{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 2: Introductory examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. USAGov Data From bit.ly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "open(path).readline()\n",
    "\n",
    "import json\n",
    "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'\n",
    "records = [json.loads(line) for line in open(path)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{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'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'America/New_York'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]['tz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "America/New_York\n"
     ]
    }
   ],
   "source": [
    "print (records[0]['tz'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting Time Zones in pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'tz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-33fd9d9b9434>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtimezones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tz'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'tz'"
     ]
    }
   ],
   "source": [
    "timezones = [rec['tz'] for rec in records]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object <genexpr> at 0x1098b13c0>\n"
     ]
    }
   ],
   "source": [
    "print (rec['tz'] for rec in records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "timezones = [rec['tz'] for rec in records if 'tz' in rec]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[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'']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timezones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_counts(sequences):\n",
    "    count = {}\n",
    "    for i in sequences:\n",
    "        if x in count:\n",
    "            count[x] += 1\n",
    "        else:\n",
    "            count[x] = 1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def get_counts(sequences):\n",
    "    count = defaultdict(int)\n",
    "    for x in count:\n",
    "        count[x] += 1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts = get_counts(timezones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<type 'int'>, {'America/New_York': 0})\n"
     ]
    }
   ],
   "source": [
    "print counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(timezones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def top_counts(count_dict, n = 10):\n",
    "    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'America/New_York')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts = Counter(timezones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(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)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting Time Zones with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.rc('figure', figsize = (10, 6))\n",
    "np.set_printoptions(precision = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'\n",
    "lines = open(path).readlines()\n",
    "records = [json.loads(line) for line in lines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame, Series\n",
    "import pandas as pd\n",
    "\n",
    "frame = DataFrame(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>a</th>\n",
       "      <th>al</th>\n",
       "      <th>c</th>\n",
       "      <th>cy</th>\n",
       "      <th>g</th>\n",
       "      <th>gr</th>\n",
       "      <th>h</th>\n",
       "      <th>hc</th>\n",
       "      <th>hh</th>\n",
       "      <th>kw</th>\n",
       "      <th>l</th>\n",
       "      <th>ll</th>\n",
       "      <th>nk</th>\n",
       "      <th>r</th>\n",
       "      <th>t</th>\n",
       "      <th>tz</th>\n",
       "      <th>u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>MA</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>1331822918</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>1331923247</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1308262393</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1331923249</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>1331919941</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>1</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>1331923250</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>BR</td>\n",
