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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1.usa.gov data from bit.ly"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A feed of anonymous data gathered from users who shorten links ending with .gov and .mil."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path = '/Users/sergulaydore/pydata-book/ch02/usagov_bitly_data2012-03-16-1331923249.txt'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "open(path).readline() # each line containsa common form of web data known as JSON (JavaScript Object Notation)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "importing a JSON string into a Python dictionary"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "record = [json.loads(line) for line in open(path)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "record[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "{u'a': u'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
        " u'al': u'en-US,en;q=0.8',\n",
        " u'c': u'US',\n",
        " u'cy': u'Danvers',\n",
        " u'g': u'A6qOVH',\n",
        " u'gr': u'MA',\n",
        " u'h': u'wfLQtf',\n",
        " u'hc': 1331822918,\n",
        " u'hh': u'1.usa.gov',\n",
        " u'l': u'orofrog',\n",
        " u'll': [42.576698, -70.954903],\n",
        " u'nk': 1,\n",
        " u'r': u'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
        " u't': 1331923247,\n",
        " u'tz': u'America/New_York',\n",
        " u'u': u'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The resulting object records is now a list of Python dicts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "record[0]['tz']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "u'America/New_York'"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Counting Time Zones in Pure Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_zones = [rec['tz'] for rec in record if 'tz' in rec]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_zones[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[u'America/New_York',\n",
        " u'America/Denver',\n",
        " u'America/New_York',\n",
        " u'America/Sao_Paulo',\n",
        " u'America/New_York',\n",
        " u'America/New_York',\n",
        " u'Europe/Warsaw',\n",
        " u'',\n",
        " u'',\n",
        " u'']"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_counts(sequence):\n",
      "    counts = {}\n",
      "    for x in sequence:\n",
      "        if x in counts: \n",
      "            counts[x] +=1\n",
      "        else:\n",
      "            counts[x] = 1\n",
      "    return counts        "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import defaultdict\n",
      "\n",
      "def get_counts2(sequence):\n",
      "    counts = defaultdict(int) # values will initialize to 0\n",
      "    for x in sequence:\n",
      "        counts[x] +=1\n",
      "    return counts    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts = get_counts(time_zones)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts['America/New_York']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "1251"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(time_zones)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "3440"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we wanted the top ten time zones and their counts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def top_counts(counts_dict,n=10):\n",
      "    value_key_pairs = [(count, tz) for tz,count in counts_dict.items()]\n",
      "    value_key_pairs.sort()\n",
      "    return value_key_pairs[-n:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_counts(counts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "[(33, u'America/Sao_Paulo'),\n",
        " (35, u'Europe/Madrid'),\n",
        " (36, u'Pacific/Honolulu'),\n",
        " (37, u'Asia/Tokyo'),\n",
        " (74, u'Europe/London'),\n",
        " (191, u'America/Denver'),\n",
        " (382, u'America/Los_Angeles'),\n",
        " (400, u'America/Chicago'),\n",
        " (521, u''),\n",
        " (1251, u'America/New_York')]"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "collections.Counter class makes this tak a lot easier."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import Counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts = Counter(time_zones)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts.most_common(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "[(u'America/New_York', 1251),\n",
        " (u'', 521),\n",
        " (u'America/Chicago', 400),\n",
        " (u'America/Los_Angeles', 382),\n",
        " (u'America/Denver', 191),\n",
        " (u'Europe/London', 74),\n",
        " (u'Asia/Tokyo', 37),\n",
        " (u'Pacific/Honolulu', 36),\n",
        " (u'Europe/Madrid', 35),\n",
        " (u'America/Sao_Paulo', 33)]"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Counting Time Zones with pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import DataFrame, Series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd; import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame = DataFrame(record)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <th>_heartbeat_</th>\n",
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        "      <th>al</th>\n",
        "      <th>c</th>\n",
        "      <th>cy</th>\n",
        "      <th>g</th>\n",
        "      <th>gr</th>\n",
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        "      <th>r</th>\n",
        "      <th>t</th>\n",
        "      <th>tz</th>\n",
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        "    </tr>\n",
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        "  <tbody>\n",
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        "      <td> 1331923247</td>\n",
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        "    <tr>\n",
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        "      <td> 9b6kNl</td>\n",
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        "1          NaN                             GoogleMaps/RochesterNY   \n",
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        "\n",
        "                  tz                                                  u  \n",
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        "1     America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
        "2   America/New_York  http://boxer.senate.gov/en/press/releases/0316...  \n",
        "3  America/Sao_Paulo            http://apod.nasa.gov/apod/ap120312.html  \n",
        "4   America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  "
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['tz'][:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "0     America/New_York\n",
        "1       America/Denver\n",
        "2     America/New_York\n",
        "3    America/Sao_Paulo\n",
        "4     America/New_York\n",
        "5     America/New_York\n",
        "6        Europe/Warsaw\n",
        "7                     \n",
        "8                     \n",
        "9                     \n",
        "Name: tz, dtype: object"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tz_counts = frame['tz'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tz_counts[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "America/New_York       1251\n",
        "                        521\n",
        "America/Chicago         400\n",
        "America/Los_Angeles     382\n",
        "America/Denver          191\n",
        "Europe/London            74\n",
        "Asia/Tokyo               37\n",
        "Pacific/Honolulu         36\n",
        "Europe/Madrid            35\n",
        "America/Sao_Paulo        33\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_tz = frame['tz'].fillna('Missing')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_tz[clean_tz=='']='Unknown'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tz_counts = clean_tz.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tz_counts[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "America/New_York       1251\n",
        "Unknown                 521\n",
        "America/Chicago         400\n",
        "America/Los_Angeles     382\n",
        "America/Denver          191\n",
        "Missing                 120\n",
        "Europe/London            74\n",
        "Asia/Tokyo               37\n",
        "Pacific/Honolulu         36\n",
        "Europe/Madrid            35\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tz_counts[:10].plot(kind='barh',rot=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1083abd90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PDwBdXV1UZmG9LrAa8CowD3gSeBp4itIVZmrXC4xP5Xe2AS8Ay/Zx3NOBk0rr\n5X3uBzZsaT8W+C2wSlqfCny+dO7m1ezhvPeK9Tjgq6X1rwHHpPIYiivyBnDaQt6HB4Bn03vwJPAa\nsEHaNr/U7iBgair/P2CpVF6ZBVej3wS+0M95BtNmwMxkf8Xa2wYZBrvwnk+9vb29C/9Y3MZyzh7h\n/HXLPT8VXbEONMd6EHBZRHRFxNiIWC91LDsNsF/TraljA0DSFoPY5zbg6NI+Yyg6pleBlyWtBeyV\ntq0CjIqIF5vN+zjencD+kkanq+X9gTslrQO8FhE/oujIxvcVRtJGwAoRsW56D8YCZwJ/N8DruIfi\n/YNifrbpZ8CRKQuSPihpzZZ9+2wz2MxmZlafgTrWg4HrWuquTfXRzz5R2nYcsFW6AedhirnGcru+\n9jkDWDXdnDML6I6I2cBM4FHgRxRDoqIYdr2t5TiTJD2TlqeB54EeYAZFZ3dROt5mwL1puPU0iivZ\n/t6Daf28Bwt7HScAJ6bXsAHwJ4CIuI1i7vlXkuZQDC2vWD5WH21+Aqw0hMyZ6647wLA0h5xylHN2\ncP665Z6/Kll/QYSkiyg6yhl1Z2klaXQsmNc9mOJGpgOW0Lmj/889Vi1/QYRZp/CX8AMRMbkdO9Vk\nQvq1mNnAF4GTluzp5WWJLO+V8+/y5ZwdnL9uueevir8ruCTdMTy2pfpLaWh2SKK4Y3dcJcEWQc5X\nUY1Gw0NKZpatrIeCrW9+HquZ2dB5KNjMzKwNuWO1tpP7PE3O+XPODs5ft9zzV8Udq5mZWYU8x9qB\nPMdqZjZ0nmM1MzNrQ+5Yre3kPk+Tc/6cs4Pz1y33/FVxx2pmZlYhz7F2IM+xmpkNXVVzrP7mpQ5V\negau1cAfbMxGLg8Fd6zIeOltgwzDWfKV+xyZ89cr9/xVqbVjlbS/pLclfXQxHX+CpPOGsf/Bkk5J\n5b0k3SfpYUkPSvpmqu+RdGAf+35A0tWLnt7MzHJU6xyrpKuA0cCDETGl4mOPiog3h3mMHuA84A3g\neuCTEfEbSUsBkyPie5KmAjdFxLXDDl0RPzaubn6UnFmOsv89VkkrAtsCxwATU123pNslXS/pd5LO\nlHSYpBmS5khaP7VbU9I1qX6GpO1S/RRJl0u6C7hM0s6SbmyeT9LUdJzZkg5I9RekK9GHJE0p5RMw\nLiJmAl8CzoiI3wBExNsR8b3Sy9lJ0i9T5gPT/l2S5qby0pK+mR7ePlvS0an+f6T8cyV9r3TurVPO\nmZLOKh3oDuxwAAAQ4ElEQVRn+dJreFBSd9V/LmZmNjx1DgXvB0yPiKeBFySNT/WbA0cBmwCHARtE\nxDbAD4BjU5vzgHNS/UFpW9PGwK4R8Xe8+4GZpwEvRsTmEbEFxUQewKkRsTWwBbCzpM1S/ZbArFTe\nFHign9chYO2I2B7YBzizjzZfANYDtkjnviLVnx8R20TEZsBoSfuk+qkUV8RbAm+y4PLzaOCtiNgc\nOAS4VNKy/eTKWKPuACNW7nNkzl+v3PNXpc67gg8Bzknlq9P6TcB9EfEcgKTHgZ+lNg8Bu6TybsAm\npTtfV5K0AkUHdENEvN7H+XYlXRkDRMRLqThR0mSK92Idig59LrAn8NNBvI6gGCYmIh6RtFY/5/4/\nEfF2avdiqv+EpJOB9wGrAQ+lq+0VI+Le1OYKig4bYHvg2+kYj0l6CvhoyttiEtCVymMoHg3bndYb\n6We7rs8aYHu7r7/7mbLN/2y87nWvt9d6o9Ggp6cHgK6uLioTEUt8oehEXgXmAU8CTwNPUfzPdGOp\nXS8wPpXf2Qa8ACzbx3FPB04qrZf3uR/YsKX9WOC3wCppfSrw+dK5V03ly4Aj+nktU4EDS+vz088u\nYG4qXwPs1rLf8sB/Ah8sZf8fwCrAvFK7zUvHmQbsUtp2B/Df+sgUEF5qWwgzy0/6tzuk/qyvpa6h\n4IOAyyKiKyLGRsR6FB3sToPc/1bguOaKpC0Gsc9tFEOpzX3GACtTdPAvpyvNvdK2VYBRseDK8izg\nFEkfSduXknTUILM2z32UpKXT/qtSdKwAf0zzzZ8BiIg/AfMlbZO2H1w6zp3AoekYG1EMLz82hBxm\nZraY1dWxHgxc11J3baqPfvaJ0rbjgK3SjUAPU8zJltv1tc8ZwKrpRqFZQHdEzAZmAo8CPwLuopgz\n3Z2iMywOEjEXOAG4UtKvKYZexy7knK3lH1Bclc9J5z4kiqHoiyiGuKcD95b2+3vgIkkzKYaJ/5Tq\nLwCWkjQH+DFweES80deblbdG3QFGrNznyJy/Xrnnr4q/0rAPki4CLoqIGTWdf4WIeDWVvwKsFRH/\nNIT9I+9ft2lQnq/MT76/blOeG86R89cr9/xV/bqNO9Y2JOmzwL9Q3FA1D5gUEX8cwv6Zd6y5y7dj\nNRvJ3LFav4qO1erkf1dm+cn+CyJs8arizra6lt7e3tozDDd/rnKfI3P+euWevyruWM3MzCrkoeAO\n5OexmpkNnYeCzczM2pA7Vms7uc/T5Jw/5+zg/HXLPX9V3LGamZlVyHOsHchzrGZmQ+c5VjMzszbk\njtXaTu7zNDnnzzk7OH/dcs9fFXesZmZmFfIcawfyVxqOPP53bDZ8nmNdTCR1SZrbUjdF0kkL2WeS\npPMXf7qhCC8jZjGzduKOdXAG+t/L/7tVqlF3gGFq1B1gkeU+R+b89co9f1XcsQ6BpF5JZ0q6V9Jj\nknboo83eku6WtLqkHknnSfqlpN9JOjC1kaSz0kPX56THxCHpu5L2TeXrJF2cykdKOkPShyU9Iun7\nkh6S9DNJyy/J98DMzBbOHevQLR0R2wInAKenOgFIOgD4MrBXen5qAGtHxPbAPsCZqf2ngS2AzYHd\ngLMkrQ3cAeyY2nwQ2CSVdwRuT+fZEPhORPw34CXgwMX0OmvUXXeAYequO8Aiy/kh1eD8dcs9f1VG\n1R2gDfU3rNusn5Z+Pgh0lbZ/AtgK2D0iXinVXw8QEY9IWivV7QBckb7F4XlJtwNbA3cCJ0jaBHgY\nGJM63I8DxwBrAk9GxJx0nAdaMpRMKm0aA4xjwX/4jfTT652xXgzBNf9Taw7Hed3rXl/4eqPRoKen\nB4Curi6q4ruCW0haEXg0ItYt1Z1H0YkdAfxzRDwgaQ3gvogYK+lwiivHscCkiHgg7TcVuCkirk3r\n8yNiJUlnA3MjYmqqvxy4KiJukvQI8H2Kq9HVgDeBz0XE1pK6gBsjYrO030nAihHx1ZbXEHlP+zbI\n+apvyedXZXcFlzvoHDl/vXLP77uCF5N0tfl/Je0CIGk1YE/groXsJuAp4CDgMkkfG+A0dwITJS0l\naU2Kod4Zads9FMPMt6d2/0wxRGxmZhlwx9q3zwOnSZoJ/AKYEhFPpG3lS4Mo/YyIeAw4FLha0vr9\ntY+I64A5wOx0/JMj4vnU5k6KedwngJnAqqmu9Zz9rXeA7roDDFN33QEWWc5XG+D8dcs9f1U8FNyB\n8h8KtqGpbijYbCTzULB1sEbdAYapUXeARZb77yE6f71yz18V3xXcsYb9ocvMzBaBh4I7kJ/HamY2\ndB4KNjMza0PuWK3t5D5Pk3P+nLOD89ct9/xVccdqZmZWIc+xdiDPsZqZDZ3nWM3MzNqQO1ZrO7nP\n0+ScP+fs4Px1yz1/VdyxmpmZVchzrB3Ic6xmZkPnOVYzM7M25K807FCSv9LQzNpfJ46u+Yq1Y0XG\nS28bZBip+XPO7vz1L0PN35kWa8cqaX9Jb0v66GI6/gRJ5w1j/4MlnSJpkqS3JG1W2vaQpPWqSfrO\nMSdL+nFpfWVJj0vqGuT+r1SZp3111x1gmLrrDjAM3XUHGKbuugMMU3fdAYapu+4AbWFxX7EeAtyU\nflZK0qiIeCAijh/GYfYEfkrx0elZ4NTStso/TkXERcCHJO2aqv4NuDgi5g20r6SlFkcmMzOr1mLr\nWCWtCGwLHANMTHXdkm6XdL2k30k6U9JhkmZImiNp/dRuTUnXpPoZkrZL9VMkXS7pLuAySTtLurF5\nPklT03FmSzog1V8g6b50BTqllE/AuIiYmapuAjaVtFEfr2UPSXdLekDSTyStIGlrSdem7ftJ+rOk\nUZKWl/S7hbw1XwTOlbQV8AngLEknSpqbluPTMbskPSbpUklzgXVLedZIefYa8h9MFhp1BximRt0B\nhqFRd4BhatQdYJgadQcYpkbdAdrC4rx5aT9gekQ8LekFSeNT/ebAxsCLwJPARRGxjaTjgGOBfwLO\nA86JiF+m4djpwMfS/hsDO0TE65K6S+c7DXgxIjYHkDQm1Z8aES9KWhr4uaTNImIusCUwu7T/28A3\ngFOASc1KSWtQXMnuGhF/kfRl4ETgfwHjUrMdgbnANsAywD39vSkRMVfSz4CfA58Ctkjn24big869\nkm4HXgI2BA6LiBkpC5LeD9yQXtcv+juPmZnVY3F2rIcA56Ty1SwYFr4vIp4DkPQ48LPU5iFgl1Te\nDdikdGfrSpJWoBgKvSEiXu/jfLuSrowBIuKlVJwoaTLFa10H2ISiE9wTuCW1aZ7oCuDUljnPj1N0\n6nenPMsCd0fEW+mqe2Nga+BsYCdgaeDOAd6b7wJ7RcQd6Qp1WkT8Jb0n0yg66huAp5qdarIs8Avg\nHyNigHNMApovYwzFZ4DutN5IP9t1vVnXLnmGut6sa5c8Q1nvbrM8Q13vbrM8Q13vbrM8Q13vHvL+\nzW9r6u5e8uuNRoOenh4Aurq6qExEVL4AqwGvAvMorkqfBp6ieCdvLLXrBcan8jvbgBeAZfs47unA\nSaX18j73Axu2tB8L/BZYJa1PBT5fOveqqXw4cH4qTwYupOh8PwzsA1zRz+v8V4qr158DawA3U8zZ\nbjrA+9MFzE3l44CvlrZ9jWL4/MPNNqVtr6TX8D8HOH5AePHixUubL0Q7SXkY7rK45lgPAi6LiK6I\nGBsR61F0sDsNcv9bKTocACRtMYh9bgOOLu0zBliZooN/WdJawF5p2yrAqIh4sdm8dJweiivmNYEA\n7gW2l7RB2ncFSR9Jbe8ETqC4gv1/wOrARhHx8CBfZ/MY+0sana7K9091ff0iagBHAhtL+tIQzpGZ\nRt0BhqlRd4BhaNQdYJgadQcYpkbdAYapUXeAtrC4OtaDgeta6q5N9dHPPlHadhywVboJ6WHgqJZ2\nfe1zBrBqugFoFtAdEbOBmcCjwI+Auyg6rN0pOuL3HCci3qCY410zrb9AMa56paTZwN1A89eHZgDv\nB+5I67MprnQHo3m+mRSd+QyKudmLUu7W15qaR1AMq39C0hcHeS4zM1tCRuR3BUu6iKIDmzFg4wxJ\niv4/v5iZtQvRTn1QVd8VPCI71k5XdKxmZu2vnfogfwl/m5P0HUkzW5bDl9T5q5iAr2vp7e2tPcNI\nzZ9zduevf1mU/J3IHetiEhHHRMSWLculdefKwaxZs+qOMCw55885Ozh/3XLPXxV3rNZ2XnrppYEb\ntbGc8+ecHZy/brnnr4o7VjMzswq5Y7W2M2/evLojDEvO+XPODs5ft9zzV8V3BXcg3xVsZrZowr9u\nY2Zm1l48FGxmZlYhd6xmZmYVcsfaQSTtKelRSb9Nz41tO5I+JKlX0sPp4fPHpfrVJN0m6TeSbi09\nTxdJ/5Je06OS9qgv/QKSlk5f+nFjWs8mv6Qxkq6R9IikX0vaNpf8KcvD6TvBr5C0XDtnl3SJpOck\nzS3VDTmvpAnpNf9W0nk15z8r/d2ZLWlaeqhJNvlL206S9Lak1SrPX/c3dXipZqF4DuzjFI+kWwaY\nBWxSd64+cq4NjEvlFYHHKJ6R+w3gS6n+y8CZqfyx9FqWSa/tcWCpNngdJ1I82OGGtJ5NfuBS4MhU\nHgWskkP+dP4ngOXS+lUUj3xs2+wUz1bektIjIIeYt3kfzAxgm1S+Bdizxvy7N99H4Mzc8qf6DwHT\nKZ66tlrV+X3F2jm2AR6PiHlRPKHnx8B+NWd6j4j4z4iYlcqvAI8AHwQ+RfEfPunn/qm8H3BlRLwR\nEfMo/rJvs0RDt5C0LvBJ4AcseLxfFvnT1cWOEXEJQES8GRF/Io/8LwNvAO+TNAp4H/AH2jh7RNwJ\nvNhSPZS820paB1gpFjw05LLSPotVX/kj4raIeDut3gusm8pZ5E/OBlofvVlZfnesneODwDOl9WdT\nXduS1EXxafJeYK2IeC5teg5YK5U/QPFamtrhdZ0DnAy8XarLJf9Y4AVJUyU9KOkiFc8Bbvv8EfFf\nwLeApyk61Jci4jYyyN5iqHlb639Pe7wOKJ4PfUsqZ5Ff0n7AsxExp2VTZfndsXaOrH5vStKKFM/o\nPT4i5pe3RTHesrDXU9trlbQP8HwUz9Ht8/fd2jk/xdDveOCCiBgPvAp8pdygXfNL2gA4gWKY7gPA\nipI+V27Trtn7M4i8bUvSqcBfI+KKurMMlqT3AacAp5erqz6PO9bO8XuKeYOmD/HuT1ltQ9IyFJ3q\n5RFxfap+TtLaafs6wPOpvvV1rZvq6rId8ClJTwJXUjxw/nLyyf8sxaf1+9L6NRQd7X9mkH8r4O6I\n+GNEvAlMA/6GPLKXDeXvyrOpft2W+lpfh6RJFNMhh5aqc8i/AcUHs9np3/C6wAOS1qLC/O5YO8f9\nwEckdUlaFpgI3FBzpveQJOBi4NcRcW5p0w0UN6KQfl5fqj9Y0rKSxgIfobiRoBYRcUpEfCgixgIH\nA/8REYeRT/7/BJ6RtFGq2g14GLiR9s//KPBxSaPT36PdgF+TR/ayIf1dSX9mL6e7twUcVtpniZO0\nJ8VUyH4R8VppU9vnj4i5EbFWRIxN/4afBcanofnq8i+JO7O8LJkF2IviLtvHgX+pO08/GXegmJuc\nBcxMy57AasDPgd8AtwJjSvuckl7To8Df1v0aSrl2ZsFdwdnkB7YA7gNmU1z1rZJLfoobTh4G5lLc\n+LNMO2enGNX4A/BXinsgjliUvMCE9JofB75dY/4jgd8CT5X+/V6QQf7Xm+9/y/YnSHcFV5nfX2lo\nZmZWIQ8Fm5mZVcgdq5mZWYXcsZqZmVXIHauZmVmF3LGamZlVyB2rmZlZhdyxmpmZVcgdq5mZWYX+\nP/0cZU5HG+ChAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1083f7510>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The a field contains information about the browser, device, or application used to perform the URL shortening:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['a'][1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "u'GoogleMaps/RochesterNY'"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['a'][50]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "u'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['a'][51]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "u'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P925/V10e Build/FRG83G) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1'"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = Series([x.split()[0] for x in frame.a.dropna()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "0               Mozilla/5.0\n",
        "1    GoogleMaps/RochesterNY\n",
        "2               Mozilla/4.0\n",
        "3               Mozilla/5.0\n",
        "4               Mozilla/5.0\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.value_counts()[:8]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "Mozilla/5.0                 2594\n",
        "Mozilla/4.0                  601\n",
        "GoogleMaps/RochesterNY       121\n",
        "Opera/9.80                    34\n",
        "TEST_INTERNET_AGENT           24\n",
        "GoogleProducer                21\n",
        "Mozilla/6.0                    5\n",
        "BlackBerry8520/5.0.0.681       4\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, suppose you wanted to decompose the top time zones into Windows and non-windows users."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cframe = frame[frame.a.notnull()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "operating_system = np.where(cframe['a'].str.contains('Windows'), 'Windows','Not Windows')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "operating_system[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "array(['Windows', 'Not Windows', 'Windows', 'Not Windows', 'Windows'], \n",
        "      dtype='|S11')"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then, you can group the data by its time zone column and this new list of operating systems:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_tz_os = cframe.groupby(['tz', operating_system])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The group counts, analogous to the value counts function above, can be computed using size. This result is then reshaped into a table with unstack:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "agg_counts = by_tz_os.size().unstack().fillna(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "agg_counts[: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>Not Windows</th>\n",
        "      <th>Windows</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tz</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td> 245</td>\n",
        "      <td> 276</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Africa/Cairo</th>\n",
        "      <td>   0</td>\n",
        "      <td>   3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Africa/Casablanca</th>\n",
        "      <td>   0</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Africa/Ceuta</th>\n",
        "      <td>   0</td>\n",
        "      <td>   2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Africa/Johannesburg</th>\n",
        "      <td>   0</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Africa/Lusaka</th>\n",
        "      <td>   0</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>America/Anchorage</th>\n",
        "      <td>   4</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>America/Argentina/Buenos_Aires</th>\n",
        "      <td>   1</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>America/Argentina/Cordoba</th>\n",
        "      <td>   0</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>America/Argentina/Mendoza</th>\n",
        "      <td>   0</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "                                Not Windows  Windows\n",
        "tz                                                  \n",
        "                                        245      276\n",
        "Africa/Cairo                              0        3\n",
        "Africa/Casablanca                         0        1\n",
        "Africa/Ceuta                              0        2\n",
        "Africa/Johannesburg                       0        1\n",
        "Africa/Lusaka                             0        1\n",
        "America/Anchorage                         4        1\n",
        "America/Argentina/Buenos_Aires            1        0\n",
        "America/Argentina/Cordoba                 0        1\n",
        "America/Argentina/Mendoza                 0        1"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "indexer = agg_counts.sum(1).argsort()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "indexer[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "tz\n",
        "                                  24\n",
        "Africa/Cairo                      20\n",
        "Africa/Casablanca                 21\n",
        "Africa/Ceuta                      92\n",
        "Africa/Johannesburg               87\n",
        "Africa/Lusaka                     53\n",
        "America/Anchorage                 54\n",
        "America/Argentina/Buenos_Aires    57\n",
        "America/Argentina/Cordoba         26\n",
        "America/Argentina/Mendoza         55\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I then use take to select the rows in that order, then slice off the last 10 rows."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_subset = agg_counts.take(indexer)[-10:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_subset"
     ],
     "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>Not Windows</th>\n",
        "      <th>Windows</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tz</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>America/Sao_Paulo</th>\n",
        "      <td>  13</td>\n",
        "      <td>  20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Europe/Madrid</th>\n",
        "      <td>  16</td>\n",
        "      <td>  19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pacific/Honolulu</th>\n",
        "      <td>   0</td>\n",
        "      <td>  36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Asia/Tokyo</th>\n",
        "      <td>   2</td>\n",
        "      <td>  35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Europe/London</th>\n",
        "      <td>  43</td>\n",
        "      <td>  31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>America/Denver</th>\n",
        "      <td> 132</td>\n",
        "      <td>  59</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>America/Los_Angeles</th>\n",
        "      <td> 130</td>\n",
        "      <td> 252</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>America/Chicago</th>\n",
        "      <td> 115</td>\n",
        "      <td> 285</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td> 245</td>\n",
        "      <td> 276</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>America/New_York</th>\n",
        "      <td> 339</td>\n",
        "      <td> 912</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "                     Not Windows  Windows\n",
        "tz                                       \n",
        "America/Sao_Paulo             13       20\n",
        "Europe/Madrid                 16       19\n",
        "Pacific/Honolulu               0       36\n",
        "Asia/Tokyo                     2       35\n",
        "Europe/London                 43       31\n",
        "America/Denver               132       59\n",
        "America/Los_Angeles          130      252\n",
        "America/Chicago              115      285\n",
        "                             245      276\n",
        "America/New_York             339      912"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_subset.plot(kind = 'barh',stacked = True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x108d1ab10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jUjHrj1k7uP5cE7v+bOFO2XEcx3HyBI8pO5XwmLLjOE798Ziy4ziO4xQY7pSd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\n4+QJ7pQdx3EcJ0/4/z3A+VF2y27WAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108d1a710>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "normed_subset = count_subset.div(count_subset.sum(1),axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "normed_subset.plot(kind = 'barh', stacked = True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x108ec0c50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jMCXcbMifB+ycc1lTHZ4H7B2wK8c7YOecy15dOmB/HKFzVfBnncY8FjGPRcxj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\nFjGPRcxjESvK/pAWswYsabu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       "text": [
        "<matplotlib.figure.Figure at 0x108d1a550>"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "MovieLens 1 M Data Set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path = '/Users/sergulaydore/pydata-book/ch02/movielens/'\n",
