlib\uc744 \uc0ac\uc6a9\ud560 \ub54c\ub9c8\ub2e4 \ubd88\ub7ec\uc624\uae30 \uac00\ub2a5"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 8.2 pandas\uc5d0\uc11c \uadf8\ub798\ud504 \uadf8\ub9ac\uae30\n",
      "\n",
      "- \ub370\uc774\ud130\ub97c \ubcf4\uc5ec\uc904 \ub2e4\uc591\ud55c \ud615\ud0dc(\uc120 \uadf8\ub798\ud504, \ub9c9\ub300 \uadf8\ub798\ud504, \ubd09 \ucc28\ud2b8, \ubd84\ud3ec\ub3c4, \ub4f1\uace0\uc120 \ub4f1)\uc640 \ubc94\ub840, \uc81c\ubaa9, \ub208\uae08 \uc774\ub984 \uac19\uc740 \uc8fc\uc11d\uc744 \uc870\ud569\ud574\uc11c \uadf8\ub798\ud504\ub97c \ub9cc\ub4e4\uc5b4\uc57c \ud55c\ub2e4.\n",
      "- \ub300\ubd80\ubd84\uc758 \ub370\uc774\ud130\ub294 \uc5ec\ub7ec\uac00\uc9c0 \uac1d\uccb4\ub85c \ud45c\ud604\ub418\uae30 \ub54c\ubb38\uc5d0 \ud558\ub098\uc758 \uc628\uc804\ud55c \uadf8\ub798\ud504\ub97c \uadf8\ub9ac\uae30 \uc704\ud574\uc11c\ub294 \uae30\ubcf8 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc870\ud569\ud574\uc57c \ud560 \ud544\uc694\n",
      "- pandas\uc5d0\ub294 \ub85c\uc6b0 \uc774\ub984, \uce7c\ub7fc \uc774\ub984 \uadf8\ub9ac\uace0 \uac00\ub2a5\ud55c \uacbd\uc6b0\uc5d0\ub294 \uadf8\ub8f9 \uc815\ubcf4\ub3c4 \ub2f4\uace0 \uc788\ub294\ub370, \uc774 \ub9d0\uc740 pandas\ub97c \uc774\uc6a9\ud558\uba74 \ub2e4\uc591\ud55c \uc885\ub958\uc758 \uc628\uc804\ud55c \uadf8\ub798\ud504\ub97c \uadf8\ub9ac\uae30 \uc704\ud574 \ud544\uc694\ud55c \ub9ce\uc740 \uc591\uc758 matplotlib \ucf54\ub4dc\ub97c \ud55c \ub450\uc904\uc758 \uac04\ub2e8\ud55c \ubb38\uc7a5\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\ub2e4\ub294 \ub9d0\n",
      "- \uc774\ub7f0 \uc774\uc810\uc744 \ud65c\uc6a9\ud574\uc11c pandas\ub97c \ud1b5\ud574 \uc77c\ubc18\uc801\uc778 \uc2dc\uac01\ud654\ub97c \uc704\ud55c \ub3c4\ud45c\ub97c \uc791\uc131\ud560 \uc218 \uc788\ub294 \uba54\uc11c\ub4dc\uac00 \ub298\uc5b4\ub098\uace0 \uc788\ub2e4.\n",
      "\n",
      "\n",
      "- \uc774 \ucc45\uc744 \uc9d1\ud544\ud558\ub294 \ud604\uc7ac pandas\uc758 \uadf8\ub798\ud504 \uae30\ub2a5\uacfc \uad00\ub828\ud55c \ub9ce\uc740 \uc791\uc5c5\uc774 \uc9c4\ud589\uc911\n",
      "- \ud55c \ud559\uc0dd\uc774 2012\ub144 Google Summer of Code \ud504\ub85c\uadf8\ub7a8 \uae30\uac04 \ub3d9\uc548 \uae30\ub2a5\uc744 \ucd94\uac00\ud558\uace0 \uc880 \ub354 \uc77c\uad00\ub418\uba70 \uc0ac\uc6a9\uc131\uc774 \uc88b\uc740 \uc778\ud130\ud398\uc774\uc2a4\ub97c \ub9cc\ub4e4\uae30 \uc704\ud574 \uc804\uc77c\uc81c\ub85c \uae30\uc5ec\n",
      "- \uc774 \ucf54\ub4dc\ub294 \uc774 \ucc45\uc5d0 \ub098\uc624\ub294 pandas\uc758 \ub2e4\ub978 \ucf54\ub4dc\ubcf4\ub2e4 \uc880 \ub354 \ube68\ub9ac \ubcc0\uacbd\ub420 \uac00\ub2a5\uc131\uc774 \uc788\uc74c\n",
      "- \uc774\uc640 \uad00\ub828\ub41c \uc815\ubcf4\ub294 pandas \uc628\ub77c\uc778 \ubb38\uc11c\uc5d0 \uac00\uc7a5 \uba3c\uc800 \uc18c\uac1c\ub420 \uac83"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.2.1 \uc120 \uadf8\ub798\ud504\n",
      "\n",
      "- Series\uc640 DataFrame\uc740 \ub458 \ub2e4 plot \uc774\ub77c\ub294 \uba54\uc11c\ub4dc\ub97c \ud1b5\ud574 \ub2e4\uc591\ud55c \ud615\ud0dc\uc758 \uadf8\ub798\ud504\ub97c \uc0dd\uc131 \uac00\ub2a5\n",
      "- \uae30\ubcf8\uc801\uc73c\ub85c plot \uba54\uc11c\ub4dc\ub294 \uc120 \uadf8\ub798\ud504\ub97c \uc0dd\uc131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# cumsum \uba54\uc18c\ub4dc\ub85c \uc778\ud574 \uac12\uc774 \ub204\uc801\ub41c\ub2e4.\n",
      "s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 146,
       "text": [
        "0    -2.708173\n",
        "10   -3.895062\n",
        "20   -4.811704\n",
        "30   -5.833185\n",
        "40   -6.410537\n",
        "50   -6.892797\n",
        "60   -6.227022\n",
        "70   -6.312690\n",
        "80   -7.326234\n",
        "90   -7.574805\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x111539210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyIexiBMR+bD/B3C0kQfwGnWQAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10075f150>"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.randn(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 137,
       "text": [
        "array([ 1.20804795, -0.90984808, -0.69856343,  0.39272464,  1.24373109,\n",
        "       -0.7912721 , -0.2316993 , -0.92572915, -0.73842767,  0.49156197])"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.randn(10).cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 138,
       "text": [
        "array([ 1.26594266, -0.34618806, -0.22816919, -0.03327351,  1.14628817,\n",
        "       -0.14389082,  1.29059151,  0.43407162,  0.61595324,  0.33726086])"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2 = Series(np.random.randn(10), index=np.arange(0, 100, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 145,
       "text": [
        "0     1.852827\n",
        "10    1.911911\n",
        "20    0.525911\n",
        "30    0.051685\n",
        "40   -0.164685\n",
        "50    0.004360\n",
        "60   -0.614278\n",
        "70   -0.748000\n",
        "80    0.189764\n",
        "90   -0.861933\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 144,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x100769690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1107c03d0>"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Series \uac1d\uccb4\uc758 \uc0c9\uc778\uc740 matplotlib\uc5d0\uc11c \uadf8\ub798\ud504\ub97c \uc0dd\uc131\ud560 \ub54c X\ucd95\uc73c\ub85c \ud574\uc11d\ub418\uba70 use_index = False \uc635\uc158\uc744 \ub118\uaca8\uc11c \uc0c9\uc778\uc744 \uadf8\ub798\ud504\uc758 \ucd95\uc73c\ub85c \uc0ac\uc6a9\ud558\ub294 \uac83\uc744 \ub9c9\uc744 \uc218 \uc788\ub2e4.\n",
      "- \ucd95\uc758 \ub208\uae08\uacfc \ud55c\uacc4\ub294 xticks \uc635\uc158\uacfc xlim \uc635\uc158\uc73c\ub85c \uc870\uc808 \uac00\ub2a5\n",
      "- Y\ucd95 \uc5ed\uc2dc yticks, ylim \uc635\uc158\uc73c\ub85c \uc870\uc808 \uac00\ub2a5\n",
      "- \uc0ac\uc6a9 \uac00\ub2a5\ud55c plot \uba54\uc11c\ub4dc\uc758 \uc635\uc158\uc744 \ud655\uc778 \uac00\ub2a5\n",
      "- \ub098\uba38\uc9c0\ub294 \ub3c5\uc790\uc758 \ubaab\uc73c\ub85c \ub0a8\uaca8\ub450\uaca0\ub2e4\u314e\u314e\u314e\n",
      "- \ub300\ubd80\ubd84\uc758 pandas \uadf8\ub798\ud504 \uba54\uc11c\ub4dc\ub294 ax \uc778\uc790\ub97c \ubc1b\ub294\ub370, \uc774 \uc778\uc790\ub294 matplotlib\uc758 \uc11c\ube0c\ud50c\ub86f \uac1d\uccb4\uac00 \ub420 \uc218 \uc788\ub2e4.\n",
      "- \uc774\ub97c \ud1b5\ud574 \uadf8\ub9ac\ub4dc \ubc30\uc5f4 \uc0c1\uc5d0\uc11c \uc11c\ube0c\ud50c\ub86f\uc758 \uc704\uce58\ub97c \uc880 \ub354 \uc720\uc5f0\ud558\uac8c \uac00\uc838\uac08 \uc218 \uc788\uc74c\n",
      "- DataFrame\uc758 plot \uba54\uc11c\ub4dc\ub294 \ud558\ub098\uc758 \uc11c\ube0c\ud50c\ub86f \uc548\uc5d0 \uac01 \uce7c\ub7fc\ubcc4\ub85c \uc120 \uadf8\ub798\ud504\ub97c \uadf8\ub9ac\uace0, \uc790\ub3d9\uc801\uc73c\ub85c \ubc94\ub840\ub97c \uc0dd\uc131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# index: x\ucd95\uc73c\ub85c \uc0ac\uc6a9\ud560 \uac12: 0 \ubd80\ud130 100\uae4c\uc9c0\uc778\ub370 \ub2e8\uc704\ub294 10\n",
      "df = DataFrame(np.random.randn(10, 4).cumsum(0),\n",
      "               columns = ['A', 'B', 'C', 'D'],\n",
      "               index = np.arange(0, 100, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 148,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x111567450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x100770b50>"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- plot\uc5d0 \ub118\uae38 \uc218 \uc788\ub294 \ub2e4\ub978 \ud0a4\uc6cc\ub4dc \uc778\uc790\ub294 \uac01\uac01 \uac19\uc740 \uc774\ub984\uc73c\ub85c \ub300\uc751\ub418\ub294 matplotlib\uc758 \ud568\uc218\ub85c \uc804\ub2ec\ub41c\ub2e4.\n",
      "- \uadf8\ub798\uc11c matplotlib API\uc5d0 \ub300\ud574\uc11c \uacf5\ubd80\ud558\uba74 \ub098\uc911\uc5d0\ub294 \uc774 \uadf8\ub798\ud504\ub97c \uc880 \ub354 \uc5ec\ub7ec\ubd84\uc758 \uc785\ub9db\uc5d0 \ub9de\ub294 \ubaa8\uc591\uc73c\ub85c \uafb8\ubbf8\uae30 \uac00\ub2a5\n",
      "\n",
      "#### Series.plot \uba54\uc11c\ub4dc \uc778\uc790\n",
      "\n",
      "\uc778\uc790 | \uc124\uba85\n",
      "--- | ---\n",
      "label | \uadf8\ub798\ud504\uc758 \ubc94\ub840 \uc774\ub984\n",
      "ax | \uadf8\ub798\ud504\ub97c \uadf8\ub9b4 matplotlib\uc758 \uc11c\ube0c\ud50c\ub86f \uac1d\uccb4. \ub9cc\uc57d\uc5d0 \uc544\ubb34\uac83\ub3c4 \ub118\uc5b4\uc624\uc9c0 \uc54a\uc73c\uba74 \ud604\uc7ac \ud65c\uc131\ud654\ub418\uc5b4 \uc788\ub294 matplotlib\uc758 \uc11c\ube0c\ud50c\ub86f\uc744 \uc0ac\uc6a9\ud55c\ub2e4.\n",
      "style | matplotlib\uc5d0 \uc804\ub2ec\ud560 'ko--' \uac19\uc740 \uc2a4\ud0c0\uc77c\uc758 \ubb38\uc790\uc5f4\n",
      "alpha | \uadf8\ub798\ud504 \ud22c\uba85\ub3c4(0\ubd80\ud130 1\uae4c\uc9c0)\n",
      "kind | \uadf8\ub798\ud504 \uc885\ub958. 'line', 'bar', 'barh', 'kde'\n",
      "logy | Y\ucd95\uc5d0 \ub300\ud55c \ub85c\uadf8 \uc2a4\ucf00\uc77c\ub9c1\n",
      "use_index | \uac1d\uccb4\uc758 \uc0c9\uc778\uc744 \ub208\uae08 \uc774\ub984\uc73c\ub85c \uc0ac\uc6a9\ud560\uc9c0\uc758 \uc5ec\ubd80\n",
      "rot | \ub208\uae08 \uc774\ub984\uc744 \ub85c\ud14c\uc774\uc158(0\ubd80\ud130 360\uae4c\uc9c0)\n",
      "xticks | X\ucd95\uc73c\ub85c \uc0ac\uc6a9\ud560 \uac12\n",
      "yticks | Y\ucd95\uc73c\ub85c \uc0ac\uc6a9\ud560 \uac12\n",
      "xlim | X\ucd95 \ud55c\uacc4\n",
      "ylim | Y\ucd95 \ud55c\uacc4\n",
      "grid | \ucd95\uc758 \uadf8\ub9ac\ub4dc\ub97c \ud45c\uc2dc\ud560\uc9c0\uc758 \uc5ec\ubd80(\uae30\ubcf8\uac12\uc740 \ucf1c\uae30)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 149
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "    Type:        instancemethod\n",
      "    String form:\n",
      "    <bound method DataFrame.plot_frame of            A         B         C         D\n",
      "               0   0.305813  0. <...>  -0.733882\n",
      "               80  1.266628  3.101826 -0.935918 -0.514306\n",
      "               90  0.641015  4.017795  1.266435  0.381728>\n",
      "    File:        /Library/Python/2.7/site-packages/pandas-0.12.0_307_g3a2fe0b-py2.7-macosx-10.8-intel.egg/pandas/tools/plotting.py\n",
      "    Definition:  df.plot(frame=None, x=None, y=None, subplots=False, sharex=True, sharey=False, use_index=True, figsize=None, grid=None, legend=True, rot=None, ax=None, style=None, title=None, xlim=None, ylim=None, logx=False, logy=False, xticks=None, yticks=None, kind='line', sort_columns=False, fontsize=None, secondary_y=False, **kwds)\n",
      "    Docstring:\n",
      "    Make line or bar plot of DataFrame's series with the index on the x-axis\n",
      "    using matplotlib / pylab.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    frame : DataFrame\n",
      "    x : label or position, default None\n",
      "    y : label or position, default None\n",
      "        Allows plotting of one column versus another\n",
      "    subplots : boolean, default False\n",
      "        Make separate subplots for each time series\n",
      "    sharex : boolean, default True\n",
      "        In case subplots=True, share x axis\n",
      "    sharey : boolean, default False\n",
      "        In case subplots=True, share y axis\n",
      "    use_index : boolean, default True\n",
      "        Use index as ticks for x axis\n",
      "    stacked : boolean, default False\n",
      "        If True, create stacked bar plot. Only valid for DataFrame input\n",
      "    sort_columns: boolean, default False\n",
      "        Sort column names to determine plot ordering\n",
      "    title : string\n",
      "        Title to use for the plot\n",
      "    grid : boolean, default None (matlab style default)\n",
      "        Axis grid lines\n",
      "    legend : boolean, default True\n",
      "        Place legend on axis subplots\n",
      "\n",
      "    ax : matplotlib axis object, default None\n",
      "    style : list or dict\n",
      "        matplotlib line style per column\n",
      "    kind : {'line', 'bar', 'barh', 'kde', 'density'}\n",
      "        bar : vertical bar plot\n",
      "        barh : horizontal bar plot\n",
      "        kde/density : Kernel Density Estimation plot\n",
      "    logx : boolean, default False\n",
      "        For line plots, use log scaling on x axis\n",
      "    logy : boolean, default False\n",
      "        For line plots, use log scaling on y axis\n",
      "    xticks : sequence\n",
      "        Values to use for the xticks\n",
      "    yticks : sequence\n",
      "        Values to use for the yticks\n",
      "    xlim : 2-tuple/list\n",
      "    ylim : 2-tuple/list\n",
      "    rot : int, default None\n",
      "        Rotation for ticks\n",
      "    secondary_y : boolean or sequence, default False\n",
      "        Whether to plot on the secondary y-axis\n",
      "        If a list/tuple, which columns to plot on secondary y-axis\n",
      "    mark_right: boolean, default True\n",
      "        When using a secondary_y axis, should the legend label the axis of\n",
      "        the various columns automatically\n",
      "    colormap : str or matplotlib colormap object, default None\n",
      "        Colormap to select colors from. If string, load colormap with that name\n",
      "        from matplotlib.\n",
      "    kwds : keywords\n",
      "        Options to pass to matplotlib plotting method\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    ax_or_axes : matplotlib.AxesSubplot or list of them"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- DataFrame\uc5d0\ub294 \uce7c\ub7fc\uc744 \uc27d\uac8c \ub2e4\ub8e8\uae30 \uc704\ud574 \uba87 \uac00\uc9c0 \uc635\uc158 \uc81c\uacf5\n",
      "- \ubaa8\ub4e0 \uce7c\ub7fc\uc744 \uac19\uc740 \uc11c\ube0c\ud50c\ub86f\uc5d0 \uadf8\ub9b4 \uac83\uc778\uc9c0? \uc544\ub2c8\uba74 \uac01\uac01\uc758 \uc11c\ube0c\ud50c\ub86f\uc744 \ub530\ub85c \ub9cc\ub4e4 \uac83\uc778\uc9c0?\n",
      "\n",
      "#### DataFrame\uc5d0\uc11c\ub9cc \uc0ac\uc6a9\ud558\ub294 plot \uc635\uc158\n",
      "\uc778\uc790 | \uc124\uba85\n",
      "--- | ---\n",
      "subplots | \uac01 DataFrame\uc758 \uce7c\ub7fc\uc744 \ub3c5\ub9bd\ub41c \uc11c\ube0c\ud50c\ub86f\uc5d0 \uadf8\ub9b0\ub2e4.\n",
      "sharex | subplots=True\uba74 \uac19\uc740 X\ucd95\uc744 \uacf5\uc720\ud558\uace0 \ub208\uae08\uacfc \ud55c\uacc4\ub97c \uc5f0\uacb0\ud55c\ub2e4.\n",
      "sharey | subplots=True\uba74 \uac19\uc740 Y\ucd95\uc744 \uacf5\uc720\ud55c\ub2e4.\n",
      "figsize | \uc0dd\uc131\ub420 \uadf8\ub798\ud504\uc758 \ud06c\uae30\ub97c \ud29c\ud50c\ub85c \uc9c0\uc815\ud55c\ub2e4.\n",
      "title | \uadf8\ub798\ud504\uc758 \uc81c\ubaa9\uc744 \ubb38\uc790\uc5f4\ub85c \uc9c0\uc815\ud55c\ub2e4.\n",
      "legend | \uc11c\ube0c\ud50c\ub86f\uc758 \ubc94\ub840\ub97c \ucd94\uac00\ud55c\ub2e4. \uae30\ubcf8\uac12\uc740 True\n",
      "sort_columns | \uce7c\ub7fc\uc744 \uc54c\ud30c\ubcb3 \uc21c\uc11c\ub85c \uadf8\ub9b0\ub2e4. \uae30\ubcf8\uac12\uc740 \uc874\uc7ac\ud558\ub294 \uce7c\ub7fc \uc21c\uc11c\ub2e4.\n",
      "\n",
      "- \uc2dc\uacc4\uc5f4 \uadf8\ub798\ud504\ub294 10\uc7a5\uc744 \ucc38\uace0\ud558\uc790"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.2.2 \ub9c9\ub300 \uadf8\ub798\ud504\n",
      "\n",
      "- \uc120 \uadf8\ub798\ud504 \ub300\uc2e0 \ub9c9\ub300 \uadf8\ub798\ud504\ub97c \uadf8\ub9ac\ub824\uba74 \uadf8\ub0e5 kind='bar'(\uc218\uc9c1 \ub9c9\ub300) \uc635\uc158\uc744 \ub118\uae30\uac70\ub098 kind='barh'(\uc218\ud3c9 \ub9c9\ub300)\ub97c \ub118\uae30\uba74 \ub428\n",
      "- barh: bar horizontality\n",
      "- \uc774 \uacbd\uc6b0 Series\ub098 DataFrame\uc758 \uc0c9\uc778\uc740 X(bar) \ub208\uae08\uc774\ub098 Y(barh) \ub208\uae08\uc73c\ub85c \uc0ac\uc6a9"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(2, 1)\n",
      "data = Series(np.random.randn(16), index=list('abcdefghijklmnop'))\n",
      "data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)\n",
      "data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 156,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x113a58950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113a3bfd0>"
       ]
      }
     ],
     "prompt_number": 156
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- plt.subplots \ud568\uc218\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \ub0b4\uc6a9\uacfc matplotlib\uc758 \ucd95\uacfc \uadf8\ub9bc\uc5d0 \ub300\ud55c \ub0b4\uc6a9\uc740 \uc774 \ub2e4\uc74c\uc7a5\uc5d0\uc11c \uc790\uc138\ud788 \uc124\uba85\n",
      "\n",
      "- DataFrame\uc5d0\uc11c \ub9c9\ub300 \uadf8\ub798\ud504\ub294 \uac01 \ub85c\uc6b0\uc758 \uac12\uc744 \ud568\uaed8 \ubb36\uc5b4\uc11c \ud558\ub098\uc758 \uadf8\ub8f9\ub9c8\ub2e4 \uac01\uac01\uc758 \ub9c9\ub300\ub97c \ubcf4\uc5ec\uc90c"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = DataFrame(np.random.rand(6, 4),\n",
      "               index=['one', 'two', 'three', 'four', 'five', 'six'],\n",
      "               columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 160
    },
    {
     "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>Genus</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>one</th>\n",
        "      <td> 0.156639</td>\n",
        "      <td> 0.714753</td>\n",
        "      <td> 0.793158</td>\n",
        "      <td> 0.883230</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>two</th>\n",
        "      <td> 0.859143</td>\n",
        "      <td> 0.281589</td>\n",
        "      <td> 0.394842</td>\n",
        "      <td> 0.226950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>three</th>\n",
        "      <td> 0.083308</td>\n",
        "      <td> 0.702602</td>\n",
        "      <td> 0.377885</td>\n",
        "      <td> 0.516319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>four</th>\n",
        "      <td> 0.489569</td>\n",
        "      <td> 0.069673</td>\n",
        "      <td> 0.953518</td>\n",
        "      <td> 0.302130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>five</th>\n",
        "      <td> 0.296956</td>\n",
        "      <td> 0.984021</td>\n",
        "      <td> 0.938269</td>\n",
        "      <td> 0.241004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>six</th>\n",
        "      <td> 0.263840</td>\n",
        "      <td> 0.051877</td>\n",
        "      <td> 0.824661</td>\n",
        "      <td> 0.054635</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 161,
       "text": [
        "Genus         A         B         C         D\n",
        "one    0.156639  0.714753  0.793158  0.883230\n",
        "two    0.859143  0.281589  0.394842  0.226950\n",
        "three  0.083308  0.702602  0.377885  0.516319\n",
        "four   0.489569  0.069673  0.953518  0.302130\n",
        "five   0.296956  0.984021  0.938269  0.241004\n",
        "six    0.263840  0.051877  0.824661  0.054635"
       ]
      }
     ],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 162,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x114242650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAETCAYAAAArjI32AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ3B93gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114243210>"
       ]
      }
     ],
     "prompt_number": 162
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "DataFrame\uc758 \uce7c\ub7fc\uc778 'Genus'\uac00 \ubc94\ub840\uc758 \uc81c\ubaa9\uc73c\ub85c \uc0ac\uc6a9\ub428\n",
      "\n",
      "- \uc313\uc778 \ub9c9\ub300 \uadf8\ub798\ud504\ub294 stacked=True \uc635\uc158\uc744 \uc0ac\uc6a9\ud574\uc11c \uc0dd\uc131\ud560 \uc218 \uc788\ub294\ub370 \uac01 \ub85c\uc6b0\uc758 \uac12\uc774 \ud558\ub098\uc758 \ub9c9\ub300\uc5d0 \uc313\uc5ec\uc838 \ucd9c\ub825"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "help(np.random.rand)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on built-in function rand:\n",
        "\n",
        "rand(...)\n",
        "    rand(d0, d1, ..., dn)\n",
        "    \n",
        "    Random values in a given shape.\n",
        "    \n",
        "    Create an array of the given shape and propagate it with\n",
        "    random samples from a uniform distribution\n",
        "    over ``[0, 1)``.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    d0, d1, ..., dn : int, optional\n",
        "        The dimensions of the returned array, should all be positive.\n",
        "        If no argument is given a single Python float is returned.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    out : ndarray, shape ``(d0, d1, ..., dn)``\n",
        "        Random values.\n",
        "    \n",
        "    See Also\n",
        "    --------\n",
        "    random\n",
        "    \n",
        "    Notes\n",
        "    -----\n",
        "    This is a convenience function. If you want an interface that\n",
        "    takes a shape-tuple as the first argument, refer to\n",
        "    np.random.random_sample .\n",
        "    \n",
        "    Examples\n",
        "    --------\n",
        "    >>> np.random.rand(3,2)\n",
        "    array([[ 0.14022471,  0.96360618],  #random\n",
        "           [ 0.37601032,  0.25528411],  #random\n",
        "           [ 0.49313049,  0.94909878]]) #random\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "help(np.random.randn)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on built-in function randn:\n",
        "\n",
        "randn(...)\n",
        "    randn(d0, d1, ..., dn)\n",
        "    \n",
        "    Return a sample (or samples) from the \"standard normal\" distribution.\n",
        "    \n",
        "    If positive, int_like or int-convertible arguments are provided,\n",
        "    `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n",
        "    with random floats sampled from a univariate \"normal\" (Gaussian)\n",
        "    distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n",
        "    floats, they are first converted to integers by truncation). A single\n",
        "    float randomly sampled from the distribution is returned if no\n",
        "    argument is provided.\n",
        "    \n",
        "    This is a convenience function.  If you want an interface that takes a\n",
        "    tuple as the first argument, use `numpy.random.standard_normal` instead.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    d0, d1, ..., dn : int, optional\n",
        "        The dimensions of the returned array, should be all positive.\n",
        "        If no argument is given a single Python float is returned.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    Z : ndarray or float\n",
        "        A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n",
        "        the standard normal distribution, or a single such float if\n",
        "        no parameters were supplied.\n",
        "    \n",
        "    See Also\n",
        "    --------\n",
        "    random.standard_normal : Similar, but takes a tuple as its argument.\n",
        "    \n",
        "    Notes\n",
        "    -----\n",
        "    For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n",
        "    \n",
        "    ``sigma * np.random.randn(...) + mu``\n",
        "    \n",
        "    Examples\n",
        "    --------\n",
        "    >>> np.random.randn()\n",
        "    2.1923875335537315 #random\n",
        "    \n",
        "    Two-by-four array of samples from N(3, 6.25):\n",
        "    \n",
        "    >>> 2.5 * np.random.randn(2, 4) + 3\n",
        "    array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random\n",
        "           [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = DataFrame(np.random.randn(6, 4),\n",
      "               index=['one', 'two', 'three', 'four', 'five', 'six'],\n",
      "               columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))\n",
      "df2.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 166,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1145bb110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAETCAYAAAAS6zytAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDZuIyBD/B5eelJpnLMJLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1145346d0>"
       ]
      }
     ],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(kind='barh', stacked=True, alpha=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 167,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x11470ccd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD7CAYAAABuSzNOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhYGIiAKwMBARUQAWBiIiCsDCQEREAf4fRrLugY+w1ZkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1142332d0>"
       ]
      }
     ],
     "prompt_number": 167
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ub9c9\ub300 \uadf8\ub798\ud504\ub97c \uc774\uc6a9\ud55c \uc720\uc6a9\ud55c \ub808\uc2dc\ud53c\ub294 \uc55e\uc5d0\uc11c \uc0b4\ud3b4\ubcf4\uc558\ub4ef\uc774 Series\uc5d0\uc11c \uac12\uc758 \ube48\ub3c4\ub97c value_counts: s.value_counts().plot(kind='bar')\uc744 \uc774\uc6a9\ud574\uc11c \uc2dc\uac01\ud654\ud558\ub294 \uac83"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc774 \ucc45\uc758 \uc55e\ubd80\ubd84\uc5d0\uc11c \uc0b4\ud3b4\ubd24\ub358 \ud301 \ub370\uc774\ud130\ub97c \ub2e4\uc2dc \uc0b4\ud3b4\ubcf4\uc790\n",
      "- \uc774 \ub370\uc774\ud130\uc5d0\uc11c \uc694\uc77c\ubcc4 \ud30c\ud2f0 \uc22b\uc790\ub97c \ubf51\uace0 \ud30c\ud2f0 \uc22b\uc790 \ub300\ube44 \ud301 \ube44\uc728\uc744 \ubcf4\uc5ec\uc8fc\ub294 \ub9c9\ub300 \uadf8\ub798\ud504\ub97c \uadf8\ub824\ubcf4\uc790\n",
      "- read_csv \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud574\uc11c \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc624\uace0 \uc694\uc77c\uacfc \ud30c\ud2f0 \uc22b\uc790\uc5d0 \ub530\ub77c \uad50\ucc28 \ud14c\uc774\ube14 \uc0dd\uc131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips = pd.read_csv('ch08/tips.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips.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>total_bill</th>\n",
        "      <th>tip</th>\n",
        "      <th>sex</th>\n",
        "      <th>smoker</th>\n",
        "      <th>day</th>\n",
        "      <th>time</th>\n",
        "      <th>size</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 16.99</td>\n",
        "      <td> 1.01</td>\n",
        "      <td> Female</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 10.34</td>\n",
        "      <td> 1.66</td>\n",
        "      <td>   Male</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 21.01</td>\n",
        "      <td> 3.50</td>\n",
        "      <td>   Male</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 23.68</td>\n",
        "      <td> 3.31</td>\n",
        "      <td>   Male</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 24.59</td>\n",
        "      <td> 3.61</td>\n",
        "      <td> Female</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 176,
       "text": [
        "   total_bill   tip     sex smoker  day    time  size\n",
        "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
        "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
        "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
        "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
        "4       24.59  3.61  Female     No  Sun  Dinner     4"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ud30c\ud2f0 \uc694\uc77c\uacfc \ud30c\ud2f0 \uc22b\uc790\uc758 crosstab\n",
      "party_counts = pd.crosstab(tips.day, tips.size)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 169
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "party_counts"
     ],
     "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>size</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>day</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>Fri</th>\n",
        "      <td> 1</td>\n",
        "      <td> 16</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sat</th>\n",
        "      <td> 2</td>\n",
        "      <td> 53</td>\n",
        "      <td> 18</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sun</th>\n",
        "      <td> 0</td>\n",
        "      <td> 39</td>\n",
        "      <td> 15</td>\n",
        "      <td> 18</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thur</th>\n",
        "      <td> 1</td>\n",
        "      <td> 48</td>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 170,
       "text": [
        "size  1   2   3   4  5  6\n",
        "day                      \n",
        "Fri   1  16   1   1  0  0\n",
        "Sat   2  53  18  13  1  0\n",
        "Sun   0  39  15  18  3  1\n",
        "Thur  1  48   4   5  1  3"
       ]
      }
     ],
     "prompt_number": 170
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Not many 1- and 6-person parties\n",
      "# 1, 6\ubc88\uc740 \ub9ce\uc9c0 \uc54a\uc73c\ubbc0\ub85c \uc81c\uc678\n",
      "party_counts = party_counts.ix[:, 2:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "party_counts"
     ],
     "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>size</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>day</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Fri</th>\n",
        "      <td> 16</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sat</th>\n",
        "      <td> 53</td>\n",
        "      <td> 18</td>\n",
        "      <td> 13</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sun</th>\n",
        "      <td> 39</td>\n",
        "      <td> 15</td>\n",
        "      <td> 18</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thur</th>\n",
        "      <td> 48</td>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 177,
       "text": [
        "size   2   3   4  5\n",
        "day                \n",
        "Fri   16   1   1  0\n",
        "Sat   53  18  13  1\n",
        "Sun   39  15  18  3\n",
        "Thur  48   4   5  1"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips.day"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 172,
       "text": [
        "0     Sun\n",
        "1     Sun\n",
        "2     Sun\n",
        "3     Sun\n",
        "4     Sun\n",
        "5     Sun\n",
        "6     Sun\n",
        "7     Sun\n",
        "8     Sun\n",
        "9     Sun\n",
        "10    Sun\n",
        "11    Sun\n",
        "12    Sun\n",
        "13    Sun\n",
        "14    Sun\n",
        "...\n",
        "229     Sat\n",
        "230     Sat\n",
        "231     Sat\n",
        "232     Sat\n",
        "233     Sat\n",
        "234     Sat\n",
        "235     Sat\n",
        "236     Sat\n",
        "237     Sat\n",
        "238     Sat\n",
        "239     Sat\n",
        "240     Sat\n",
        "241     Sat\n",
        "242     Sat\n",
        "243    Thur\n",
        "Name: day, Length: 244, dtype: object"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips.size"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 173,
       "text": [
        "0     2\n",
        "1     3\n",
        "2     3\n",
        "3     2\n",
        "4     4\n",
        "5     4\n",
        "6     2\n",
        "7     4\n",
        "8     2\n",
        "9     2\n",
        "10    2\n",
        "11    4\n",
        "12    2\n",
        "13    4\n",
        "14    2\n",
        "...\n",
        "229    2\n",
        "230    4\n",
        "231    3\n",
        "232    2\n",
        "233    2\n",
        "234    2\n",
        "235    2\n",
        "236    2\n",
        "237    2\n",
        "238    3\n",
        "239    3\n",
        "240    2\n",
        "241    2\n",
        "242    2\n",
        "243    2\n",
        "Name: size, Length: 244, dtype: int64"
       ]
      }
     ],
     "prompt_number": 173
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "help(pd.crosstab)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on function crosstab in module pandas.tools.pivot:\n",
        "\n",
        "crosstab(rows, cols, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, dropna=True)\n",
        "    Compute a simple cross-tabulation of two (or more) factors. By default\n",
        "    computes a frequency table of the factors unless an array of values and an\n",
        "    aggregation function are passed\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    rows : array-like, Series, or list of arrays/Series\n",
        "        Values to group by in the rows\n",
        "    cols : array-like, Series, or list of arrays/Series\n",
        "        Values to group by in the columns\n",
        "    values : array-like, optional\n",
        "        Array of values to aggregate according to the factors\n",
        "    aggfunc : function, optional\n",
        "        If no values array is passed, computes a frequency table\n",
        "    rownames : sequence, default None\n",
        "        If passed, must match number of row arrays passed\n",