       "      <td>Braz</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>27</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1331923068</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331923249</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>1273672411</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1331923251</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   _heartbeat_                                                  a  \\\n",
       "0          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "1          NaN                             GoogleMaps/RochesterNY   \n",
       "2          NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "3          NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "4          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "\n",
       "               al   c          cy       g  gr       h          hc         hh  \\\n",
       "0  en-US,en;q=0.8  US     Danvers  A6qOVH  MA  wfLQtf  1331822918  1.usa.gov   \n",
       "1             NaN  US       Provo  mwszkS  UT  mwszkS  1308262393       j.mp   \n",
       "2           en-US  US  Washington  xxr3Qb  DC  xxr3Qb  1331919941  1.usa.gov   \n",
       "3           pt-br  BR        Braz  zCaLwp  27  zUtuOu  1331923068  1.usa.gov   \n",
       "4  en-US,en;q=0.8  US  Shrewsbury  9b6kNl  MA  9b6kNl  1273672411     bit.ly   \n",
       "\n",
       "    kw         l                        ll  nk  \\\n",
       "0  NaN   orofrog   [42.576698, -70.954903]   1   \n",
       "1  NaN     bitly  [40.218102, -111.613297]   0   \n",
       "2  NaN     bitly     [38.9007, -77.043098]   1   \n",
       "3  NaN  alelex88  [-23.549999, -46.616699]   0   \n",
       "4  NaN     bitly   [42.286499, -71.714699]   0   \n",
       "\n",
       "                                                   r           t  \\\n",
       "0  http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...  1331923247   \n",
       "1                           http://www.AwareMap.com/  1331923249   \n",
       "2                               http://t.co/03elZC4Q  1331923250   \n",
       "3                                             direct  1331923249   \n",
       "4                http://www.shrewsbury-ma.gov/selco/  1331923251   \n",
       "\n",
       "                  tz                                                  u  \n",
       "0   America/New_York        http://www.ncbi.nlm.nih.gov/pubmed/22415991  \n",
       "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...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['tz'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = frame['tz'].value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')\n",
    "clean_tz[clean_tz == ''] = 'Unknown'\n",
    "tz_counts = clean_tz.value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aed97d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aed97d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c09af10>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ods42wNbAz4B7kiym9wGyy5JsDSyqqjv6zh8dZK8CTmzrXe+iF1Q/nuTngPur\n6twk3wPOGKsz7Rr70VsO8GArO4beDPalY53TfIve0oIPJ3kJsE0rvxQ4L8kJVfWf7ReIrarqh319\nH7MOvV8IJu3zessmPixJkjQAQ0NDDA0NPbS/fPnyGWt7smUJR7D+T+4jzqH3wbL+T/aP9yn/44AX\ntA8/XUdvtnEyHwC2bR/EWgkMVdVqYBVwA/A54IpW9yDga6POPybJD5PcmuSH9N7jHwLD9JY0XF1V\nFwBPAYbbNc5odcZyGL21sw/2lZ0P/FqSR0/w3pcDByVZTe8OC7cB91TVDcAfARcnuZbeLwDbtXMK\nYII6U+2zJEnSgjSvH7+b5GTgM1V11aD7MloLvmuram27u8JJ7cNhm+r6Pn5Xs8jH70qSZk5m8PG7\n8zrczmVJnkXvg3ib0VuT+3tVtWITXt9wq1lkuJUkzRzD7SxJ8klgH3qpMO3riVV12kA7tgEMt5pd\nhltJ0swx3GpShlvNLsOtJGnmzGS4nerjdyVJkqQ5b7JbgWlem5FfgKRHWLx4yaC7IEnSmAy3Heaf\njSVJ0kLjsgRJkiR1huFWkiRJnWG4lSRJUmcYbiVJktQZhltJkiR1huFWkiRJnWG4lSRJUmcYbiVJ\nktQZhltJkiR1huFWkiRJnWG4lSRJUmcYbiVJktQZhltJkiR1huFWkiRJnWG4lSRJUmdsPugOaPYk\nGXQXtAAtXryE2267ZdDdkCQtUKmqQfdBsyBJgd9bDULw/yuSpOlIQlXNyKycyxIkSZLUGQMNt0kO\nS7IuyY6z1P4eSU7YiPOPSPLetn1okquTXJdkRZKPtPJTk7xyjHP/d5IvbnjvJUmSNF2Dnrk9Evgy\ncNRMN5xkUVWtqKq3b0QzhwIXJvlF4BPAq6tqZ+AFwE0TnVhV/1FVv7kR15YkSdI0DSzcJnkcsDdw\nLL2QS5L9kwwnOS/JTUk+mOQ1Sa5Kcm2SHVq9JyX5UpIr2+uFrXxpktOTXAGc3tq7YOR6SU5JsjrJ\nqiSvaOUntfbXJFk6qpu7VtVK4N3AB6rq+wDV81d99fZP8q3W51e2dpckWdO2N0vykXaNVUmObeXv\nb/1fneQv+8Zmz/Z+r0ny4b52tuh7DyuSDM3gt0SSJGneG+TM7cuBi6rqVuD2JLu18l2ANwE7Aa8F\nnlVVewGfBd7a6pwIfKyq9gZe1Y6NeC5wQFUd3fZHPtnyfuDOqtqlqp