      "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "users = pd.read_table(path + 'users.dat', sep = '::', header = None, names = unames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/sergulaydore/anaconda/lib/python2.7/site-packages/pandas/io/parsers.py:624: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.\n",
        "  ParserWarning)\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
      "ratings = pd.read_table(path + 'ratings.dat', sep = '::', header = None, names = rnames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mnames = ['movie_id', 'title', 'genres']\n",
      "movies = pd.read_table(path + 'movies.dat', sep='::', header = None, names = mnames)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "users[: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>user_id</th>\n",
        "      <th>gender</th>\n",
        "      <th>age</th>\n",
        "      <th>occupation</th>\n",
        "      <th>zip</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> F</td>\n",
        "      <td>  1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 48067</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> M</td>\n",
        "      <td> 56</td>\n",
        "      <td> 16</td>\n",
        "      <td> 70072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> M</td>\n",
        "      <td> 25</td>\n",
        "      <td> 15</td>\n",
        "      <td> 55117</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> M</td>\n",
        "      <td> 45</td>\n",
        "      <td>  7</td>\n",
        "      <td> 02460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td> M</td>\n",
        "      <td> 25</td>\n",
        "      <td> 20</td>\n",
        "      <td> 55455</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "   user_id gender  age  occupation    zip\n",
        "0        1      F    1          10  48067\n",
        "1        2      M   56          16  70072\n",
        "2        3      M   25          15  55117\n",
        "3        4      M   45           7  02460\n",
        "4        5      M   25          20  55455"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings[: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>user_id</th>\n",
        "      <th>movie_id</th>\n",
        "      <th>rating</th>\n",
        "      <th>timestamp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1193</td>\n",
        "      <td> 5</td>\n",
        "      <td> 978300760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td>  661</td>\n",
        "      <td> 3</td>\n",
        "      <td> 978302109</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1</td>\n",
        "      <td>  914</td>\n",
        "      <td> 3</td>\n",
        "      <td> 978301968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 3408</td>\n",
        "      <td> 4</td>\n",
        "      <td> 978300275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2355</td>\n",
        "      <td> 5</td>\n",
        "      <td> 978824291</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "   user_id  movie_id  rating  timestamp\n",
        "0        1      1193       5  978300760\n",
        "1        1       661       3  978302109\n",
        "2        1       914       3  978301968\n",
        "3        1      3408       4  978300275\n",
        "4        1      2355       5  978824291"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "movies[: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>movie_id</th>\n",
        "      <th>title</th>\n",
        "      <th>genres</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>                   Toy Story (1995)</td>\n",
        "      <td>  Animation|Children's|Comedy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>                     Jumanji (1995)</td>\n",
        "      <td> Adventure|Children's|Fantasy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td>            Grumpier Old Men (1995)</td>\n",
        "      <td>               Comedy|Romance</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>           Waiting to Exhale (1995)</td>\n",
        "      <td>                 Comedy|Drama</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td> Father of the Bride Part II (1995)</td>\n",
        "      <td>                       Comedy</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "   movie_id                               title                        genres\n",
        "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
        "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
        "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
        "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
        "4         5  Father of the Bride Part II (1995)                        Comedy"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.merge(pd.merge(ratings,users), movies)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.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>user_id</th>\n",
        "      <th>movie_id</th>\n",
        "      <th>rating</th>\n",
        "      <th>timestamp</th>\n",
        "      <th>gender</th>\n",
        "      <th>age</th>\n",
        "      <th>occupation</th>\n",
        "      <th>zip</th>\n",
        "      <th>title</th>\n",
        "      <th>genres</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  1</td>\n",
        "      <td> 1193</td>\n",
        "      <td> 5</td>\n",
        "      <td> 978300760</td>\n",
        "      <td> F</td>\n",
        "      <td>  1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 48067</td>\n",
        "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
        "      <td> Drama</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  2</td>\n",
        "      <td> 1193</td>\n",
        "      <td> 5</td>\n",
        "      <td> 978298413</td>\n",
        "      <td> M</td>\n",
        "      <td> 56</td>\n",
        "      <td> 16</td>\n",
        "      <td> 70072</td>\n",
        "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
        "      <td> Drama</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 12</td>\n",
        "      <td> 1193</td>\n",
        "      <td> 4</td>\n",
        "      <td> 978220179</td>\n",
        "      <td> M</td>\n",
        "      <td> 25</td>\n",
        "      <td> 12</td>\n",
        "      <td> 32793</td>\n",
        "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
        "      <td> Drama</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 15</td>\n",
        "      <td> 1193</td>\n",
        "      <td> 4</td>\n",
        "      <td> 978199279</td>\n",
        "      <td> M</td>\n",
        "      <td> 25</td>\n",
        "      <td>  7</td>\n",
        "      <td> 22903</td>\n",
        "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
        "      <td> Drama</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 17</td>\n",
        "      <td> 1193</td>\n",
        "      <td> 5</td>\n",
        "      <td> 978158471</td>\n",
        "      <td> M</td>\n",
        "      <td> 50</td>\n",
        "      <td>  1</td>\n",
        "      <td> 95350</td>\n",
        "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
        "      <td> Drama</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "   user_id  movie_id  rating  timestamp gender  age  occupation    zip  \\\n",
        "0        1      1193       5  978300760      F    1          10  48067   \n",
        "1        2      1193       5  978298413      M   56          16  70072   \n",
        "2       12      1193       4  978220179      M   25          12  32793   \n",
        "3       15      1193       4  978199279      M   25           7  22903   \n",
        "4       17      1193       5  978158471      M   50           1  95350   \n",
        "\n",
        "                                    title genres  \n",
        "0  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
        "1  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
        "2  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
        "3  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
        "4  One Flew Over the Cuckoo's Nest (1975)  Drama  "
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "user_id                                            1\n",
        "movie_id                                        1193\n",
        "rating                                             5\n",
        "timestamp                                  978300760\n",
        "gender                                             F\n",
        "age                                                1\n",
        "occupation                                        10\n",
        "zip                                            48067\n",
        "title         One Flew Over the Cuckoo's Nest (1975)\n",
        "genres                                         Drama\n",
        "Name: 0, dtype: object"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To get mean movie ratings for each film grouped by gender, we can use the pivot_table method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings = data.pivot_table('rating',rows='title',cols='gender',aggfunc = 'mean') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/sergulaydore/anaconda/lib/python2.7/site-packages/pandas/util/decorators.py:53: FutureWarning: cols is deprecated, use columns instead\n",
        "  warnings.warn(msg, FutureWarning)\n",
        "/Users/sergulaydore/anaconda/lib/python2.7/site-packages/pandas/util/decorators.py:53: FutureWarning: rows is deprecated, use index instead\n",
        "  warnings.warn(msg, FutureWarning)\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings[: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>gender</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>title</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>$1,000,000 Duck (1971)</th>\n",
        "      <td> 3.375000</td>\n",
        "      <td> 2.761905</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>'Night Mother (1986)</th>\n",
        "      <td> 3.388889</td>\n",
        "      <td> 3.352941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>'Til There Was You (1997)</th>\n",
        "      <td> 2.675676</td>\n",
        "      <td> 2.733333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>'burbs, The (1989)</th>\n",
        "      <td> 2.793478</td>\n",
        "      <td> 2.962085</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...And Justice for All (1979)</th>\n",
        "      <td> 3.828571</td>\n",
        "      <td> 3.689024</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "gender                                F         M\n",
        "title                                            \n",
        "$1,000,000 Duck (1971)         3.375000  2.761905\n",
        "'Night Mother (1986)           3.388889  3.352941\n",
        "'Til There Was You (1997)      2.675676  2.733333\n",
        "'burbs, The (1989)             2.793478  2.962085\n",
        "...And Justice for All (1979)  3.828571  3.689024"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings_by_title = data.groupby('title').size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings_by_title[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "title\n",
        "$1,000,000 Duck (1971)                37\n",
        "'Night Mother (1986)                  70\n",
        "'Til There Was You (1997)             52\n",
        "'burbs, The (1989)                   303\n",
        "...And Justice for All (1979)        199\n",
        "1-900 (1994)                           2\n",
        "10 Things I Hate About You (1999)    700\n",
        "101 Dalmatians (1961)                565\n",
        "101 Dalmatians (1996)                364\n",