        "    colnames : sequence, default None\n",
        "        If passed, must match number of column arrays passed\n",
        "    margins : boolean, default False\n",
        "        Add row/column margins (subtotals)\n",
        "    dropna : boolean, default True\n",
        "        Do not include columns whose entries are all NaN\n",
        "    \n",
        "    Notes\n",
        "    -----\n",
        "    Any Series passed will have their name attributes used unless row or column\n",
        "    names for the cross-tabulation are specified\n",
        "    \n",
        "    Examples\n",
        "    --------\n",
        "    >>> a\n",
        "    array([foo, foo, foo, foo, bar, bar,\n",
        "           bar, bar, foo, foo, foo], dtype=object)\n",
        "    >>> b\n",
        "    array([one, one, one, two, one, one,\n",
        "           one, two, two, two, one], dtype=object)\n",
        "    >>> c\n",
        "    array([dull, dull, shiny, dull, dull, shiny,\n",
        "           shiny, dull, shiny, shiny, shiny], dtype=object)\n",
        "    \n",
        "    >>> crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])\n",
        "    b    one          two\n",
        "    c    dull  shiny  dull  shiny\n",
        "    a\n",
        "    bar  1     2      1     0\n",
        "    foo  2     2      1     2\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    crosstab : DataFrame\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uac01 \ub85c\uc6b0\uc758 \ud569\uc774 1\uc774 \ub418\ub3c4\ub85d \uc815\uaddc\ud654(\ud30c\uc774\uc36c 2.7\uc5d0\uc11c \uc815\uc218 \ub098\ub217\uc148 \uc774\uc288\ub97c \ud53c\ud558\uae30 \uc704\ud574 \uc2e4\uc218\ud615\uc73c\ub85c \ud615 \ubcc0\ud658\uc744 \ud574\uc57c\ub9cc \ud55c\ub2e4)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ucd1d\ud569\uc774 1\uc774 \ub418\ub3c4\ub85d \uc815\uaddc\ud654\ud55c\ub2e4\n",
      "party_pacts = party_counts.div(party_counts.sum(1).astype(float), axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 178
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "party_counts.sum(1).astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 183,
       "text": [
        "day\n",
        "Fri     18\n",
        "Sat     85\n",
        "Sun     75\n",
        "Thur    58\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 183
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# day = Fri, size = 2\n",
      "16/18.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 185,
       "text": [
        "0.8888888888888888"
       ]
      }
     ],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "party_pacts"
     ],
     "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>size</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>day</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Fri</th>\n",
        "      <td> 0.888889</td>\n",
        "      <td> 0.055556</td>\n",
        "      <td> 0.055556</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sat</th>\n",
        "      <td> 0.623529</td>\n",
        "      <td> 0.211765</td>\n",
        "      <td> 0.152941</td>\n",
        "      <td> 0.011765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sun</th>\n",
        "      <td> 0.520000</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.240000</td>\n",
        "      <td> 0.040000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thur</th>\n",
        "      <td> 0.827586</td>\n",
        "      <td> 0.068966</td>\n",
        "      <td> 0.086207</td>\n",
        "      <td> 0.017241</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 179,
       "text": [
        "size         2         3         4         5\n",
        "day                                         \n",
        "Fri   0.888889  0.055556  0.055556  0.000000\n",
        "Sat   0.623529  0.211765  0.152941  0.011765\n",
        "Sun   0.520000  0.200000  0.240000  0.040000\n",
        "Thur  0.827586  0.068966  0.086207  0.017241"
       ]
      }
     ],
     "prompt_number": 179
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc694\uc77c\ubcc4 \ud30c\ud2f0 \uaddc\ubaa8\n",
      "party_pacts.plot(kind='bar', stacked=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 180,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x114a86fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEcCAYAAADa2j8jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nciIizbGRExFp7v8BETv3u4GWtsYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114a76690>"
       ]
      }
     ],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "party_pacts.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 186,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x114ab13d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEcCAYAAADa2j8jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114abb290>"
       ]
      }
     ],
     "prompt_number": 186
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc774 \ub370\uc774\ud130\ub97c \ubcf4\uba74 \ud30c\ud2f0 \uaddc\ubaa8\ub294 \uc8fc\ub9d0\uc5d0 \ucee4\uc9c0\ub294 \uacbd\ud5a5\n",
      "\n",
      "#### [\ubbf8\uad6d\uc758 \ud30c\ud2f0\uc640 \ud55c\uad6d\uc758 \uc220\uc790\ub9ac, \uc5b4\ub5bb\uac8c \ub2e4\ub97c\uae4c?](http://puwazaza.com/292): \uc774 \uae00\uc744 \uc77d\uc5b4\ubcf4\uba74 \uc678\uad6d\uc758 \ud30c\ud2f0 \ubb38\ud654\uc5d0 \ub300\ud574\uc11c \uc870\uae08\uc740 \uc774\ud574\ud560 \uc218 \uc788\ub2e4."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.2.3 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uacfc \ubc00\ub3c4 \uadf8\ub798\ud504\n",
      "\n",
      "- \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc740 \ub9c9\ub300 \uadf8\ub798\ud504\uc758 \ud55c \uc885\ub958, \uac12\uc758 \ube48\ub3c4\ub97c \ubd84\ub9ac\ud574\uc11c \ubcf4\uc5ec\uc90c\n",
      "- \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\ub294 \ubd84\ub9ac\ub418\uc5b4 \uace0\ub978 \uac04\uaca9\uc758 \ub9c9\ub300\ub85c \ud45c\ud604\ub418\uba70 \ub370\uc774\ud130\uc758 \uc22b\uc790\uac00 \ub9c9\ub300 \ub192\uc774\ub85c \ud45c\ud604 \ub428\n",
      "- \uc55e\uc5d0\uc11c \uc0b4\ud3b4\ubcf8 \ud301 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud574\uc11c \uc804\uccb4 \uacb0\uc81c \uae08\uc561 \ub300\ube44 \ud301\uc758 \ube44\uc728\uc744 Series\uc758 hist \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud574\uc11c \ub9cc\ub4e4\uc5b4\ubcf4\uc790"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ud301\uc758 \ube44\uc728 = \ud301 / \uc804\uccb4 \uacb0\uc81c \uae08\uc561\n",
      "tips['tip_pct'] = tips['tip'] / tips['total_bill']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ud301\uc758 \ube44\uc728\uc774 \uc5bc\ub9c8\ub098 \ub418\ub294\uc9c0 histogray\uc73c\ub85c \ub098\ud0c0\ub0c4\n",
      "tips['tip_pct'].hist(bins=50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 188,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x114d23d90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUZTgQCciihIc6EREUeL/APj1aWnXmV7nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114d272d0>"
       ]
      }
     ],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips['tip_pct'].hist(bins=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 192,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x11547c650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDwhrw1/KTbWQAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114f9d0d0>"
       ]
      }
     ],
     "prompt_number": 192
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips['tip_pct'].hist?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 196
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips['tip_pct'].hist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "    bins : integer or array_like, optional, default: 10\n",
      "        If an integer is given, `bins + 1` bin edges are returned,\n",
      "        consistently with :func:`numpy.histogram` for numpy version >=\n",
      "        1.3.\n",
      "\n",
      "        Unequally spaced bins are supported if `bins` is a sequence."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips['tip_pct'].hist(bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 195,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x115458590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEaUJDnQiojTBgU5ElCY40ImI0sT/B/fctxthdvvJAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114ac7e90>"
       ]
      }
     ],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips['tip_pct'].hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 193,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1154cf690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQi8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114dc0250>"
       ]
      }
     ],
     "prompt_number": 193
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# default bins=10\n",
      "tips['tip_pct'].hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 202,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x11653ed10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDwhrw1/KTbWQAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115d7c450>"
       ]
      }
     ],
     "prompt_number": 202
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(tips)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 203,
       "text": [
        "244"
       ]
      }
     ],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips['tip_pct'].hist(bins=len(tips))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 204,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x116530d50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHQB8goEOAD7BQAcAn2CgA4BP/B+M6lFoDwdtaQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115f2a1d0>"
       ]
      }
     ],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tips.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>total_bill</th>\n",
        "      <th>tip</th>\n",
        "      <th>sex</th>\n",
        "      <th>smoker</th>\n",
        "      <th>day</th>\n",
        "      <th>time</th>\n",
        "      <th>size</th>\n",
        "      <th>tip_pct</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 16.99</td>\n",
        "      <td> 1.01</td>\n",
        "      <td> Female</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0.059447</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 10.34</td>\n",
        "      <td> 1.66</td>\n",
        "      <td>   Male</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0.160542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 21.01</td>\n",
        "      <td> 3.50</td>\n",
        "      <td>   Male</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0.166587</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 23.68</td>\n",
        "      <td> 3.31</td>\n",
        "      <td>   Male</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0.139780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 24.59</td>\n",
        "      <td> 3.61</td>\n",
        "      <td> Female</td>\n",
        "      <td> No</td>\n",
        "      <td> Sun</td>\n",
        "      <td> Dinner</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0.146808</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 189,
       "text": [
        "   total_bill   tip     sex smoker  day    time  size   tip_pct\n",
        "0       16.99  1.01  Female     No  Sun  Dinner     2  0.059447\n",
        "1       10.34  1.66    Male     No  Sun  Dinner     3  0.160542\n",
        "2       21.01  3.50    Male     No  Sun  Dinner     3  0.166587\n",
        "3       23.68  3.31    Male     No  Sun  Dinner     2  0.139780\n",
        "4       24.59  3.61  Female     No  Sun  Dinner     4  0.146808"
       ]
      }
     ],
     "prompt_number": 189
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc774\uc640 \uad00\ub828\uc788\ub294 \ub2e4\ub978 \ub3c4\ud45c\ub85c \ubc00\ub3c4 \uadf8\ub798\ud504 \uc874\uc7ac\n",
      "- \ubc00\ub3c4 \uadf8\ub798\ud504\ub294 \uad00\ucc30\uac12\uc744 \uc0ac\uc6a9\ud574\uc11c \ucd94\uc815\ub418\ub294 \uc5f0\uc18d\ub41c \ud655\ub960\ubd84\ud3ec \ub098\ud0c0\ub0c4\n",
      "- \uc77c\ubc18\uc801\uc73c\ub85c\ub294 kernels \uba54\uc11c\ub4dc\ub97c \uc798 \uc11e\uc5b4\uc11c \uc774 \ubd84\ud3ec\ub97c \uadfc\uc0ac\ud558\ub294 \uc2dd\uc73c\ub85c \uadf8\ub9ac\ub294\ub370, \uc774\uac83\uc740 \uc880 \ub354 \ub2e8\uc21c\ud55c \uc815\uaddc(\uac00\uc6b0\uc2dc\uc548) \ubd84\ud3ec\ub2e4.\n",
      "- \ubc00\ub3c4 \uadf8\ub798\ud504\ub294 KDE(Kernel Density Estimate), \ucee4\ub110 \ubc00\ub3c4 \ucd94\uc815 \uadf8\ub798\ud504\ub77c\uace0\ub3c4 \uc54c\ub824\uc838 \uc788\ub2e4.\n",
      "- plot \uba54\uc11c\ub4dc\uc758 \uc778\uc790\ub85c kind='kde'\ub97c \ub118\uae30\uba74 \ubc00\ub3c4 \uadf8\ub798\ud504\ub97c \ud45c\uc900 KDE \ud615\uc2dd\uc73c\ub85c \uc0dd\uc131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ud301 \ube44\uc728\uc758 \ubc00\ub3c4 \uadf8\ub798\ud504\n",
      "tips['tip_pct'].plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 205,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1165cf450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x116507ad0>"
       ]
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \ud788\uc2a4\ud1a0\uadf8\ub7a8\uacfc \ubc00\ub3c4 \uadf8\ub798\ud504\ub294 \ud568\uaed8 \uc790\uc8fc \uc0ac\uc6a9\n",
      "- \uc815\uaddc\ud654\ub41c \ud788\uc2a4\ud1a0\uadf8\ub7a8 \uc704\uc5d0 \ucee4\ub110 \ubc00\ub3c4 \ucd94\uc815 \uadf8\ub798\ud504\ub97c \ud568\uaed8 \uadf8\ub9b0\ub2e4.\n",
      "- 2\uac1c\uc758 \ub2e4\ub978 \ud45c\uc900 \uc815\uaddc\ubd84\ud3ec\ub85c \uc774\ub8e8\uc5b4\uc9c4 \uc591\ubd09\ubd84\ud3ec\ub97c \uc0dd\uac01\ud574\ubcf4\uc790"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comp1 = np.random.normal(0, 1, size=200) # N(0, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 206
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comp2 = np.random.normal(10, 2, size=200) # N(10, 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 207
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values = Series(np.concatenate([comp1, comp2]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 208
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comp1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 219,
       "text": [
        "array([-0.03006432,  0.62416921, -0.82979016, -0.07626137,  0.33006727,\n",
        "       -0.2226163 , -1.4490153 , -0.57840093, -1.11460083,  0.7553205 ,\n",
        "       -0.21047422,  0.81083657,  0.25409624, -0.73677549,  1.59938654,\n",
        "        0.30897776, -0.52118929, -0.98074448,  1.02452457,  1.26628246,\n",
        "       -1.08982414, -1.8863159 , -1.49819582, -0.57088863, -1.52569729,\n",
        "       -0.21462303, -1.91300802, -0.97689937,  0.69224438,  1.19066594,\n",
        "       -0.04543148, -1.68464209,  0.16377322, -0.41100469,  1.65563584,\n",
        "       -0.43548471, -0.0686075 ,  0.87443718, -0.09561706,  0.34318721,\n",
        "       -0.33434316, -0.6943807 ,  1.05739929, -0.08260261, -0.26344038,\n",
        "       -1.05453138,  1.11027371,  1.09987408, -2.05056012,  0.7458223 ,\n",
        "        1.80206639, -0.29385907, -0.30099934, -1.48906035, -0.25789229,\n",
        "        0.6368622 ,  1.35291641, -0.37697077, -0.63794597, -0.07542487,\n",
        "        1.14693734, -1.47784192, -0.37739846,  0.69789293, -0.2241004 ,\n",
        "       -0.47868564,  1.03858588, -0.26952836, -0.36315754,  0.75307931,\n",
        "        1.90836703,  0.84463412, -0.07939539,  1.23437271,  1.43452041,\n",
        "        0.61490596, -0.3134146 ,  1.75492581,  0.69160287,  1.39466142,\n",
        "       -0.59527496,  0.04232359,  0.49865358,  0.92069305,  0.71648503,\n",
        "        0.32103442, -1.37489713,  0.3128524 ,  1.2121235 , -0.64490816,\n",
        "        0.87252652,  0.04927038, -0.40507724, -1.23035539,  0.5222532 ,\n",
        "       -1.26006006,  0.58750751, -0.81574104,  2.03006589,  1.79778166,\n",
        "       -1.18547603, -0.19684619, -1.2061639 , -0.67239921, -1.06537364,\n",
        "        1.89551087,  0.69125902,  0.24671771,  0.1799499 ,  0.13539037,\n",
        "        2.52973433, -0.15766208,  0.21710313, -0.39874482, -0.09711104,\n",
        "       -0.68850282,  0.42143671,  2.03038841, -1.06994309, -1.15533822,\n",
        "        0.267592  ,  0.76303184,  0.92527421, -0.18773323,  0.68041131,\n",
        "        0.52873759,  0.02513809,  0.2401861 , -2.01104222,  0.19282394,\n",
        "       -0.98472998,  1.10906995, -1.46220406,  0.04029807,  3.70747642,\n",
        "        0.13750037, -0.17335615, -0.04595318, -0.00703485,  0.13321913,\n",
        "       -0.1880394 ,  0.60217843, -0.50454323,  2.13825018, -0.86848241,\n",
        "       -0.15371265,  1.05188101, -0.6490668 , -0.03762459,  0.12019895,\n",
        "       -0.85142469, -0.34167035, -1.67959706, -0.44873759, -1.55704742,\n",
        "       -0.45973605, -1.34374166,  0.20502535,  0.34766993, -0.02929194,\n",
        "       -1.62667786,  0.35566227,  0.99383155, -0.3068941 ,  0.99818634,\n",
        "        0.46747651,  0.51479121,  0.0885481 ,  1.74646427,  0.13564841,\n",
        "       -0.71792006,  1.22226059,  0.08595269,  1.26449524, -0.25562476,\n",
        "       -0.2302898 ,  0.47995741,  0.80062927,  0.77013342, -2.45091037,\n",
        "        0.15850605,  0.32502658,  0.42511228, -0.83601846, -1.50093046,\n",
        "        0.25244309,  0.88262766, -0.20556962, -0.20643288,  0.19607876,\n",
        "        1.19494803,  2.51500466,  0.22907423,  0.28595409, -1.04140855,\n",
        "       -1.60573034, -0.83226837, -0.38434512,  0.40409659, -0.2794942 ])"
       ]
      }
     ],
     "prompt_number": 219
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comp2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 220,
       "text": [
        "array([ 12.12979238,  11.31287139,   9.11185445,   6.21412288,\n",
        "        12.02195632,   7.98072736,   8.88810476,  10.59032813,\n",
        "         6.90336211,   8.31517484,  10.32299948,   9.59569113,\n",
        "        10.61039485,   7.7421701 ,   9.34527707,   7.10553918,\n",
        "        10.43539833,   7.77376647,   9.3063226 ,   8.99981389,\n",
        "        10.2348831 ,   8.87808666,   9.90299934,  10.26468104,\n",
        "        10.31592146,   9.23504796,  12.75585353,  10.37984266,\n",
        "        13.12118208,  10.75283153,  10.38349071,   7.70727205,\n",
        "         8.76031168,  12.64752461,  13.72756757,   9.35243395,\n",
        "        11.43970632,  11.93780346,  13.41580251,   9.45244386,\n",
        "        13.93512561,  12.4077175 ,  15.41248813,   8.65833233,\n",
        "         9.98494934,   9.45740357,   8.37800771,   8.4866978 ,\n",
        "         8.94076236,   9.16867676,   9.96257804,  10.01438034,\n",
        "        13.56244584,   7.72115934,   7.98352198,  10.86779896,\n",
        "         7.53621441,   8.89007687,   8.75529358,   6.45836627,\n",
        "         9.26307598,   9.26817923,  11.23709731,   9.4024986 ,\n",
        "        10.48216662,   9.81099859,   9.61800828,   9.2411512 ,\n",
        "         9.02121394,  13.0542351 ,   9.30883883,   8.75658223,\n",
        "         8.84301486,  12.36004597,  10.44683533,   4.96961209,\n",
        "         9.62606839,   8.09027939,   8.73693674,  13.29556102,\n",
        "        12.43524757,   9.5924543 ,   7.49019263,  10.53756646,\n",
        "         9.03188349,  12.91507698,  13.11381014,  11.01024165,\n",
        "        11.40147758,  11.05118302,   8.15736822,   6.85889665,\n",
        "        10.37081389,  10.62889047,   8.78887665,   8.28573431,\n",
        "         5.51950026,  12.28506857,   9.32171887,  10.50588577,\n",
        "         6.03667584,   7.41734292,   8.20309615,  10.42889666,\n",
        "        12.67629172,  14.09353288,   9.89166436,   8.00360409,\n",
        "         5.99305938,   9.33975857,  10.50234367,   9.95875703,\n",
        "         9.13212607,   8.95817617,   9.61129025,   9.54071445,\n",
        "         9.17114096,   9.54379153,   7.79176814,   8.2786597 ,\n",
        "         9.53316156,   7.7926394 ,  10.88550414,  12.96031431,\n",
        "         9.42429864,   8.54464011,  11.2917608 ,  11.8457744 ,\n",
        "         9.54300979,   8.30888867,  13.54993186,  11.05272319,\n",
        "        12.11968601,   9.88714917,  10.77184716,  11.02172827,\n",
        "         7.40669767,   9.36458248,  10.66301035,   7.68434779,\n",
        "        10.00564907,  11.10394758,   7.22035275,   8.08423978,\n",
        "        11.0795675 ,   7.40255238,  10.29544307,   7.7234862 ,\n",
        "        11.59979629,  10.94676699,   9.58586261,   8.43918392,\n",
        "         9.86616604,   9.65168141,   7.41870469,  11.16809778,\n",
        "         7.22804576,  12.53855354,  12.29421695,  11.47979516,\n",
        "         9.94691419,  10.2979203 ,  10.66189723,   7.1988263 ,\n",
        "        11.65957401,   6.19223188,  11.67952798,   6.60079654,\n",
        "        11.69536621,   8.55243384,  14.66606056,  10.59700618,\n",
        "        14.51955882,  12.66453724,   8.4263302 ,  10.80717172,\n",
        "         9.78971246,   9.49791593,   8.58696967,   8.0595833 ,\n",
        "        11.35096868,  10.05825319,   8.59931553,  10.9292835 ,\n",
        "        14.82178706,   9.64108128,   7.15230379,   9.98130276,\n",
        "        10.16814889,  12.50442524,   9.41593221,   7.23832656,\n",
        "         8.57751347,  11.36690595,   8.68819674,  11.7532276 ,\n",
        "         9.59768956,  10.37324093,   6.73252232,   7.6218148 ])"
       ]
      }
     ],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.concatenate([comp1, comp2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 216,
       "text": [
        "array([ -3.00643194e-02,   6.24169215e-01,  -8.29790162e-01,\n",
        "        -7.62613746e-02,   3.30067271e-01,  -2.22616296e-01,\n",
        "        -1.44901530e+00,  -5.78400932e-01,  -1.11460083e+00,\n",
        "         7.55320501e-01,  -2.10474218e-01,   8.10836567e-01,\n",
        "         2.54096238e-01,  -7.36775488e-01,   1.59938654e+00,\n",
        "         3.08977756e-01,  -5.21189295e-01,  -9.80744477e-01,\n",
        "         1.02452457e+00,   1.26628246e+00,  -1.08982414e+00,\n",
        "        -1.88631590e+00,  -1.49819582e+00,  -5.70888632e-01,\n",
        "        -1.52569729e+00,  -2.14623027e-01,  -1.91300802e+00,\n",
        "        -9.76899373e-01,   6.92244377e-01,   1.19066594e+00,\n",
        "        -4.54314784e-02,  -1.68464209e+00,   1.63773225e-01,\n",
        "        -4.11004693e-01,   1.65563584e+00,  -4.35484706e-01,\n",
        "        -6.86075043e-02,   8.74437184e-01,  -9.56170616e-02,\n",
        "         3.43187210e-01,  -3.34343163e-01,  -6.94380695e-01,\n",
        "         1.05739929e+00,  -8.26026111e-02,  -2.63440378e-01,\n",
        "        -1.05453138e+00,   1.11027371e+00,   1.09987408e+00,\n",
        "        -2.05056012e+00,   7.45822303e-01,   1.80206639e+00,\n",
        "        -2.93859066e-01,  -3.00999341e-01,  -1.48906035e+00,\n",
        "        -2.57892293e-01,   6.36862198e-01,   1.35291641e+00,\n",
        "        -3.76970772e-01,  -6.37945971e-01,  -7.54248741e-02,\n",
        "         1.14693734e+00,  -1.47784192e+00,  -3.77398457e-01,\n",
        "         6.97892928e-01,  -2.24100400e-01,  -4.78685642e-01,\n",
        "         1.03858588e+00,  -2.69528364e-01,  -3.63157539e-01,\n",
        "         7.53079311e-01,   1.90836703e+00,   8.44634122e-01,\n",
        "        -7.93953923e-02,   1.23437271e+00,   1.43452041e+00,\n",
        "         6.14905962e-01,  -3.13414601e-01,   1.75492581e+00,\n",
        "         6.91602868e-01,   1.39466142e+00,  -5.95274958e-01,\n",
        "         4.23235905e-02,   4.98653581e-01,   9.20693051e-01,\n",
        "         7.16485030e-01,   3.21034425e-01,  -1.37489713e+00,\n",
        "         3.12852398e-01,   1.21212350e+00,  -6.44908165e-01,\n",
        "         8.72526521e-01,   4.92703824e-02,  -4.05077235e-01,\n",
        "        -1.23035539e+00,   5.22253204e-01,  -1.26006006e+00,\n",
        "         5.87507505e-01,  -8.15741043e-01,   2.03006589e+00,\n",
        "         1.79778166e+00,  -1.18547603e+00,  -1.96846185e-01,\n",
        "        -1.20616390e+00,  -6.72399213e-01,  -1.06537364e+00,\n",
        "         1.89551087e+00,   6.91259021e-01,   2.46717711e-01,\n",
        "         1.79949899e-01,   1.35390370e-01,   2.52973433e+00,\n",
        "        -1.57662075e-01,   2.17103135e-01,  -3.98744823e-01,\n",
        "        -9.71110403e-02,  -6.88502818e-01,   4.21436714e-01,\n",
        "         2.03038841e+00,  -1.06994309e+00,  -1.15533822e+00,\n",
        "         2.67592000e-01,   7.63031840e-01,   9.25274205e-01,\n",
        "        -1.87733232e-01,   6.80411308e-01,   5.28737593e-01,\n",
        "         2.51380939e-02,   2.40186101e-01,  -2.01104222e+00,\n",
        "         1.92823944e-01,  -9.84729985e-01,   1.10906995e+00,\n",
        "        -1.46220406e+00,   4.02980737e-02,   3.70747642e+00,\n",
        "         1.37500372e-01,  -1.73356154e-01,  -4.59531831e-02,\n",
        "        -7.03484946e-03,   1.33219127e-01,  -1.88039396e-01,\n",
        "         6.02178428e-01,  -5.04543229e-01,   2.13825018e+00,\n",
        "        -8.68482412e-01,  -1.53712650e-01,   1.05188101e+00,\n",
        "        -6.49066801e-01,  -3.76245876e-02,   1.20198950e-01,\n",
        "        -8.51424687e-01,  -3.41670352e-01,  -1.67959706e+00,\n",
        "        -4.48737587e-01,  -1.55704742e+00,  -4.59736054e-01,\n",
        "        -1.34374166e+00,   2.05025350e-01,   3.47669928e-01,\n",
        "        -2.92919436e-02,  -1.62667786e+00,   3.55662274e-01,\n",
        "         9.93831547e-01,  -3.06894098e-01,   9.98186342e-01,\n",
        "         4.67476511e-01,   5.14791212e-01,   8.85480975e-02,\n",
        "         1.74646427e+00,   1.35648405e-01,  -7.17920061e-01,\n",
        "         1.22226059e+00,   8.59526924e-02,   1.26449524e+00,\n",
        "        -2.55624763e-01,  -2.30289803e-01,   4.79957414e-01,\n",
        "         8.00629272e-01,   7.70133416e-01,  -2.45091037e+00,\n",
        "         1.58506053e-01,   3.25026581e-01,   4.25112278e-01,\n",
        "        -8.36018462e-01,  -1.50093046e+00,   2.52443085e-01,\n",
        "         8.82627665e-01,  -2.05569621e-01,  -2.06432880e-01,\n",
        "         1.96078761e-01,   1.19494803e+00,   2.51500466e+00,\n",
        "         2.29074231e-01,   2.85954093e-01,  -1.04140855e+00,\n",
        "        -1.60573034e+00,  -8.32268371e-01,  -3.84345125e-01,\n",
        "         4.04096595e-01,  -2.79494201e-01,   1.21297924e+01,\n",
        "         1.13128714e+01,   9.11185445e+00,   6.21412288e+00,\n",
        "         1.20219563e+01,   7.98072736e+00,   8.88810476e+00,\n",
        "         1.05903281e+01,   6.90336211e+00,   8.31517484e+00,\n",
        "         1.03229995e+01,   9.59569113e+00,   1.06103949e+01,\n",
        "         7.74217010e+00,   9.34527707e+00,   7.10553918e+00,\n",
        "         1.04353983e+01,   7.77376647e+00,   9.30632260e+00,\n",
        "         8.99981389e+00,   1.02348831e+01,   8.87808666e+00,\n",
        "         9.90299934e+00,   1.02646810e+01,   1.03159215e+01,\n",
        "         9.23504796e+00,   1.27558535e+01,   1.03798427e+01,\n",
        "         1.31211821e+01,   1.07528315e+01,   1.03834907e+01,\n",
        "         7.70727205e+00,   8.76031168e+00,   1.26475246e+01,\n",
        "         1.37275676e+01,   9.35243395e+00,   1.14397063e+01,\n",
        "         1.19378035e+01,   1.34158025e+01,   9.45244386e+00,\n",
        "         1.39351256e+01,   1.24077175e+01,   1.54124881e+01,\n",
        "         8.65833233e+00,   9.98494934e+00,   9.45740357e+00,\n",
        "         8.37800771e+00,   8.48669780e+00,   8.94076236e+00,\n",
        "         9.16867676e+00,   9.96257804e+00,   1.00143803e+01,\n",
        "         1.35624458e+01,   7.72115934e+00,   7.98352198e+00,\n",
        "         1.08677990e+01,   7.53621441e+00,   8.89007687e+00,\n",
        "         8.75529358e+00,   6.45836627e+00,   9.26307598e+00,\n",
        "         9.26817923e+00,   1.12370973e+01,   9.40249860e+00,\n",
        "         1.04821666e+01,   9.81099859e+00,   9.61800828e+00,\n",
        "         9.24115120e+00,   9.02121394e+00,   1.30542351e+01,\n",
        "         9.30883883e+00,   8.75658223e+00,   8.84301486e+00,\n",
        "         1.23600460e+01,   1.04468353e+01,   4.96961209e+00,\n",
        "         9.62606839e+00,   8.09027939e+00,   8.73693674e+00,\n",
        "         1.32955610e+01,   1.24352476e+01,   9.59245430e+00,\n",
        "         7.49019263e+00,   1.05375665e+01,   9.03188349e+00,\n",
        "         1.29150770e+01,   1.31138101e+01,   1.10102417e+01,\n",
        "         1.14014776e+01,   1.10511830e+01,   8.15736822e+00,\n",
        "         6.85889665e+00,   1.03708139e+01,   1.06288905e+01,\n",
        "         8.78887665e+00,   8.28573431e+00,   5.51950026e+00,\n",
        "         1.22850686e+01,   9.32171887e+00,   1.05058858e+01,\n",
        "         6.03667584e+00,   7.41734292e+00,   8.20309615e+00,\n",
        "         1.04288967e+01,   1.26762917e+01,   1.40935329e+01,\n",
        "         9.89166436e+00,   8.00360409e+00,   5.99305938e+00,\n",
        "         9.33975857e+00,   1.05023437e+01,   9.95875703e+00,\n",
        "         9.13212607e+00,   8.95817617e+00,   9.61129025e+00,\n",
        "         9.54071445e+00,   9.17114096e+00,   9.54379153e+00,\n",
        "         7.79176814e+00,   8.27865970e+00,   9.53316156e+00,\n",
        "         7.79263940e+00,   1.08855041e+01,   1.29603143e+01,\n",
        "         9.42429864e+00,   8.54464011e+00,   1.12917608e+01,\n",
        "         1.18457744e+01,   9.54300979e+00,   8.30888867e+00,\n",
        "         1.35499319e+01,   1.10527232e+01,   1.21196860e+01,\n",
        "         9.88714917e+00,   1.07718472e+01,   1.10217283e+01,\n",
        "         7.40669767e+00,   9.36458248e+00,   1.06630104e+01,\n",
        "         7.68434779e+00,   1.00056491e+01,   1.11039476e+01,\n",
        "         7.22035275e+00,   8.08423978e+00,   1.10795675e+01,\n",
        "         7.40255238e+00,   1.02954431e+01,   7.72348620e+00,\n",
        "         1.15997963e+01,   1.09467670e+01,   9.58586261e+00,\n",
        "         8.43918392e+00,   9.86616604e+00,   9.65168141e+00,\n",
        "         7.41870469e+00,   1.11680978e+01,   7.22804576e+00,\n",
        "         1.25385535e+01,   1.22942170e+01,   1.14797952e+01,\n",
        "         9.94691419e+00,   1.02979203e+01,   1.06618972e+01,\n",
        "         7.19882630e+00,   1.16595740e+01,   6.19223188e+00,\n",
        "         1.16795280e+01,   6.60079654e+00,   1.16953662e+01,\n",
        "         8.55243384e+00,   1.46660606e+01,   1.05970062e+01,\n",
        "         1.45195588e+01,   1.26645372e+01,   8.42633020e+00,\n",
        "         1.08071717e+01,   9.78971246e+00,   9.49791593e+00,\n",
        "         8.58696967e+00,   8.05958330e+00,   1.13509687e+01,\n",
        "         1.00582532e+01,   8.59931553e+00,   1.09292835e+01,\n",
        "         1.48217871e+01,   9.64108128e+00,   7.15230379e+00,\n",
        "         9.98130276e+00,   1.01681489e+01,   1.25044252e+01,\n",
        "         9.41593221e+00,   7.23832656e+00,   8.57751347e+00,\n",
        "         1.13669059e+01,   8.68819674e+00,   1.17532276e+01,\n",
        "         9.59768956e+00,   1.03732409e+01,   6.73252232e+00,\n",
        "         7.62181480e+00])"
       ]
      }
     ],
     "prompt_number": 216
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(comp1), len(comp2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 218,
       "text": [
        "(200, 200)"
       ]
      }
     ],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(np.concatenate([comp1, comp2]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 217,
       "text": [
        "400"
       ]
      }
     ],
     "prompt_number": 217
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 214,
       "text": [
        "0    -0.030064\n",
        "1     0.624169\n",
        "2    -0.829790\n",
        "3    -0.076261\n",
        "4     0.330067\n",
        "5    -0.222616\n",
        "6    -1.449015\n",
        "7    -0.578401\n",
        "8    -1.114601\n",
        "9     0.755321\n",
        "10   -0.210474\n",
        "11    0.810837\n",
        "12    0.254096\n",
        "13   -0.736775\n",
        "14    1.599387\n",
        "...\n",
