{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import datadive.diveindata.diveindata as dv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/walrus/.virtualenvs/py3/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2821: DtypeWarning: Columns (4,61,62,66,116,117,123) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if self.run_code(code, result):\n"
     ]
    }
   ],
   "source": [
    "terror = dv.DataInfo('./globalterrorismdb_0616dist.csv', params={'encoding': 'latin1'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Columns metadata and basic stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  'type': 'INT',\n",
       "  'uniques': 3},\n",
       " 'INT_IDEO': {'idxmin': -9,\n",
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       " 'alternative': {'equivalents': ['alternative_txt'],\n",
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       " 'nperps': {'idxmin': -99.0,\n",
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       " 'nreleased': {'idxmin': -99.0,\n",
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       " 'nwoundus': {'idxmin': 0.0,\n",
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       " 'propcomment': {'type': 'CATEGORY', 'uniques': 17459},\n",
       " 'property': {'idxmin': -9,\n",
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       "  'min': -9,\n",
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       "  'type': 'INT',\n",
       "  'uniques': 3},\n",
       " 'propextent': {'equivalents': ['propextent_txt'],\n",
       "  'has_equivalent': True,\n",
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       " 'propextent_txt': {'equivalents': ['propextent'],\n",
       "  'has_equivalent': True,\n",
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       "  'uniques': 605},\n",
       " 'provstate': {'type': 'CATEGORY', 'uniques': 2510},\n",
       " 'ransom': {'idxmin': -9.0,\n",
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       "  'min': -9.0,\n",
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       " 'ransomamt': {'idxmin': -99.0,\n",
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       "  'uniques': 351},\n",
       " 'ransomamtus': {'idxmin': -99.0,\n",
       "  'max': 132000000.0,\n",
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       "  'std': 6665966.884489282,\n",
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       "  'type': 'FLOAT',\n",
       "  'uniques': 22},\n",
       " 'ransomnote': {'type': 'CATEGORY', 'uniques': 297},\n",
       " 'ransompaid': {'idxmin': -99.0,\n",
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       "  'min': -99.0,\n",
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       "  'type': 'FLOAT',\n",
       "  'uniques': 123},\n",
       " 'ransompaidus': {'idxmin': -99.0,\n",
       "  'max': 48000.0,\n",
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       "  'min': -99.0,\n",
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       "  'sum': 122689.0,\n",
       "  'type': 'FLOAT',\n",
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       " 'region': {'equivalents': ['region_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1,\n",
       "  'max': 12,\n",
       "  'mean': 6.9700967009414949,\n",
       "  'min': 1,\n",
       "  'std': 2.9678026050631856,\n",
       "  'sum': 1092716,\n",
       "  'type': 'INT',\n",
       "  'uniques': 12},\n",
       " 'region_txt': {'equivalents': ['region'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 12},\n",
       " 'related': {'type': 'CATEGORY', 'uniques': 20030},\n",
       " 'resolution': {'type': 'CATEGORY', 'uniques': 2658},\n",
       " 'scite1': {'type': 'CATEGORY', 'uniques': 66823},\n",
       " 'scite2': {'type': 'CATEGORY', 'uniques': 50240},\n",
       " 'scite3': {'type': 'CATEGORY', 'uniques': 28555},\n",
       " 'specificity': {'idxmin': 1,\n",
       "  'max': 5,\n",
       "  'mean': 1.4526318475237925,\n",
       "  'min': 1,\n",
       "  'std': 1.0169709073052788,\n",
       "  'sum': 227732,\n",
       "  'type': 'INT',\n",
       "  'uniques': 5},\n",
       " 'success': {'idxmin': 0,\n",
       "  'max': 1,\n",
       "  'mean': 0.90361161431888348,\n",
       "  'min': 0,\n",
       "  'std': 0.29512407621110542,\n",
       "  'sum': 141661,\n",
       "  'type': 'INT',\n",
       "  'uniques': 2},\n",
       " 'suicide': {'idxmin': 0,\n",
       "  'max': 1,\n",
       "  'mean': 0.030432730334498507,\n",
       "  'min': 0,\n",
       "  'std': 0.1717753401202016,\n",
       "  'sum': 4771,\n",
       "  'type': 'INT',\n",
       "  'uniques': 2},\n",
       " 'summary': {'type': 'CATEGORY', 'uniques': 88704},\n",
       " 'target1': {'type': 'CATEGORY', 'uniques': 79948},\n",
       " 'target2': {'type': 'CATEGORY', 'uniques': 4563},\n",
       " 'target3': {'type': 'CATEGORY', 'uniques': 632},\n",
       " 'targsubtype1': {'equivalents': ['targsubtype1_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 111.0,\n",
       "  'mean': 46.654884703634174,\n",
       "  'min': 1.0,\n",
       "  'std': 31.10804746151652,\n",
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       "  'type': 'FLOAT',\n",
       "  'uniques': 111},\n",
       " 'targsubtype1_txt': {'equivalents': ['targsubtype1'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 111},\n",
       " 'targsubtype2': {'equivalents': ['targsubtype2_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 111.0,\n",
       "  'mean': 54.622623686385644,\n",
       "  'min': 1.0,\n",
       "  'std': 25.830595023453668,\n",
       "  'sum': 462599.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 104},\n",
       " 'targsubtype2_txt': {'equivalents': ['targsubtype2'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 104},\n",
       " 'targsubtype3': {'equivalents': ['targsubtype3_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 109.0,\n",