        "12 Angry Men (1957)                  616\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "active_titles = ratings_by_title.index[ratings_by_title >= 250]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "active_titles"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "Index([u''burbs, The (1989)', u'10 Things I Hate About You (1999)', u'101 Dalmatians (1961)', u'101 Dalmatians (1996)', u'12 Angry Men (1957)', u'13th Warrior, The (1999)', u'2 Days in the Valley (1996)', u'20,000 Leagues Under the Sea (1954)', u'2001: A Space Odyssey (1968)', u'2010 (1984)', u'28 Days (2000)', u'39 Steps, The (1935)', u'54 (1998)', u'7th Voyage of Sinbad, The (1958)', u'8MM (1999)', u'About Last Night... (1986)', u'Absent Minded Professor, The (1961)', u'Absolute Power (1997)', u'Abyss, The (1989)', u'Ace Ventura: Pet Detective (1994)', u'Ace Ventura: When Nature Calls (1995)', u'Addams Family Values (1993)', u'Addams Family, The (1991)', u'Adventures in Babysitting (1987)', u'Adventures of Buckaroo Bonzai Across the 8th Dimension, The (1984)', u'Adventures of Priscilla, Queen of the Desert, The (1994)', u'Adventures of Robin Hood, The (1938)', u'African Queen, The (1951)', u'Age of Innocence, The (1993)', u'Agnes of God (1985)', u'Air America (1990)', u'Air Force One (1997)', u'Airplane II: The Sequel (1982)', u'Airplane! (1980)', u'Akira (1988)', u'Aladdin (1992)', u'Alice in Wonderland (1951)', u'Alien (1979)', u'Alien Nation (1988)', u'Alien: Resurrection (1997)', u'Aliens (1986)', u'Alien\ufffd (1992)', u'Alive (1993)', u'All About Eve (1950)', u'All About My Mother (Todo Sobre Mi Madre) (1999)', u'All Quiet on the Western Front (1930)', u'All That Jazz (1979)', u'Almost Famous (2000)', u'Amadeus (1984)', u'American Beauty (1999)', u'American Gigolo (1980)', u'American Graffiti (1973)', u'American History X (1998)', u'American Movie (1999)', u'American Pie (1999)', u'American President, The (1995)', u'American Psycho (2000)', u'American Tail, An (1986)', u'American Werewolf in London, An (1981)', u'American Werewolf in Paris, An (1997)', u'American in Paris, An (1951)', u'Amistad (1997)', u'Amityville Horror, The (1979)', u'Anaconda (1997)', u'Analyze This (1999)', u'Anastasia (1997)', u'And Now for Something Completely Different (1971)', u'Angel Heart (1987)', u'Animal House (1978)', u'Anna and the King (1999)', u'Annie Hall (1977)', u'Antz (1998)', u'Any Given Sunday (1999)', u'Apartment, The (1960)', u'Apocalypse Now (1979)', u'Apollo 13 (1995)', u'Apostle, The (1997)', u'Arachnophobia (1990)', u'Aristocats, The (1970)', u'Arlington Road (1999)', u'Armageddon (1998)', u'Army of Darkness (1993)', u'Around the World in 80 Days (1956)', u'Arrival, The (1996)', u'Arsenic and Old Lace (1944)', u'Arthur (1981)', u'As Good As It Gets (1997)', u'Astronaut's Wife, The (1999)', u'Atlantic City (1980)', u'Auntie Mame (1958)', u'Austin Powers: International Man of Mystery (1997)', u'Austin Powers: The Spy Who Shagged Me (1999)', u'Avengers, The (1998)', u'Awakenings (1990)', u'Babe (1995)', u'Babe: Pig in the City (1998)', u'Bachelor Party (1984)', u'Bachelor, The (1999)', u'Back to School (1986)', u'Back to the Future (1985)', ...], dtype='object')"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The index of titles receiving at least 250 ratings can then be used to select rows from mean_ratings above:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings = mean_ratings.ix[active_titles]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings.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>gender</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>title</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>'burbs, The (1989)</th>\n",
        "      <td> 2.793478</td>\n",
        "      <td> 2.962085</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 Things I Hate About You (1999)</th>\n",
        "      <td> 3.646552</td>\n",
        "      <td> 3.311966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>101 Dalmatians (1961)</th>\n",
        "      <td> 3.791444</td>\n",
        "      <td> 3.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>101 Dalmatians (1996)</th>\n",
        "      <td> 3.240000</td>\n",
        "      <td> 2.911215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 Angry Men (1957)</th>\n",
        "      <td> 4.184397</td>\n",
        "      <td> 4.328421</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "gender                                    F         M\n",
        "title                                                \n",
        "'burbs, The (1989)                 2.793478  2.962085\n",
        "10 Things I Hate About You (1999)  3.646552  3.311966\n",
        "101 Dalmatians (1961)              3.791444  3.500000\n",
        "101 Dalmatians (1996)              3.240000  2.911215\n",
        "12 Angry Men (1957)                4.184397  4.328421"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To see the top films among female viewers, we can sort by the F column in descending order:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_female_ratings = mean_ratings.sort_index(by = 'F', ascending = False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_female_ratings[: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>gender</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>title</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Close Shave, A (1995)</th>\n",
        "      <td> 4.644444</td>\n",
        "      <td> 4.473795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Wrong Trousers, The (1993)</th>\n",
        "      <td> 4.588235</td>\n",
        "      <td> 4.478261</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</th>\n",
        "      <td> 4.572650</td>\n",
        "      <td> 4.464589</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Wallace &amp; Gromit: The Best of Aardman Animation (1996)</th>\n",
        "      <td> 4.563107</td>\n",
        "      <td> 4.385075</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Schindler's List (1993)</th>\n",
        "      <td> 4.562602</td>\n",
        "      <td> 4.491415</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Shawshank Redemption, The (1994)</th>\n",
        "      <td> 4.539075</td>\n",
        "      <td> 4.560625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Grand Day Out, A (1992)</th>\n",
        "      <td> 4.537879</td>\n",
        "      <td> 4.293255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>To Kill a Mockingbird (1962)</th>\n",
        "      <td> 4.536667</td>\n",
        "      <td> 4.372611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Creature Comforts (1990)</th>\n",
        "      <td> 4.513889</td>\n",
        "      <td> 4.272277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Usual Suspects, The (1995)</th>\n",
        "      <td> 4.513317</td>\n",
        "      <td> 4.518248</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "gender                                                         F         M\n",
        "title                                                                     \n",
        "Close Shave, A (1995)                                   4.644444  4.473795\n",
        "Wrong Trousers, The (1993)                              4.588235  4.478261\n",
        "Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)           4.572650  4.464589\n",
        "Wallace & Gromit: The Best of Aardman Animation (1996)  4.563107  4.385075\n",
        "Schindler's List (1993)                                 4.562602  4.491415\n",
        "Shawshank Redemption, The (1994)                        4.539075  4.560625\n",
        "Grand Day Out, A (1992)                                 4.537879  4.293255\n",
        "To Kill a Mockingbird (1962)                            4.536667  4.372611\n",
        "Creature Comforts (1990)                                4.513889  4.272277\n",
        "Usual Suspects, The (1995)                              4.513317  4.518248"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Measuring rating disagreement"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings['diff'] = mean_ratings['M']-mean_ratings['F']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sorted_by_diff = mean_ratings.sort_index(by='diff')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sorted_by_diff[:15]"
     ],
     "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>gender</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "      <th>diff</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>title</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Dirty Dancing (1987)</th>\n",
        "      <td> 3.790378</td>\n",
        "      <td> 2.959596</td>\n",
        "      <td>-0.830782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jumpin' Jack Flash (1986)</th>\n",
        "      <td> 3.254717</td>\n",
        "      <td> 2.578358</td>\n",
        "      <td>-0.676359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Grease (1978)</th>\n",
        "      <td> 3.975265</td>\n",
        "      <td> 3.367041</td>\n",
        "      <td>-0.608224</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Little Women (1994)</th>\n",
        "      <td> 3.870588</td>\n",
        "      <td> 3.321739</td>\n",
        "      <td>-0.548849</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Steel Magnolias (1989)</th>\n",
        "      <td> 3.901734</td>\n",
        "      <td> 3.365957</td>\n",
        "      <td>-0.535777</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Anastasia (1997)</th>\n",
        "      <td> 3.800000</td>\n",
        "      <td> 3.281609</td>\n",
        "      <td>-0.518391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rocky Horror Picture Show, The (1975)</th>\n",
        "      <td> 3.673016</td>\n",
        "      <td> 3.160131</td>\n",
        "      <td>-0.512885</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Color Purple, The (1985)</th>\n",
        "      <td> 4.158192</td>\n",
        "      <td> 3.659341</td>\n",
        "      <td>-0.498851</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Age of Innocence, The (1993)</th>\n",
        "      <td> 3.827068</td>\n",
        "      <td> 3.339506</td>\n",
        "      <td>-0.487561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Free Willy (1993)</th>\n",
        "      <td> 2.921348</td>\n",
        "      <td> 2.438776</td>\n",
        "      <td>-0.482573</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>French Kiss (1995)</th>\n",
        "      <td> 3.535714</td>\n",
        "      <td> 3.056962</td>\n",
        "      <td>-0.478752</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Little Shop of Horrors, The (1960)</th>\n",
        "      <td> 3.650000</td>\n",
        "      <td> 3.179688</td>\n",
        "      <td>-0.470312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Guys and Dolls (1955)</th>\n",
        "      <td> 4.051724</td>\n",
        "      <td> 3.583333</td>\n",
        "      <td>-0.468391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mary Poppins (1964)</th>\n",
        "      <td> 4.197740</td>\n",
        "      <td> 3.730594</td>\n",
        "      <td>-0.467147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Patch Adams (1998)</th>\n",
        "      <td> 3.473282</td>\n",
        "      <td> 3.008746</td>\n",
        "      <td>-0.464536</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "gender                                        F         M      diff\n",
        "title                                                              \n",
        "Dirty Dancing (1987)                   3.790378  2.959596 -0.830782\n",
        "Jumpin' Jack Flash (1986)              3.254717  2.578358 -0.676359\n",
        "Grease (1978)                          3.975265  3.367041 -0.608224\n",
        "Little Women (1994)                    3.870588  3.321739 -0.548849\n",
        "Steel Magnolias (1989)                 3.901734  3.365957 -0.535777\n",
        "Anastasia (1997)                       3.800000  3.281609 -0.518391\n",
        "Rocky Horror Picture Show, The (1975)  3.673016  3.160131 -0.512885\n",
        "Color Purple, The (1985)               4.158192  3.659341 -0.498851\n",
        "Age of Innocence, The (1993)           3.827068  3.339506 -0.487561\n",
        "Free Willy (1993)                      2.921348  2.438776 -0.482573\n",
        "French Kiss (1995)                     3.535714  3.056962 -0.478752\n",
        "Little Shop of Horrors, The (1960)     3.650000  3.179688 -0.470312\n",
        "Guys and Dolls (1955)                  4.051724  3.583333 -0.468391\n",
        "Mary Poppins (1964)                    4.197740  3.730594 -0.467147\n",
        "Patch Adams (1998)                     3.473282  3.008746 -0.464536"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sorted_by_diff[::-1][:15]"
     ],
     "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>gender</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "      <th>diff</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>title</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Good, The Bad and The Ugly, The (1966)</th>\n",
        "      <td> 3.494949</td>\n",
        "      <td> 4.221300</td>\n",