4PfL2VH9/a3xUYSrIzQOvP\nta3OzsDT0yg2AAAQTUlEQVSKCd7LdlW1D/DrwIf6ykeu/WZgCTBy7b9t5Z+oqr2rahfgsUle2spP\nAd5YVbsDa/vaORZY1+q/GjgtyaMn6JckSdKCMshwexQwsib1LHphDeDqqrq9qu6n96f/i1r5GmD7\ntn0g8MkkK4HzgccneWw7dn47d7QDgU+N7FTVXW3zyCQrgJX0AvVOrfwQ4KtTfC/ntTZvAJ48xvEX\nA39V7SPkVXXnSHmS7yRZDfwK8ItJngA8vqquanX+rq+dfYHPtTZuBG4BZmW9siRJ0nw0kPvcJnki\ncACwc++WVSyiNzv5FeC+vqrr+vbXsb6/AfauqgdGtQtw7zT6sT3wTmCPqro7yanAlu3wS4CRD4pd\nR2+d7Zpxmurv85RuY5FkC3phe/eq+ve2JGLk2lO9FcYk9Zb1bQ+1lyRJ0mANDw8zPDw8K20P6iEO\nhwOnV9XvjhQkuQzYb4rnXwwcB3y0nbtrVV078SlcQu/P+u9o52wDbA38DLgnyWJ6HyC7LMnWwKKq\nuqOd+1Hg7CRXVNX3k2xGb9nAXz3iKmMHzkuANycZrqq1Ldyvoxfof5Lk8fSWV5xVVXcluTvJnlV1\nNW09cnM5cDQw3O4w8TTgxvHf8rJJhkSSJGnTGxoaYmho6KH95cuXz1jbg1qWcARw7qiyc+gFuf67\nv493J/jjgBe0D11dR29N62Q+AGzbPtS1EhiqqtXAKuAGen/uv6LVPQj42kOdqFoDvB04M8n1wGpg\nh3H6OFafPwPcCqxu1z6qLYv4DHA9veUPV/XVfwPwmSTXAI8FRpZQnAQsassYzgSOGT17LUmStJD5\nhLIxJDkZ+EzfutdNff3HVdW9bfs99D6w9gfTbMMnlGlAfEKZJGl6ZvIJZYbbOSjJbwLvpbds5Bbg\nt6rqJ9Nsw3CrATHcSpKmx3CrSRluNTiGW0nS9MxkuB30E8okSZKkGWO4lSRJUmcM6lZg2iRmZHZf\nmpbFi5cMuguSpAXMcNthrnuUJEkLjcsSJEmS1BmGW0mSJHWG4VaSJEmdYbiVJElSZxhuJUmS1BmG\nW0mSJHWG4VaSJEmdYbiVJElSZxhuJUmS1BmGW0mSJHWG4VaSJEmdYbiVJElSZxhuJUmS1BmGW0mS\nJHWG4VaSJEmdsfmgO6DZk2TQXZDmncWLl3DbbbcMuhuSpA2Uqhp0HzQLkhT4vZWmL/j/RUnatJJQ\nVTMyK+eyBEmSJHWG4VaSJEmdYbgdJcmSJGtGlS1N8o4JzjkmySdmv3eSJEmaiOF2bBuy4M5FepIk\nSQNmuJ26JLksyQeTXJnkn5PsM0allyb5VpJtk5ya5MS2f1OSV/bV+0iSNUmuTXJ4K/tkkl9r2+cm\n+Uzbfn2S/9tmlb+b5OQk1yW5MMkWm2oAJEmS5jrD7fQtqqq9gT8AlvUfSHIY8G7g0Kr6aSverqr2\nAX4d+FCr9xvALlX1POAg4KNJFgOXA/u1834e2Klt7wd8s20/C/hEVe0M3AX8xoy/Q0mSpHnKcPtI\n4y0vqPY6p+2vAJb0HX8xvWD70qq6u6/8PICqugF4civbBzizld8ODAN70gu3v5zkucB3gR8n2Q54\nIfDtdu7NVTWyJngFsP2036EkSVJH+RCHR/oJsO2osm2BH7Tt+9rXtTx8/P4F2AH4BXqhk1H1Aca7\nf1sAqurfk2wDHAx8o133N4F7qureJE8a1d5aYMvx38qyvu2h9pIkSRqs4eFhhoeHZ6Vtw+0oLUT+\ne5JfqarLkmxLL2yeAPz2qOr9YfUW4P8A5yZ5VZupHW2k/uXAm5KcDvwcvWUH/6cd+w69JQ+/AjwJ\n+BJw1jjXnMSyqVeVJEnaRIaGhhgaGnpof/ny5TPWtssSxvY64P1JVgJfA5ZV1c08csnCw/ar6nvA\n0cBZSXYYr35VnQusBq5t7b+rLU+AXvBdVFU/AK4Bnsj69baPuKYkSZLW8/G7HeXjd6UN5eN3JWlT\n8/G7kiRJ0hgMt5IkSeoMw60kSZI6w3ArSZKkzvBWYJ02I+uypQVl8eIlk1eSJM1ZhtsO8xPfkiRp\noXFZgiRJkjrDcCtJkqTOMNxKkiSpMwy3kiRJ6gzDrSRJkjrDcCtJkqTOMNxKkiSpMwy3kiRJ6gzD\nrSRJkjrDcCtJkqTOMNxKkiSpMwy3kiRJ6gzDrSRJkjrDcCtJkqTOMNxKkiSpMzYfdAc0e5IMuguS\nJGkDLF68hNtuu2XQ3ZiXUlWD7oNmQZICv7eSJM1PYSFltCRU1YzMyrksQZIkSZ1huJUkSVJnzFq4\nTXJYknVJdpyl9vdIcsJGnH9EkvcmOSbJ2iQ79x1bk+TpM9PTh9p8Q5LP9+1vleSmJNtPo40zkrxs\nJvslSZLUJbM5c3sk8GXgqJluOMmiqlpRVW/fiGYOBS5s27cC7+s7NuOLXKrqM8BTkxzQiv4E+ExV\n3TKV85Msmuk+SZIkdc2shNskjwP2Bo6lF3JJsn+S4STntRnLDyZ5TZKrklybZIdW70lJvpTkyvZ6\nYStfmuT0JFcAp7f2Lhi5XpJTkqxOsirJK1r5Sa39NUmWjurmrlW1sm1/BfjFJM8eeQt97+WgJN9O\n8k9JvpDksUlekOTsdvzlSf47yeZJtkjyLxMMze8CJybZAzgA+GhrY/ck32l9PyvJVq388iQfS3JV\nG8v+Mf6zJH895W+KJEnSAjBbM7cvBy6qqluB25Ps1sp3Ad4E7AS8FnhWVe0FfBZ4a6tzIvCxqtob\neFU7NuK5wAFVdXTbH5lhfT9wZ1XtUlXPB77eyo9v7e8KDI0sPWj9ubav3bXAh3n47C1Jfg74I+DF\nVfUCYAXwDmBlaxNgX2ANsCe9QP+d8QalqtYAFwGXAr9fVQ+2Q2cAb299/157PyM2q6q9qurj67uV\njwFbVdUbx7uWJEnSQjRb97k9CviLtn0W8Gp6SxSurqrbAZLcRC/oQS8cDrXtA4HnZv1NWh+f5LFt\n+/yqun+M6x0IHDGyU1V3tc0jk7yR3vvcjl6ovg44BPjqqDbOBN43ag3sL7VzvtX68yjg21W1Nsm/\nJHkOsBfwMWB/YBFw+fjDAsCngEOq6vI2DtsCW1TVSCg+DTi9r/4XRp2/HLiiqn5/kusAy/q2h1g/\nxJIkSYMzPDzM8PDwrLQ94+E2yRPp/cl95969VllEb4b1K8B9fVXX9e2v6+tLgL2r6oFR7QLcO41+\nbA+8E9ijqu5OciqwZTv8EuCV/fVbYP1z4D2snxEOcHHfTHG/b9Jbt3s/8DV6oXQz4F2TdG1dez2s\nuxPUH/2erwT2TLJNVd058aWWTdIVSZKkTW9oaIihoaGH9pcvXz5jbc/GsoTDgdOraoeqekZVLQFu\nBvab4vkXA8eN7CTZdYK6Iy6hb01qkm2ArYGfAfckWUwviJJka2BRVd0xRjun0ZsF/l9t/zvAPkme\n2c59bN+63CuAt9Obyf0J8HPAL1TV9VPo70Nhtqp+Cvx3kl9qRa8FvjHBuV8B/hz4clvbLEmSpGY2\nwu0RwLmjys6h98Gy/rsQjHdHguOAF7QPmV0HvHkK1/wAsG374NhKYKiqVgOrgBuAz9ELowAH0Ztp\nfYQ2W/xx4Mlt/7+A3wLOTHIt8G3gF1r1K1u9b7b91e01FaPf+2uBE5Ksoreu+APj1KvWry8CfwOc\nl+TRU7ymJElS5y24x+8mOZneLbiuGnRfZpOP35UkaT7z8bsb3NZCGriFxHArSdJ8ZrjdULN1t4QF\nLckngX3opcu0rydW1WkD7ZgkSVLHOXPbUc7cSpI0nzlzu6Gcue20GfkZkSRJm9jixUsG3YV5y3Db\nYQvpNz5JkiSYvcfvSpIkSZuc4VaSJEmdYbiVJElSZxhupVGGh4cH3YV5zfHbOI7fhnPsNo7jt3Ec\nv7nDcCuN4v+gNo7jt3Ecvw3n2G0cx2/jOH5zh+FWkiRJnWG4lSRJUmf4hLKO6j2hTJIkaX6YqSeU\nGW4lSZLUGS5LkCRJUmcYbiVJktQZhtuOSXJIkn9O8r0k7xl0f+aaJE9N8vUk1ydZk+RtrfyJSS5O\ncmOSi5I8oe+c9yb5fpIbkrxkcL2fO5JsluSaJOe3fcdvipI8IclZbTyuT7K34zc1bSyuT7I6yd8m\nebRjN7Ekn03y4ySr+8qmPWZJdm/j/r0kJ2zq9zEI44zdh9vYrEpydpKt+445dn3GGr++Y+9