       "  'mean': 54.86279069767442,\n",
       "  'min': 1.0,\n",
       "  'std': 26.779003496982764,\n",
       "  'sum': 47182.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 86},\n",
       " 'targsubtype3_txt': {'equivalents': ['targsubtype3'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 86},\n",
       " 'targtype1': {'equivalents': ['targtype1_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1,\n",
       "  'max': 22,\n",
       "  'mean': 8.3051118822238656,\n",
       "  'min': 1,\n",
       "  'std': 6.64251830538063,\n",
       "  'sum': 1302009,\n",
       "  'type': 'INT',\n",
       "  'uniques': 22},\n",
       " 'targtype1_txt': {'equivalents': ['targtype1'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 22},\n",
       " 'targtype2': {'equivalents': ['targtype2_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 22.0,\n",
       "  'mean': 10.117858345493321,\n",
       "  'min': 1.0,\n",
       "  'std': 5.7649110534432575,\n",
       "  'sum': 90140.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 23},\n",
       " 'targtype2_txt': {'equivalents': ['targtype2'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 23},\n",
       " 'targtype3': {'equivalents': ['targtype3_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 22.0,\n",
       "  'mean': 9.7982832618025757,\n",
       "  'min': 1.0,\n",
       "  'std': 5.835194274069373,\n",
       "  'sum': 9132.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 21},\n",
       " 'targtype3_txt': {'equivalents': ['targtype3'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 21},\n",
       " 'vicinity': {'idxmin': -9,\n",
       "  'max': 1,\n",
       "  'mean': 0.067422754063225579,\n",
       "  'min': -9,\n",
       "  'std': 0.28904050760340855,\n",
       "  'sum': 10570,\n",
       "  'type': 'INT',\n",
       "  'uniques': 3},\n",
       " 'weapdetail': {'type': 'CATEGORY', 'uniques': 16988},\n",
       " 'weapsubtype1': {'equivalents': ['weapsubtype1_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 29.0,\n",
       "  'mean': 10.949519317322579,\n",
       "  'min': 1.0,\n",
       "  'std': 6.4389857794042182,\n",
       "  'sum': 1520505.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 29},\n",
       " 'weapsubtype1_txt': {'equivalents': ['weapsubtype1'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 29},\n",
       " 'weapsubtype2': {'equivalents': ['weapsubtype2_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 28.0,\n",
       "  'mean': 10.591715976331361,\n",
       "  'min': 1.0,\n",
       "  'std': 7.4922207854013534,\n",
       "  'sum': 98450.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 27},\n",
       " 'weapsubtype2_txt': {'equivalents': ['weapsubtype2'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 27},\n",
       " 'weapsubtype3': {'equivalents': ['weapsubtype3_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 28.0,\n",
       "  'mean': 11.426493108728943,\n",
       "  'min': 1.0,\n",
       "  'std': 8.2059432409625419,\n",
       "  'sum': 14923.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 23},\n",
       " 'weapsubtype3_txt': {'equivalents': ['weapsubtype3'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 23},\n",
       " 'weapsubtype4': {'equivalents': ['weapsubtype4_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 2.0,\n",
       "  'max': 28.0,\n",
       "  'mean': 10.788732394366198,\n",
       "  'min': 2.0,\n",
       "  'std': 8.1467179946593848,\n",
       "  'sum': 766.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 17},\n",
       " 'weapsubtype4_txt': {'equivalents': ['weapsubtype4'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 17},\n",
       " 'weaptype1': {'equivalents': ['weaptype1_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1,\n",
       "  'max': 13,\n",
       "  'mean': 6.4085869925752048,\n",
       "  'min': 1,\n",
       "  'std': 2.1307853828571552,\n",
       "  'sum': 1004687,\n",
       "  'type': 'INT',\n",
       "  'uniques': 12},\n",
       " 'weaptype1_txt': {'equivalents': ['weaptype1'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 12},\n",
       " 'weaptype2': {'equivalents': ['weaptype2_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 1.0,\n",
       "  'max': 13.0,\n",
       "  'mean': 6.6269086718524921,\n",
       "  'min': 1.0,\n",
       "  'std': 2.0854659636748027,\n",
       "  'sum': 69006.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 12},\n",
       " 'weaptype2_txt': {'equivalents': ['weaptype2'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 12},\n",
       " 'weaptype3': {'equivalents': ['weaptype3_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 2.0,\n",
       "  'max': 13.0,\n",
       "  'mean': 6.8044692737430168,\n",
       "  'min': 2.0,\n",
       "  'std': 2.0977595110524638,\n",
       "  'sum': 9744.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 11},\n",
       " 'weaptype3_txt': {'equivalents': ['weaptype3'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 11},\n",
       " 'weaptype4': {'equivalents': ['weaptype4_txt'],\n",
       "  'has_equivalent': True,\n",
       "  'idxmin': 5.0,\n",
       "  'max': 12.0,\n",
       "  'mean': 6.243243243243243,\n",
       "  'min': 5.0,\n",
       "  'std': 1.4971279427652517,\n",
       "  'sum': 462.0,\n",
       "  'type': 'FLOAT',\n",
       "  'uniques': 6},\n",
       " 'weaptype4_txt': {'equivalents': ['weaptype4'],\n",
       "  'has_equivalent': True,\n",
       "  'type': 'CATEGORY',\n",
       "  'uniques': 6}}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terror.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'idxmin': 0.0,\n",
       " 'max': 1500.0,\n",
       " 'mean': 2.3592374870502684,\n",
       " 'min': 0.0,\n",
       " 'std': 11.42127034765741,\n",