        "      <td> 0.726351</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kentucky Fried Movie, The (1977)</th>\n",
        "      <td> 2.878788</td>\n",
        "      <td> 3.555147</td>\n",
        "      <td> 0.676359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dumb &amp; Dumber (1994)</th>\n",
        "      <td> 2.697987</td>\n",
        "      <td> 3.336595</td>\n",
        "      <td> 0.638608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Longest Day, The (1962)</th>\n",
        "      <td> 3.411765</td>\n",
        "      <td> 4.031447</td>\n",
        "      <td> 0.619682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cable Guy, The (1996)</th>\n",
        "      <td> 2.250000</td>\n",
        "      <td> 2.863787</td>\n",
        "      <td> 0.613787</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Evil Dead II (Dead By Dawn) (1987)</th>\n",
        "      <td> 3.297297</td>\n",
        "      <td> 3.909283</td>\n",
        "      <td> 0.611985</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hidden, The (1987)</th>\n",
        "      <td> 3.137931</td>\n",
        "      <td> 3.745098</td>\n",
        "      <td> 0.607167</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Rocky III (1982)</th>\n",
        "      <td> 2.361702</td>\n",
        "      <td> 2.943503</td>\n",
        "      <td> 0.581801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Caddyshack (1980)</th>\n",
        "      <td> 3.396135</td>\n",
        "      <td> 3.969737</td>\n",
        "      <td> 0.573602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>For a Few Dollars More (1965)</th>\n",
        "      <td> 3.409091</td>\n",
        "      <td> 3.953795</td>\n",
        "      <td> 0.544704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Porky's (1981)</th>\n",
        "      <td> 2.296875</td>\n",
        "      <td> 2.836364</td>\n",
        "      <td> 0.539489</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Animal House (1978)</th>\n",
        "      <td> 3.628906</td>\n",
        "      <td> 4.167192</td>\n",
        "      <td> 0.538286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Exorcist, The (1973)</th>\n",
        "      <td> 3.537634</td>\n",
        "      <td> 4.067239</td>\n",
        "      <td> 0.529605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fright Night (1985)</th>\n",
        "      <td> 2.973684</td>\n",
        "      <td> 3.500000</td>\n",
        "      <td> 0.526316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Barb Wire (1996)</th>\n",
        "      <td> 1.585366</td>\n",
        "      <td> 2.100386</td>\n",
        "      <td> 0.515020</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "gender                                         F         M      diff\n",
        "title                                                               \n",
        "Good, The Bad and The Ugly, The (1966)  3.494949  4.221300  0.726351\n",
        "Kentucky Fried Movie, The (1977)        2.878788  3.555147  0.676359\n",
        "Dumb & Dumber (1994)                    2.697987  3.336595  0.638608\n",
        "Longest Day, The (1962)                 3.411765  4.031447  0.619682\n",
        "Cable Guy, The (1996)                   2.250000  2.863787  0.613787\n",
        "Evil Dead II (Dead By Dawn) (1987)      3.297297  3.909283  0.611985\n",
        "Hidden, The (1987)                      3.137931  3.745098  0.607167\n",
        "Rocky III (1982)                        2.361702  2.943503  0.581801\n",
        "Caddyshack (1980)                       3.396135  3.969737  0.573602\n",
        "For a Few Dollars More (1965)           3.409091  3.953795  0.544704\n",
        "Porky's (1981)                          2.296875  2.836364  0.539489\n",
        "Animal House (1978)                     3.628906  4.167192  0.538286\n",
        "Exorcist, The (1973)                    3.537634  4.067239  0.529605\n",
        "Fright Night (1985)                     2.973684  3.500000  0.526316\n",
        "Barb Wire (1996)                        1.585366  2.100386  0.515020"
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     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rating_std_by_title = data.groupby('title')['rating'].std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rating_std_by_title = rating_std_by_title.ix[active_titles]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rating_std_by_title.order(ascending=False)[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "title\n",
        "Dumb & Dumber (1994)                     1.321333\n",
        "Blair Witch Project, The (1999)          1.316368\n",
        "Natural Born Killers (1994)              1.307198\n",
        "Tank Girl (1995)                         1.277695\n",
        "Rocky Horror Picture Show, The (1975)    1.260177\n",
        "Eyes Wide Shut (1999)                    1.259624\n",
        "Evita (1996)                             1.253631\n",
        "Billy Madison (1995)                     1.249970\n",
        "Fear and Loathing in Las Vegas (1998)    1.246408\n",
        "Bicentennial Man (1999)                  1.245533\n",
        "Name: rating, dtype: float64"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "US Baby Names 1880 - 2010"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path = '/Users/sergulaydore/pydata-book/ch02/names/'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -n 10 '/Users/sergulaydore/pydata-book/ch02/names/yob1880.txt'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mary,F,7065\r",
        "\r\n",
        "Anna,F,2604\r",
        "\r\n",
        "Emma,F,2003\r",
        "\r\n",
        "Elizabeth,F,1939\r",
        "\r\n",
        "Minnie,F,1746\r",
        "\r\n",
        "Margaret,F,1578\r",
        "\r\n",
        "Ida,F,1472\r",
        "\r\n",
        "Alice,F,1414\r",
        "\r\n",
        "Bertha,F,1320\r",
        "\r\n",
        "Sarah,F,1288\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names1880 = pd.read_csv(path + 'yob1880.txt', names = ['name','sex','births'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names1880[: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>name</th>\n",
        "      <th>sex</th>\n",
        "      <th>births</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>      Mary</td>\n",
        "      <td> F</td>\n",
        "      <td> 7065</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      Anna</td>\n",
        "      <td> F</td>\n",
        "      <td> 2604</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      Emma</td>\n",
        "      <td> F</td>\n",
        "      <td> 2003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Elizabeth</td>\n",
        "      <td> F</td>\n",
        "      <td> 1939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    Minnie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1746</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>  Margaret</td>\n",
        "      <td> F</td>\n",
        "      <td> 1578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>       Ida</td>\n",
        "      <td> F</td>\n",
        "      <td> 1472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>     Alice</td>\n",
        "      <td> F</td>\n",
        "      <td> 1414</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>    Bertha</td>\n",
        "      <td> F</td>\n",
        "      <td> 1320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>     Sarah</td>\n",
        "      <td> F</td>\n",
        "      <td> 1288</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "        name sex  births\n",
        "0       Mary   F    7065\n",
        "1       Anna   F    2604\n",
        "2       Emma   F    2003\n",
        "3  Elizabeth   F    1939\n",
        "4     Minnie   F    1746\n",
        "5   Margaret   F    1578\n",
        "6        Ida   F    1472\n",
        "7      Alice   F    1414\n",
        "8     Bertha   F    1320\n",
        "9      Sarah   F    1288"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names1880.groupby('sex').births.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "sex\n",
        "F       90993\n",
        "M      110493\n",
        "Name: births, dtype: int64"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years = range(1880, 2011)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pieces = []\n",
      "columns = ['name','sex','births']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for year in years:\n",
      "    path_file = path + 'yob%d.txt' %year\n",
      "    frame = pd.read_csv(path_file, names=columns)\n",
      "    frame['year']=year\n",
      "    pieces.append(frame)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Concatenate everything into a single DataFrame\n",
      "names = pd.concat(pieces, ignore_index=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births = names.pivot_table('births',rows='year',cols='sex',aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>sex</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 1896468</td>\n",
        "      <td> 2050234</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 1916888</td>\n",
        "      <td> 2069242</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 1883645</td>\n",
        "      <td> 2032310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 1827643</td>\n",
        "      <td> 1973359</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 1759010</td>\n",
        "      <td> 1898382</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 143,
       "text": [
        "sex         F        M\n",
        "year                  \n",
        "2006  1896468  2050234\n",
        "2007  1916888  2069242\n",
        "2008  1883645  2032310\n",
        "2009  1827643  1973359\n",
        "2010  1759010  1898382"
       ]
      }
     ],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births.plot(title='Total births by sex and year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 144,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x114efac10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TztGiG1KZ+GIYZoQRC456/yqlWLZsGS+++KJjBDC4JSevn2TElhHseHcHaTzS8GqpV/mo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\n55xLy/8AJvL4RW/9lNwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113f3df10>"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, let's insert a column prop with the fraction of babies given each name realtive to the total number of births."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def  add_prop(group):\n",
      "    # integer division floors\n",
      "    births = group.births.astype('float')\n",
      "    \n",
      "    group['prop'] = births / births.sum()\n",
      "    return group"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names = names.groupby(['year','sex']).apply(add_prop)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names.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>name</th>\n",
        "      <th>sex</th>\n",
        "      <th>births</th>\n",
        "      <th>year</th>\n",
        "      <th>prop</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>      Mary</td>\n",
        "      <td> F</td>\n",
        "      <td> 7065</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.077643</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      Anna</td>\n",
        "      <td> F</td>\n",
        "      <td> 2604</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.028618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      Emma</td>\n",
        "      <td> F</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.022013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Elizabeth</td>\n",
        "      <td> F</td>\n",
        "      <td> 1939</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.021309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    Minnie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1746</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.019188</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 148,
       "text": [
        "        name sex  births  year      prop\n",
        "0       Mary   F    7065  1880  0.077643\n",
        "1       Anna   F    2604  1880  0.028618\n",
        "2       Emma   F    2003  1880  0.022013\n",
        "3  Elizabeth   F    1939  1880  0.021309\n",
        "4     Minnie   F    1746  1880  0.019188"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.allclose(names.groupby(['year','sex']).prop.sum(),1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 149,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 149