        "385     9.641081\n",
        "386     7.152304\n",
        "387     9.981303\n",
        "388    10.168149\n",
        "389    12.504425\n",
        "390     9.415932\n",
        "391     7.238327\n",
        "392     8.577513\n",
        "393    11.366906\n",
        "394     8.688197\n",
        "395    11.753228\n",
        "396     9.597690\n",
        "397    10.373241\n",
        "398     6.732522\n",
        "399     7.621815\n",
        "Length: 400, dtype: float64"
       ]
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values.hist(bins=100, alpha=0.3, color='k', normed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 212,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x119490290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHZtJREFUeJzt3W9MFPn9B/D3LirdBXJYHnS9bWWpKfHBrdFLVYwneg9O\nE4KS1tDENBHCg4uGWJs+KKaJ0UtjUiQ1XBtyJBbObDFnIPVOUS8XrzJim3QxmzMsNO16lfMKZvFA\nCOXP7Xkwvwf+dj+7yzLsjiw7MO9XQs6ZHb5897OffJj7MN8Zi6qqKoiIyBSsmZ4AEREtHxZ9IiIT\nYdEnIjIRFn0iIhNh0SciMhEWfSIiE1m06Hd2dqKoqAiFhYVob29f8Lh///vfKC4uht1ux+7du3Hl\nypWUxyAiovSyaF2nHwqFsGHDBrzxxhuw2Wy4efMmgsEgcnNz5x07MDCA69evo7i4GC0tLbhz5w7+\n+9//Ys2aNUmPQURE6aVZ9O/du4e9e/eiv78fOTk5cLlcuHHjBsrKyhYccHJyEg0NDfjDH/6A4eFh\neL3elMcgIqL0WKP1YjAYBAC0tLTAZrPF7Evk888/R3FxMXJycvDJJ59g3bp1KY9BRETpk9Qfcmtr\na3Hy5MlFj9u4cSMURcFbb72FyspKfPvttymPQURE6aN5pu9wOAAAMzMzyMrKitmXyLp161BaWors\n7Gzs2rULXq836TGcTieePHmi710QEZnUpk2b8Pnnnyf/DaqGr7/+Wl2/fr1aXl6uVlZWqrm5uerU\n1JSqqqp66NAh9fXXX48ce+3aNfWPf/yjeuPGDfUnP/mJumbNGvU///mP5hjRFpmKqZw5cybTUzAM\nxkIwFoKxEKnWTs32TnZ2NjweD/r6+uD1etHa2gq73Q4AmJiYwNjYWORYi8WCCxcuoLKyEhMTE/B4\nPPjhD3+oOQYl9sUXX2R6CobBWAjGQjAW+mm2dwCgvLwc5eXl8/Z3dXXFbB88eBAHDx5MaQwiIlpe\nixZ9Sr+Ojg6Mjo6ioKAAlZWVqK6uzvSUDIOxEIyFYCz007xOfzlZLBYYZCrLrrm5GU6nE0NDQzh2\n7Fimp0NEK0iqtZP33jEgRVEyPQXDYCwEYyEYC/1Y9ImITIRF34D27duX6SkYBmMhGAvBWOjHok9E\nZCIs+gbEfqVgLARjIRgL/Vj0iYhMhEXfgNivFIyFYCwEY6Efiz4RkYmw6BsQ+5WCsRCMhWAs9GPR\nJyIyERZ9A2K/UjAWgrEQjIV+LPpERCbCom9A7FcKxkIwFoKx0I9Fn4jIRFj0DYj9SsFYCMZCMBb6\nsegTEZkIi74BsV8pGAvBWAjGQj8WfSIiE2HRNyD2KwVjIRgLwVjox6JPRGQiLPoGxH6lYCwEYyEY\nC/1Y9ImITIRF34DYrxSMhWAsBGOhH4s+EZGJsOgbEPuVgrEQjIVgLPRj0SciMpFFi35nZyeKiopQ\nWFiI9vb2BY/r6enBli1bkJeXh7KyMty+fVt+iNUa+Vq7du3SzHwVY79SMBaCsRCMhX6aRT8UCqGq\nqgputxslJSWoqanB5ORkwmPHxsZQUVGBy5cvY2pqCocPH8bs7Gzk9StXrkBRFNy5c2dp3wERESVN\ns+j39PRgfHwc9fX1aGhowPT0NLq7uxMee+DAAfz2t7/FoUOHUF1djcnJyZhfENu3b0dpaSn27Nmz\ntO9gFWK/UjAWgrEQjIV+mkU/GAwCAFpaWnDx4sWYfQtRVRVtbW0oLS3FK6+8Etm/efNmFBQU4Pz5\n8y87ZyIi0impP+TW1tbi5MmTSQ14+vRp9Pb2oqWlJbKvqakJt27dwsGDB3Hq1Cl4vV59szUJ9isF\nYyEYC8FY6LdG60WHwwEAmJmZQVZWVsy+RN5//31cuHABn376KTZt2hTZf/z4cQAvzvY9Hg/6+/ux\nc+fOed9fXV0Nl8sFAMjPz8fWrVsjH274f+dW67bf78fIyEgkFpmeD7e5zW1jbiuKgkuXLgFApF6m\nwqKqqrrQi6FQCBs2bMDu3bths9nw8ccfY3h4GHa7HRUVFRgcHITP54tM6q233sKJEydw6NAhAMCu\nXbvw5ZdfQlEUOBwOtLW14S9/+Qvu37+Pbdu2xU7EYoHGVFa15uZmOJ1ODA0N4dixY1AUJfJhmx1j\nIRgLwViIVGun5pl+dnY2PB4PTpw4gbm5ObS2tsJutwMAJiYmMDY2Fjn27t27mJ2dRWNjIxobG2Gx\nWDAwMIBQKISGhgZ8+eWXePXVV9HU1DSv4BMR0fLQPNNfTjzTlzN9IqJkpVo7uSKXiMhEWPQNKPxH\nG2IsojEWgrHQj0WfiMhEWPQNiFclCMZCMBaCsdCPRZ+IyERY9A3E5/OhubkZv/rVr9DR0ZHp6RgC\ne7eCsRCMhX6a1+nT8pqamoLT6cSzZ88wOjqa6ekQ0SrEM30DcrvdmZ6CYbB3KxgLwVjox6JPRGQi\nLPoG5Pf7Mz0Fw2DvVjAWgrHQj0WfiMhEWPQNiD19wd6tYCwEY6Efiz4RkYmw6BsQe/qCvVvBWAjG\nQj8WfSIiE+HiLANyu90YGhoCAHR0dGB0dBQFBQWorKzM8MyWH3u3grEQjIV+PNM3uNHRUTidTq7Q\nJaIlwaJvQOzpC/ZuBWMhGAv9WPSJiEyERd+AeJ2+YO9WMBaCsdCPRZ+IyERY9A2IPX3B3q1gLARj\noR+LPhGRibDoGxB7+oK9W8FYCMZCPxZ9IiITYdE3IPb0BXu3grEQjIV+vA3DCmT2WzMQkX480zeg\nxXr6Zro1A3u3grEQjIV+LPpERCayaNHv7OxEUVERCgsL0d7evuBxPT092LJlC/Ly8lBWVobbt2+n\nPAa9wJ6+YO9WMBaCsdBPs+iHQiFUVVXB7XajpKQENTU1mJycTHjs2NgYKioqcPnyZUxNTeHw4cOY\nnZ1NaQwiIkovzT/k9vT0YHx8HPX19cjJyUFHRwe6u7tRVlY279gDBw7gwIEDAF70nO/du4fJyUn0\n9vYmPQa9EH0/fbNj71YwFoKx0E+z6AeDQQBAS0sLbDZbzL6FqKqKtrY2lJaW4pVXXtE1BhERpUdS\nl2zW1tYiLy8P586dW/TY06dPo7e3F//4xz9SHqO6uhoulwsAkJ+fj61bt0Z+o4d7eKt12+/3Y3h4\nOPLvkZGRmL5lIBCAoigxx4+MjERez/T807Ud3meU+WRy+8GDB/jlL39pmPlkcruxsdFU9SF6W1EU\nXLp0CQAi9TIlqobu7m7VYrGo/f396uPHj1WLxaLevHlzweNbW1tVm82m/v3vf095jEWmsqq99957\n6vXr19UjR46o169fV8+dO6e+9957Ma+Ftxfat1p1dXVlegqGwVgIxkKkWjs1z/R37NiB/Px81NXV\nwWazIScnJ/Kbp6KiAoODg/D5fJHfRG+//TZOnDiBb775BoqiYNeuXZpjUGLs6QvmimAsBGOhn2bR\nz87OhsfjwYkTJzA3N4fW1lbY7XYAwMTEBMbGxiLH3r17F7Ozs2hsbERjYyMsFgsGBgawcePGBccw\ns/CqWgDw+XxwOp0xr/t8PjQ3Nyd8jYhIr0Wv0y8vL8fAwAAeP34cs+S/q6sLjx49imyfOXMGc3Nz\nka/Z2Vls3LhRcwwzC6+qdTqdmJqainnN7/djamoq4WtmE93bNzvGQjAW+nFFLhGRibDoGxDvpy/Y\nuxWMhWAs9GPRJyIyERZ9A+K9dwR7t4KxEIyFfiz6REQmwqJvQOzpC/ZuBWMhGAv9WPSJiEyERd+A\n2NMX7N0KxkIwFvrxGblEBhK9UpvPQKZ04Jm+AbGnL8zWu41eqR3/DGSzxUILY6Efiz4RkYmw6BsQ\ne/qCvVvBWAjGQj8WfSIiE2HRNyD29AV7t4KxEIyFfiz6REQmwqJvQOzpC/ZuBWMhGAv9WPSJiEyE\nRd+A2NMX7N0KxkIwFvqx6BMRmQiLvgGxpy/YuxWMhWAs9GPRJyIyERZ9A2JPX7B3KxgLwVjox6JP\nRGQiLPoGxJ6+YO9WMBaCsdCPRZ+IyERY9A2IPX3B3q1gLARjoR+LPhGRibDoGxB7+oK9W8FYCMZC\nv0WLfmdnJ4qKilBYWIj29vYFj1NVFfv374fdbsf69etjf4jVGvlau3bty8+aiIh00Sz6oVAIVVVV\ncLvdKCkpQU1NDSYnJxMeq6oq7HY7du3aBYvFMu/1K1euQFEU3LlzZ2lmvoqxpy/YuxWMhWAs9NMs\n+j09PRgfH0d9fT0aGhowPT2N7u7uxANZrfjoo4+wZ88eqKo67/Xt27ejtLQUe/bsWZqZExFRyjSL\nfjAYBAC0tLTg4sWLMftStXnzZhQUFOD8+fO6vt9M2NMX7N0KxkIwFvqtSeag2tpa5OXl4dy5c7p+\nSFNTE4qLi/HnP/8Zp06dwt69e7Fz5855x1VXV8PlcgEA8vPzsXXr1sj/xoU/5NWyHQgE8OzZs0gr\nx+/3Y3h4OBKL4eHhmOIfCASgKErk+/1+P0ZGRiKvZ/r9pGt7tb+/hd5vos/3wYMHGZ+fUbYfPHhg\nqPks57aiKLh06RIAROplKixqol7M/7t37x727t2Lvr4+5ObmwuVy4caNGygrK1twwLNnz+Ldd9/F\n2NjYvNeGhobwgx/8AH/6059QU1MTOxGLJWFbaLVqbm6G0+kEAHzwwQc4cuRI5L+J9g0NDeHYsWMx\n3xu9j1aH6Lzg50vJSLV2ap7p79ixA/n5+airq4PNZkNOTk7kN09FRQUGBwfh8/kAANPT07h69Sp6\ne3vx/PlzXL58GVu2bMF3vvMdKIoCh8OBtrY2WK1WbNu2Tf87JCIi3TR7+tnZ2fB4POjr64PX60Vr\nayvsdjsAYGJiIuZs/unTpzh69CiuXbuGmZkZHD16FB9++CFCoRAaGhpQWVmJ+/fvo6mpiUV/Eezp\ni/i2h5kxFoKx0G/Rnn55eTnKy8vn7e/q6orZdrlcmJubSzhGIBDQOT0iIlpKXJFrQLxOX4TbicRY\nRGMs9GPRJyIyERZ9A2JPX7B3KxgLwVjol9R1+kSUXh0dHRgdHYXP54tcshn/WiAQwFdffYXKysrI\nvoKCAlRWVmZo1rQS8UzfgNjTF2bp3Y6OjsLpdGJqamrB1958802Mjo7G7Atvm41Z8iIdWPSJiEyE\nRd+A2NMX7N0K5oVgXujHok9EZCIs+gbEnr5g71YwLwTzQj8WfSIiE2HRNyD2bgV7t4J5IZgX+rHo\nExGZCIu+AbF3K9i7FcwLwbzQjytyl0k6VlD6fD40NzcDeHEn0+LiYq7QXGG0VuISpQPP9JdJKiso\nk+3dTk1Nwel0wul0IhgMrsoVmqu9d6u1Ejcee/pitedFOrHoExGZCIu+AbF3K9i7FcwLwbzQj0Wf\niMhEWPQNiL1bwd6tYF4I5oV+LPpERCbCom9A7N0K9m4F80IwL/Rj0SciMhEWfQNi71awdyuYF4J5\noR+LPhGRifA2DC8pvIweQOQWCFq3XAjfOkFr2b3b7UZfX1/C7wv/e6HvDR+3Wm7HwN6tcLvdGBoa\nyvQ0DIF5oR/P9F9SeBl99C0QtG65EL51QjLL7hN932LfGz5utd2OgYiWBou+AbF3K9i7FcwLwbzQ\nj0WfiMhEFi36nZ2dKCoqQmFhIdrb2xc8TlVV7N+/H3a7HevXr9c1Br3A67EFe7eCeSGYF/ppFv1Q\nKISqqiq43W6UlJSgpqYGk5OTCY9VVRV2ux27du2CxWLRNQYREaWXZtHv6enB+Pg46uvr0dDQgOnp\naXR3dyceyGrFRx99hD179kBVVV1j0Avs3Qr2bgXzQjAv9NO8ZDMYDAIAWlpaYLPZYvYlaynGICKi\npZHUdfq1tbXIy8vDuXPndP+gZMaorq6Gy+UCAOTn52Pr1q2R3l34N7vRtsP8fj9GRkYW3A4EAnj2\n7Flke3h4GH6/P9Kn9fv9GB4eBvCid/vXv/415sxO6/jo16N/nqIoGY8Pt7W3w+I/v0T5FC2cT9/9\n7ncN9X6Wazu8zyjzWc5tRVFw6dIlAIjUy1RY1OheTJx79+5h79696OvrQ25uLlwuF27cuIGysrIF\nBzx79izeffddjI2NpTSGxWKBxlQMq7m5ObJQamhoCMeOHYvsC29HH/fBBx/gyJEjkf8CSGpfqsdH\n/2wyLq28iM+nRPv4OVOqtVPzTH/Hjh3Iz89HXV0dbDYbcnJyIr95KioqMDg4CJ/PBwCYnp7G1atX\n0dvbi+fPn+Py5cvYsmULdu7cueAYlBh7tyL6bG4liF+NnWjFtl5+vz9yZh8WvVI7EAiguLg45udo\nrQ5fyVZaXhiJ5h9ys7Oz4fF40NfXB6/Xi9bWVtjtdgDAxMRE5GweAJ4+fYqjR4/i2rVrmJmZwdGj\nR/Hhhx9i3bp1C45BtNrEr8ZOtGJ7KUWv1A4Gg/N+jtbqcDKnRXv65eXlKC8vn7e/q6srZtvlcmFu\nbi6lMSixRPfeMSuezQnee0cwL/TjilwiIhNh0Tcg9vRF/FUuZsa8EMwL/Vj0iYhMhEXfgHiPFcHe\nrWBeCOaFfiz6REQmwqJvQOzdCvZuBfNCMC/0Y9EnIjIRPiPXgHidvsh071bvitZEz0J+2ecXL8V1\n+ku5QjiTMp0XKxnP9Ik06F3RmuhZyEZ4fnG6VwiT8bHoGxB7t4K9W8G8EMwL/Vj0iYhMhEXfgHg9\ntmDvVjAvBPNCPxZ9IiITYdE3IPZuBXu3gnkhmBf6segTEZkIi74BsXcr2LsVzAvBvNCPRZ+IyERY\n9A2IvVvB3q1gXgjmhX68DcMSil96H/3Q6ujl+LTyRH+W8Q8913uLhvC/lzovmHekhWf6Syh+6X30\nQ6ujl+Mvhr1bYZTebfRnGf/Qc723aEhXXugdfyUxSl6sRCz6REQmwqJvQOzdCvZuBfNCMC/0Y9En\nIjIRFn0DYk9fsHcrmBeCeaEfiz4RkYmw6BsQe7eCvVvBvBDMC/1Y9ImITIRF34DYuxXs3QrmhWBe\n6LfoitzOzk784he/wNzcHBoaGvCzn/0s5WOtVvndkpWVhefPny/B1DMrvBpzJa14XC0PxU636Dgl\n+nwTPfTcDPSuQCZj0TzTD4VCqKqqgtvtRklJCWpqajA5OZn0sdGrAa9cuQJFUXDnzp2lfQcZEl6N\nmY4Vj+nq3a7Eh2JnoncbHadEn2+ih54vh0z39PWuQE4H9vT10zzT7+npwfj4OOrr65GTk4OOjg50\nd3ejrKwsqWPv3r0bOXb79u0oKipKz7sgIqKkaBb9YDAIAGhpaYHNZovZl+qxmzdvRm5uLurq6vDr\nX//65We+irndbvT19WV6GobA3q1wu90YGhrK9DQMgXmhX1J/yK2trcXJkyeTGjDRsU1NTbh16xYO\nHjyIU6dOwev1pj5TIiJ6aZpn+g6HAwAwMzODrKysmH2pHHv8+HEAL872PR4P+vv7sXPnznljVFdX\nw+VyAQDy8/OxdevWyG/0cA/PKNuBQADPnj2LzN3v92N4eHjB7eHh4ZiebHg7fEVG9PHhfyd7fKLx\nA4EAFEWJmX8gEIj84THR68sZv2S3w/uW++fH988Xi398jzn+80iUH4sdPzIyErP96NEj/PjHPwaQ\nXP5FC3/eC42fTHyi8yfT+dHY2Gjo+pDObUVRcOnSJQCI1MtUaBb9HTt2ID8/H3V1dbDZbMjJyYlM\noqKiAoODg/D5fJrHPnz4EIqiwOFwoK2tDVarFdu2bUv488JvJJH4/53L9HZxcTGcTmekDRPfkonf\n/t73vhezL7ydruOLi4tj5rxv3z7861//ipl//OsvE490bccn/3L9/HCsk41/+PvDMY7/PBLlR6Lx\no4+PbuXEX66ZTP5Fjx/+vMPzix8/mfhE50+m8yO64BthPsu5vW/fvpjtd955B6nQbO9kZ2fD4/Gg\nr68PXq8Xra2tsNvtAICJiQmMjY0temwoFEJDQwMqKytx//59NDU1LVj06QVejy3ik9/MmBeCeaHf\notfpl5eXo7y8fN7+rq6upI597bXXEAgEXmKKRES0VLgi14AyfT22kfB6bMG8EMwL/Vj0iYhMhA9G\nN6CluE4/0YO8EzH60vqV0LtN14PI42/38DLX6WvdOiLR7TnC+wKBAIqLiyNjOJ3OyFjRObPcebQS\n8sKoeKa/SiV6kHciRlpav1Kl60HkS3m7B62xEt2eI7wvGAzOe2/hsaJzhnm0crDoGxB7t4K9W8G8\nEMwL/Vj0iYhMhEXfgHg9tmDvVjAvBPNCPxZ9IiITYdE3IPZuBXu3gnkhmBf6segTEZkIi74BsXcr\n2LsVzAvBvNCPRZ+IyES4IjcJWqsT02Gpe7eJVmMms0LTCKt0o+/5n6z4z2ux9xF/vFEfeO73+zEw\nMLBkD2VPNS9SGRNAJJ7Jfg7xEq0UDtOTF/QCz/SToLU6cSVItBozmRWaK3V1Zfzntdj7iD/eyJ9t\nulfpvuz40auTw/FM9nOIl2ilML08Fn0DYu9W8GxOMC8E80I/Fn0iIhNh0TcgXo8teD22YF4I5oV+\nLPpERCbCom9A7N0K9m4F80IwL/Rj0SciMhEWfQNi71awdyuYF4J5oR8XZ8X5+uuvoaoqsrKysG7d\nukxPh4hoSbHoRwkGg7h27RoAwG634+c//zms1uX/n6GleEZuJmmtpNQzxldffRUzRqrja60SDb9u\nxBW48YyWF6k+G3ixz0Hrs4x/Lm90T99IK8hXArZ3oszOzgIAXn31VUxPT0NV1QzPaGVaipWUWmOk\nOr7WKlGjr8A1slSfDbzY56D1WSZ6Lm/YSl9BvtxY9A2IvVvBWAjGQrCnrx+LPhGRibDoGxCvxxaM\nhWAsBK/T149Fn4jIRBYt+p2dnSgqKkJhYSHa29t1HZvKGMTebTTGQjAWgj19/TSLfigUQlVVFdxu\nN0pKSlBTU4PJycmkj52amkppDHrh0aNHmZ6CYTAWgrEQDx48yPQUVizNot/T04Px8XHU19ejoaEB\n09PT6O7uTvrYu3fvpjQGvcBLCAVjIRgLMT4+nukprFiai7OCwSAAoKWlBTabLWZfsseGEzWZMYiI\nKL2S+kNubW0tTp48mdSACx2byhiZYrFY8O233+LJkyfIysrK2DyePn2asZ9tNIyFYCzEF198kekp\nrFyqhu7ubtVisaj9/f3q48ePVYvFot68eTOlY+/du5fUGJs2bVIB8Itf/OIXv1L42rRpk1YZn8ei\natxrIBQKYcOGDdi9ezdsNhs+/vhjDA8Pw263o6KiAoODg/D5fJrHZmVlLTgGEREtL832TnZ2Njwe\nD/r6+uD1etHa2hop1hMTExgbG1v0WK0xiIhoeWme6RMR0epimBW5Vqs18rV27dpMTydjuJBNmDkn\nVFXF/v37YbfbsX79+sh+M+bHQrEwW3709PRgy5YtyMvLQ1lZGW7fvg0g9ZwwzJm+1WrFlStX4HA4\nYLFYsGfPnkxPadmF/y7yxhtvwGaz4ebNmwgGg8jNzc301DLCzDkxNzeHn/70p/jf//6Hzz77DM+e\nPTNtfiSKBWC+/Pjkk0/wt7/9Ddu3b8fvf/97fPbZZxgZGYHD4UgtJ1L6s28aWSwW9dGjR5meRkaF\nr4D65z//uejVUmbAnFDVM2fOqPn5+aqqMj+iY6Gq5s6P1tZW1WKxRK6OTCUnDNPeAYDNmzejoKAA\n58+fz/RUMiJ6gdvFixdj9pmV2XMiGvNjPjPmh6qqaGtrQ2lpqa6cWPaiX11dHdOLs1qtePPNN9HU\n1IRbt27h4MGDOHXqFLxe73JPzTBWwkK25cCcSIz58YJZ8+P06dPo7e1FS0tL5Ol+qeTEsj8j93e/\n+x1+85vfxOyz2+34/ve/D+DFb26Px4P+/n7s3LlzuaeXUQ6HAwAwMzMTWREc3mdGx48fB2DunIjG\n/Ihlxvx4//33ceHCBXz66afYtGkTnjx5AiC1nFj2ou9wOOZN6uHDh7h48SIcDgfa2tpgtVqxbdu2\n5Z5axu3YsQP5+fmoq6uDzWZDTk6OaR8W8fDhQyiKYtqcmJ6extWrV9Hb24vnz5/j8uXLeO2110yZ\nH4li4Xa74fV6TZUfiqLg7bffxokTJ/DNN99AURS8/vrrqefEsvzVYRF+v1/90Y9+pGZnZ6tFRUVq\nc3NzpqeUMZ2dnarL5VI3btyotre3Z3o6GWP2nBgYGFAtFotqtVojX++8844p8yNRLA4fPmy6/Dh7\n9qxqsVgiX1arVX38+HHKOWGYSzaJiCj9DHX1DhERpReLPhGRibDoExGZCIs+EZGJsOgTEZkIiz4R\nkYmw6BMRmQiLPhGRifwfwWW4vJ0M7s8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x118dc1550>"
       ]
      }
     ],
     "prompt_number": 212
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### values.hist \ud568\uc218 \uc778\uc790\n",
      "\n",
      "- bins: \uba87 \uac1c\ub97c \ubcf4\uc5ec\uc904\uc9c0\n",
      "- alpha: \ud22c\uba85\ub3c4\n",
      "- normed: [\uc815\uaddc\ud654](http://adnoctum.tistory.com/184): \ub9ce\uc740 \uc591\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud568\uc5d0 \uc788\uc5b4 \uc5ec\ub7ec \uc774\uc720\ub85c \uc815\uaddc\ud654, \uc989 \ub370\uc774\ud130\uc758 \ubc94\uc704\ub97c \uc77c\uce58\uc2dc\ud0a4\uac70\ub098 \ubd84\ud3ec\ub97c \uc720\uc0ac\ud558\uac8c \ub9cc\ub4e4\uc5b4\uc8fc\ub294 \ub4f1\uc758 \uc791\uc5c5\uc740 \uaf2d \ud544\uc694\ud55c \uc77c\uc774\ub2e4.\n",
      "- values.hist? \ud588\ub294\ub370 \uc5b4\ub5a4 \uc778\uc790\ub97c \ub118\uaca8\uc8fc\ub294\uc9c0 \uc548\ub098\uc624\ub124..?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values.hist(bins=100, alpha=0.8, color='k', normed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 221,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1197bc4d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115fa6a90>"
       ]
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values.hist(bins=100, alpha=0.8, color='k', normed=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 222,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x11ad4c550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhDgAhNj/AW246FsqdiBJAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1197e0e50>"
       ]
      }
     ],
     "prompt_number": 222
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values.plot(kind='kde', style='k--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 210,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x116539650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQohJqGAQQggxyf8BNbvmVK3o/CYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x116517810>"
       ]
      }
     ],
     "prompt_number": 210
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "-----\n",
      "\n",
      "#### \ud55c plot\uc5d0 \uc5f0\uc18d\uc801\uc73c\ub85c \uadf8\ub9ac\uae30 \uc704\ud574\uc11c \uc8fc\ub294 \uba85\ub839\uc774 \uc788\ub358\uac78\ub85c \uae30\uc5b5\ub098\ub294\ub370..\n",
      "\n",
      "- \ubb54\uc9c0 \ucc3e\uc544\ubd10\uc57c\uaca0\ub2e4.\n",
      "- \uc9c0\uae08 notebook\uc5d0\uc11c\ub294 \ud55c \uac1c\uc758 \uba85\ub839\ucc3d\uc5d0 \uc785\ub825\ud574\uc57c \ub3d9\uc77c\ud55c plot\uc5d0 \uc785\ub825\ud558\ub77c\uace0 \uc778\uc2dd\ub418\ub124\n",
      "\n",
      "------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values.hist(bins=100, alpha=0.3, color='k', normed=True)\n",
      "values.plot(kind='kde', style='k--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 213,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1194bfc90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1194abb50>"
       ]
      }
     ],
     "prompt_number": 213
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.2.4 \uc0b0\ud3ec\ub3c4\n",
      "\n",
      "- \uc0b0\ud3ec\ub3c4\ub294 2\uac1c\uc758 1\ucc28\uc6d0 \ub370\uc774\ud130 \ubb36\uc74c \uac04\uc758 \uad00\uacc4\ub97c \ub098\ud0c0\ub0b4\uace0\uc790 \ud560 \ub54c \uc720\uc6a9\ud55c \uadf8\ub798\ud504\n",
      "- matplotlib\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 scatter \uba54\uc11c\ub4dc\ub97c \uc774\uc6a9\ud558\uc5ec \uc0b0\ud3ec\ub3c4 \uadf8\ub9b4 \uc218 \uc788\ub2e4\n",
      "- [Statsmodel \ud504\ub85c\uc81d\ud2b8](http://statsmodels.sourceforge.net/)\uc5d0\uc11c macrodata \ub370\uc774\ud130 \ubb36\uc74c\uc744 \ubd88\ub7ec\uc628 \ub2e4\uc74c **\uba87 \uac00\uc9c0 \ubcc0\uc218\ub97c \uc120\ud0dd**\ud558\uace0 **\ub85c\uadf8\ucc28**\ub97c \uad6c\ud574\ubcf4\uc790\n",
      "\n",
      "#### [\uc790\ub8cc\uc758 \ud0d0\uc0c9 \ubc0f \uc815\uaddc\uc131 \uac80\uc815](http://dermabae.tistory.com/148): \ud1b5\uacc4\ub294 \ub108\ubb34 \uc5b4\ub824\uc6cc. \uc0b0\ud3ec\ub3c4 \uc27d\uac8c \uc124\uba85\ud55c \uae00\n",
      "#### [14. \uc790\ub8cc\uc758 \ud769\uc5b4\uc9c4 \uc815\ub3c4\ub97c \ub098\ud0c0\ub0b4\ub294 \uc0b0\ud3ec\ub3c4\ub97c \ubaa8\ub978\ub2e4?](http://classroom.re.kr/uploadfile/content/content2013/mis_error_study/%EC%A4%91%EB%93%B1%EC%88%98%ED%95%99/%ED%99%95%EB%A5%A0%EA%B3%BC%ED%86%B5%EA%B3%84/%ED%99%95%EB%A5%A0%EA%B3%BC%ED%86%B5%EA%B3%844-14.pdf)\n",
      "\n",
      "#### Statsmodel Project\n",
      "\n",
      "Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. Researchers across fields may find that statsmodels fully meets their needs for statistical computing and data analysis in Python. Features include:\n",
      "\n",
      "- Linear regression models\n",
      "- Generalized linear models\n",
      "- Discrete choice models\n",
      "- Robust linear models\n",
      "- Many models and functions for time series analysis\n",
      "- Nonparametric estimators\n",
      "- A collection of datasets for examples\n",
      "- A wide range of statistical tests\n",
      "- Input-output tools for producing tables in a number of formats (Text, LaTex, HTML) and for reading Stata files into NumPy and Pandas.\n",
      "- Plotting functions\n",
      "- Extensive unit tests to ensure correctness of results\n",
      "- Many more models and extensions in development\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "macro = pd.read_csv('ch08/macrodata.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 224
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "macro"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 203 entries, 0 to 202\n",
        "Data columns (total 14 columns):\n",
        "year        203  non-null values\n",
        "quarter     203  non-null values\n",
        "realgdp     203  non-null values\n",
        "realcons    203  non-null values\n",
        "realinv     203  non-null values\n",
        "realgovt    203  non-null values\n",
        "realdpi     203  non-null values\n",
        "cpi         203  non-null values\n",
        "m1          203  non-null values\n",
        "tbilrate    203  non-null values\n",
        "unemp       203  non-null values\n",
        "pop         203  non-null values\n",
        "infl        203  non-null values\n",
        "realint     203  non-null values\n",
        "dtypes: float64(14)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 225,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 203 entries, 0 to 202\n",
        "Data columns (total 14 columns):\n",
        "year        203  non-null values\n",
        "quarter     203  non-null values\n",
        "realgdp     203  non-null values\n",
        "realcons    203  non-null values\n",
        "realinv     203  non-null values\n",
        "realgovt    203  non-null values\n",
        "realdpi     203  non-null values\n",
        "cpi         203  non-null values\n",
        "m1          203  non-null values\n",
        "tbilrate    203  non-null values\n",
        "unemp       203  non-null values\n",
        "pop         203  non-null values\n",
        "infl        203  non-null values\n",
        "realint     203  non-null values\n",
        "dtypes: float64(14)"
       ]
      }
     ],
     "prompt_number": 225
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "macro[:10]"
     ],
     "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>quarter</th>\n",
        "      <th>realgdp</th>\n",
        "      <th>realcons</th>\n",
        "      <th>realinv</th>\n",
        "      <th>realgovt</th>\n",
        "      <th>realdpi</th>\n",
        "      <th>cpi</th>\n",
        "      <th>m1</th>\n",
        "      <th>tbilrate</th>\n",
        "      <th>unemp</th>\n",
        "      <th>pop</th>\n",
        "      <th>infl</th>\n",
        "      <th>realint</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1959</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2710.349</td>\n",
        "      <td> 1707.4</td>\n",
        "      <td> 286.898</td>\n",
        "      <td> 470.045</td>\n",
        "      <td> 1886.9</td>\n",
        "      <td> 28.98</td>\n",
        "      <td> 139.7</td>\n",
        "      <td> 2.82</td>\n",
        "      <td> 5.8</td>\n",
        "      <td> 177.146</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1959</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2778.801</td>\n",
        "      <td> 1733.7</td>\n",
        "      <td> 310.859</td>\n",
        "      <td> 481.301</td>\n",
        "      <td> 1919.7</td>\n",
        "      <td> 29.15</td>\n",
        "      <td> 141.7</td>\n",
        "      <td> 3.08</td>\n",
        "      <td> 5.1</td>\n",
        "      <td> 177.830</td>\n",
        "      <td> 2.34</td>\n",
        "      <td> 0.74</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1959</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2775.488</td>\n",
        "      <td> 1751.8</td>\n",
        "      <td> 289.226</td>\n",
        "      <td> 491.260</td>\n",
        "      <td> 1916.4</td>\n",
        "      <td> 29.35</td>\n",
        "      <td> 140.5</td>\n",
        "      <td> 3.82</td>\n",
        "      <td> 5.3</td>\n",
        "      <td> 178.657</td>\n",
        "      <td> 2.74</td>\n",
        "      <td> 1.09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1959</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2785.204</td>\n",
        "      <td> 1753.7</td>\n",
        "      <td> 299.356</td>\n",
        "      <td> 484.052</td>\n",
        "      <td> 1931.3</td>\n",
        "      <td> 29.37</td>\n",
        "      <td> 140.0</td>\n",
        "      <td> 4.33</td>\n",
        "      <td> 5.6</td>\n",
        "      <td> 179.386</td>\n",
        "      <td> 0.27</td>\n",
        "      <td> 4.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1960</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2847.699</td>\n",
        "      <td> 1770.5</td>\n",
        "      <td> 331.722</td>\n",
        "      <td> 462.199</td>\n",
        "      <td> 1955.5</td>\n",
        "      <td> 29.54</td>\n",
        "      <td> 139.6</td>\n",
        "      <td> 3.50</td>\n",
        "      <td> 5.2</td>\n",
        "      <td> 180.007</td>\n",
        "      <td> 2.31</td>\n",
        "      <td> 1.19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 1960</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2834.390</td>\n",
        "      <td> 1792.9</td>\n",
        "      <td> 298.152</td>\n",
        "      <td> 460.400</td>\n",
        "      <td> 1966.1</td>\n",
        "      <td> 29.55</td>\n",
        "      <td> 140.2</td>\n",
        "      <td> 2.68</td>\n",
        "      <td> 5.2</td>\n",
        "      <td> 180.671</td>\n",
        "      <td> 0.14</td>\n",
        "      <td> 2.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 1960</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2839.022</td>\n",
        "      <td> 1785.8</td>\n",
        "      <td> 296.375</td>\n",
        "      <td> 474.676</td>\n",
        "      <td> 1967.8</td>\n",
        "      <td> 29.75</td>\n",
        "      <td> 140.9</td>\n",
        "      <td> 2.36</td>\n",
        "      <td> 5.6</td>\n",
        "      <td> 181.528</td>\n",
        "      <td> 2.70</td>\n",
        "      <td>-0.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 1960</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2802.616</td>\n",
        "      <td> 1788.2</td>\n",
        "      <td> 259.764</td>\n",
        "      <td> 476.434</td>\n",
        "      <td> 1966.6</td>\n",
        "      <td> 29.84</td>\n",
        "      <td> 141.1</td>\n",
        "      <td> 2.29</td>\n",
        "      <td> 6.3</td>\n",
        "      <td> 182.287</td>\n",
        "      <td> 1.21</td>\n",
        "      <td> 1.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 1961</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2819.264</td>\n",
        "      <td> 1787.7</td>\n",
        "      <td> 266.405</td>\n",
        "      <td> 475.854</td>\n",
        "      <td> 1984.5</td>\n",
        "      <td> 29.81</td>\n",
        "      <td> 142.1</td>\n",
        "      <td> 2.37</td>\n",
        "      <td> 6.8</td>\n",
        "      <td> 182.992</td>\n",
        "      <td>-0.40</td>\n",
        "      <td> 2.77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> 1961</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2872.005</td>\n",
        "      <td> 1814.3</td>\n",
        "      <td> 286.246</td>\n",
        "      <td> 480.328</td>\n",
        "      <td> 2014.4</td>\n",
        "      <td> 29.92</td>\n",
        "      <td> 142.9</td>\n",
        "      <td> 2.29</td>\n",
        "      <td> 7.0</td>\n",
        "      <td> 183.691</td>\n",
        "      <td> 1.47</td>\n",
        "      <td> 0.81</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 226,
       "text": [
        "   year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
        "0  1959        1  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
        "1  1959        2  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
        "2  1959        3  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
        "3  1959        4  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
        "4  1960        1  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
        "5  1960        2  2834.390    1792.9  298.152   460.400   1966.1  29.55   \n",
        "6  1960        3  2839.022    1785.8  296.375   474.676   1967.8  29.75   \n",
        "7  1960        4  2802.616    1788.2  259.764   476.434   1966.6  29.84   \n",
        "8  1961        1  2819.264    1787.7  266.405   475.854   1984.5  29.81   \n",
        "9  1961        2  2872.005    1814.3  286.246   480.328   2014.4  29.92   \n",
        "\n",
        "      m1  tbilrate  unemp      pop  infl  realint  \n",
        "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
        "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
        "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
        "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
        "4  139.6      3.50    5.2  180.007  2.31     1.19  \n",
        "5  140.2      2.68    5.2  180.671  0.14     2.55  \n",
        "6  140.9      2.36    5.6  181.528  2.70    -0.34  \n",
        "7  141.1      2.29    6.3  182.287  1.21     1.08  \n",
        "8  142.1      2.37    6.8  182.992 -0.40     2.77  \n",
        "9  142.9      2.29    7.0  183.691  1.47     0.81  "
       ]
      }
     ],
     "prompt_number": 226
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cpi</th>\n",
        "      <th>m1</th>\n",
        "      <th>tbilrate</th>\n",
        "      <th>unemp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>198</th>\n",
        "      <td> 216.889</td>\n",
        "      <td> 1474.7</td>\n",
        "      <td> 1.17</td>\n",
        "      <td> 6.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>199</th>\n",
        "      <td> 212.174</td>\n",
        "      <td> 1576.5</td>\n",
        "      <td> 0.12</td>\n",
        "      <td> 6.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>200</th>\n",
        "      <td> 212.671</td>\n",
        "      <td> 1592.8</td>\n",
        "      <td> 0.22</td>\n",
        "      <td> 8.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201</th>\n",
        "      <td> 214.469</td>\n",
        "      <td> 1653.6</td>\n",
        "      <td> 0.18</td>\n",
        "      <td> 9.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>202</th>\n",
        "      <td> 216.385</td>\n",
        "      <td> 1673.9</td>\n",
        "      <td> 0.12</td>\n",
        "      <td> 9.6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 231,
       "text": [
        "         cpi      m1  tbilrate  unemp\n",
        "198  216.889  1474.7      1.17    6.0\n",
        "199  212.174  1576.5      0.12    6.9\n",
        "200  212.671  1592.8      0.22    8.1\n",
        "201  214.469  1653.6      0.18    9.2\n",
        "202  216.385  1673.9      0.12    9.6"
       ]
      }
     ],
     "prompt_number": 231
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# data\uc5d0 log\ub97c \ucde8\ud558\uace0 diff\n",
      "trans_data = np.log(data).diff().dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 228
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(trans_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 240,
       "text": [
        "202"
       ]
      }
     ],
     "prompt_number": 240
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# NA\uac12\uc774 1\uac1c \uc788\ub124. 203 - 202 = 1\n",
      "len(np.log(data).diff())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 241,
       "text": [
        "203"
       ]
      }
     ],
     "prompt_number": 241
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# log \ucde8\ud558\uba74 216.889 -> 5.3793\n",
      "np.log(216.889)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 245,
       "text": [
        "5.3793857019317688"
       ]
      }
     ],
     "prompt_number": 245
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# diff: \uc55e\uc758 \uc22b\uc790\uc640\uc758 \ucc28\uc774\n",
      "# \uc544..\uc601\uc5b4 \ud574\uc11d\uc774 \uc548\ub418\ub2c8 \ud798\ub4e4\uad6c\ub9cc\n",
      "# \uc608\uc81c input \uac12\uacfc output \uac12 \ud14c\uc2a4\ud2b8 \ud574\ubcf4\uace0 \uc54c\uc558\ub2e4.\n",
      "np.log(data).diff?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 246
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "    Type:        function\n",
      "    String form: <function diff at 0x1027d0578>\n",
      "    File:        /Library/Python/2.7/site-packages/numpy-1.9.0.dev_c50e60d-py2.7-macosx-10.8-x86_64.egg/numpy/lib/function_base.py\n",
      "    Definition:  diff(a, n=1, axis=-1)\n",
      "    Docstring:\n",
      "    Calculate the n-th order discrete difference along given axis.\n",
      "\n",
      "    The first order difference is given by ``out[n] = a[n+1] - a[n]`` along\n",
      "    the given axis, higher order differences are calculated by using `diff`\n",
      "    recursively.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    a : array_like\n",
      "        Input array\n",
      "    n : int, optional\n",
      "        The number of times values are differenced.\n",
      "    axis : int, optional\n",
      "        The axis along which the difference is taken, default is the last axis.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    diff : ndarray\n",
      "        The `n` order differences. The shape of the output is the same as `a`\n",
      "        except along `axis` where the dimension is smaller by `n`.\n",
      "\n",
      "    See Also\n",
      "    --------\n",
      "    gradient, ediff1d, cumsum\n",
      "\n",
      "    Examples\n",
      "    --------\n",
      "    >>> x = np.array([1, 2, 4, 7, 0])\n",
      "    >>> np.diff(x)\n",
      "    array([ 1,  2,  3, -7])\n",
      "    >>> np.diff(x, n=2)\n",
      "    array([  1,   1, -10])\n",
      "\n",
      "    >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])\n",
      "    >>> np.diff(x)\n",
      "    array([[2, 3, 4],\n",
      "           [5, 1, 2]])\n",
      "    >>> np.diff(x, axis=0)\n",
      "    array([[-1,  2,  0, -2]])"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.log(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",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cpi</th>\n",
        "      <th>m1</th>\n",
        "      <th>tbilrate</th>\n",
        "      <th>unemp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>198</th>\n",
        "      <td> 5.379386</td>\n",
        "      <td> 7.296210</td>\n",
        "      <td> 0.157004</td>\n",
        "      <td> 1.791759</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>199</th>\n",
        "      <td> 5.357407</td>\n",
        "      <td> 7.362962</td>\n",
        "      <td>-2.120264</td>\n",
        "      <td> 1.931521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>200</th>\n",
        "      <td> 5.359746</td>\n",
        "      <td> 7.373249</td>\n",
        "      <td>-1.514128</td>\n",
        "      <td> 2.091864</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201</th>\n",
        "      <td> 5.368165</td>\n",
        "      <td> 7.410710</td>\n",
        "      <td>-1.714798</td>\n",
        "      <td> 2.219203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>202</th>\n",
        "      <td> 5.377059</td>\n",
        "      <td> 7.422912</td>\n",
        "      <td>-2.120264</td>\n",
        "      <td> 2.261763</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 233,
       "text": [
        "          cpi        m1  tbilrate     unemp\n",
        "198  5.379386  7.296210  0.157004  1.791759\n",
        "199  5.357407  7.362962 -2.120264  1.931521\n",
        "200  5.359746  7.373249 -1.514128  2.091864\n",
        "201  5.368165  7.410710 -1.714798  2.219203\n",
        "202  5.377059  7.422912 -2.120264  2.261763"
       ]
      }
     ],
     "prompt_number": 233
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.log(data).diff()[-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",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cpi</th>\n",
        "      <th>m1</th>\n",
        "      <th>tbilrate</th>\n",
        "      <th>unemp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>198</th>\n",
        "      <td>-0.007904</td>\n",