Msi7J\ntn1lMzd+VeWrIy96v6zcBCwBHgWsAp4z6H7NpRewHfD8tv144EbgOcCHgHe38vcAH2zbOwErgc2B\n7dv4ZtDvY9Av4A+AzwHnt33Hb+pj9zfA69v25sATHL8pjdsS4AfAo9v+F4BjHLtJx21f4PnA6r6y\naY8ZcCWwZ9v+B+DgQb+3AY3dgcBmbfuDwP/n2E19/Fr5U4ELgZuBbVvZc2dy/Jy57Za9gO9X1b9W\n1QPA54GXD7hPc0pV3VZVq9r2z4Ab6P2H9nLgtFbtNOCwtv0y4PNV9WBV3QJ8n944L1hJngr8KvCZ\nvmLHbwraLM9+VXUqQBuXu3D8puJu4H7gcUk2Bx4D/AjHbkJVdQVwx6jiaY1Zku2Ararq6lbv9L5z\nOmussauqr1XVurb7HXr/foBj9wjj/OwB/AXwrlFlL2cGx89w2y1PAW7t2/+3VqYxJNme3m+V3wEW\nV9WPoReAgSe3aqPH9Ec4piP/Y+q/1YrjNzU7AP+V5NS2rOPkJI/F8ZtUVd0B/DnwQ3rjcFdVfQ3H\nbkM8eZpj9hR6/56M8N+Wnt+mN5MIjt2UJHkZcGtVrRl1aEbHz3CrBSnJ44EvAce1GdzR98TzHnlj\nSPJS4Mdt9nui+xE6fmPbHNgd+FRV7Q7cC/wh/vxNKskz6C2HWQL8PL0Z3KNx7GaCYzZNSd4HPFBV\nZw66L/NFkscAxwNLZ/tahttu+RHw9L79p7Yy9Wl/0vwScEZV/X0r/nGSxe34dsDtrfxHwNP6Tl/o\nY7oP8LIkPwDOBA5IcgZwm+M3Jf9Gb9bin9r+2fTCrj9/k3sB8K2q+mlVrQXOBV6EY7chpjtmjmWf\nJL9Fb2nWq/uKHbvJPZPeetprk9xMbyyuSfJkxs8vGzR+httuuRp4VpIlSR4NHAmcP+A+zUWnAN+t\nqhP7ys4HfqttHwP8fV/5ke1T2TsAzwKu2lQdnWuq6viqenpVPYPez9fXq+q1wAU4fpNqfwq+NcmO\nrejFwPX48zcVNwK/lGTLJKE3dt/FsZuK8PC/tExrzNrShbuS7NXG/nV953Tdw8YuySH0lmW9rKru\n66vn2I3tofGrquuqaruqekZV7UDvl/3dqup2euN3xIyN36A/Tedrxj+deAi9fwS+D/zhoPsz1170\nZh7X0ruTxErgmjZm2wJfa2N3MbBN3znvpffJzRuAlwz6PcyVF7A/6++W4PhNfdx2pfeL6CrgHHp3\nS3D8pjZ276L3y8Bqeh+EepRjN+mY/R3w78B99NYrvx544nTHDNgDWNP+bTlx0O9rgGP3feBf278d\n1wAnOXZTH79Rx39Au1vCTI+fj9+VJElSZ7gsQZIkSZ1huJUkSVJnGG4lSZLUGYZbSZIkdYbhVpIk\nSZ1huJUkSVJnGG4lSZLUGYZbSZIkdcb/DwV5SEtMzEXBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d9aed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tz_counts[:10].plot(kind = 'barh', rot = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = Series([x.split()[0] for x in frame.a.dropna()])\n",
    "results[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cframe = frame[frame.a.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Windows', 'Not Windows', 'Windows', 'Not Windows', 'Windows'], \n",
       "      dtype='|S11')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operating_system = np.where(cframe['a'].str.contains('Windows'), 'Windows', 'Not Windows')\n",
    "operating_system[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz', operating_system])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "<p>10 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                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\n",
       "\n",
       "[10 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
    "agg_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use to sort ascending order\n",
    "indexer = agg_counts.sum(1).argsort()\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "<p>10 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     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\n",
       "\n",
       "[10 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = agg_counts.take(indexer)[-10:]\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb885721e10>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb885721e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fb885407a90>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jR4+yY8cORo0aRZkyZbjhhhuYM2cOt956q2Me6skVl7KpJ1dERPLPY3ty586dy6BBg2jY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\nkSuEEEIIIZzO/wDuKqlzvVX28gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb885407250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_subset.plot(kind = 'barh', stacked = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb885218f10>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb885218f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fb8854b2790>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tp0+nf//+xMTE3NL9WpalC71EREREruG2FrV2u53q1asTFRXlKPJsNht/+9vf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\ne/J0fwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb88573d690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_subset = count_subset.div(count_subset.sum(1), axis = 0)\n",
    "normed_subset.plot(kind = 'barh', stacked = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###MovieLens 1M Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "encoding = 'latin1'\n",
    "\n",
    "upath = os.path.expanduser('ch02/movielens/users.dat')\n",
    "rpath = os.path.expanduser('ch02/movielens/ratings.dat')\n",