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "the top 1000 names for each sex/year combination"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_top1000(group):\n",
      "    return group.sort_index(by='births',ascending=False)[:1000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 150
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped = names.groupby(['year','sex'])\n",
      "top1000 = grouped.apply(get_top1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top1000[:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>name</th>\n",
        "      <th>sex</th>\n",
        "      <th>births</th>\n",
        "      <th>year</th>\n",
        "      <th>prop</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>sex</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"15\" valign=\"top\">1880</th>\n",
        "      <th rowspan=\"15\" valign=\"top\">F</th>\n",
        "      <th>0 </th>\n",
        "      <td>      Mary</td>\n",
        "      <td> F</td>\n",
        "      <td> 7065</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.077643</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>      Anna</td>\n",
        "      <td> F</td>\n",
        "      <td> 2604</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.028618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>      Emma</td>\n",
        "      <td> F</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.022013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> Elizabeth</td>\n",
        "      <td> F</td>\n",
        "      <td> 1939</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.021309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>    Minnie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1746</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.019188</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  Margaret</td>\n",
        "      <td> F</td>\n",
        "      <td> 1578</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.017342</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>       Ida</td>\n",
        "      <td> F</td>\n",
        "      <td> 1472</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.016177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>     Alice</td>\n",
        "      <td> F</td>\n",
        "      <td> 1414</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.015540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>    Bertha</td>\n",
        "      <td> F</td>\n",
        "      <td> 1320</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.014507</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>     Sarah</td>\n",
        "      <td> F</td>\n",
        "      <td> 1288</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.014155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>     Annie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1258</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.013825</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>     Clara</td>\n",
        "      <td> F</td>\n",
        "      <td> 1226</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.013474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>      Ella</td>\n",
        "      <td> F</td>\n",
        "      <td> 1156</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.012704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>  Florence</td>\n",
        "      <td> F</td>\n",
        "      <td> 1063</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.011682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>      Cora</td>\n",
        "      <td> F</td>\n",
        "      <td> 1045</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.011484</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 153,
       "text": [
        "                  name sex  births  year      prop\n",
        "year sex                                          \n",
        "1880 F   0        Mary   F    7065  1880  0.077643\n",
        "         1        Anna   F    2604  1880  0.028618\n",
        "         2        Emma   F    2003  1880  0.022013\n",
        "         3   Elizabeth   F    1939  1880  0.021309\n",
        "         4      Minnie   F    1746  1880  0.019188\n",
        "         5    Margaret   F    1578  1880  0.017342\n",
        "         6         Ida   F    1472  1880  0.016177\n",
        "         7       Alice   F    1414  1880  0.015540\n",
        "         8      Bertha   F    1320  1880  0.014507\n",
        "         9       Sarah   F    1288  1880  0.014155\n",
        "         10      Annie   F    1258  1880  0.013825\n",
        "         11      Clara   F    1226  1880  0.013474\n",
        "         12       Ella   F    1156  1880  0.012704\n",
        "         13   Florence   F    1063  1880  0.011682\n",
        "         14       Cora   F    1045  1880  0.011484"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Analyzing Naming Trends"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boys = top1000[top1000.sex=='M']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "girls = top1000[top1000.sex=='F']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 156
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births = top1000.pivot_table('births',rows='year',cols='name',aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 157
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "</table>\n",
        "<p>10 rows \u00d7 6868 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 158,
       "text": [
        "name  Aaden  Aaliyah  Aarav  Aaron  Aarush  Ab  Abagail  Abb  Abbey  Abbie  \\\n",
        "year                                                                         \n",
        "1880    NaN      NaN    NaN    102     NaN NaN      NaN  NaN    NaN     71   \n",
        "1881    NaN      NaN    NaN     94     NaN NaN      NaN  NaN    NaN     81   \n",
        "1882    NaN      NaN    NaN     85     NaN NaN      NaN  NaN    NaN     80   \n",
        "1883    NaN      NaN    NaN    105     NaN NaN      NaN  NaN    NaN     79   \n",
        "1884    NaN      NaN    NaN     97     NaN NaN      NaN  NaN    NaN     98   \n",
        "1885    NaN      NaN    NaN     88     NaN   6      NaN  NaN    NaN     88   \n",
        "1886    NaN      NaN    NaN     86     NaN NaN      NaN  NaN    NaN     84   \n",
        "1887    NaN      NaN    NaN     78     NaN NaN      NaN  NaN    NaN    104   \n",
        "1888    NaN      NaN    NaN     90     NaN NaN      NaN  NaN    NaN    137   \n",
        "1889    NaN      NaN    NaN     85     NaN NaN      NaN  NaN    NaN    107   \n",
        "\n",
        "name  ...    Zoa  Zoe  Zoey  Zoie  Zola  Zollie  Zona  Zora  Zula  Zuri  \n",
        "year  ...                                                                \n",
        "1880  ...      8   23   NaN   NaN     7     NaN     8    28    27   NaN  \n",
        "1881  ...    NaN   22   NaN   NaN    10     NaN     9    21    27   NaN  \n",
        "1882  ...      8   25   NaN   NaN     9     NaN    17    32    21   NaN  \n",
        "1883  ...    NaN   23   NaN   NaN    10     NaN    11    35    25   NaN  \n",
        "1884  ...     13   31   NaN   NaN    14       6     8    58    27   NaN  \n",
        "1885  ...      6   27   NaN   NaN    12       6    14    48    38   NaN  \n",
        "1886  ...     13   25   NaN   NaN     8     NaN    20    52    43   NaN  \n",
        "1887  ...      9   34   NaN   NaN    23     NaN    28    46    33   NaN  \n",
        "1888  ...     11   42   NaN   NaN    23       7    30    42    45   NaN  \n",
        "1889  ...     14   29   NaN   NaN    22     NaN    29    53    55   NaN  \n",
        "\n",
        "[10 rows x 6868 columns]"
       ]
      }
     ],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subset = total_births[['John','Harry','Mary','Marilyn']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 159
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subset.plot(subplots=True, figsize=(12,10), grid=False, \n",
      "            title='Number of births per year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 160,
       "text": [
        "array([<matplotlib.axes._subplots.AxesSubplot object at 0x10f360e10>,\n",
        "       <matplotlib.axes._subplots.AxesSubplot object at 0x10dcca610>,\n",
        "       <matplotlib.axes._subplots.AxesSubplot object at 0x10298cb10>,\n",
        "       <matplotlib.axes._subplots.AxesSubplot object at 0x110200410>], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fnkqVKhEbG0ufPn245ZZbqFKlSoHPf/7550lMTKR9+/YkJCTQpk0b6tWrp92p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\n4uLiwoEDB3j66adv65fWsbd6/vnnOXXqFB06dCB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1fzrgb91vD8wVkQQRCQWOAV7GmFJA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\nR/dNKiwsTAsUKKDh4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JykvZ6IhDjP/TetMRljXGvq1KnUqlXLkmbjdnLnzs23337LyZMn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\nEf4/WVfN1ognOuIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f395890>"
       ]
      }
     ],
     "prompt_number": 160
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Measuring the increase in naming diversity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = top1000.pivot_table('prop',rows='year',cols='sex',aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.plot(title='Sum of table1000.prop by year ans sex',\n",
      "           yticks=np.linspace(0,1.2,13), xticks=range(1880,2020,10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 164,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10fc64a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Y/PfJ/PztnzP387kUlhVGRb+5NDjOXlVnA7PDts0MWV8ADPK/aoZhtDQiMHas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\nLhg7nymxX5kAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x118ac9cd0>"
       ]
      }
     ],
     "prompt_number": 164
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = boys[boys.year==2010]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[: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></th>\n",
        "      <th></th>\n",
        "      <th>name</th>\n",
        "      <th>sex</th>\n",
        "      <th>births</th>\n",
        "      <th>year</th>\n",
        "      <th>prop</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>sex</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"10\" valign=\"top\">2010</th>\n",
        "      <th rowspan=\"10\" valign=\"top\">M</th>\n",
        "      <th>1676644</th>\n",
        "      <td>     Jacob</td>\n",
        "      <td> M</td>\n",
        "      <td> 21875</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.011523</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676645</th>\n",
        "      <td>     Ethan</td>\n",
        "      <td> M</td>\n",
        "      <td> 17866</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.009411</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676646</th>\n",
        "      <td>   Michael</td>\n",
        "      <td> M</td>\n",
        "      <td> 17133</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.009025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676647</th>\n",
        "      <td>    Jayden</td>\n",
        "      <td> M</td>\n",
        "      <td> 17030</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.008971</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676648</th>\n",
        "      <td>   William</td>\n",
        "      <td> M</td>\n",
        "      <td> 16870</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.008887</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676649</th>\n",
        "      <td> Alexander</td>\n",
        "      <td> M</td>\n",
        "      <td> 16634</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.008762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676650</th>\n",
        "      <td>      Noah</td>\n",
        "      <td> M</td>\n",
        "      <td> 16281</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.008576</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676651</th>\n",
        "      <td>    Daniel</td>\n",
        "      <td> M</td>\n",
        "      <td> 15679</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.008259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676652</th>\n",
        "      <td>     Aiden</td>\n",
        "      <td> M</td>\n",