        "      <td> 0.045361</td>\n",
        "      <td>-0.396881</td>\n",
        "      <td> 0.105361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>199</th>\n",
        "      <td>-0.021979</td>\n",
        "      <td> 0.066753</td>\n",
        "      <td>-2.277267</td>\n",
        "      <td> 0.139762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>200</th>\n",
        "      <td> 0.002340</td>\n",
        "      <td> 0.010286</td>\n",
        "      <td> 0.606136</td>\n",
        "      <td> 0.160343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201</th>\n",
        "      <td> 0.008419</td>\n",
        "      <td> 0.037461</td>\n",
        "      <td>-0.200671</td>\n",
        "      <td> 0.127339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>202</th>\n",
        "      <td> 0.008894</td>\n",
        "      <td> 0.012202</td>\n",
        "      <td>-0.405465</td>\n",
        "      <td> 0.042560</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 235,
       "text": [
        "          cpi        m1  tbilrate     unemp\n",
        "198 -0.007904  0.045361 -0.396881  0.105361\n",
        "199 -0.021979  0.066753 -2.277267  0.139762\n",
        "200  0.002340  0.010286  0.606136  0.160343\n",
        "201  0.008419  0.037461 -0.200671  0.127339\n",
        "202  0.008894  0.012202 -0.405465  0.042560"
       ]
      }
     ],
     "prompt_number": 235
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trans_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",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>cpi</th>\n",
        "      <th>m1</th>\n",
        "      <th>tbilrate</th>\n",
        "      <th>unemp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>198</th>\n",
        "      <td>-0.007904</td>\n",
        "      <td> 0.045361</td>\n",
        "      <td>-0.396881</td>\n",
        "      <td> 0.105361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>199</th>\n",
        "      <td>-0.021979</td>\n",
        "      <td> 0.066753</td>\n",
        "      <td>-2.277267</td>\n",
        "      <td> 0.139762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>200</th>\n",
        "      <td> 0.002340</td>\n",
        "      <td> 0.010286</td>\n",
        "      <td> 0.606136</td>\n",
        "      <td> 0.160343</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201</th>\n",
        "      <td> 0.008419</td>\n",
        "      <td> 0.037461</td>\n",
        "      <td>-0.200671</td>\n",
        "      <td> 0.127339</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>202</th>\n",
        "      <td> 0.008894</td>\n",
        "      <td> 0.012202</td>\n",
        "      <td>-0.405465</td>\n",
        "      <td> 0.042560</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 229,
       "text": [
        "          cpi        m1  tbilrate     unemp\n",
        "198 -0.007904  0.045361 -0.396881  0.105361\n",
        "199 -0.021979  0.066753 -2.277267  0.139762\n",
        "200  0.002340  0.010286  0.606136  0.160343\n",
        "201  0.008419  0.037461 -0.200671  0.127339\n",
        "202  0.008894  0.012202 -0.405465  0.042560"
       ]
      }
     ],
     "prompt_number": 229
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uac04\ub2e8\ud55c \uc0b0\ud3ec\ub3c4\ub294 plt.scatter \uba54\uc18c\ub4dc \uc774\uc6a9\ud574\uc11c \uadf8\ub9b4 \uc218 \uc788\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(trans_data['m1'], trans_data['unemp'])\n",
      "plt.title('Change in log %s vs. log %s' % ('m1', 'unemp'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 249,
       "text": [
        "<matplotlib.text.Text at 0x11b92c850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEKCAYAAADn+anLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11b3da190>"
       ]
      }
     ],
     "prompt_number": 249
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Make a scatter plot of x vs y, where x and y are sequence like objects\n",
      "# of the same lengths.\n",
      "plt.scatter?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 250
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- **\ud0d0\uc0c9 \ub370\uc774\ud130 \ubd84\uc11d**\uc5d0\uc11c\ub294 **\ubcc0\uc218\uc758 \uadf8\ub8f9 \uac04 \ubaa8\ub4e0 \uc0b0\ud3ec\ub3c4\ub97c \uc0b4\ud3b4\ubcf4\ub294 \uc77c**\uc774 \ub9e4\uc6b0 \uc720\uc6a9\ud558\ub2e4.\n",
      "- \uc774\ub294 **\uc9dd\uc9c0\uc740 \uadf8\ub798\ud504** \ub610\ub294 **\uc0b0\ud3ec\ub3c4 \ud589\ub82c**\uc774\ub77c\uace0 \ubd80\ub984\n",
      "- \uc774\ub7f0 \uadf8\ub798\ud504\ub97c \uc9c1\uc811 \uadf8\ub9ac\ub294 \uacfc\uc815\uc740 \ub2e4\uc18c \ubcf5\uc7a1\n",
      "- pandas\uc5d0\ub294 **scatter_matrix** \ud568\uc218\uac00 \uc788\uc5b4 DataFrame \uac1d\uccb4\ub85c\ubd80\ud130 \uc0b0\ud3ec\ub3c4 \ud589\ub82c\uc744 \uac04\ub2e8\ud788 \uc0dd\uc131\n",
      "- \ub610\ud55c pandas\ub294 **\ub300\uac01\uc120\uc744 \ub530\ub77c \uac01 \ubcc0\uc218\uc5d0 \ub300\ud55c \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc774\ub098 \ubc00\ub3c4 \uadf8\ub798\ud504\ub3c4 \uc0dd\uc131** \uac00\ub2a5"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.scatter_matrix(trans_data, diagonal='kde', color='k', alpha=0.3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 251,
       "text": [
        "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11b9a8350>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b9cdd90>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b9719d0>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b98eed0>],\n",
        "       [<matplotlib.axes._subplots.AxesSubplot object at 0x115554a10>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x115578f50>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bd7e410>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bda1950>],\n",
        "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11bd8ca10>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bde15d0>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bf1f990>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bf3d4d0>],\n",
        "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11bf5fc90>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bf715d0>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bfa0fd0>,\n",
        "        <matplotlib.axes._subplots.AxesSubplot object at 0x11bfc73d0>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEcCAYAAADUX4MJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11b39a490>"
       ]
      }
     ],
     "prompt_number": 251
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 8.3 \uc9c0\ub3c4 \uadf8\ub9ac\uae30: \uc544\uc774\ud2f0 \uc9c0\uc9c4 \ub370\uc774\ud130 \uc2dc\uac01\ud654\ud558\uae30\n",
      "\n",
      "- [\uc6b0\uc0e4\ud788\ub514(Ushahidi)](http://ushahidi.com/)\ub294 \ube44\uc601\ub9ac \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uae30\uc5c5\n",
      "- [Ushahidi - Wiki](http://en.wikipedia.org/wiki/Ushahidi)\n",
      "- \ubb38\uc790 \uba54\uc2dc\uc9c0\ub97c \ud1b5\ud574 \uc790\uc5f0\uc7ac\ud574\ub098 \uc9c0\ub9ac\uc801\uc778 \uc0ac\uac74\uacfc \uad00\ub828\ub41c \uc815\ubcf4\ub97c \ud06c\ub77c\uc6b0\ub4dc \uc18c\uc2f1\ud558\uace0 \uc774\ub807\uac8c \ubaa8\uc778 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \uc6f9 \uc0ac\uc774\ud2b8\ub97c \ud1b5\ud574 \uacf5\uac1c\ud558\uc5ec \ubd84\uc11d\uc774\ub098 \uc2dc\uac01\ud654\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\uace0 \uc788\uc74c\n",
      "- 2010\ub144\uc5d0 \uc218\uc9d1\ub41c \uc544\uc774\ud2f0 \uc9c0\uc9c4 \ub370\uc774\ud130\uc640 \uadf8 \uc5ec\ud30c\uc5d0 \ub300\ud55c \uc790\ub8cc\ub97c \ub0b4\ub824\ubc1b\uc544 \uc5b4\ub5bb\uac8c \ubd84\uc11d\ud558\uace0 \uc2dc\uac01\ud654\ud558\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uc790"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_csv('ch08/Haiti.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 279
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \ub2e4\ub978 \ub370\uc774\ud130\ub4e4\uc740 3593\uc778\ub370, \uce74\ud14c\uace0\ub9ac\ub294 3587\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 3593 entries, 0 to 3592\n",
        "Data columns (total 10 columns):\n",
        "Serial            3593  non-null values\n",
        "INCIDENT TITLE    3593  non-null values\n",
        "INCIDENT DATE     3593  non-null values\n",
        "LOCATION          3593  non-null values\n",
        "DESCRIPTION       3593  non-null values\n",
        "CATEGORY          3587  non-null values\n",
        "LATITUDE          3593  non-null values\n",
        "LONGITUDE         3593  non-null values\n",
        "APPROVED          3593  non-null values\n",
        "VERIFIED          3593  non-null values\n",
        "dtypes: float64(2), int64(1), object(7)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 270,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 3593 entries, 0 to 3592\n",
        "Data columns (total 10 columns):\n",
        "Serial            3593  non-null values\n",
        "INCIDENT TITLE    3593  non-null values\n",
        "INCIDENT DATE     3593  non-null values\n",
        "LOCATION          3593  non-null values\n",
        "DESCRIPTION       3593  non-null values\n",
        "CATEGORY          3587  non-null values\n",
        "LATITUDE          3593  non-null values\n",
        "LONGITUDE         3593  non-null values\n",
        "APPROVED          3593  non-null values\n",
        "VERIFIED          3593  non-null values\n",
        "dtypes: float64(2), int64(1), object(7)"
       ]
      }
     ],
     "prompt_number": 270
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Serial</th>\n",
        "      <th>INCIDENT TITLE</th>\n",
        "      <th>INCIDENT DATE</th>\n",
        "      <th>LOCATION</th>\n",
        "      <th>DESCRIPTION</th>\n",
        "      <th>CATEGORY</th>\n",
        "      <th>LATITUDE</th>\n",
        "      <th>LONGITUDE</th>\n",
        "      <th>APPROVED</th>\n",
        "      <th>VERIFIED</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 4052</td>\n",
        "      <td> * URGENT * Type O blood donations needed in #J...</td>\n",
        "      <td> 05/07/2010 17:26</td>\n",
        "      <td>      Jacmel, Haiti</td>\n",
        "      <td> Birthing Clinic in Jacmel #Haiti urgently need...</td>\n",
        "      <td>       1. Urgences | Emergency, 3. Public Health, </td>\n",
        "      <td> 18.233333</td>\n",
        "      <td> -72.533333</td>\n",
        "      <td> YES</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 4051</td>\n",
        "      <td>                    Food-Aid sent to Fondwa, Haiti</td>\n",
        "      <td> 28/06/2010 23:06</td>\n",
        "      <td>             fondwa</td>\n",
        "      <td> Please help food-aid.org deliver more food to ...</td>\n",
        "      <td> 1. Urgences | Emergency, 2. Urgences logistiqu...</td>\n",
        "      <td> 50.226029</td>\n",
        "      <td>   5.729886</td>\n",
        "      <td>  NO</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 4050</td>\n",
        "      <td> how haiti is right now and how it was during t...</td>\n",
        "      <td> 24/06/2010 16:21</td>\n",
        "      <td>            centrie</td>\n",
        "      <td> i feel so bad for you i know i am supposed to ...</td>\n",
        "      <td> 2. Urgences logistiques | Vital Lines, 8. Autr...</td>\n",
        "      <td> 22.278381</td>\n",
        "      <td> 114.174287</td>\n",
        "      <td>  NO</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4049</td>\n",
        "      <td>                                       Lost person</td>\n",
        "      <td> 20/06/2010 21:59</td>\n",
        "      <td>             Genoca</td>\n",
        "      <td> We are family members of Juan Antonio Zuniga O...</td>\n",
        "      <td>                         1. Urgences | Emergency, </td>\n",
        "      <td> 44.407062</td>\n",
        "      <td>   8.933989</td>\n",
        "      <td>  NO</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 4042</td>\n",
        "      <td>                                Citi Soleil school</td>\n",
        "      <td> 18/05/2010 16:26</td>\n",
        "      <td> Citi Soleil, Haiti</td>\n",
        "      <td> We are working with Haitian (NGO) -The Christi...</td>\n",
        "      <td>                         1. Urgences | Emergency, </td>\n",
        "      <td> 18.571084</td>\n",
        "      <td> -72.334671</td>\n",
        "      <td> YES</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 271,
       "text": [
        "   Serial                                     INCIDENT TITLE  \\\n",
        "0    4052  * URGENT * Type O blood donations needed in #J...   \n",
        "1    4051                     Food-Aid sent to Fondwa, Haiti   \n",
        "2    4050  how haiti is right now and how it was during t...   \n",
        "3    4049                                        Lost person   \n",
        "4    4042                                 Citi Soleil school   \n",
        "\n",
        "      INCIDENT DATE            LOCATION  \\\n",
        "0  05/07/2010 17:26       Jacmel, Haiti   \n",
        "1  28/06/2010 23:06              fondwa   \n",
        "2  24/06/2010 16:21             centrie   \n",
        "3  20/06/2010 21:59              Genoca   \n",
        "4  18/05/2010 16:26  Citi Soleil, Haiti   \n",
        "\n",
        "                                         DESCRIPTION  \\\n",
        "0  Birthing Clinic in Jacmel #Haiti urgently need...   \n",
        "1  Please help food-aid.org deliver more food to ...   \n",
        "2  i feel so bad for you i know i am supposed to ...   \n",
        "3  We are family members of Juan Antonio Zuniga O...   \n",
        "4  We are working with Haitian (NGO) -The Christi...   \n",
        "\n",
        "                                            CATEGORY   LATITUDE   LONGITUDE  \\\n",
        "0        1. Urgences | Emergency, 3. Public Health,   18.233333  -72.533333   \n",
        "1  1. Urgences | Emergency, 2. Urgences logistiqu...  50.226029    5.729886   \n",
        "2  2. Urgences logistiques | Vital Lines, 8. Autr...  22.278381  114.174287   \n",
        "3                          1. Urgences | Emergency,   44.407062    8.933989   \n",
        "4                          1. Urgences | Emergency,   18.571084  -72.334671   \n",
        "\n",
        "  APPROVED VERIFIED  \n",
        "0      YES       NO  \n",
        "1       NO       NO  \n",
        "2       NO       NO  \n",
        "3       NO       NO  \n",
        "4      YES       NO  "
       ]
      }
     ],
     "prompt_number": 271
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc774\uc81c \uc27d\uac8c \ub370\uc774\ud130\ub97c \ub4e4\uc5ec\ub2e4\ubcf4\uba70 \uc6d0\ud558\ub294 \uc791\uc5c5\uc744 \ud560 \uc218 \uc788\uac8c \ub410\ub2e4.\n",
      "- \uac01\uac01\uc758 \ub85c\uc6b0\ub294 \uc775\uba85\uc758 \uc0ac\uc6a9\uc790\uac00 \ud734\ub300\ud3f0\uc73c\ub85c \uc751\uae09\uc0c1\ud669\uc774\ub098 \ub2e4\ub978 \ubb38\uc81c\uac00 \ubc1c\uc0dd\ud588\uc74c\uc744 \uc54c\ub824\uc628 \ub0b4\uc5f0\n",
      "- \uac01 \ub0b4\uc5ed\uc740 \ud0c0\uc784\uc2a4\ud0ec\ud504\uc640 \uc704\uce58 \uc815\ubcf4(\uc704\ub3c4, \uacbd\ub3c4)\ub97c \ud3ec\ud568"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[['INCIDENT DATE', 'LATITUDE', 'LONGITUDE']][:10]"
     ],
     "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>INCIDENT DATE</th>\n",
        "      <th>LATITUDE</th>\n",
        "      <th>LONGITUDE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 05/07/2010 17:26</td>\n",
        "      <td> 18.233333</td>\n",
        "      <td> -72.533333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 28/06/2010 23:06</td>\n",
        "      <td> 50.226029</td>\n",
        "      <td>   5.729886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 24/06/2010 16:21</td>\n",
        "      <td> 22.278381</td>\n",
        "      <td> 114.174287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 20/06/2010 21:59</td>\n",
        "      <td> 44.407062</td>\n",
        "      <td>   8.933989</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 18/05/2010 16:26</td>\n",
        "      <td> 18.571084</td>\n",
        "      <td> -72.334671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 26/04/2010 13:14</td>\n",
        "      <td> 18.593707</td>\n",
        "      <td> -72.310079</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 26/04/2010 14:19</td>\n",
        "      <td> 18.482800</td>\n",
        "      <td> -73.638800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 26/04/2010 14:27</td>\n",
        "      <td> 18.415000</td>\n",
        "      <td> -73.195000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 15/03/2010 10:58</td>\n",
        "      <td> 18.517443</td>\n",
        "      <td> -72.236841</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> 15/03/2010 11:00</td>\n",
        "      <td> 18.547790</td>\n",
        "      <td> -72.410010</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 272,
       "text": [
        "      INCIDENT DATE   LATITUDE   LONGITUDE\n",
        "0  05/07/2010 17:26  18.233333  -72.533333\n",
        "1  28/06/2010 23:06  50.226029    5.729886\n",
        "2  24/06/2010 16:21  22.278381  114.174287\n",
        "3  20/06/2010 21:59  44.407062    8.933989\n",
        "4  18/05/2010 16:26  18.571084  -72.334671\n",
        "5  26/04/2010 13:14  18.593707  -72.310079\n",
        "6  26/04/2010 14:19  18.482800  -73.638800\n",
        "7  26/04/2010 14:27  18.415000  -73.195000\n",
        "8  15/03/2010 10:58  18.517443  -72.236841\n",
        "9  15/03/2010 11:00  18.547790  -72.410010"
       ]
      }
     ],
     "prompt_number": 272
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['CATEGORY'][:6]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 273,
       "text": [
        "0          1. Urgences | Emergency, 3. Public Health, \n",
        "1    1. Urgences | Emergency, 2. Urgences logistiqu...\n",
        "2    2. Urgences logistiques | Vital Lines, 8. Autr...\n",
        "3                            1. Urgences | Emergency, \n",
        "4                            1. Urgences | Emergency, \n",
        "5                       5e. Communication lines down, \n",
        "Name: CATEGORY, dtype: object"
       ]
      }
     ],
     "prompt_number": 273
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc774 data \uc694\uc57d\uc744 \ubcf4\uba74 \uba87\uba87 \uce74\ud14c\uace0\ub9ac\uac00 \ube60\uc838\uc788\ub294 \uac83\uc744 \ud655\uc778\n",
      "- \uce74\ud14c\uace0\ub9ac\uac00 \ub204\ub77d\ub41c \ub370\uc774\ud130\ub97c \uc81c\uc678\uc2dc\ud0a4\uace0 describe \uba54\uc11c\ub4dc\ub97c \ud638\ucd9c\ud558\uc5ec \uc774\uc0c1\ud55c \uc704\uce58\uac00 \uc788\ub294\uc9c0 \ud655\uc778\ud574\ubcf4\uc790"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.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>Serial</th>\n",
        "      <th>LATITUDE</th>\n",
        "      <th>LONGITUDE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 3593.000000</td>\n",
        "      <td> 3593.000000</td>\n",
        "      <td> 3593.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 2080.277484</td>\n",
        "      <td>   18.611495</td>\n",
        "      <td>  -72.322680</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 1171.100360</td>\n",
        "      <td>    0.738572</td>\n",
        "      <td>    3.650776</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    4.000000</td>\n",
        "      <td>   18.041313</td>\n",
        "      <td>  -74.452757</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 1074.000000</td>\n",
        "      <td>   18.524070</td>\n",
        "      <td>  -72.417500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 2163.000000</td>\n",
        "      <td>   18.539269</td>\n",
        "      <td>  -72.335000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 3088.000000</td>\n",
        "      <td>   18.561820</td>\n",
        "      <td>  -72.293570</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 4052.000000</td>\n",
        "      <td>   50.226029</td>\n",
        "      <td>  114.174287</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 282,
       "text": [
        "            Serial     LATITUDE    LONGITUDE\n",
        "count  3593.000000  3593.000000  3593.000000\n",
        "mean   2080.277484    18.611495   -72.322680\n",
        "std    1171.100360     0.738572     3.650776\n",
        "min       4.000000    18.041313   -74.452757\n",
        "25%    1074.000000    18.524070   -72.417500\n",
        "50%    2163.000000    18.539269   -72.335000\n",
        "75%    3088.000000    18.561820   -72.293570\n",
        "max    4052.000000    50.226029   114.174287"
       ]
      }
     ],
     "prompt_number": 282
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc798\ubabb\ub41c \uc704\uce58 \uc815\ubcf4\uc640 \ub204\ub77d\ub41c \uce74\ud14c\uace0\ub9ac\ub294 \ub2e4\uc74c\ucc98\ub7fc \uac04\ub2e8\ud788 \uc81c\uac70"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[data.CATEGORY.isnull()]"
     ],
     "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>Serial</th>\n",
        "      <th>INCIDENT TITLE</th>\n",
        "      <th>INCIDENT DATE</th>\n",
        "      <th>LOCATION</th>\n",
        "      <th>DESCRIPTION</th>\n",
        "      <th>CATEGORY</th>\n",
        "      <th>LATITUDE</th>\n",
        "      <th>LONGITUDE</th>\n",
        "      <th>APPROVED</th>\n",
        "      <th>VERIFIED</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1198</th>\n",
        "      <td> 2777</td>\n",
        "      <td> Food needed in Castro area, near airport 10 De...</td>\n",
        "      <td> 24/01/2010 23:31</td>\n",
        "      <td>                                 10 Dessaline Road</td>\n",
        "      <td> hungry  grangou.  We are in the Cit?? Castro a...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 18.567320</td>\n",
        "      <td>-72.293280</td>\n",
        "      <td> YES</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1531</th>\n",
        "      <td> 2436</td>\n",
        "      <td>                       Food Needed in Fontamara 27</td>\n",
        "      <td> 22/01/2010 23:04</td>\n",
        "      <td> Fontamara 27 (off Rue National 2) Coordinates ...</td>\n",
        "      <td> NOU NAN FONTAMARA 27 PROLONGE NOU PA WE PES?N\\...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 18.532570</td>\n",
        "      <td>-72.367540</td>\n",
        "      <td> YES</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1828</th>\n",
        "      <td> 2130</td>\n",
        "      <td> 15 People, 5 Babies, Water and Food Needed at ...</td>\n",
        "      <td> 23/01/2010 18:07</td>\n",
        "      <td> Airport Runway Entrance - one end of the runwa...</td>\n",
        "      <td> nou sou wout aewopo a antre pis la nou se 15 f...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 18.580320</td>\n",
        "      <td>-72.312390</td>\n",
        "      <td> YES</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2620</th>\n",
        "      <td> 1182</td>\n",
        "      <td>                                    Trapped person</td>\n",
        "      <td> 19/01/2010 05:19</td>\n",
        "      <td> Delmas, 33 Rue Derrosier Impas Du Crist, Numbe...</td>\n",
        "      <td> Mwen.se MADAM LAINE FRITZ MWEN RETE DELMAS 33 ...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 18.559451</td>\n",
        "      <td>-72.295522</td>\n",
        "      <td> YES</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2622</th>\n",
        "      <td> 1179</td>\n",
        "      <td> 500 to 600 people in a temporary shelter with ...</td>\n",
        "      <td> 19/01/2010 05:57</td>\n",
        "      <td>                                Grand Goave, Haiti</td>\n",
        "      <td> Nou anviron 500 a 600 moun nan abri pwoviswa g...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 18.431400</td>\n",
        "      <td>-72.787200</td>\n",
        "      <td> YES</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2623</th>\n",
        "      <td> 1178</td>\n",
        "      <td> 500 to 600 people in a temporary shelter with ...</td>\n",
        "      <td> 19/01/2010 05:57</td>\n",
        "      <td>                                Grand Goave, Haiti</td>\n",
        "      <td> Nou anviron 500 a 600 moun nan abri pwoviswa g...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 18.424764</td>\n",
        "      <td>-72.770300</td>\n",
        "      <td> YES</td>\n",
        "      <td> NO</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 283,
       "text": [
        "      Serial                                     INCIDENT TITLE  \\\n",
        "1198    2777  Food needed in Castro area, near airport 10 De...   \n",
        "1531    2436                        Food Needed in Fontamara 27   \n",
        "1828    2130  15 People, 5 Babies, Water and Food Needed at ...   \n",
        "2620    1182                                     Trapped person   \n",
        "2622    1179  500 to 600 people in a temporary shelter with ...   \n",
        "2623    1178  500 to 600 people in a temporary shelter with ...   \n",
        "\n",
        "         INCIDENT DATE                                           LOCATION  \\\n",
        "1198  24/01/2010 23:31                                  10 Dessaline Road   \n",
        "1531  22/01/2010 23:04  Fontamara 27 (off Rue National 2) Coordinates ...   \n",
        "1828  23/01/2010 18:07  Airport Runway Entrance - one end of the runwa...   \n",
        "2620  19/01/2010 05:19  Delmas, 33 Rue Derrosier Impas Du Crist, Numbe...   \n",
        "2622  19/01/2010 05:57                                 Grand Goave, Haiti   \n",
        "2623  19/01/2010 05:57                                 Grand Goave, Haiti   \n",
        "\n",
        "                                            DESCRIPTION CATEGORY   LATITUDE  \\\n",
        "1198  hungry  grangou.  We are in the Cit?? Castro a...      NaN  18.567320   \n",
        "1531  NOU NAN FONTAMARA 27 PROLONGE NOU PA WE PES?N\\...      NaN  18.532570   \n",
        "1828  nou sou wout aewopo a antre pis la nou se 15 f...      NaN  18.580320   \n",
        "2620  Mwen.se MADAM LAINE FRITZ MWEN RETE DELMAS 33 ...      NaN  18.559451   \n",
        "2622  Nou anviron 500 a 600 moun nan abri pwoviswa g...      NaN  18.431400   \n",
        "2623  Nou anviron 500 a 600 moun nan abri pwoviswa g...      NaN  18.424764   \n",
        "\n",
        "      LONGITUDE APPROVED VERIFIED  \n",
        "1198 -72.293280      YES       NO  \n",
        "1531 -72.367540      YES       NO  \n",
        "1828 -72.312390      YES       NO  \n",
        "2620 -72.295522      YES       NO  \n",
        "2622 -72.787200      YES       NO  \n",
        "2623 -72.770300      YES       NO  "
       ]
      }
     ],
     "prompt_number": 283
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 18 < \uacbd\ub3c4 < 20\n",
      "# -75 < \uc704\ub3c4 < -70\n",
      "# \ubaa8\ub4e0 \uc870\uac74\ub4e4\uc774 \ucc38\uc774\uc5b4\uc57c \ud558\ub294 &(and) \uc5f0\uc0b0\uc790 \uc0ac\uc6a9\n",
      "data = data[(data.LATITUDE > 18) & (data.LATITUDE < 20) &\n",
      "            (data.LONGITUDE > -75) & (data.LONGITUDE < -70)\n",
      "            & data.CATEGORY.notnull()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 286
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.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>Serial</th>\n",
        "      <th>LATITUDE</th>\n",
        "      <th>LONGITUDE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 3569.000000</td>\n",
        "      <td> 3569.000000</td>\n",
        "      <td> 3569.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 2081.498459</td>\n",
        "      <td>   18.592503</td>\n",
        "      <td>  -72.424994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 1170.311824</td>\n",
        "      <td>    0.273695</td>\n",
        "      <td>    0.291018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    4.000000</td>\n",
        "      <td>   18.041313</td>\n",
        "      <td>  -74.452757</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 1074.000000</td>\n",
        "      <td>   18.524200</td>\n",
        "      <td>  -72.417498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 2166.000000</td>\n",
        "      <td>   18.539269</td>\n",
        "      <td>  -72.335000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 3089.000000</td>\n",
        "      <td>   18.561800</td>\n",
        "      <td>  -72.293939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 4052.000000</td>\n",
        "      <td>   19.940630</td>\n",
        "      <td>  -71.099489</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 287,
       "text": [
        "            Serial     LATITUDE    LONGITUDE\n",
        "count  3569.000000  3569.000000  3569.000000\n",
        "mean   2081.498459    18.592503   -72.424994\n",
        "std    1170.311824     0.273695     0.291018\n",
        "min       4.000000    18.041313   -74.452757\n",
        "25%    1074.000000    18.524200   -72.417498\n",
        "50%    2166.000000    18.539269   -72.335000\n",
        "75%    3089.000000    18.561800   -72.293939\n",
        "max    4052.000000    19.940630   -71.099489"
       ]
      }
     ],
     "prompt_number": 287
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \uc544\uc774\ud2f0 \uc9c0\uc9c4 \uc704\uce58 \uc815\ubcf4\n",
      "\n",
      "- 18 < \uacbd\ub3c4 < 20\n",
      "- -75 < \uc704\ub3c4 < -70\n",
      "- \uc544\uc774\ud2f0 \uc9c0\uc9c4 \ub370\uc774\ud130\uc774\uae30 \ub54c\ubb38\uc5d0 \uc544\uc774\ud2f0 \ubd80\uadfc \ub370\uc774\ud130\ub9cc \ubd84\uc11d\ud558\uaca0\ub2e4\ub294 \uc758\ub3c4\ub2e4.\n",
      "- \ucc98\uc74c\uc5d0 \ud5f7\uae54\ub9b0\uac8c Ushahidi\uac00 \ucf00\ub0d0\uc758 \uc815\ubd80\ub97c \uc218\uc9d1\ud55c\ub2e4\uace0 \ud574\uc11c google map \uc73c\ub85c \ucf00\ub0d0 \ubd80\uadfc\ub9cc \ucc3e\uc558\ub294\ub370 \uacbd\ub3c4, \uc704\ub3c4\uac00 \ub9de\uc9c0 \uc54a\ub294 \uac83\uc774\ub2e4. \uc870\uae08 \ub354 \uc0dd\uac01\ud574\ubcf4\ub2c8 \ucf00\ub0d0 \ub370\uc774\ud130\ub9cc \ubaa8\uc740\uac8c \uc544\ub2c8\ub77c \uc804 \uc138\uacc4 \uc815\ubcf4\ub97c \ubaa8\uc740 \uac83\uc774\uace0 \uadf8 \uc911\uc5d0\uc11c \uc544\uc774\ud2f0 \ubd80\uadfc \ub370\uc774\ud130\ub9cc \ubd84\uc11d\ud558\uaca0\ub2e4\ub294 \uc815\ubcf4\ub85c \ubcf4\uba74 \ub418\uaca0\ub2e4.\n",
      "- \ud2b9\uc815 \uc9c0\uc5ed\ub9cc \ubd84\uc11d\ud560 \uc218 \uc788\uace0 null \uac12\uc740 \ud55c \ubc88\uc5d0 \uc81c\uc678\uc2dc\ud0ac \uc218 \uc788\ub294 \ubb38\ubc95\uc774 \uad49\uc7a5\ud788 \uc26c\uc6b4\uac70 \ubcf4\ub2c8 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc815\ub9d0 \uc798 \ub9cc\ub4e4\uc5c8\ub2e4.\n",
      "\n",
      "<img src=\"files/images/it_earthquake.png\" width=\"500px\" />"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 10 columns):\n",
        "Serial            3569  non-null values\n",
        "INCIDENT TITLE    3569  non-null values\n",
        "INCIDENT DATE     3569  non-null values\n",
        "LOCATION          3569  non-null values\n",
        "DESCRIPTION       3569  non-null values\n",
        "CATEGORY          3569  non-null values\n",
        "LATITUDE          3569  non-null values\n",
        "LONGITUDE         3569  non-null values\n",
        "APPROVED          3569  non-null values\n",
        "VERIFIED          3569  non-null values\n",
        "dtypes: float64(2), int64(1), object(7)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 277,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 10 columns):\n",
        "Serial            3569  non-null values\n",
        "INCIDENT TITLE    3569  non-null values\n",
        "INCIDENT DATE     3569  non-null values\n",
        "LOCATION          3569  non-null values\n",
        "DESCRIPTION       3569  non-null values\n",
        "CATEGORY          3569  non-null values\n",
        "LATITUDE          3569  non-null values\n",
        "LONGITUDE         3569  non-null values\n",
        "APPROVED          3569  non-null values\n",
        "VERIFIED          3569  non-null values\n",
        "dtypes: float64(2), int64(1), object(7)"
       ]
      }
     ],
     "prompt_number": 277
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uce74\ud14c\uace0\ub9ac\ubcc4 \ubd84\uc11d\uc774\ub098 \uc2dc\uac01\ud654\n",
      "- \uac01 \uce74\ud14c\uace0\ub9ac \ud544\ub4dc\uc5d0\ub294 \ub2e4\uc218\uc758 \uce74\ud14c\uace0\ub9ac\uac00 \uc874\uc7ac\n",