    "mpath = os.path.expanduser('ch02/movielens/movies.dat')\n",
    "\n",
    "unames = ['user_id', 'gender', 'age', 'accupation', 'zip']\n",
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "mnames = ['movie_id', 'title', 'genres']\n",
    "\n",
    "users = pd.read_csv(upath, sep = '::', header = None, names = unames, encoding = encoding)\n",
    "ratings = pd.read_csv(rpath, sep = '::', header = None, names = rnames, encoding = encoding)\n",
    "movies = pd.read_csv(mpath, sep = '::', header = None, names = mnames, encoding = encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>accupation</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",
       "<p>5 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id gender  age  accupation    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\n",
       "\n",
       "[5 rows x 5 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "<p>5 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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\n",
       "\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td> 1</td>\n",
       "      <td>                   Toy Story (1995)</td>\n",
       "      <td>  Animation|Children's|Comedy</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2</td>\n",
       "      <td>                     Jumanji (1995)</td>\n",
       "      <td> Adventure|Children's|Fantasy</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 3</td>\n",
       "      <td>            Grumpier Old Men (1995)</td>\n",
       "      <td>               Comedy|Romance</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td> 4</td>\n",
       "      <td>           Waiting to Exhale (1995)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 5</td>\n",
       "      <td> Father of the Bride Part II (1995)</td>\n",
       "      <td>                       Comedy</td>\n",
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      "text/plain": [
       "   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\n",
       "\n",
       "[5 rows x 3 columns]"
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     "execution_count": 80,
     "metadata": {},
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   "source": [
    "movies[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
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       "      <th>timestamp</th>\n",
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       "      <th>0 </th>\n",
       "      <td> 1</td>\n",
       "      <td> 1193</td>\n",
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       "      <td> 978300760</td>\n",
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       "      <th>1 </th>\n",
       "      <td> 1</td>\n",
       "      <td>  661</td>\n",
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       "      <td> 978302109</td>\n",
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       "      <th>2 </th>\n",
       "      <td> 1</td>\n",
       "      <td>  914</td>\n",
       "      <td> 3</td>\n",
       "      <td> 978301968</td>\n",
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       "      <th>3 </th>\n",
       "      <td> 1</td>\n",
       "      <td> 3408</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978300275</td>\n",
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       "      <th>4 </th>\n",
       "      <td> 1</td>\n",
       "      <td> 2355</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978824291</td>\n",
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       "      <th>5 </th>\n",
       "      <td> 1</td>\n",
       "      <td> 1197</td>\n",
       "      <td> 3</td>\n",
       "      <td> 978302268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td> 1</td>\n",
       "      <td> 1287</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978302039</td>\n",
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       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td> 1</td>\n",
       "      <td> 2804</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978300719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td> 1</td>\n",
       "      <td>  594</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302268</td>\n",
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       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td> 1</td>\n",