        "      <td> 15403</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.008114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1676653</th>\n",
        "      <td>   Anthony</td>\n",
        "      <td> M</td>\n",
        "      <td> 15364</td>\n",
        "      <td> 2010</td>\n",
        "      <td> 0.008093</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 166,
       "text": [
        "                       name sex  births  year      prop\n",
        "year sex                                               \n",
        "2010 M   1676644      Jacob   M   21875  2010  0.011523\n",
        "         1676645      Ethan   M   17866  2010  0.009411\n",
        "         1676646    Michael   M   17133  2010  0.009025\n",
        "         1676647     Jayden   M   17030  2010  0.008971\n",
        "         1676648    William   M   16870  2010  0.008887\n",
        "         1676649  Alexander   M   16634  2010  0.008762\n",
        "         1676650       Noah   M   16281  2010  0.008576\n",
        "         1676651     Daniel   M   15679  2010  0.008259\n",
        "         1676652      Aiden   M   15403  2010  0.008114\n",
        "         1676653    Anthony   M   15364  2010  0.008093"
       ]
      }
     ],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prop_cumsum = df.sort_index(by='prop',ascending=False).prop.cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 167
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prop_cumsum[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 168,
       "text": [
        "year  sex         \n",
        "2010  M    1676644    0.011523\n",
        "           1676645    0.020934\n",
        "           1676646    0.029959\n",
        "           1676647    0.038930\n",
        "           1676648    0.047817\n",
        "           1676649    0.056579\n",
        "           1676650    0.065155\n",
        "           1676651    0.073414\n",
        "           1676652    0.081528\n",
        "           1676653    0.089621\n",
        "Name: prop, dtype: float64"
       ]
      }
     ],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prop_cumsum.values.searchsorted(0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 171,
       "text": [
        "116"
       ]
      }
     ],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_quantile_count(group, q=0.5):\n",
      "    group = group.sort_index(by='prop',ascending=False)\n",
      "    return group.prop.cumsum().values.searchsorted(q)+1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 172
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diversity = top1000.groupby(['year','sex']).apply(get_quantile_count)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 173
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diversity.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 174,
       "text": [
        "year  sex\n",
        "1880  F      38\n",
        "      M      14\n",
        "1881  F      38\n",
        "      M      14\n",
        "1882  F      38\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 174
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diversity = diversity.unstack('sex')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diversity.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>sex</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
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        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1880</th>\n",
        "      <td> 38</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1881</th>\n",
        "      <td> 38</td>\n",
        "      <td> 14</td>\n",
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        "    <tr>\n",
        "      <th>1882</th>\n",
        "      <td> 38</td>\n",
        "      <td> 15</td>\n",
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        "    <tr>\n",
        "      <th>1883</th>\n",
        "      <td> 39</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1884</th>\n",
        "      <td> 39</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
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       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 176,
       "text": [
        "sex    F   M\n",
        "year        \n",
        "1880  38  14\n",
        "1881  38  14\n",
        "1882  38  15\n",
        "1883  39  15\n",
        "1884  39  16"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diversity.plot(title='Number of popular names in top 50%')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 177,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x1124d0050>"
       ]
      },
      {
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sAR4EJgKjgQZk7n74AK774VHgdlX9LIs8NbdyDcMwwsVXX8Ff/gI//QR5mIMq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\npwhdJcUwSpVyP/Kvvup6/zz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       "text": [
        "<matplotlib.figure.Figure at 0x1124d0810>"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The \"last letter\" revolution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# extract last letter from name column\n",
      "get_last_letter = lambda x: x[-1]\n",
      "last_letters = names.name.map(get_last_letter)\n",
      "last_letters.name='last_letter'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 178
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = names.pivot_table('births', rows=last_letters,\n",
      "                          cols=['sex','year'], aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 179
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table[: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>\n",
        "      <th>sex</th>\n",
        "      <th colspan=\"10\" halign=\"left\">F</th>\n",
        "      <th>...</th>\n",
        "      <th colspan=\"10\" halign=\"left\">M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>1880</th>\n",
        "      <th>1881</th>\n",
        "      <th>1882</th>\n",
        "      <th>1883</th>\n",
        "      <th>1884</th>\n",
        "      <th>1885</th>\n",
        "      <th>1886</th>\n",
        "      <th>1887</th>\n",
        "      <th>1888</th>\n",
        "      <th>1889</th>\n",
        "      <th>...</th>\n",
        "      <th>2001</th>\n",
        "      <th>2002</th>\n",
        "      <th>2003</th>\n",
        "      <th>2004</th>\n",
        "      <th>2005</th>\n",
        "      <th>2006</th>\n",
        "      <th>2007</th>\n",
        "      <th>2008</th>\n",
        "      <th>2009</th>\n",
        "      <th>2010</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>last_letter</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></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>a</th>\n",
        "      <td> 31446</td>\n",
        "      <td> 31581</td>\n",
        "      <td> 36536</td>\n",
        "      <td> 38330</td>\n",
        "      <td> 43680</td>\n",
        "      <td> 45408</td>\n",
        "      <td> 49100</td>\n",
        "      <td> 48942</td>\n",
        "      <td> 59442</td>\n",
        "      <td> 58631</td>\n",
        "      <td>...</td>\n",
        "      <td>  39124</td>\n",
        "      <td>  38815</td>\n",
        "      <td>  37825</td>\n",
        "      <td>  38650</td>\n",
        "      <td>  36838</td>\n",
        "      <td>  36156</td>\n",
        "      <td>  34654</td>\n",
        "      <td>  32901</td>\n",
        "      <td>  31430</td>\n",
        "      <td>  28438</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>  50950</td>\n",
        "      <td>  49284</td>\n",
        "      <td>  48065</td>\n",
        "      <td>  45914</td>\n",
        "      <td>  43144</td>\n",
        "      <td>  42600</td>\n",
        "      <td>  42123</td>\n",
        "      <td>  39945</td>\n",
        "      <td>  38862</td>\n",
        "      <td>  38859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     5</td>\n",
        "      <td>     5</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>  27113</td>\n",
        "      <td>  27238</td>\n",
        "      <td>  27697</td>\n",
        "      <td>  26778</td>\n",
        "      <td>  26078</td>\n",
        "      <td>  26635</td>\n",
        "      <td>  26864</td>\n",
        "      <td>  25318</td>\n",
        "      <td>  24048</td>\n",
        "      <td>  23125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>d</th>\n",
        "      <td>   609</td>\n",
        "      <td>   607</td>\n",
        "      <td>   734</td>\n",
        "      <td>   810</td>\n",
        "      <td>   916</td>\n",
        "      <td>   862</td>\n",
        "      <td>  1007</td>\n",
        "      <td>  1027</td>\n",
        "      <td>  1298</td>\n",
        "      <td>  1374</td>\n",
        "      <td>...</td>\n",
        "      <td>  60838</td>\n",
        "      <td>  55829</td>\n",
        "      <td>  53391</td>\n",
        "      <td>  51754</td>\n",
        "      <td>  50670</td>\n",
        "      <td>  51410</td>\n",
        "      <td>  50595</td>\n",
        "      <td>  47910</td>\n",
        "      <td>  46172</td>\n",
        "      <td>  44398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>e</th>\n",
        "      <td> 33378</td>\n",
        "      <td> 34080</td>\n",
        "      <td> 40399</td>\n",
        "      <td> 41914</td>\n",
        "      <td> 48089</td>\n",
        "      <td> 49616</td>\n",
        "      <td> 53884</td>\n",
        "      <td> 54353</td>\n",
        "      <td> 66750</td>\n",
        "      <td> 66663</td>\n",
        "      <td>...</td>\n",
        "      <td> 145395</td>\n",
        "      <td> 144651</td>\n",
        "      <td> 144769</td>\n",
        "      <td> 142098</td>\n",
        "      <td> 141123</td>\n",
        "      <td> 142999</td>\n",
        "      <td> 143698</td>\n",
        "      <td> 140966</td>\n",
        "      <td> 135496</td>\n",
        "      <td> 129012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>f</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>   1758</td>\n",
        "      <td>   1817</td>\n",
        "      <td>   1819</td>\n",
        "      <td>   1904</td>\n",
        "      <td>   1985</td>\n",
        "      <td>   1968</td>\n",
        "      <td>   2090</td>\n",
        "      <td>   2195</td>\n",
        "      <td>   2212</td>\n",
        "      <td>   2255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>g</th>\n",
        "      <td>     7</td>\n",
        "      <td>     5</td>\n",
        "      <td>    12</td>\n",
        "      <td>     8</td>\n",
        "      <td>    24</td>\n",
        "      <td>    11</td>\n",
        "      <td>    18</td>\n",
        "      <td>    25</td>\n",
        "      <td>    44</td>\n",
        "      <td>    28</td>\n",
        "      <td>...</td>\n",
        "      <td>   2151</td>\n",
        "      <td>   2084</td>\n",
        "      <td>   2009</td>\n",
        "      <td>   1837</td>\n",
        "      <td>   1882</td>\n",
        "      <td>   1929</td>\n",
        "      <td>   2040</td>\n",
        "      <td>   2059</td>\n",
        "      <td>   2396</td>\n",
        "      <td>   2666</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>h</th>\n",
        "      <td>  4863</td>\n",
        "      <td>  4784</td>\n",
        "      <td>  5567</td>\n",
        "      <td>  5701</td>\n",
        "      <td>  6602</td>\n",
        "      <td>  6624</td>\n",
        "      <td>  7146</td>\n",
        "      <td>  7141</td>\n",
        "      <td>  8630</td>\n",
        "      <td>  8826</td>\n",
        "      <td>...</td>\n",
        "      <td>  85959</td>\n",
        "      <td>  88085</td>\n",
        "      <td>  88226</td>\n",
        "      <td>  89620</td>\n",
        "      <td>  92497</td>\n",
        "      <td>  98477</td>\n",
        "      <td>  99414</td>\n",
        "      <td> 100250</td>\n",
        "      <td>  99979</td>\n",
        "      <td>  98090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>i</th>\n",
        "      <td>    61</td>\n",
        "      <td>    78</td>\n",
        "      <td>    81</td>\n",
        "      <td>    76</td>\n",
        "      <td>    84</td>\n",
        "      <td>    92</td>\n",
        "      <td>    85</td>\n",
        "      <td>   105</td>\n",
        "      <td>   141</td>\n",
        "      <td>   134</td>\n",
        "      <td>...</td>\n",
        "      <td>  20980</td>\n",
        "      <td>  23610</td>\n",
        "      <td>  26011</td>\n",
        "      <td>  28500</td>\n",
        "      <td>  31317</td>\n",