      "- \uac01 \uce74\ud14c\uace0\ub9ac\ub294 \ucf54\ub4dc\uc640 \uc601\uc5b4, \ubd88\uc5b4 \ucf54\ub4dc \uc774\ub984\uc73c\ub85c \uad6c\uc131\n",
      "- \uadf8\ub798\uc11c \ub370\uc774\ud130\ub97c \uc880 \ub354 \uc27d\uac8c \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \uc57d\uac04\uc758 \uc218\uace0\uac00 \ud544\uc694\n",
      "- \uba3c\uc800 \uc874\uc7ac\ud558\ub294 \ubaa8\ub4e0 \uce74\ud14c\uace0\ub9ac\ub97c \ubf51\uc544\ub0b4\uc5b4 \uce74\ud14c\uace0\ub9ac \ucf54\ub4dc\uc640 \uc601\uc5b4 \uc774\ub984\uc73c\ub85c \ubd84\ub9ac\ub418\ub3c4\ub85d \ud568\uc218 \uc791\uc131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def to_cat_list(catstr):\n",
      "    # \uce74\ud14c\uace0\ub9ac \uad6c\ubd84\uc790\ub85c ,\ub97c \uc0ac\uc6a9\n",
      "    stripped = (x.strip() for x in catstr.split(','))\n",
      "    # ,\ub85c \uad6c\ubd84\ud588\ub294\ub370 x\uac00 \uc544\ubb34\uac83\ub3c4 \uc5c6\uc73c\uba74 \ud3ec\ud568\ud558\uc9c0 \uc54a\ub294\ub2e4. \n",
      "    # \uc704\uc5d0\uc11c x.strip()\uc73c\ub85c \uacf5\ubc31\uc744 \ubaa8\ub450 \ub0a0\ub838\uae30 \ub54c\ubb38\uc5d0 \uacf5\ubc31\ub9cc \uc788\ub358 \uac83\uc740 \uc544\ubb34\uac83\ub3c4 \uc5c6\ub294 ''\uc774 \ub42c\uc744 \uac83\uc774\ub2e4.\n",
      "    return [x for x in stripped if x]\n",
      "\n",
      "def get_all_categories(cat_series):\n",
      "    # set\uc73c\ub85c unique\ud55c \uac12\ub9cc \ubc1b\ub294\ub2e4.\n",
      "    cat_sets = (set(to_cat_list(x)) for x in cat_series)\n",
      "    # set.union\uc73c\ub85c \ud569\uc9d1\ud569 \ub9cc\ub4e4\uc5b4\uc11c sorting\n",
      "    return sorted(set.union(*cat_sets))\n",
      "\n",
      "def get_english(cat):\n",
      "    # code\uc640 names \uad6c\ubd84\uc790\ub85c . \uc0ac\uc6a9\n",
      "    code, names = cat.split('.')\n",
      "    # names\uc5d0\uc11c |\uac00 \uc788\ub2e4\uba74 \ubd88\uc5b4\uc640 \uc601\uc5b4 \uad6c\ubd84\uc790 \uc774\ubbc0\ub85c\n",
      "    if '|' in names:\n",
      "        # |\ub97c \uae30\uc900\uc73c\ub85c \ub098\ub220\uc11c \ubc30\uc5f4\uc758 [1]\ubc88\uc9f8\uc778 \uc601\uc5b4\ub97c \uc120\ud0dd\ud55c\ub2e4.\n",
      "        names = names.split(' | ')[1]\n",
      "    # \uc78a\uc9c0\uc54a\uace0 names\uac12\uc740 strip \ud574\uc8fc\uc5b4 \uacf5\ubc31\uc744 \uc81c\uac70\ud55c\ub2e4.\n",
      "    # \uc0ac\uc18c\ud55c \uac70\uc9c0\ub9cc \ubb38\uc790\uc5f4 \uc791\uc5c5\ud560 \ub54c\ub294 \uacf5\ubc31\uc744 \uc81c\uac70\ud574 \uc8fc\ub294\uac8c \uc911\uc694\ud558\ub2e4.\n",
      "    return code, names.strip()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 288
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# ,: \uce74\ud14c\uace0\ub9ac \uad6c\ubd84\uc790\n",
      "# .: \ucf54\ub4dc, \ubd88\uc5b4 \uad6c\ubd84\uc790\n",
      "# |: \ubd88\uc5b4, \uc601\uc5b4 \uad6c\ubd84\uc790\n",
      "data['CATEGORY'][:20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 292,
       "text": [
        "0           1. Urgences | Emergency, 3. Public Health, \n",
        "4                             1. Urgences | Emergency, \n",
        "5                        5e. Communication lines down, \n",
        "6     4. Menaces | Security Threats, 4e. Assainissem...\n",
        "7                       4. Menaces | Security Threats, \n",
        "8     2. Urgences logistiques | Vital Lines, 2f. San...\n",
        "9     2. Urgences logistiques | Vital Lines, 2d. Ref...\n",
        "10    1a. Highly vulnerable, 2. Urgences logistiques...\n",
        "11    4. Menaces | Security Threats, 4e. Assainissem...\n",
        "12    2. Urgences logistiques | Vital Lines, 2d. Ref...\n",
        "13    2. Urgences logistiques | Vital Lines, 2b. Pen...\n",
        "14                                   3. Public Health, \n",
        "15              2. Urgences logistiques | Vital Lines, \n",
        "16    1. Urgences | Emergency, 7. Secours | Services...\n",
        "17           3. Public Health, 3b. Chronic care needs, \n",
        "18    2. Urgences logistiques | Vital Lines, 2b. Pen...\n",
        "19    1. Urgences | Emergency, 7. Secours | Services...\n",
        "20    1. Urgences | Emergency, 2. Urgences logistiqu...\n",
        "21    2. Urgences logistiques | Vital Lines, 2d. Ref...\n",
        "22    2. Urgences logistiques | Vital Lines, 2d. Ref...\n",
        "Name: CATEGORY, dtype: object"
       ]
      }
     ],
     "prompt_number": 292
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### \ubd88\uc5b4, \uc601\uc5b4 \ucc28\uc774\n",
      "\n",
      "<img src=\"files/images/fren.png\" width=\"500px\" />\n",
      "<img src=\"files/images/eng.png\" width=\"500px\" />\n",
      "\n",
      "- \ubd88\uc5b4: menaces\n",
      "- \uc601\uc5b4: Security Threats"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uce74\ud14c\uace0\ub9ac \uad6c\uc131\n",
      "# 1. Code: 2\n",
      "# 2. \ubd88\uc5b4: Urgences logistiques\n",
      "# 3. \uc601\uc5b4: Vital Lines\n",
      "# \uc774\ub807\uac8c 3\uac1c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\ub294\ub370 \uc774\uac83\ub4e4\uc744 \uc5b4\ub5a4 \uae30\uc900\uc73c\ub85c \ub098\ub20c \uac83\uc778\uac00?\n",
      "get_english('2. Urgences logistiques | Vital Lines')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 289,
       "text": [
        "('2', 'Vital Lines')"
       ]
      }
     ],
     "prompt_number": 289
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc774 \ucf54\ub4dc\ub97c \ubd84\uc11d\uc5d0 \uc0ac\uc6a9\ud560 \uac83\n",
      "- \ucf54\ub4dc\uc640 \ucf54\ub4dc \uc774\ub984\uc744 \ub9e4\ud551\ud560 \uc218 \uc788\ub294 \ucf54\ub4dc \uc791\uc131\n",
      "- \uc774\ub294 \ub098\uc911\uc5d0 \ub3c4\ud45c\ub97c \uafb8\ubc00 \ub54c \uc0ac\uc6a9(\ub9ac\uc2a4\ud2b8 \ub0b4\ud3ec \uc790\ub9ac\uc5d0 \uc81c\ub108\ub808\uc774\ud130 \ud45c\ud604\uc2dd\uc744 \uc0ac\uc6a9\ud588\uc74c \uc8fc\ubaa9)\n",
      "\n",
      "#### \uc880 \ubcf5\uc7a1\ud55c \ucf54\ub4dc\ub294 \ub514\ubc84\uae45 \ud558\uba74\uc11c \ud55c \uc904 \ud55c \uc904 \uc5b4\ub5bb\uac8c \ubcc0\uacbd\ub418\ub294\uc9c0 \ucd94\uc801\ud574\ubcf4\ub294\uac8c \uc88b\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_cats = get_all_categories(data.CATEGORY)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 293
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_cats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 294,
       "text": [
        "['1. Urgences | Emergency',\n",
        " '1a. Highly vulnerable',\n",
        " '1b. Urgence medicale | Medical Emergency',\n",
        " '1c. Personnes prises au piege | People trapped',\n",
        " '1d. Incendie | Fire',\n",
        " '2. Urgences logistiques | Vital Lines',\n",
        " \"2a. Penurie d'aliments | Food Shortage\",\n",
        " \"2b. Penurie d'eau | Water shortage\",\n",
        " '2c. Eau contaminee | Contaminated water',\n",
        " '2c. Probleme de securite | Security Concern',\n",
        " '2d. Refuge | Shelter needed',\n",
        " '2e. Penurie de carburant | Fuel shortage',\n",
        " '2f. Sans courant | Power Outage',\n",
        " '3. Public Health',\n",
        " '3a. Infectious human disease',\n",
        " '3b. Chronic care needs',\n",
        " '3c. Besoins en materiels et medicaments | Medical equipment and supply needs',\n",
        " \"3d. OBGYN/Women's Health\",\n",
        " '3e. Psychiatric need',\n",
        " '4. Menaces | Security Threats',\n",
        " '4a. Pillage | Looting',\n",
        " '4c. Group violence',\n",
        " '4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion',\n",
        " '5. Infrastructure Damage',\n",
        " '5a. Structure effondres | Collapsed structure',\n",
        " '5b. Structures a risque | Unstable Structure',\n",
        " '5c. Route barree | Road blocked',\n",
        " '5d. Compromised bridge',\n",
        " '5e. Communication lines down',\n",
        " '6. Natural Hazards',\n",
        " '6a. Deces | Deaths',\n",
        " '6b. Personnes Disparues | Missing Persons',\n",
        " '6c. Demandant de transmettre un message | Asking to forward a message',\n",
        " '6c. Seisme et repliques | Earthquake and aftershocks',\n",
        " '7. Secours | Services Available',\n",
        " \"7a. Distribution d'aliments | Food distribution point\",\n",
        " \"7b. Distribution d'eau | Water distribution point\",\n",
        " '7c. Denrees non alimentaires | Non-food aid distribution point',\n",
        " '7d. Services de sante | Hospital/Clinics Operating',\n",
        " '7g. Morgue | Human remains management',\n",
        " '7h. Deblayage de gravats | Rubble removal',\n",
        " '8. Autre | Other',\n",
        " '8a. IDP concentration',\n",
        " '8c. Price gouging',\n",
        " '8d. Recherche et sauvetage | Search and Rescue',\n",
        " '8e. Nouvelles de Personnes | Persons News',\n",
        " '8f. Other']"
       ]
      }
     ],
     "prompt_number": 294
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \uc81c\ub108\ub808\uc774\ud130 \ud45c\ud604\n",
      "english_mapping = dict(get_english(x) for x in all_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 326
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "----\n",
      "\n",
      "#### \uc81c\ub108\ub808\uc774\ud130 \ud45c\ud604\uc2dd\uc774 \uc774\ud574 \uc548\ub418\uc11c \uc0bd\uc9c8\uc911"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_english(all_cats[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 343,
       "text": [
        "('1', 'Emergency')"
       ]
      }
     ],
     "prompt_number": 343
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dict(get_english(all_cats[0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "dictionary update sequence element #0 has length 1; 2 is required",
       "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-327-10cf166fd0b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_english\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_cats\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mValueError\u001b[0m: dictionary update sequence element #0 has length 1; 2 is required"
       ]
      }
     ],
     "prompt_number": 327
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dict(one=1, two=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 328,
       "text": [
        "{'one': 1, 'two': 2}"
       ]
      }
     ],
     "prompt_number": 328
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dict(emergency=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 329,
       "text": [
        "{'emergency': 1}"
       ]
      }
     ],
     "prompt_number": 329
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dict('1a'=emergency)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "keyword can't be an expression (<ipython-input-330-1a7d13e9a231>, line 1)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-330-1a7d13e9a231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    dict('1a'=emergency)\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m keyword can't be an expression\n"
       ]
      }
     ],
     "prompt_number": 330
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(get_english(x) for x in all_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 338,
       "text": [
        "generator"
       ]
      }
     ],
     "prompt_number": 338
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(get_english(x) for x in all_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 342,
       "text": [
        "<generator object <genexpr> at 0x11da768c0>"
       ]
      }
     ],
     "prompt_number": 342
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dict(get_english(x) for x in all_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 340,
       "text": [
        "{'1': 'Emergency',\n",
        " '1a': 'Highly vulnerable',\n",
        " '1b': 'Medical Emergency',\n",
        " '1c': 'People trapped',\n",
        " '1d': 'Fire',\n",
        " '2': 'Vital Lines',\n",
        " '2a': 'Food Shortage',\n",
        " '2b': 'Water shortage',\n",
        " '2c': 'Security Concern',\n",
        " '2d': 'Shelter needed',\n",
        " '2e': 'Fuel shortage',\n",
        " '2f': 'Power Outage',\n",
        " '3': 'Public Health',\n",
        " '3a': 'Infectious human disease',\n",
        " '3b': 'Chronic care needs',\n",
        " '3c': 'Medical equipment and supply needs',\n",
        " '3d': \"OBGYN/Women's Health\",\n",
        " '3e': 'Psychiatric need',\n",
        " '4': 'Security Threats',\n",
        " '4a': 'Looting',\n",
        " '4c': 'Group violence',\n",
        " '4e': 'Water sanitation and hygiene promotion',\n",
        " '5': 'Infrastructure Damage',\n",
        " '5a': 'Collapsed structure',\n",
        " '5b': 'Unstable Structure',\n",
        " '5c': 'Road blocked',\n",
        " '5d': 'Compromised bridge',\n",
        " '5e': 'Communication lines down',\n",
        " '6': 'Natural Hazards',\n",
        " '6a': 'Deaths',\n",
        " '6b': 'Missing Persons',\n",
        " '6c': 'Earthquake and aftershocks',\n",
        " '7': 'Services Available',\n",
        " '7a': 'Food distribution point',\n",
        " '7b': 'Water distribution point',\n",
        " '7c': 'Non-food aid distribution point',\n",
        " '7d': 'Hospital/Clinics Operating',\n",
        " '7g': 'Human remains management',\n",
        " '7h': 'Rubble removal',\n",
        " '8': 'Other',\n",
        " '8a': 'IDP concentration',\n",
        " '8c': 'Price gouging',\n",
        " '8d': 'Search and Rescue',\n",
        " '8e': 'Persons News',\n",
        " '8f': 'Other'}"
       ]
      }
     ],
     "prompt_number": 340
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(dict(get_english(x) for x in all_cats))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 339,
       "text": [
        "dict"
       ]
      }
     ],
     "prompt_number": 339
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(english_mapping)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 333,
       "text": [
        "dict"
       ]
      }
     ],
     "prompt_number": 333
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "english_mapping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 332,
       "text": [
        "{'1': 'Emergency',\n",
        " '1a': 'Highly vulnerable',\n",
        " '1b': 'Medical Emergency',\n",
        " '1c': 'People trapped',\n",
        " '1d': 'Fire',\n",
        " '2': 'Vital Lines',\n",
        " '2a': 'Food Shortage',\n",
        " '2b': 'Water shortage',\n",
        " '2c': 'Security Concern',\n",
        " '2d': 'Shelter needed',\n",
        " '2e': 'Fuel shortage',\n",
        " '2f': 'Power Outage',\n",
        " '3': 'Public Health',\n",
        " '3a': 'Infectious human disease',\n",
        " '3b': 'Chronic care needs',\n",
        " '3c': 'Medical equipment and supply needs',\n",
        " '3d': \"OBGYN/Women's Health\",\n",
        " '3e': 'Psychiatric need',\n",
        " '4': 'Security Threats',\n",
        " '4a': 'Looting',\n",
        " '4c': 'Group violence',\n",
        " '4e': 'Water sanitation and hygiene promotion',\n",
        " '5': 'Infrastructure Damage',\n",
        " '5a': 'Collapsed structure',\n",
        " '5b': 'Unstable Structure',\n",
        " '5c': 'Road blocked',\n",
        " '5d': 'Compromised bridge',\n",
        " '5e': 'Communication lines down',\n",
        " '6': 'Natural Hazards',\n",
        " '6a': 'Deaths',\n",
        " '6b': 'Missing Persons',\n",
        " '6c': 'Earthquake and aftershocks',\n",
        " '7': 'Services Available',\n",
        " '7a': 'Food distribution point',\n",
        " '7b': 'Water distribution point',\n",
        " '7c': 'Non-food aid distribution point',\n",
        " '7d': 'Hospital/Clinics Operating',\n",
        " '7g': 'Human remains management',\n",
        " '7h': 'Rubble removal',\n",
        " '8': 'Other',\n",
        " '8a': 'IDP concentration',\n",
        " '8c': 'Price gouging',\n",
        " '8d': 'Search and Rescue',\n",
        " '8e': 'Persons News',\n",
        " '8f': 'Other'}"
       ]
      }
     ],
     "prompt_number": 332
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "english_mapping['2a']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 299,
       "text": [
        "'Food Shortage'"
       ]
      }
     ],
     "prompt_number": 299
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "english_mapping['6c']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 344,
       "text": [
        "'Earthquake and aftershocks'"
       ]
      }
     ],
     "prompt_number": 344
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uce74\ud14c\uace0\ub9ac\ubcc4\ub85c \ub0b4\uc5ed\uc744 \uc27d\uac8c \ubaa8\uc744 \uc218 \uc788\ub3c4\ub85d \ub370\uc774\ud130\ub97c \ud655\uc7a5\ud558\ub294 \ub2e4\uc591\ud55c \ubc29\ubc95\n",
      "- \uc2dd\ubcc4\uc6a9(\ud639\uc740 \ub354\ubbf8) \uce7c\ub7fc\uc744 \uac01 \uce74\ud14c\uace0\ub9ac\uc5d0 \ud558\ub098\uc529 \ucd94\uac00\ud558\ub294 \uac83\uc778\ub370, \uadf8\ub7ec\ub824\uba74 \uba3c\uc800 \uc911\ubcf5\ub418\uc9c0 \uc54a\uc740 \uce74\ud14c\uace0\ub9ac \ucf54\ub4dc\ub97c \ubf51\uc544\ub0b8 \ub2e4\uc74c data\uc640 \uac19\uc740 \uc0c9\uc778\uc744 \uac00\uc9c0\ub294 DataFrame \uac1d\uccb4\uc5d0 \uac12\uc744 \ubaa8\ub450 0\uc73c\ub85c \ucc44\uc6cc \ub123\uc5b4 \ub9cc\ub4e4\uc5b4\uc57c \ud55c\ub2e4."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_code(seq):\n",
      "    return [x.split('.')[0] for x in seq if x]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 346
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_codes = get_code(all_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 347
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_codes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 348,
       "text": [
        "['1',\n",
        " '1a',\n",
        " '1b',\n",
        " '1c',\n",
        " '1d',\n",
        " '2',\n",
        " '2a',\n",
        " '2b',\n",
        " '2c',\n",
        " '2c',\n",
        " '2d',\n",
        " '2e',\n",
        " '2f',\n",
        " '3',\n",
        " '3a',\n",
        " '3b',\n",
        " '3c',\n",
        " '3d',\n",
        " '3e',\n",
        " '4',\n",
        " '4a',\n",
        " '4c',\n",
        " '4e',\n",
        " '5',\n",
        " '5a',\n",
        " '5b',\n",
        " '5c',\n",
        " '5d',\n",
        " '5e',\n",
        " '6',\n",
        " '6a',\n",
        " '6b',\n",
        " '6c',\n",
        " '6c',\n",
        " '7',\n",
        " '7a',\n",
        " '7b',\n",
        " '7c',\n",
        " '7d',\n",
        " '7g',\n",
        " '7h',\n",
        " '8',\n",
        " '8a',\n",
        " '8c',\n",
        " '8d',\n",
        " '8e',\n",
        " '8f']"
       ]
      }
     ],
     "prompt_number": 348
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "code_index = pd.Index(np.unique(all_codes))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 349
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "code_index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 350,
       "text": [
        "Index([u'1', u'1a', u'1b', u'1c', u'1d', u'2', u'2a', u'2b', u'2c', u'2d', u'2e', u'2f', u'3', u'3a', u'3b', u'3c', u'3d', u'3e', u'4', u'4a', u'4c', u'4e', u'5', u'5a', u'5b', u'5c', u'5d', u'5e', u'6', u'6a', u'6b', u'6c', u'7', u'7a', u'7b', u'7c', u'7d', u'7g', u'7h', u'8', u'8a', u'8c', u'8d', u'8e', u'8f'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 350
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(all_cats), len(all_codes), len(code_index), len(english_mapping)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 353,
       "text": [
        "(47, 47, 45, 45)"
       ]
      }
     ],
     "prompt_number": 353
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dummy_frame = DataFrame(np.zeros((len(data), len(code_index))),\n",
      "                        index=data.index, columns=code_index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 354
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 355,
       "text": [
        "Int64Index([0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239, 2240, 2241, 2242, 2243, 2244, 2245, 2246, 2247, 2248, 2249, 2251, 2252, 2253, 2254, 2255, 2256, 2257, 2258, 2259, 2260, 2261, 2262, 2263, 2264, 2265, 2266, 2267, 2268, 2269, 2270, 2271, 2272, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2280, 2281, 2282, 2283, 2284, 2285, 2286, 2287, 2288, 2289, 2290, 2291, 2292, 2293, 2294, 2295, 2296, 2297, 2298, 2299, 2300, 2301, 2302, 2303, 2304, 2305, 2306, 2307, 2308, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325, 2326, 2327, 2328, 2329, 2330, 2331, 2332, 2333, 2334, 2335, 2336, 2337, 2338, 2339, 2340, 2341, 2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357, 2358, 2359, 2360, 2361, 2362, 2363, 2364, 2365, 2366, 2367, 2368, 2369, 2371, 2372, 2373, 2374, 2375, 2376, 2377, 2378, 2379, 2380, 2381, 2382, 2383, 2384, 2385, 2386, 2387, 2388, 2389, 2390, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399, 2400, 2401, 2402, 2403, 2404, 2405, 2406, 2407, 2408, 2409, 2410, 2411, 2412, 2413, 2414, 2415, 2416, 2417, 2418, 2419, 2420, 2421, 2422, 2423, 2424, 2425, 2426, 2427, 2428, 2429, 2430, 2431, 2432, 2433, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456, 2457, 2458, 2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516, 2517, 2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539, 2540, 2541, 2542, 2543, 2544, 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2569, 2570, 2571, 2572, 2573, 2574, 2575, 2576, 2577, 2578, 2579, 2580, 2581, 2582, 2583, 2584, 2585, 2586, 2587, 2588, 2589, 2590, 2591, 2592, 2593, 2594, 2595, 2597, 2598, 2600, 2601, 2602, 2603, 2604, 2605, 2606, 2607, 2608, 2609, 2610, 2611, 2612, 2613, 2614, 2615, 2616, 2617, 2618, 2619, 2621, 2624, 2625, 2626, 2627, 2628, 2629, 2630, 2631, 2632, 2633, 2634, 2635, 2636, 2637, 2638, 2639, 2640, 2641, 2642, 2643, 2644, 2645, 2646, 2647, 2648, 2649, 2650, 2651, 2652, 2653, 2654, 2655, 2656, 2657, 2658, 2659, 2660, 2661, 2662, 2663, 2664, 2665, 2666, 2667, 2668, 2669, 2670, 2671, 2672, 2673, 2674, 2675, 2676, 2677, 2678, 2679, 2680, 2681, 2682, 2683, 2684, 2685, 2686, 2687, 2688, 2689, 2690, 2691, 2692, 2693, 2694, 2695, 2696, 2697, 2698, 2699, 2700, 2701, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2718, 2719, 2720, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821, 2822, 2823, 2824, 2825, 2826, 2827, 2828, 2829, 2830, 2831, 2832, 2833, 2834, 2835, 2836, 2837, 2838, 2839, 2840, 2841, 2842, 2843, 2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853, 2854, 2855, 2856, 2857, 2858, 2859, 2860, 2861, 2862, 2863, 2864, 2865, 2866, 2867, 2868, 2869, 2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2892, 2893, 2894, 2895, 2896, 2897, 2898, 2899, 2900, 2901, 2902, 2903, 2904, 2905, 2906, 2907, 2908, 2909, 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 2925, 2926, 2927, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938, 2939, 2940, 2941, 2942, 2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3042, 3043, 3044, 3045, 3046, 3047, 3049, 3050, 3051, 3052, 3053, 3054, 3055, 3056, 3057, 3058, 3059, 3060, 3061, 3062, 3063, 3064, 3065, 3066, 3067, 3068, 3069, 3070, 3071, 3072, 3073, 3074, 3075, 3076, 3077, 3078, 3079, 3080, 3081, 3082, 3083, 3084, 3085, 3086, 3087, 3088, 3089, 3090, 3091, 3092, 3093, 3094, 3095, 3096, 3097, 3098, 3099, 3100, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3110, 3111, 3112, 3113, 3114, 3115, 3116, 3117, 3118, 3119, 3120, 3121, 3122, 3123, 3124, 3125, 3126, 3127, 3128, 3129, 3130, 3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140, 3141, 3142, 3143, 3144, 3145, 3146, 3147, 3148, 3149, 3150, 3151, 3152, 3153, 3154, 3155, 3156, 3157, 3158, 3159, 3160, 3161, 3163, 3164, 3165, 3166, 3167, 3168, 3169, 3170, 3171, 3172, 3173, 3174, 3175, 3176, 3177, 3178, 3179, 3180, 3181, 3182, 3183, 3184, 3185, 3186, 3187, 3188, 3189, 3190, 3191, 3192, 3193, 3194, 3195, 3197, 3198, 3199, 3200, 3201, 3202, 3203, 3204, 3205, 3206, 3207, 3208, 3209, 3210, 3211, 3212, 3213, 3214, 3215, 3216, 3217, 3218, 3219, 3220, 3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3296, 3297, 3298, 3299, 3300, 3301, 3302, 3303, 3304, 3305, 3306, 3307, 3308, 3309, 3310, 3311, 3312, 3313, 3314, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3341, 3342, 3343, 3344, 3345, 3346, 3347, 3348, 3349, 3350, 3351, 3352, 3353, 3354, 3355, 3356, 3357, 3358, 3359, 3360, 3361, 3362, 3363, 3364, 3365, 3366, 3367, 3368, 3369, 3370, 3371, 3372, 3373, 3374, 3375, 3376, 3377, 3378, 3379, 3380, 3381, 3382, 3383, 3384, 3385, 3386, 3387, 3388, 3389, 3390, 3391, 3392, 3393, 3394, 3395, 3396, 3397, 3398, 3399, 3400, 3401, 3402, 3403, 3404, 3405, 3406, 3407, 3408, 3409, 3410, 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3418, 3419, 3420, 3421, 3422, 3423, 3424, 3425, 3426, 3427, 3428, 3429, 3430, 3431, 3432, 3433, 3434, 3435, 3436, 3437, 3438, 3439, 3440, 3441, 3442, 3444, 3445, 3446, 3447, 3448, 3449, 3450, 3451, 3452, 3453, 3454, 3455, 3456, 3457, 3458, 3459, 3460, 3461, 3462, 3463, 3464, 3465, 3466, 3467, 3468, 3469, 3470, 3471, 3472, 3473, 3474, 3475, 3476, 3477, 3478, 3479, 3480, 3481, 3482, 3483, 3484, 3485, 3486, 3487, 3488, 3489, 3490, 3491, 3492, 3493, 3494, 3495, 3496, 3497, 3498, 3499, 3500, 3501, 3502, 3503, 3504, 3505, 3506, 3507, 3508, 3509, 3510, 3511, 3512, 3513, 3514, 3515, 3516, 3517, 3518, 3519, 3520, 3521, 3522, 3523, 3524, 3525, 3526, 3527, 3528, 3529, 3530, 3531, 3532, 3533, 3534, 3535, 3536, 3537, 3538, 3539, 3540, 3541, 3542, 3543, 3544, 3545, 3546, 3547, 3548, 3549, 3550, 3551, 3552, 3553, 3554, 3555, 3556, 3557, 3558, 3559, 3560, 3561, 3562, 3563, 3564, 3565, 3566, 3567, 3568, 3569, 3570, 3571, 3572, 3573, 3574, 3575, 3576, 3577, 3578, 3579, 3580, 3581, 3582, 3583, 3584, 3585, 3586, 3587, 3588, 3589, 3590, 3591, 3592], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 355
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(data.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 357,
       "text": [
        "3569"
       ]
      }
     ],
     "prompt_number": 357
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dummy_frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 45 columns):\n",
        "1     3569  non-null values\n",
        "1a    3569  non-null values\n",
        "1b    3569  non-null values\n",
        "1c    3569  non-null values\n",
        "1d    3569  non-null values\n",
        "2     3569  non-null values\n",
        "2a    3569  non-null values\n",
        "2b    3569  non-null values\n",
        "2c    3569  non-null values\n",
        "2d    3569  non-null values\n",
        "2e    3569  non-null values\n",
        "2f    3569  non-null values\n",
        "3     3569  non-null values\n",
        "3a    3569  non-null values\n",
        "3b    3569  non-null values\n",
        "3c    3569  non-null values\n",
        "3d    3569  non-null values\n",
        "3e    3569  non-null values\n",
        "4     3569  non-null values\n",
        "4a    3569  non-null values\n",
        "4c    3569  non-null values\n",
        "4e    3569  non-null values\n",
        "5     3569  non-null values\n",
        "5a    3569  non-null values\n",
        "5b    3569  non-null values\n",
        "5c    3569  non-null values\n",
        "5d    3569  non-null values\n",
        "5e    3569  non-null values\n",
        "6     3569  non-null values\n",
        "6a    3569  non-null values\n",
        "6b    3569  non-null values\n",
        "6c    3569  non-null values\n",
        "7     3569  non-null values\n",
        "7a    3569  non-null values\n",
        "7b    3569  non-null values\n",
        "7c    3569  non-null values\n",
        "7d    3569  non-null values\n",
        "7g    3569  non-null values\n",
        "7h    3569  non-null values\n",
        "8     3569  non-null values\n",
        "8a    3569  non-null values\n",
        "8c    3569  non-null values\n",
        "8d    3569  non-null values\n",
        "8e    3569  non-null values\n",
        "8f    3569  non-null values\n",
        "dtypes: float64(45)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 359,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 45 columns):\n",
        "1     3569  non-null values\n",
        "1a    3569  non-null values\n",
        "1b    3569  non-null values\n",
        "1c    3569  non-null values\n",
        "1d    3569  non-null values\n",
        "2     3569  non-null values\n",
        "2a    3569  non-null values\n",
        "2b    3569  non-null values\n",
        "2c    3569  non-null values\n",
        "2d    3569  non-null values\n",
        "2e    3569  non-null values\n",
        "2f    3569  non-null values\n",
        "3     3569  non-null values\n",
        "3a    3569  non-null values\n",
        "3b    3569  non-null values\n",
        "3c    3569  non-null values\n",
        "3d    3569  non-null values\n",
        "3e    3569  non-null values\n",
        "4     3569  non-null values\n",
        "4a    3569  non-null values\n",
        "4c    3569  non-null values\n",
        "4e    3569  non-null values\n",
        "5     3569  non-null values\n",
        "5a    3569  non-null values\n",
        "5b    3569  non-null values\n",
        "5c    3569  non-null values\n",
        "5d    3569  non-null values\n",
        "5e    3569  non-null values\n",
        "6     3569  non-null values\n",
        "6a    3569  non-null values\n",
        "6b    3569  non-null values\n",
        "6c    3569  non-null values\n",
        "7     3569  non-null values\n",
        "7a    3569  non-null values\n",
        "7b    3569  non-null values\n",
        "7c    3569  non-null values\n",
        "7d    3569  non-null values\n",
        "7g    3569  non-null values\n",
        "7h    3569  non-null values\n",
        "8     3569  non-null values\n",
        "8a    3569  non-null values\n",
        "8c    3569  non-null values\n",
        "8d    3569  non-null values\n",
        "8e    3569  non-null values\n",
        "8f    3569  non-null values\n",
        "dtypes: float64(45)"
       ]
      }
     ],
     "prompt_number": 359
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dummy_frame.ix[: , :6]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 6 columns):\n",
        "1     3569  non-null values\n",
        "1a    3569  non-null values\n",
        "1b    3569  non-null values\n",
        "1c    3569  non-null values\n",
        "1d    3569  non-null values\n",
        "2     3569  non-null values\n",
        "dtypes: float64(6)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 363,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 6 columns):\n",
        "1     3569  non-null values\n",
        "1a    3569  non-null values\n",
        "1b    3569  non-null values\n",
        "1c    3569  non-null values\n",
        "1d    3569  non-null values\n",
        "2     3569  non-null values\n",
        "dtypes: float64(6)"
       ]
      }
     ],
     "prompt_number": 363
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc5ec\uae30\uc5d0 \uc801\uc808\ud55c \ub85c\uc6b0\ub294 1\ub85c \uc124\uc815\ud558\uace0 \ub9c8\uc9c0\ub9c9\uc5d0 data\uc640 \uc870\uc778"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for row, cat in zip(data.index, data.CATEGORY):\n",
      "    # code \ucd94\ucd9c\n",
      "    codes = get_code(to_cat_list(cat))\n",
      "    # dummy_frame\uc5d0 \ud589:row, \uc5f4:codes \uc5d0 1 \uc124\uc815\n",
      "    dummy_frame.ix[row, codes] = 1\n",
      "    \n",
      "data = data.join(dummy_frame.add_prefix('category_'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 364
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zip(data.index, data.CATEGORY)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 365,
       "text": [
        "[(0, '1. Urgences | Emergency, 3. Public Health, '),\n",
        " (4, '1. Urgences | Emergency, '),\n",
        " (5, '5e. Communication lines down, '),\n",
        " (6,\n",
        "  '4. Menaces | Security Threats, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (7, '4. Menaces | Security Threats, '),\n",
        " (8,\n",
        "  '2. Urgences logistiques | Vital Lines, 2f. Sans courant | Power Outage, '),\n",
        " (9,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (10,\n",
        "  \"1a. Highly vulnerable, 2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (11,\n",
        "  '4. Menaces | Security Threats, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (12,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (13,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (14, '3. Public Health, '),\n",
        " (15, '2. Urgences logistiques | Vital Lines, '),\n",
        " (16,\n",