       "      <td>  919</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978301368</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td> 1</td>\n",
       "      <td>  595</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978824268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td> 1</td>\n",
       "      <td>  938</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978301752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2398</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302281</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2918</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1035</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978301753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2791</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2687</td>\n",
       "      <td> 3</td>\n",
       "      <td> 978824268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2018</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978301777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td> 1</td>\n",
       "      <td> 3105</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978301713</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2797</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2321</td>\n",
       "      <td> 3</td>\n",
       "      <td> 978302205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td> 1</td>\n",
       "      <td>  720</td>\n",
       "      <td> 3</td>\n",
       "      <td> 978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1270</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td> 1</td>\n",
       "      <td>  527</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978824195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2340</td>\n",
       "      <td> 3</td>\n",
       "      <td> 978300103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td> 1</td>\n",
       "      <td>   48</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978824351</td>\n",
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       "      <th>26</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1097</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978301953</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1721</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1545</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978824139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td> 1</td>\n",
       "      <td>  745</td>\n",
       "      <td> 3</td>\n",
       "      <td> 978824268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2294</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978824291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td> 1</td>\n",
       "      <td> 3186</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978300019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1566</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978824330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td> 1</td>\n",
       "      <td>  588</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978824268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1907</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978824330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td> 1</td>\n",
       "      <td>  783</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978824291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1836</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978300172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1022</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978300055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2762</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td> 1</td>\n",
       "      <td>  150</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978301777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td> 1</td>\n",
       "      <td>    1</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978824268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1961</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978301590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1962</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978301753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2692</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978301570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td> 1</td>\n",