       " 'sum': 348758.99999818002,\n",
       " 'type': 'FLOAT',\n",
       " 'uniques': 339}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terror.columns['nkill']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'equivalents': ['country'],\n",
       " 'has_equivalent': True,\n",
       " 'type': 'CATEGORY',\n",
       " 'uniques': 206}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terror.columns['country_txt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group by region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1892bb47f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wpbw0RfHLgdts72T7xprj3hQ4qxzTTsB7yqq7gb3KZFdfBP67Rbz3A0dLaujq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\nKtnyVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1892c45dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terror.bar_groups(['region_txt']).plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter a list of regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1892be3390>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18c2da6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terror.bar_groups([('region_txt', ['North America', 'Middle East & North Africa'])]).plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f188a84cc50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qxWKxsG7dOnJycoiIiMBiseDp6cnkyZMBmDFjBv7+/nh7e9O5c2cyMzMBWLhwIRaLBW9v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\nvb0ZPXp03v/+92f33XfPm9/85jzzzDPr6NWDsAg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PEFSX5+Wn5ukl+cPlzl0EXOtTHJq6tqc5LPLDGepbb52W0flJLkC0n+z3b7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\nkiRJkiRJjVlYlCRJkiRJktTYH8qSPD/URV2jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188b086a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If 'level = 1' we use column 1 ('arracktype1_txt') for the groups\n",
    "\n",
    "terror.bar_groups(['region_txt', 'attacktype1_txt']).unstack(level=1).plot.barh(figsize=(20,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f188a239e10>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BSU4RERERERERkaYsrk0Dj1dUa7VpmowaNYopU6Y4EogHDx6ksLDwqpOcvr6+1SY5y8rK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\nvvCFL+Sggw7KFltskVGjRnWGksvr6OjIiSeemGHDhqVv376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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188a3fa828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If 'level = 0' we use column 0 ('region_txt') for the groups\n",
    "\n",
    "terror.bar_groups(['region_txt', 'attacktype1_txt']).unstack(level=0).plot.barh(figsize=(20,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple columns and filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f188a639eb8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gZt8lAJK6AxvM7LsE+oLoc+qrSCOiRCpWC+CdBNtyzjlXCTyZiVwNDAlTQQOJ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\n1Zo7M2ZmZlZr7syYmZlZrf0PkaahYvpIHUIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188a67aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terror.bar_groups([('region_txt', ['North America', 'Middle East & North Africa']), 'attacktype1_txt']).unstack(level=0).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f188b66f5c0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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27Yqfn1+WlMY3b940bnsEBATken65XZdq1aoxe/ZsZsyYgatr/hPexcXFERER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\nKS/4in6kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188b69c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terror.bar_groups([('region_txt', ['North America', 'Middle East & North Africa']), 'attacktype1_txt']).unstack(level=1).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1888a0f208>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YxwP8CLgf2EbSJGAzsqf2mpmZmTWbgpOXiJgu6avAdmRfWPdiRCxrssjMzMzM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\nbxBwFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188a233d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terror.bar_groups([('country_txt', 'Iraq'), 'attacktype1_txt']).unstack(level=0).plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18888e4400>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9VnFMJQ0GRpPdAbk5cJOknmRJ2845dSZHxD9y1t+MiPI8/R0E/DEiPgSoeAd2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\nBotJySkdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18888f73c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terror.bar_groups([('country_txt', ['Iraq', 'United States']), 'attacktype1_txt']).unstack(level=0).plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select another column to calculate\n",
    "\n",
    "By default the bar graph is going to display the count of entries in a given category. If you pass a column name, it is going to use it for the weight of the bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1888565b38>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aYOBJkppg4EmSmmDgSZKaYOBJkppg4EmSmmDgSZKaYOBJkppg4EmSmmDgSZKaYOBJkppg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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188834cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terror.bar_groups([('country_txt', ['Iraq', 'United States'])], 'nkill').plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default is to sum the values in this column, if you want other types of aggregation, you can pass a argument 'aggs' with possible values as ['sum', 'count', 'mean', 'std']:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f188867f400>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f188863a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terror.bar_groups([('country_txt', ['Iraq', 'United States'])], 'nkill', aggs=['count', 'sum']).plot.barh()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}