        "  '1. Urgences | Emergency, 7. Secours | Services Available, 8. Autre | Other, 6. Natural Hazards, '),\n",
        " (17, '3. Public Health, 3b. Chronic care needs, '),\n",
        " (18,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, \"),\n",
        " (19,\n",
        "  '1. Urgences | Emergency, 7. Secours | Services Available, 8. Autre | Other, 6. Natural Hazards, '),\n",
        " (20,\n",
        "  \"1. Urgences | Emergency, 2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (21,\n",
        "  '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 8. Autre | Other, '),\n",
        " (22, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (23, \"2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (24,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (25,\n",
        "  \"1b. Urgence medicale | Medical Emergency, 2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7d. Services de sante | Hospital/Clinics Operating, \"),\n",
        " (26,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, \"),\n",
        " (27, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (28,\n",
        "  '2. Urgences logistiques | Vital Lines, 2f. Sans courant | Power Outage, '),\n",
        " (29, '2. Urgences logistiques | Vital Lines, '),\n",
        " (30, '2. Urgences logistiques | Vital Lines, '),\n",
        " (31, '6. Natural Hazards, '),\n",
        " (32, '2. Urgences logistiques | Vital Lines, '),\n",
        " (33, '4. Menaces | Security Threats, '),\n",
        " (34,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 3. Public Health, 3d. OBGYN/Women's Health, \"),\n",
        " (35,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (36,\n",
        "  '2. Urgences logistiques | Vital Lines, 2f. Sans courant | Power Outage, '),\n",
        " (37,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, \"),\n",
        " (38,\n",
        "  '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 8. Autre | Other, 8f. Other, '),\n",
        " (39,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (40,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (41,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (42,\n",
        "  '1. Urgences | Emergency, 1d. Incendie | Fire, 1b. Urgence medicale | Medical Emergency, '),\n",
        " (43, '2. Urgences logistiques | Vital Lines, 3. Public Health, '),\n",
        " (44,\n",
        "  '1. Urgences | Emergency, 1d. Incendie | Fire, 1b. Urgence medicale | Medical Emergency, '),\n",
        " (45,\n",
        "  '2. Urgences logistiques | Vital Lines, 2f. Sans courant | Power Outage, '),\n",
        " (46,\n",
        "  '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 8. Autre | Other, 8a. IDP concentration, '),\n",
        " (47,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, \"),\n",
        " (48, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (49, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (50,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (51,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 3a. Infectious human disease, \"),\n",
        " (52,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 3a. Infectious human disease, \"),\n",
        " (53, '2d. Refuge | Shelter needed, '),\n",
        " (54,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, 8a. IDP concentration, \"),\n",
        " (55, \"2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (56,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (57,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, 8f. Other, \"),\n",
        " (58,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 3. Public Health, \"),\n",
        " (59,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (60,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3. Public Health, \"),\n",
        " (61, '2. Urgences logistiques | Vital Lines, '),\n",
        " (62, '2. Urgences logistiques | Vital Lines, '),\n",
        " (63,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (64, '2. Urgences logistiques | Vital Lines, '),\n",
        " (65,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (66, '3. Public Health, '),\n",
        " (67,\n",
        "  '7d. Services de sante | Hospital/Clinics Operating, 3b. Chronic care needs, '),\n",
        " (68, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (69,\n",
        "  '2. Urgences logistiques | Vital Lines, 8. Autre | Other, 8c. Price gouging, 3. Public Health, '),\n",
        " (70, '4. Menaces | Security Threats, 4c. Group violence, '),\n",
        " (71,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (72,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (73,\n",
        "  '1. Urgences | Emergency, 1b. Urgence medicale | Medical Emergency, 1a. Highly vulnerable, 3. Public Health, 3a. Infectious human disease, '),\n",
        " (74, '2d. Refuge | Shelter needed, 8a. IDP concentration, '),\n",
        " (75, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (76,\n",
        "  \"5. Infrastructure Damage, 2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, 8a. IDP concentration, 3. Public Health, 3a. Infectious human disease, 3d. OBGYN/Women's Health, 3e. Psychiatric need, \"),\n",
        " (77,\n",
        "  \"5. Infrastructure Damage, 2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2e. Penurie de carburant | Fuel shortage, 8. Autre | Other, 8a. IDP concentration, 3. Public Health, 3a. Infectious human disease, 3d. OBGYN/Women's Health, 3e. Psychiatric need, \"),\n",
        " (78,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, 8a. IDP concentration, 3. Public Health, 3d. OBGYN/Women's Health, 3e. Psychiatric need, \"),\n",
        " (79,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, 8a. IDP concentration, 3. Public Health, 3a. Infectious human disease, 3d. OBGYN/Women's Health, \"),\n",
        " (80,\n",
        "  \"5. Infrastructure Damage, 5d. Compromised bridge, 2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 8. Autre | Other, 8a. IDP concentration, 3a. Infectious human disease, 3d. OBGYN/Women's Health, \"),\n",
        " (81,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, 3a. Infectious human disease, 3d. OBGYN/Women's Health, 3e. Psychiatric need, \"),\n",
        " (82,\n",
        "  '1. Urgences | Emergency, 2. Urgences logistiques | Vital Lines, 3. Public Health, '),\n",
        " (83, '8. Autre | Other, 8f. Other, '),\n",
        " (84,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (85,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (86,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (87,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (88,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (89,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (90,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (91,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (92,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (93,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (94,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (95,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (96,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (97,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (98,\n",
        "  '1. Urgences | Emergency, 2. Urgences logistiques | Vital Lines, 7. Secours | Services Available, '),\n",
        " (99,\n",
        "  '1. Urgences | Emergency, 5. Infrastructure Damage, 2. Urgences logistiques | Vital Lines, 4. Menaces | Security Threats, 7. Secours | Services Available, 8. Autre | Other, 6. Natural Hazards, 3. Public Health, '),\n",
        " (100,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, 8a. IDP concentration, \"),\n",
        " (101, '2d. Refuge | Shelter needed, '),\n",
        " (102,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (103,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (104, '1b. Urgence medicale | Medical Emergency, 3. Public Health, '),\n",
        " (105, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (106,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (107,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (108, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (109,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (110, '8. Autre | Other, '),\n",
        " (111,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (112,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (113,\n",
        "  '1b. Urgence medicale | Medical Emergency, 3a. Infectious human disease, '),\n",
        " (114,\n",
        "  '4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (115, \"2a. Penurie d'aliments | Food Shortage, 3. Public Health, \"),\n",
        " (116,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (117, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (118,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (119, '2. Urgences logistiques | Vital Lines, 8f. Other, '),\n",
        " (120,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (121, \"1. Urgences | Emergency, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (122,\n",
        "  '1. Urgences | Emergency, 2. Urgences logistiques | Vital Lines, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (123, '1c. Personnes prises au piege | People trapped, '),\n",
        " (124, '2. Urgences logistiques | Vital Lines, '),\n",
        " (125, '2d. Refuge | Shelter needed, '),\n",
        " (126,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (127,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (128,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (129,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (130,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (131,\n",
        "  '4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (132, '2f. Sans courant | Power Outage, '),\n",
        " (133, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (134, '4. Menaces | Security Threats, '),\n",
        " (135,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (136,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (137, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (138, '4a. Pillage | Looting, 8. Autre | Other, '),\n",
        " (139,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (140,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (141,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (142, '1b. Urgence medicale | Medical Emergency, '),\n",
        " (143, '1c. Personnes prises au piege | People trapped, '),\n",
        " (144, '1c. Personnes prises au piege | People trapped, '),\n",
        " (145,\n",
        "  '1b. Urgence medicale | Medical Emergency, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (146,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (147,\n",
        "  '5a. Structure effondres | Collapsed structure, 2d. Refuge | Shelter needed, '),\n",
        " (148, '8. Autre | Other, '),\n",
        " (149, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (150, '8. Autre | Other, '),\n",
        " (151, '2f. Sans courant | Power Outage, '),\n",
        " (152,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (153,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (154,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (155,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (156, '7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (157, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (158,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (159, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (160,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (161,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (162,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (163,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (164, '2d. Refuge | Shelter needed, '),\n",
        " (165, '8. Autre | Other, '),\n",
        " (166, '2. Urgences logistiques | Vital Lines, '),\n",
        " (167, '7. Secours | Services Available, '),\n",
        " (168, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (169, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (170,\n",
        "  \"7. Secours | Services Available, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (171,\n",
        "  \"7. Secours | Services Available, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (172,\n",
        "  '1. Urgences | Emergency, 5a. Structure effondres | Collapsed structure, 1b. Urgence medicale | Medical Emergency, '),\n",
        " (173,\n",
        "  '1. Urgences | Emergency, 5a. Structure effondres | Collapsed structure, 7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (174, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (175, '8. Autre | Other, '),\n",
        " (176, '2d. Refuge | Shelter needed, '),\n",
        " (177, '8. Autre | Other, '),\n",
        " (178, '2f. Sans courant | Power Outage, '),\n",
        " (179,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (180,\n",
        "  '2. Urgences logistiques | Vital Lines, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (181, '2d. Refuge | Shelter needed, '),\n",
        " (182,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (183, '2d. Refuge | Shelter needed, '),\n",
        " (184,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (185,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (186, '2. Urgences logistiques | Vital Lines, '),\n",
        " (187,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (188, '2. Urgences logistiques | Vital Lines, '),\n",
        " (189, '2. Urgences logistiques | Vital Lines, '),\n",
        " (190,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, '),\n",
        " (191, \"4. Menaces | Security Threats, 2b. Penurie d'eau | Water shortage, \"),\n",
        " (192,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7d. Services de sante | Hospital/Clinics Operating, \"),\n",
        " (193,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (194,\n",
        "  '2. Urgences logistiques | Vital Lines, 7. Secours | Services Available, '),\n",
        " (195,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7d. Services de sante | Hospital/Clinics Operating, \"),\n",
        " (196,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (197,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, 8. Autre | Other, \"),\n",
        " (198,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (199,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (200, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (201, '2. Urgences logistiques | Vital Lines, '),\n",
        " (202, '2. Urgences logistiques | Vital Lines, '),\n",
        " (203, '2. Urgences logistiques | Vital Lines, 2d. Refuge | Shelter needed, '),\n",
        " (204, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (205,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (206,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (207, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (208,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (209,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (210,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (211,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (212,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (213, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (214,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (215, '8. Autre | Other, '),\n",
        " (216,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (217,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (218, '4. Menaces | Security Threats, '),\n",
        " (219,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (220,\n",
        "  '7d. Services de sante | Hospital/Clinics Operating, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (221,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (222, '1. Urgences | Emergency, 2. Urgences logistiques | Vital Lines, '),\n",
        " (223,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (224, '2d. Refuge | Shelter needed, '),\n",
        " (225,\n",
        "  \"2b. Penurie d'eau | Water shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (226, '2d. Refuge | Shelter needed, '),\n",
        " (227,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, 8. Autre | Other, \"),\n",
        " (228, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (229,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (230,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (231,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (232,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (233, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (234,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (235, '8. Autre | Other, '),\n",
        " (236,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, \"),\n",
        " (237,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (238,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (239,\n",
        "  '7. Secours | Services Available, 7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (240,\n",
        "  '8e. Nouvelles de Personnes | Persons News, 6b. Personnes Disparues | Missing Persons, '),\n",
        " (241,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (242,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (243,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (244, '7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (245,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (246,\n",
        "  '1b. Urgence medicale | Medical Emergency, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (247,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (248, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (249,\n",
        "  '2d. Refuge | Shelter needed, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (250, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (251, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (252, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (253, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (254, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (255,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (256,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (257, '2. Urgences logistiques | Vital Lines, '),\n",
        " (258, '1b. Urgence medicale | Medical Emergency, '),\n",
        " (259, '1. Urgences | Emergency, '),\n",
        " (260, '2. Urgences logistiques | Vital Lines, '),\n",
        " (261,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (262, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (263,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (264,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (265,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (266, '2. Urgences logistiques | Vital Lines, '),\n",
        " (267,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, \"),\n",
        " (268, '2. Urgences logistiques | Vital Lines, '),\n",
        " (269, '2. Urgences logistiques | Vital Lines, '),\n",
        " (270, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (271, '2. Urgences logistiques | Vital Lines, '),\n",
        " (272, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (273,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (274, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (275,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (276, '2. Urgences logistiques | Vital Lines, '),\n",
        " (277,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (278, '2. Urgences logistiques | Vital Lines, '),\n",
        " (279,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, 8. Autre | Other, \"),\n",
        " (280, '2d. Refuge | Shelter needed, '),\n",
        " (281, '2d. Refuge | Shelter needed, '),\n",
        " (282,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (283, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (284,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (285, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (286, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (287, \"2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, \"),\n",
        " (288,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (289, '2d. Refuge | Shelter needed, '),\n",
        " (290,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (291, '2d. Refuge | Shelter needed, '),\n",
        " (292, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (293, '2d. Refuge | Shelter needed, '),\n",
        " (294, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (295, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (296, '4. Menaces | Security Threats, 2d. Refuge | Shelter needed, '),\n",
        " (297, '8. Autre | Other, '),\n",
        " (298,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (299,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (300,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (301,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (302,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (303,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (304, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (305,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (306, '2d. Refuge | Shelter needed, '),\n",
        " (307, '2. Urgences logistiques | Vital Lines, '),\n",
        " (308,\n",
        "  '7. Secours | Services Available, 2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, '),\n",
        " (309, '2d. Refuge | Shelter needed, '),\n",
        " (310, '7. Secours | Services Available, '),\n",
        " (311,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (312, '2d. Refuge | Shelter needed, '),\n",
        " (313, '2d. Refuge | Shelter needed, '),\n",
        " (314, '8. Autre | Other, '),\n",
        " (315, '8. Autre | Other, '),\n",
        " (316, '8. Autre | Other, '),\n",
        " (317, '2d. Refuge | Shelter needed, '),\n",
        " (318, '8. Autre | Other, '),\n",
        " (319,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (320,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (321,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (322, '2. Urgences logistiques | Vital Lines, '),\n",
        " (323, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (324,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (325,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (326,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (327,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (328, '2. Urgences logistiques | Vital Lines, '),\n",
        " (329, \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (330, '2d. Refuge | Shelter needed, '),\n",
        " (331, '2d. Refuge | Shelter needed, '),\n",
        " (332, '2d. Refuge | Shelter needed, '),\n",
        " (333,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (334, '2d. Refuge | Shelter needed, '),\n",
        " (335, '2d. Refuge | Shelter needed, '),\n",
        " (336,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (337,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (338,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (339,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (340,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (341, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (342, \"2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, \"),\n",
        " (343,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (344,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (345,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (346,\n",
        "  \"1b. Urgence medicale | Medical Emergency, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (347,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (348, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (349, \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (350,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (351, '2d. Refuge | Shelter needed, '),\n",
        " (352,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (353,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (354,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (355,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (356,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (357,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (358,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (359,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (360,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (361,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (362, '2d. Refuge | Shelter needed, '),\n",
        " (363, '2d. Refuge | Shelter needed, '),\n",
        " (364,\n",
        "  '4a. Pillage | Looting, 2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, '),\n",
        " (365, '2d. Refuge | Shelter needed, '),\n",
        " (366,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (367,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (368,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (369,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (370, '2d. Refuge | Shelter needed, '),\n",
        " (371,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (372, '7. Secours | Services Available, '),\n",
        " (373, '2d. Refuge | Shelter needed, '),\n",
        " (374, '2d. Refuge | Shelter needed, '),\n",
        " (375, '7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (376,\n",
        "  '2d. Refuge | Shelter needed, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (377,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (378, '2d. Refuge | Shelter needed, '),\n",
        " (379, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (380,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (381, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (382,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (383, '2d. Refuge | Shelter needed, '),\n",
        " (384, '2d. Refuge | Shelter needed, '),\n",
        " (385,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (386, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (387, '1b. Urgence medicale | Medical Emergency, '),\n",
        " (388,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (389,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, \"),\n",
        " (390,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (391,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (392, \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (393,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (394,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (395,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (396,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (397,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (398, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (399,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (400, '5b. Structures a risque | Unstable Structure, '),\n",
        " (401,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (402,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (403,\n",
        "  \"4. Menaces | Security Threats, 2c. Probleme de securite | Security Concern, 2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (404, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (405,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (406,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (407,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (408, '2d. Refuge | Shelter needed, '),\n",
        " (409,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (410,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (411,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (412,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (413,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (414,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (415,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (416, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (417,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (418,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, \"),\n",
        " (419,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (420,\n",
        "  '7. Secours | Services Available, 7d. Services de sante | Hospital/Clinics Operating, 8. Autre | Other, '),\n",
        " (421,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (422, '2d. Refuge | Shelter needed, '),\n",
        " (423, '7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (424, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (425,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (426,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (427,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (428,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (429, '8. Autre | Other, '),\n",
        " (430,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (431,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 8. Autre | Other, \"),\n",
        " (433,\n",
        "  '4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (434,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (435,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (436, '2d. Refuge | Shelter needed, '),\n",
        " (437,\n",
        "  \"5a. Structure effondres | Collapsed structure, 2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (438, '8. Autre | Other, '),\n",
        " (439,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (440,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (441, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (442, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (443, '2d. Refuge | Shelter needed, '),\n",
        " (444,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (445, '2d. Refuge | Shelter needed, '),\n",
        " (446,\n",
        "  '2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, '),\n",
        " (447, '2d. Refuge | Shelter needed, '),\n",
        " (448, '2d. Refuge | Shelter needed, '),\n",
        " (449, '2d. Refuge | Shelter needed, '),\n",
        " (450, '2d. Refuge | Shelter needed, '),\n",
        " (451, '2d. Refuge | Shelter needed, '),\n",
        " (452, '7. Secours | Services Available, '),\n",
        " (453,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (454, '7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (455, '5a. Structure effondres | Collapsed structure, '),\n",
        " (456, '5a. Structure effondres | Collapsed structure, '),\n",
        " (457,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (458, '7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (459, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (460, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (461, '2f. Sans courant | Power Outage, '),\n",
        " (462,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (463,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (464, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (465, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (466, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (467,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (468,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (469, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (470,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (471,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (472,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (473,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (474,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (475,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (476,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (477, \"2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, \"),\n",
        " (478,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (479, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (480, '4. Menaces | Security Threats, '),\n",
        " (481, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (482, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (483,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (484,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (485,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (486, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (487, '1d. Incendie | Fire, '),\n",
        " (488, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (489, \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (490,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (491,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (492,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (493, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (494,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (495,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (496,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (497,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (498,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (499,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (500, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (501, '7g. Morgue | Human remains management, '),\n",