        "      <td>  33558</td>\n",
        "      <td>  35231</td>\n",
        "      <td>  38151</td>\n",
        "      <td>  40912</td>\n",
        "      <td>  42956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>j</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>   1069</td>\n",
        "      <td>   1088</td>\n",
        "      <td>   1203</td>\n",
        "      <td>   1094</td>\n",
        "      <td>   1291</td>\n",
        "      <td>   1241</td>\n",
        "      <td>   1254</td>\n",
        "      <td>   1381</td>\n",
        "      <td>   1416</td>\n",
        "      <td>   1459</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 262 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 180,
       "text": [
        "sex              F                                                          \\\n",
        "year          1880   1881   1882   1883   1884   1885   1886   1887   1888   \n",
        "last_letter                                                                  \n",
        "a            31446  31581  36536  38330  43680  45408  49100  48942  59442   \n",
        "b              NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "c              NaN    NaN      5      5    NaN    NaN    NaN    NaN    NaN   \n",
        "d              609    607    734    810    916    862   1007   1027   1298   \n",
        "e            33378  34080  40399  41914  48089  49616  53884  54353  66750   \n",
        "f              NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "g                7      5     12      8     24     11     18     25     44   \n",
        "h             4863   4784   5567   5701   6602   6624   7146   7141   8630   \n",
        "i               61     78     81     76     84     92     85    105    141   \n",
        "j              NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
        "\n",
        "sex                 ...         M                                          \\\n",
        "year          1889  ...      2001    2002    2003    2004    2005    2006   \n",
        "last_letter         ...                                                     \n",
        "a            58631  ...     39124   38815   37825   38650   36838   36156   \n",
        "b              NaN  ...     50950   49284   48065   45914   43144   42600   \n",
        "c              NaN  ...     27113   27238   27697   26778   26078   26635   \n",
        "d             1374  ...     60838   55829   53391   51754   50670   51410   \n",
        "e            66663  ...    145395  144651  144769  142098  141123  142999   \n",
        "f              NaN  ...      1758    1817    1819    1904    1985    1968   \n",
        "g               28  ...      2151    2084    2009    1837    1882    1929   \n",
        "h             8826  ...     85959   88085   88226   89620   92497   98477   \n",
        "i              134  ...     20980   23610   26011   28500   31317   33558   \n",
        "j              NaN  ...      1069    1088    1203    1094    1291    1241   \n",
        "\n",
        "sex                                          \n",
        "year           2007    2008    2009    2010  \n",
        "last_letter                                  \n",
        "a             34654   32901   31430   28438  \n",
        "b             42123   39945   38862   38859  \n",
        "c             26864   25318   24048   23125  \n",
        "d             50595   47910   46172   44398  \n",
        "e            143698  140966  135496  129012  \n",
        "f              2090    2195    2212    2255  \n",
        "g              2040    2059    2396    2666  \n",
        "h             99414  100250   99979   98090  \n",
        "i             35231   38151   40912   42956  \n",
        "j              1254    1381    1416    1459  \n",
        "\n",
        "[10 rows x 262 columns]"
       ]
      }
     ],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subtable.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>\n",
        "      <th>sex</th>\n",
        "      <th colspan=\"3\" halign=\"left\">F</th>\n",
        "      <th colspan=\"3\" halign=\"left\">M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>1910</th>\n",
        "      <th>1960</th>\n",
        "      <th>2010</th>\n",
        "      <th>1910</th>\n",
        "      <th>1960</th>\n",
        "      <th>2010</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>last_letter</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>a</th>\n",
        "      <td> 108376</td>\n",
        "      <td> 691247</td>\n",
        "      <td> 670605</td>\n",
        "      <td>   977</td>\n",
        "      <td>   5204</td>\n",
        "      <td>  28438</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>b</th>\n",
        "      <td>    NaN</td>\n",
        "      <td>    694</td>\n",
        "      <td>    450</td>\n",
        "      <td>   411</td>\n",
        "      <td>   3912</td>\n",
        "      <td>  38859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>c</th>\n",
        "      <td>      5</td>\n",
        "      <td>     49</td>\n",
        "      <td>    946</td>\n",
        "      <td>   482</td>\n",
        "      <td>  15476</td>\n",
        "      <td>  23125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>d</th>\n",
        "      <td>   6750</td>\n",
        "      <td>   3729</td>\n",
        "      <td>   2607</td>\n",
        "      <td> 22111</td>\n",
        "      <td> 262112</td>\n",
        "      <td>  44398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>e</th>\n",
        "      <td> 133569</td>\n",
        "      <td> 435013</td>\n",
        "      <td> 313833</td>\n",
        "      <td> 28655</td>\n",
        "      <td> 178823</td>\n",
        "      <td> 129012</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 182,
       "text": [
        "sex               F                      M                \n",
        "year           1910    1960    2010   1910    1960    2010\n",
        "last_letter                                               \n",
        "a            108376  691247  670605    977    5204   28438\n",
        "b               NaN     694     450    411    3912   38859\n",
        "c                 5      49     946    482   15476   23125\n",
        "d              6750    3729    2607  22111  262112   44398\n",
        "e            133569  435013  313833  28655  178823  129012"
       ]
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     ],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subtable.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 183,
       "text": [
        "sex  year\n",
        "F    1910     396416\n",
        "     1960    2022062\n",
        "     2010    1759010\n",
        "M    1910     194198\n",
        "     1960    2132588\n",
        "     2010    1898382\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 183
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "letter_prop = subtable/subtable.sum().astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(2,1,figsize=(10,8))\n",
      "letter_prop['M'].plot(kind='bar',rot=0,ax=axes[0],title='Male')\n",
      "letter_prop['F'].plot(kind='bar',rot=0,ax=axes[1],title='Female',\n",
      "                      legend=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 190,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x117c9cad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WAbtKapG0OXAscNNAn9PvQydImpy8nwgcDtw7pMwtVVn+HNXMzKzsBm1URcR6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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117c1d910>"
       ]
      }
     ],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "letter_prop = table/table.sum().astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 191
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dny_ts = letter_prop.ix[['d','n','y'],'M'].T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 192
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dny_ts.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>d</th>\n",
        "      <th>n</th>\n",
        "      <th>y</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1880</th>\n",
        "      <td> 0.083055</td>\n",
        "      <td> 0.153213</td>\n",
        "      <td> 0.075760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1881</th>\n",
        "      <td> 0.083247</td>\n",
        "      <td> 0.153214</td>\n",
        "      <td> 0.077451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1882</th>\n",
        "      <td> 0.085340</td>\n",
        "      <td> 0.149560</td>\n",
        "      <td> 0.077537</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1883</th>\n",
        "      <td> 0.084066</td>\n",
        "      <td> 0.151646</td>\n",
        "      <td> 0.079144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1884</th>\n",
        "      <td> 0.086120</td>\n",
        "      <td> 0.149915</td>\n",
        "      <td> 0.080405</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 193,
       "text": [
        "             d         n         y\n",
        "year                              \n",
        "1880  0.083055  0.153213  0.075760\n",
        "1881  0.083247  0.153214  0.077451\n",
        "1882  0.085340  0.149560  0.077537\n",
        "1883  0.084066  0.151646  0.079144\n",
        "1884  0.086120  0.149915  0.080405"
       ]
      }
     ],
     "prompt_number": 193
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dny_ts.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 194,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x116ca0f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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up1SRUpQpVoakw0m8Nus13pr7FrXL1OacsuewYMsCbm5yM/dfcH/uv6EcEhcX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1124fe8d0>"
       ]
      }
     ],
     "prompt_number": 194
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Boy names that became girl names (and vice versa)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_names = top1000.name.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mask = np.array(['lesl' in x.lower() for x in all_names])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 196
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lesley_like = all_names[mask]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 197
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lesley_like"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 198,
       "text": [
        "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 198
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filtered = top1000[top1000.name.isin(lesley_like)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filtered.groupby('name').births.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 200,
       "text": [
        "name\n",
        "Leslee      1082\n",
        "Lesley     35022\n",
        "Lesli        929\n",
        "Leslie    370429\n",
        "Lesly      10067\n",
        "Name: births, dtype: int64"
       ]
      }
     ],
     "prompt_number": 200
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = filtered.pivot_table('births',rows='year',\n",
      "                             cols='sex', aggfunc='sum')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 201
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = table.div(table.sum(1),axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 202
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>sex</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 203,
       "text": [
        "sex   F   M\n",
        "year       \n",
        "2006  1 NaN\n",
        "2007  1 NaN\n",
        "2008  1 NaN\n",
        "2009  1 NaN\n",
        "2010  1 NaN"
       ]
      }
     ],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.plot(style={'M':'k-','F':'k--'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 204,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x11379ed10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xYk5fOyMjA5MmTUKlSpUQGBiIc+fOwcTEBB06dMCcOXMwc+ZMWeLmnjczWuPG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\nNEGTJk3kDqNQ3PNmjDE9xj1vxhhTkCK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       "text": [
        "<matplotlib.figure.Figure at 0x1094cba10>"
       ]
      }
     ],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 205,
       "text": [
        "u'/Users/sergulaydore/python_for_data_analysis'"
       ]
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}