        " (502, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (503,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (504, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (505,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (506,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (507,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (508,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, 2d. Refuge | Shelter needed, \"),\n",
        " (509, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (510,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (511, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (512,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (513,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (514, '2d. Refuge | Shelter needed, '),\n",
        " (515, '2d. Refuge | Shelter needed, '),\n",
        " (516,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (517,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (518,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (519,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (520, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (521,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (522, '2d. Refuge | Shelter needed, '),\n",
        " (523,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (524,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (525, '2d. Refuge | Shelter needed, '),\n",
        " (526, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (527, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (528, '4. Menaces | Security Threats, '),\n",
        " (529, '2d. Refuge | Shelter needed, '),\n",
        " (530, '2d. Refuge | Shelter needed, '),\n",
        " (531,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (532,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (533,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (534,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (535,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (536,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (537,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (538,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (539, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (540, '1c. Personnes prises au piege | People trapped, '),\n",
        " (541,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (542,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (543,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (544,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (545,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, 8. Autre | Other, \"),\n",
        " (546, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (547,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (548,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (549,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (550,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (551, '2d. Refuge | Shelter needed, '),\n",
        " (552,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (553, '2d. Refuge | Shelter needed, '),\n",
        " (554,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (555, '1b. Urgence medicale | Medical Emergency, '),\n",
        " (556,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (557,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (558, '2. Urgences logistiques | Vital Lines, '),\n",
        " (559,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (560,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (561,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (562, '2. Urgences logistiques | Vital Lines, '),\n",
        " (563, '2d. Refuge | Shelter needed, '),\n",
        " (564,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (565, '2d. Refuge | Shelter needed, '),\n",
        " (566,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (567,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (568,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (569,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (570, '2d. Refuge | Shelter needed, '),\n",
        " (571,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (572,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (573,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (574, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (575, '2d. Refuge | Shelter needed, '),\n",
        " (576, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (577,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (578, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (579,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (580,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (581,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (582,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (583,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (584,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (585, '2d. Refuge | Shelter needed, '),\n",
        " (586,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (587,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (588,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (589, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (590,\n",
        "  \"4a. Pillage | Looting, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (591,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (592,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (593,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (594, '7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (595,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (596,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (597,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 8. Autre | Other, \"),\n",
        " (598,\n",
        "  '2. Urgences logistiques | Vital Lines, 2f. Sans courant | Power Outage, '),\n",
        " (599,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (600, '7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (601,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (602,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (603, '8. Autre | Other, '),\n",
        " (604,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (605,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (606,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (607,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (608,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (609,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2d. Refuge | Shelter needed, \"),\n",
        " (610,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (611,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (612,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (613,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (614,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (615,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (616,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (617, '2. Urgences logistiques | Vital Lines, '),\n",
        " (618, '2. Urgences logistiques | Vital Lines, '),\n",
        " (619, '2. Urgences logistiques | Vital Lines, '),\n",
        " (620,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (621, '2. Urgences logistiques | Vital Lines, '),\n",
        " (622,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (623,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (624, '2d. Refuge | Shelter needed, '),\n",
        " (625,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (626, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (627,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (628, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (629,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (630, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (631,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (632,\n",
        "  '4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (633, '7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (634,\n",
        "  '4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, '),\n",
        " (635,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (636,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (637, '7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (638,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (639,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, 7b. Distribution d'eau | Water distribution point, \"),\n",
        " (640, '2d. Refuge | Shelter needed, '),\n",
        " (641,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (642,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (643,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (644,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (645,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (646,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (647,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (648,\n",
        "  \"2b. Penurie d'eau | Water shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (649, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (650,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (651,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (652,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (653,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (654, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (655,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (656,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (657, '7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (658, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (659,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (660,\n",
        "  '5a. Structure effondres | Collapsed structure, 1b. Urgence medicale | Medical Emergency, '),\n",
        " (661,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (662, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (663, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (664,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (665, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (666, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (667,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (668,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (669, '1c. Personnes prises au piege | People trapped, '),\n",
        " (670, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (671,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (672,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (673,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (674,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (675,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (676,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (677,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (678,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (679,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (680, '1c. Personnes prises au piege | People trapped, '),\n",
        " (681,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (682,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (683,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (684,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (685,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (686, '2. Urgences logistiques | Vital Lines, '),\n",
        " (687,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (688,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (689, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (690, '2. Urgences logistiques | Vital Lines, '),\n",
        " (691,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (692,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (693,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (694, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (695,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (696,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (697,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (698, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (699, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (700,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (701,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (702,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (703,\n",
        "  '5a. Structure effondres | Collapsed structure, 1c. Personnes prises au piege | People trapped, '),\n",
        " (704,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (705, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (706, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (707, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (708,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (709,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (710, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (711,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2d. Refuge | Shelter needed, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (712,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (713,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (714,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (715,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (716,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (717, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (718,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (719,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (720, '2d. Refuge | Shelter needed, '),\n",
        " (721,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (722, '2d. Refuge | Shelter needed, '),\n",
        " (723, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (724, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (725,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (726,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (727, '1c. Personnes prises au piege | People trapped, '),\n",
        " (728, '1b. Urgence medicale | Medical Emergency, '),\n",
        " (729,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (730,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (731, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (732,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (733,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (734,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (735,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (736,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (737,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (738,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (739, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (740,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (741,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (742,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (743, '4. Menaces | Security Threats, 5c. Route barree | Road blocked, '),\n",
        " (744, '2d. Refuge | Shelter needed, '),\n",
        " (745,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (746, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (747,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (748,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (749,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (750,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (751,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (752,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (753,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (754, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (755,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (756, '2d. Refuge | Shelter needed, '),\n",
        " (757,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (758,\n",
        "  \"5a. Structure effondres | Collapsed structure, 1c. Personnes prises au piege | People trapped, 5b. Structures a risque | Unstable Structure, 2b. Penurie d'eau | Water shortage, 5c. Route barree | Road blocked, 2a. Penurie d'aliments | Food Shortage, 8d. Recherche et sauvetage | Search and Rescue, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, 7c. Denrees non alimentaires | Non-food aid distribution point, 7b. Distribution d'eau | Water distribution point, 8e. Nouvelles de Personnes | Persons News, \"),\n",
        " (759, '2d. Refuge | Shelter needed, '),\n",
        " (760,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (761,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (762, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (763,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (764, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (765,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (766,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (767, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (768,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (769, '8. Autre | Other, '),\n",
        " (770, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (771,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (772,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (773,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (774, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (775,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (776,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (777,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (778,\n",
        "  '1b. Urgence medicale | Medical Emergency, 2d. Refuge | Shelter needed, '),\n",
        " (779,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (780,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (781,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (782,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (783,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (784,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, \"),\n",
        " (785,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (786,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (787,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (788,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (789, '7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (790,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (791,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (792, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (793, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (794,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (795,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (796,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (797,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (798, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (799,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (800,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (801,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (802, '2d. Refuge | Shelter needed, '),\n",
        " (803,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (804,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (805,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (806, \"4a. Pillage | Looting, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (807,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (808,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (809,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (810,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (811,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (812,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (813, '2d. Refuge | Shelter needed, '),\n",
        " (814,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (815,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (816,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (817,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (818,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (819,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (820,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (821,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (822,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (823,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (824,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (825,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (826,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (827, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (828,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (829, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (830, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (831,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (832, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (833, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (834, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (835, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (836,\n",
        "  \"1. Urgences | Emergency, 1b. Urgence medicale | Medical Emergency, 2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (837,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (838, '2d. Refuge | Shelter needed, '),\n",
        " (839,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (841, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (842,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (843,\n",
        "  \"7. Secours | Services Available, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (844,\n",
        "  \"7. Secours | Services Available, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (845,\n",
        "  \"7. Secours | Services Available, 2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (846,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (847, '7. Secours | Services Available, '),\n",
        " (848,\n",
        "  \"1b. Urgence medicale | Medical Emergency, 2b. Penurie d'eau | Water shortage, 2f. Sans courant | Power Outage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (849, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (850, '2d. Refuge | Shelter needed, '),\n",
        " (851,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (852, '1. Urgences | Emergency, '),\n",
        " (853,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (854, '7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (855, '7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (856, '2. Urgences logistiques | Vital Lines, '),\n",
        " (857,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (858,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (859, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (860, '2. Urgences logistiques | Vital Lines, '),\n",
        " (861,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (862,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (863, '2d. Refuge | Shelter needed, '),\n",
        " (864, '8. Autre | Other, '),\n",
        " (865, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (866, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (867,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, \"),\n",
        " (868, '2. Urgences logistiques | Vital Lines, '),\n",
        " (869, '7. Secours | Services Available, 2d. Refuge | Shelter needed, '),\n",
        " (870,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (871,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (872,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (873, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (874,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (875,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (876,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (877,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7. Secours | Services Available, 2d. Refuge | Shelter needed, \"),\n",
        " (878, '2f. Sans courant | Power Outage, '),\n",
        " (879,\n",
        "  '6c. Seisme et repliques | Earthquake and aftershocks, 4. Menaces | Security Threats, '),\n",
        " (880,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2e. Penurie de carburant | Fuel shortage, \"),\n",
        " (881, '2d. Refuge | Shelter needed, '),\n",
        " (882,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (883,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (884,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 2d. Refuge | Shelter needed, \"),\n",
        " (885,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (886,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (887, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (888,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (889,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (890,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (891,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (892,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (893,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, '),\n",
        " (894, \"7a. Distribution d'aliments | Food distribution point, \"),\n",
        " (895,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (896,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (897,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (898,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (899,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (900,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (901,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (902,\n",
        "  \"1b. Urgence medicale | Medical Emergency, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (903,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (904,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (905,\n",
        "  \"3c. Besoins en materiels et medicaments | Medical equipment and supply needs, 7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (906,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (907,\n",
        "  \"4. Menaces | Security Threats, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (908,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (909,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (910,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (911,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (912,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (913,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (915,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (916,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (917,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (918,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (919,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (920,\n",
        "  \"2d. Refuge | Shelter needed, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (921,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (922,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (923,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (924,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (925,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (926,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (927, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (928,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (929,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (931,\n",
        "  \"7d. Services de sante | Hospital/Clinics Operating, 7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (932, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (933, '2. Urgences logistiques | Vital Lines, '),\n",
        " (934,\n",
        "  \"7a. Distribution d'aliments | Food distribution point, 4e. Assainissement eau et hygiene | Water sanitation and hygiene promotion, \"),\n",
        " (935, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (936,\n",
        "  '3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (937,\n",
        "  '1b. Urgence medicale | Medical Emergency, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (938,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (939,\n",
        "  '5a. Structure effondres | Collapsed structure, 2. Urgences logistiques | Vital Lines, '),\n",
        " (940, '2. Urgences logistiques | Vital Lines, '),\n",
        " (941, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (942,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (943,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (944,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (945,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (946,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (947,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (948,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (949,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (950,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (951,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (952, '2. Urgences logistiques | Vital Lines, '),\n",
        " (953,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (954, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (955,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (956,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (957, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (958,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (959, '7g. Morgue | Human remains management, '),\n",
        " (960,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (961, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (962, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (963,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (964, \"2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, \"),\n",
        " (965,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (966, '1. Urgences | Emergency, 2. Urgences logistiques | Vital Lines, '),\n",
        " (967,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (968,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (969,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, 7c. Denrees non alimentaires | Non-food aid distribution point, \"),\n",
        " (970,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (971,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (972, '2. Urgences logistiques | Vital Lines, '),\n",
        " (973,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (974,\n",
        "  \"2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (975, '2. Urgences logistiques | Vital Lines, '),\n",
        " (976, '2. Urgences logistiques | Vital Lines, '),\n",
        " (977,\n",
        "  \"1b. Urgence medicale | Medical Emergency, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (978, '2. Urgences logistiques | Vital Lines, '),\n",
        " (979,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (980,\n",
        "  \"4. Menaces | Security Threats, 2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (981,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (982,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (983, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (984, '2. Urgences logistiques | Vital Lines, '),\n",
        " (985, '8. Autre | Other, '),\n",
        " (986,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (987,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 2d. Refuge | Shelter needed, \"),\n",
        " (988,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 8. Autre | Other, \"),\n",
        " (989,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (990,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (991,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (992,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (993, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (994, '8. Autre | Other, '),\n",
        " (995, '2d. Refuge | Shelter needed, '),\n",
        " (996,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (997,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (998, \"2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (999, \"2b. Penurie d'eau | Water shortage, \"),\n",
        " (1000,\n",
        "  \"1b. Urgence medicale | Medical Emergency, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (1001,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, \"),\n",
        " (1002,\n",
        "  \"2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (1003,\n",
        "  '1b. Urgence medicale | Medical Emergency, 3c. Besoins en materiels et medicaments | Medical equipment and supply needs, '),\n",
        " (1004,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2b. Penurie d'eau | Water shortage, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " (1005, '2d. Refuge | Shelter needed, '),\n",
        " (1006,\n",
        "  \"2. Urgences logistiques | Vital Lines, 2a. Penurie d'aliments | Food Shortage, \"),\n",
        " ...]"
       ]
      }
     ],
     "prompt_number": 365
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dummy_frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 45 columns):\n",
        "1     3569  non-null values\n",
        "1a    3569  non-null values\n",
        "1b    3569  non-null values\n",
        "1c    3569  non-null values\n",
        "1d    3569  non-null values\n",
        "2     3569  non-null values\n",
        "2a    3569  non-null values\n",
        "2b    3569  non-null values\n",
        "2c    3569  non-null values\n",
        "2d    3569  non-null values\n",
        "2e    3569  non-null values\n",
        "2f    3569  non-null values\n",
        "3     3569  non-null values\n",
        "3a    3569  non-null values\n",
        "3b    3569  non-null values\n",
        "3c    3569  non-null values\n",
        "3d    3569  non-null values\n",
        "3e    3569  non-null values\n",
        "4     3569  non-null values\n",
        "4a    3569  non-null values\n",
        "4c    3569  non-null values\n",
        "4e    3569  non-null values\n",
        "5     3569  non-null values\n",
        "5a    3569  non-null values\n",
        "5b    3569  non-null values\n",
        "5c    3569  non-null values\n",
        "5d    3569  non-null values\n",
        "5e    3569  non-null values\n",
        "6     3569  non-null values\n",
        "6a    3569  non-null values\n",
        "6b    3569  non-null values\n",
        "6c    3569  non-null values\n",
        "7     3569  non-null values\n",
        "7a    3569  non-null values\n",
        "7b    3569  non-null values\n",
        "7c    3569  non-null values\n",
        "7d    3569  non-null values\n",
        "7g    3569  non-null values\n",
        "7h    3569  non-null values\n",
        "8     3569  non-null values\n",
        "8a    3569  non-null values\n",
        "8c    3569  non-null values\n",
        "8d    3569  non-null values\n",
        "8e    3569  non-null values\n",
        "8f    3569  non-null values\n",
        "dtypes: float64(45)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 367,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 45 columns):\n",
        "1     3569  non-null values\n",
        "1a    3569  non-null values\n",
        "1b    3569  non-null values\n",
        "1c    3569  non-null values\n",
        "1d    3569  non-null values\n",
        "2     3569  non-null values\n",
        "2a    3569  non-null values\n",
        "2b    3569  non-null values\n",
        "2c    3569  non-null values\n",
        "2d    3569  non-null values\n",
        "2e    3569  non-null values\n",
        "2f    3569  non-null values\n",
        "3     3569  non-null values\n",
        "3a    3569  non-null values\n",
        "3b    3569  non-null values\n",
        "3c    3569  non-null values\n",
        "3d    3569  non-null values\n",
        "3e    3569  non-null values\n",
        "4     3569  non-null values\n",
        "4a    3569  non-null values\n",
        "4c    3569  non-null values\n",
        "4e    3569  non-null values\n",
        "5     3569  non-null values\n",
        "5a    3569  non-null values\n",
        "5b    3569  non-null values\n",
        "5c    3569  non-null values\n",
        "5d    3569  non-null values\n",
        "5e    3569  non-null values\n",
        "6     3569  non-null values\n",
        "6a    3569  non-null values\n",
        "6b    3569  non-null values\n",
        "6c    3569  non-null values\n",
        "7     3569  non-null values\n",
        "7a    3569  non-null values\n",
        "7b    3569  non-null values\n",
        "7c    3569  non-null values\n",
        "7d    3569  non-null values\n",
        "7g    3569  non-null values\n",
        "7h    3569  non-null values\n",
        "8     3569  non-null values\n",
        "8a    3569  non-null values\n",
        "8c    3569  non-null values\n",
        "8d    3569  non-null values\n",
        "8e    3569  non-null values\n",
        "8f    3569  non-null values\n",
        "dtypes: float64(45)"
       ]
      }
     ],
     "prompt_number": 367
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1\ub85c \uc138\ud305\ub41c\uac8c \uac04\ud639 \ubcf4\uc778\ub2e4.\n",
      "dummy_frame.ix[:, 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 374,
       "text": [
        "0     1\n",
        "4     1\n",
        "5     0\n",
        "6     0\n",
        "7     0\n",
        "8     0\n",
        "9     0\n",
        "10    0\n",
        "11    0\n",
        "12    0\n",
        "13    0\n",
        "14    0\n",
        "15    0\n",
        "16    1\n",
        "17    0\n",
        "...\n",
        "3578    0\n",
        "3579    0\n",
        "3580    0\n",
        "3581    0\n",
        "3582    0\n",
        "3583    0\n",
        "3584    0\n",
        "3585    0\n",
        "3586    1\n",
        "3587    1\n",
        "3588    0\n",
        "3589    0\n",
        "3590    0\n",
        "3591    0\n",
        "3592    0\n",
        "Name: 1, Length: 3569, dtype: float64"
       ]
      }
     ],
     "prompt_number": 374
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 55 columns):\n",
        "Serial            3569  non-null values\n",
        "INCIDENT TITLE    3569  non-null values\n",
        "INCIDENT DATE     3569  non-null values\n",
        "LOCATION          3569  non-null values\n",
        "DESCRIPTION       3569  non-null values\n",
        "CATEGORY          3569  non-null values\n",
        "LATITUDE          3569  non-null values\n",
        "LONGITUDE         3569  non-null values\n",
        "APPROVED          3569  non-null values\n",
        "VERIFIED          3569  non-null values\n",
        "category_1        3569  non-null values\n",
        "category_1a       3569  non-null values\n",
        "category_1b       3569  non-null values\n",
        "category_1c       3569  non-null values\n",
        "category_1d       3569  non-null values\n",
        "category_2        3569  non-null values\n",
        "category_2a       3569  non-null values\n",
        "category_2b       3569  non-null values\n",
        "category_2c       3569  non-null values\n",
        "category_2d       3569  non-null values\n",
        "category_2e       3569  non-null values\n",
        "category_2f       3569  non-null values\n",
        "category_3        3569  non-null values\n",
        "category_3a       3569  non-null values\n",
        "category_3b       3569  non-null values\n",
        "category_3c       3569  non-null values\n",
        "category_3d       3569  non-null values\n",
        "category_3e       3569  non-null values\n",
        "category_4        3569  non-null values\n",
        "category_4a       3569  non-null values\n",
        "category_4c       3569  non-null values\n",
        "category_4e       3569  non-null values\n",
        "category_5        3569  non-null values\n",
        "category_5a       3569  non-null values\n",
        "category_5b       3569  non-null values\n",
        "category_5c       3569  non-null values\n",
        "category_5d       3569  non-null values\n",
        "category_5e       3569  non-null values\n",
        "category_6        3569  non-null values\n",
        "category_6a       3569  non-null values\n",
        "category_6b       3569  non-null values\n",
        "category_6c       3569  non-null values\n",
        "category_7        3569  non-null values\n",
        "category_7a       3569  non-null values\n",
        "category_7b       3569  non-null values\n",
        "category_7c       3569  non-null values\n",
        "category_7d       3569  non-null values\n",
        "category_7g       3569  non-null values\n",
        "category_7h       3569  non-null values\n",
        "category_8        3569  non-null values\n",
        "category_8a       3569  non-null values\n",
        "category_8c       3569  non-null values\n",
        "category_8d       3569  non-null values\n",
        "category_8e       3569  non-null values\n",
        "category_8f       3569  non-null values\n",
        "dtypes: float64(47), int64(1), object(7)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 366,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 55 columns):\n",
        "Serial            3569  non-null values\n",
        "INCIDENT TITLE    3569  non-null values\n",
        "INCIDENT DATE     3569  non-null values\n",
        "LOCATION          3569  non-null values\n",
        "DESCRIPTION       3569  non-null values\n",
        "CATEGORY          3569  non-null values\n",
        "LATITUDE          3569  non-null values\n",
        "LONGITUDE         3569  non-null values\n",
        "APPROVED          3569  non-null values\n",
        "VERIFIED          3569  non-null values\n",
        "category_1        3569  non-null values\n",
        "category_1a       3569  non-null values\n",
        "category_1b       3569  non-null values\n",
        "category_1c       3569  non-null values\n",
        "category_1d       3569  non-null values\n",
        "category_2        3569  non-null values\n",
        "category_2a       3569  non-null values\n",
        "category_2b       3569  non-null values\n",
        "category_2c       3569  non-null values\n",
        "category_2d       3569  non-null values\n",
        "category_2e       3569  non-null values\n",
        "category_2f       3569  non-null values\n",
        "category_3        3569  non-null values\n",
        "category_3a       3569  non-null values\n",
        "category_3b       3569  non-null values\n",
        "category_3c       3569  non-null values\n",
        "category_3d       3569  non-null values\n",
        "category_3e       3569  non-null values\n",
        "category_4        3569  non-null values\n",
        "category_4a       3569  non-null values\n",
        "category_4c       3569  non-null values\n",
        "category_4e       3569  non-null values\n",
        "category_5        3569  non-null values\n",
        "category_5a       3569  non-null values\n",
        "category_5b       3569  non-null values\n",
        "category_5c       3569  non-null values\n",
        "category_5d       3569  non-null values\n",
        "category_5e       3569  non-null values\n",
        "category_6        3569  non-null values\n",
        "category_6a       3569  non-null values\n",
        "category_6b       3569  non-null values\n",
        "category_6c       3569  non-null values\n",
        "category_7        3569  non-null values\n",
        "category_7a       3569  non-null values\n",
        "category_7b       3569  non-null values\n",
        "category_7c       3569  non-null values\n",
        "category_7d       3569  non-null values\n",
        "category_7g       3569  non-null values\n",
        "category_7h       3569  non-null values\n",
        "category_8        3569  non-null values\n",
        "category_8a       3569  non-null values\n",
        "category_8c       3569  non-null values\n",
        "category_8d       3569  non-null values\n",
        "category_8e       3569  non-null values\n",
        "category_8f       3569  non-null values\n",
        "dtypes: float64(47), int64(1), object(7)"
       ]
      }
     ],
     "prompt_number": 366
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc774\ub807\uac8c \ud574\uc11c \ucd5c\uc885\uc801\uc73c\ub85c data\uc5d0\ub294 \uc0c8\ub85c\uc6b4 \uce7c\ub7fc\uc774 \ucd94\uac00 \ub428"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[:, 10:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 5 columns):\n",
        "category_1     3569  non-null values\n",
        "category_1a    3569  non-null values\n",
        "category_1b    3569  non-null values\n",
        "category_1c    3569  non-null values\n",
        "category_1d    3569  non-null values\n",
        "dtypes: float64(5)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 375,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 3569 entries, 0 to 3592\n",
        "Data columns (total 5 columns):\n",
        "category_1     3569  non-null values\n",
        "category_1a    3569  non-null values\n",
        "category_1b    3569  non-null values\n",
        "category_1c    3569  non-null values\n",
        "category_1d    3569  non-null values\n",
        "dtypes: float64(5)"
       ]
      }
     ],
     "prompt_number": 375
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- \uc774\uc81c \ub3c4\ud45c\ub97c \ub9cc\ub4e4\uc5b4\ubcf4\uc790.\n",
      "- \uc774 \ub370\uc774\ud130\ub294 \uc9c0\ub9ac \uc815\ubcf4\uc774\ubbc0\ub85c \uce74\ud14c\uace0\ub9ac\ubcc4 \ub370\uc774\ud130\ub97c \uc544\uc774\ud2f0 \uc9c0\ub3c4 \uc704\uc5d0 \uadf8\ub9b4 \uac83\n",
      "- matplotlib\uc758 \ud655\uc7a5 \ud234\ud0b7\uc778 [basemap \ud234\ud0b7](http://matplotlib.github.com/basemap)\uc744 \uc0ac\uc6a9\ud574\uc11c \ud30c\uc774\uc36c\uc73c\ub85c \uc9c0\ub3c4 \uc704\uc5d0 2\ucc28\uc6d0 \ub370\uc774\ud130 \ub3c4\ud45c\ub97c \uadf8\ub9b4 \uc218 \uc788\ub2e4.\n",
      "- basemap\uc740 \uc5ec\ub7ec\uac00\uc9c0 \uc9c0\ub3c4 \ud22c\uc601\ubc95\uc744 \uc9c0\uc6d0\ud558\uba70 \uc704\ub3c4 \uacbd\ub3c4 \uc88c\ud45c\ub97c 2\ucc28\uc6d0 \ud3c9\uba74 matplotlib\uc758 \uc88c\ud45c\ub85c \ubcc0\ud658\n",
      "- \uc774 \ub370\uc774\ud130\ub97c \uac00\uc9c0\uace0 \uac04\ub2e8\ud55c \ud751\ubc31 \uc544\uc774\ud2f0 \uc9c0\ub3c4\ub97c \uadf8\ub9ac\ub294 \ud568\uc218 \uc791\uc131\n",
      "\n",
      "------\n",
      "\n",
      "#### basemap \ud234\ud0b7 install\n",
      "\n",
      "- [basemap download](http://sourceforge.net/projects/matplotlib/files/matplotlib-toolkits/)\n",
      "- canopy\uac00 \uc788\ub294 \ubd84\ub4e4 \uc870\uc2ec: canopy\uc640 \ucda9\ub3cc\uc774 \ubc1c\uc0dd\ud574\uc11c basemap\uc774 \uc124\uce58\uac00 \ub418\uc9c0 \uc54a\uc558\ub124\uc694. \uadf8\ub798\uc11c virtual \ud658\uacbd \uc7a0\uc2dc \uaebc\ub450\uace0 install \ud588\uc2b5\ub2c8\ub2e4. \uc544.. \uc774\uac83 \ub54c\ubb38\uc5d0 \uae4c\uba39\uc740 \uc2dc\uac04\uc774 \uc5bc\ub9c8\uc778\uc9c0 -_-;\n",
      "- \uacb0\uad6d \ud3ec\uae30. canopy\uc640 \ubb54\uac00 \ucda9\ub3cc\uc774 \uc788\ub294\uac83 \uac19\uc74c\n",
      "- \ub9ac\ub205\uc2a4 \uacc4\uc5f4\uc774\ub098 \uc708\ub3c4\uc6b0\uc5d0\uc11c \uc2e4\ud5d8\uc744 \ud574\ubd10\uc57c \ud560\ub4ef. \uc77c\ub2e8 skip. **\ub370\uc774\ud130 \ub2e4 \uac00\uacf5\ud574\ub1a8\ub294\ub370 \uc774\uac78 \uc9c0\ub3c4\uc5d0 \ubabb \ubfcc\ub9ac\ub2e4\ub2c8 \uc9dc\uc99d..**\n",
      "- \uc9c0\uae08 \uc0dd\uac01\ud574\ubcf4\ub2c8 matplotlib\uac00 \uc5d0\ub7ec\uac00 \ub09c \uc774\uc720\uac00 \ub0b4\uac00 \uc124\uce58\ub97c \uc548\ud574\uc11c \uc778\uac83 \uac19\uc740\ub370..?\n",
      "- \ub0b4\uac00 canopy\ub9cc \uc124\uce58\ud588\uc9c0 matplotlib\ub97c \ub530\ub85c \uc124\uce58\ud55c \uae30\uc5b5\uc774 \uc5c6\ub2e4. \uadf8\ub7ec\ub2c8 canopy \uac00\uc0c1 \ud658\uacbd\uc774 \uc138\ud305\uc774 \ub418\uc9c0 \uc54a\uc73c\uba74 matplotlib\uac00 \ub2f9\uc5f0\ud788 \ub3d9\uc791\uc744 \ud558\uc9c0 \uc54a\uc544 \uadf8\ub798\ud504\ub97c \ubcf4\uc5ec\uc8fc\uc9c0 \uc54a\uc558\ub358\uac70\ub124.. \uc774\uc81c\uc57c \uc65c \uadf8\ub798\ud504\uac00 \uc548 \uadf8\ub824\uc84c\ub294\uc9c0 \uc54c\uac8c \ub410\ub2e4.\n",
      "\n",
      "----"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 8.4 \ud30c\uc774\uc36c \uc2dc\uac01\ud654 \ub3c4\uad6c \uc0dd\ud0dc\uacc4\n",
      "\n",
      "- matplotlib\uc740 \ud30c\uc774\uc36c\uc5d0\uc11c \uadf8\ub798\ud504\ub97c \uadf8\ub9ac\uae30 \uc704\ud55c \ub3c4\uad6c\n",
      "- \ud1b5\uacc4 \uadf8\ub798\ud53d\uc744 \ub9cc\ub4e4\uace0 \ud45c\uc2dc\ud558\ub294\ub370 \uc788\uc5b4\uc11c matplotlib\uc740 \ub2e4\uc18c \uacb0\uc810\n",
      "- R \uc0ac\uc6a9\uc790\ub4e4\uc740 ggplot2\uc640 trellis \ud328\ud0a4\uc9c0\uc5d0 \uc775\uc219\ud55c \uc0ac\uc6a9\uc790\ub4e4\uc740 \uc57d\uac04 \uc2e4\ub9dd\ud560\uc9c0\ub3c4 \ubaa8\ub974\uaca0\ub2e4.\n",
      "- matplotlib\uc73c\ub85c \uc6f9\uc0c1\uc5d0\uc11c \ud45c\uc2dc\ud560 \ud6cc\ub96d\ud55c \uadf8\ub798\ud504\ub97c \ub9cc\ub4e4\uc5b4\ub0bc \uc218 \uc788\uc9c0\ub9cc \uc6d0\ub798\ub294 \ucd9c\ud310\ubb3c\uc744 \uc704\ud55c \uadf8\ub798\ud53d\uc744 \ub9cc\ub4e4\uae30 \uc704\ud574 \uc124\uacc4\ub41c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc778 \uad00\uacc4\ub85c \uc6f9\uc6a9 \uadf8\ub798\ud53d\uc744 \uc0dd\uc131\ud558\ub824\uba74 \uc801\uc9c0 \uc54a\uc740 \ub178\ub825 \ud544\uc694\n",
      "- \uc2ec\ubbf8\uc801\uc778 \ubd80\ubd84 \uc678\uc5d0\ub294 \ub300\ubd80\ubd84\uc758 \uc694\uad6c\uc0ac\ud56d\uc744 \ucda9\ubd84\ud788 \ub9cc\uc871"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.4.1 [Chaco](http://code.enthought.com/chaco/)\n",
      "\n",
      "- \uc815\uc801 \uadf8\ub798\ud504\uc640 \ub300\ud654\ud615 \uc2dc\uac01\ud654 2\uac00\uc9c0 \ubaa8\ub450\uc5d0 \uc798 \uc5b4\uc6b8\ub9ac\ub294 \ud234\ud0b7\n",
      "- \ub370\uc774\ud130 \ub0b4\ubd80 \uad00\uacc4\uc5d0 \ub530\ub77c \ubcf5\uc7a1\ud55c \uc2dc\uac01\ud654 \uc798 \ud45c\ud604\n",
      "- \uadf8\ub798\ud504 \uc694\uc18c\uc640\uc758 \uc778\ud130\ub809\uc158\uc744 \uc880 \ub354 \uc798 \uc9c0\uc6d0\ud560 \ubfd0\ub9cc \uc544\ub2c8\ub77c \uadf8\ub798\ud504\ub97c \uadf8\ub9ac\ub294 \uc18d\ub3c4\ub3c4 \uc544\uc8fc \ube68\ub77c\uc11c \ub300\ud654\ud615 GUI \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \uc791\uc131\ud560 \ub54c \uc720\uc6a9\ud55c \uc120\ud0dd"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 8.4.2 mayavi\n",
      "\n",
      "- IPython\uc5d0 \ud1b5\ud569\n",
      "\n",
      "### 8.4.3 \uae30\ud0c0 \ud328\ud0a4\uc9c0\n",
      "\n",
      "- PySqt\n",
      "- Veusz\n",
      "- gnuplot-py\n",
      "- biggles\n",
      "\n",
      "### 8.4.4 \uc2dc\uac01\ud654 \ub3c4\uad6c\uc758 \ubbf8\ub798\n",
      "\n",
      "- \uc6f9 \uae30\uc220 \uae30\ubc18\uc758 \uc2dc\uac01\ud654, \uc989 \uc790\ubc14\uc2a4\ud06c\ub9bd\ud2b8 \uae30\ubc18\uc758 \uc2dc\uac01\ud654\ub294 \ud53c\ud560 \uc218 \uc5c6\ub294 \ubbf8\ub798\n",
      "- \uc774\ubbf8 \ub9ce\uc740 \uc885\ub958\uc758 \uc815\uc801 \ud639\uc740 \ub300\ud654\u314f\ud615 \uc2dc\uac01\ud654\ub97c \ud50c\ub798\uc2dc\ub098 \uc790\ubc14\uc2a4\ud06c\ub9bd\ud2b8\ub97c \ud1b5\ud574 \uc0ac\uc6a9\n",
      "- d3.js\uc640 \uac70\uae30\uc11c \ud30c\uc0dd\ub41c \ub2e4\uc591\ud55c \ub2e4\ub978 \ud504\ub85c\uc81d\ud2b8 \uac19\uc740 \uc0c8\ub85c\uc6b4 \ud234\ud0b7\uc774 \uacc4\uc18d \ub098\uc634\n",
      "- \ud558\uc9c0\ub9cc \uc774\uc640\ub294 \ub2e4\ub974\uac8c \uc6f9 \uae30\ubc18\uc774 \uc544\ub2cc \ud658\uacbd\uc5d0\uc11c\uc758 \uc2dc\uac01\ud654\ub294 \ub208\uc5d0 \ub744\uac8c \ub354\ub518 \ubc1c\uc804\n",
      "- \uc774\ub294 \ud30c\uc774\uc36c\ubfd0\ub9cc \uc544\ub2c8\ub77c R \uac19\uc740 \ub2e4\ub978 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \ud1b5\uacc4 \ucef4\ud4e8\ud305 \ud658\uacbd\uc5d0\uc11c\ub3c4 \ub9c8\ucc2c\uac00\uc9c0\n",
      "- \uadf8\ub7ec\uba74 \uac1c\ubc1c\uc5d0 \uc788\uc5b4\uc11c \ub3c4\uc804 \uacfc\uc81c\ub294 pandas\uc640 \uc6f9\ube0c\ub77c\uc6b0\uc800 \uac19\uc740 \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \uc900\ube44 \ub3c4\uad6c\uac04\uc758 \ubc00\uc811\ud55c \ud1b5\ud569\uc744 \uc774\ub904\ub0b4\ub294 \uac83\n",
      "- \uc774\ub7f0 \uacfc\uc81c\uac00 \ud30c\uc774\uc36c\uacfc \ud30c\uc774\uc36c \uc0ac\uc6a9\uc790 \uac04\uc758 \ud611\uc5c5\uc744 \uc704\ud55c \uc0dd\uc0b0\uc801\uc778 \uc9c0\uc810\uc774 \ub420 \uac83\uc774\ub77c \uae30\ub300\n",
      "\n",
      "-----\n",
      "\n",
      "#### [\ub370\uc774\ud130 \uc2dc\uac01\ud654 \ub77c\uc774\ube0c\ub7ec\ub9ac \ubaa9\ub85d](http://codefactory.kr/data-visualization-libraries/)\n",
      "\n",
      "- \uc0c1\ub2f9\ud55c \ub370\uc774\ud130 \uc2dc\uac01\ud654 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub4e4\uc744 \ub370\uc774\ud130\ud504\ub808\uc784 \ud615\uc2dd\uc73c\ub85c \ub098\ud0c0\ub0b4\uc5b4 \uc0ac\uc6a9\uc790\ub4e4\uc774 \ud55c \ub208\uc5d0 \uc54c\uc544\ubcfc \uc218 \uc788\ub3c4\ub85d \ub9cc\ub4e4\uc5c8\uc74c. \n",
      "- \uac01 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub4e4 \uc0ac\uc6a9\uc790 \ud6c4\uae30\uae4c\uc9c0 \ub2ec\ub9ac\uba74 \ub531\uc778\ub370 \uadf8\uac8c \uc544\uc27d\ub2e4.\n",
      "\n",
      "------"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## [About my IPython in github](https://github.com/re4lfl0w/ipython)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}