       "      <td>  260</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1028</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978301777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1029</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978302205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1207</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978300719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2028</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978301619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td> 1</td>\n",
       "      <td>  531</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td> 1</td>\n",
       "      <td> 3114</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td> 1</td>\n",
       "      <td>  608</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978301398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td> 1</td>\n",
       "      <td> 1246</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978302091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td> 2</td>\n",
       "      <td> 1357</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978298709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td> 2</td>\n",
       "      <td> 3068</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978299000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td> 2</td>\n",
       "      <td> 1537</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978299620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td> 2</td>\n",
       "      <td>  647</td>\n",
       "      <td> 3</td>\n",
       "      <td> 978299351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td> 2</td>\n",
       "      <td> 2194</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978299297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td> 2</td>\n",
       "      <td>  648</td>\n",
       "      <td> 4</td>\n",
       "      <td> 978299913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td> 2</td>\n",
       "      <td> 2268</td>\n",
       "      <td> 5</td>\n",
       "      <td> 978299297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000209 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    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\n",
       "5         1      1197       3  978302268\n",
       "6         1      1287       5  978302039\n",
       "7         1      2804       5  978300719\n",
       "8         1       594       4  978302268\n",
       "9         1       919       4  978301368\n",
       "10        1       595       5  978824268\n",
       "11        1       938       4  978301752\n",
       "12        1      2398       4  978302281\n",
       "13        1      2918       4  978302124\n",
       "14        1      1035       5  978301753\n",
       "15        1      2791       4  978302188\n",
       "16        1      2687       3  978824268\n",
       "17        1      2018       4  978301777\n",
       "18        1      3105       5  978301713\n",
       "19        1      2797       4  978302039\n",
       "20        1      2321       3  978302205\n",
       "21        1       720       3  978300760\n",
       "22        1      1270       5  978300055\n",
       "23        1       527       5  978824195\n",
       "24        1      2340       3  978300103\n",
       "25        1        48       5  978824351\n",
       "26        1      1097       4  978301953\n",
       "27        1      1721       4  978300055\n",
       "28        1      1545       4  978824139\n",
       "29        1       745       3  978824268\n",
       "30        1      2294       4  978824291\n",
       "31        1      3186       4  978300019\n",
       "32        1      1566       4  978824330\n",
       "33        1       588       4  978824268\n",
       "34        1      1907       4  978824330\n",
       "35        1       783       4  978824291\n",
       "36        1      1836       5  978300172\n",
       "37        1      1022       5  978300055\n",
       "38        1      2762       4  978302091\n",
       "39        1       150       5  978301777\n",
       "40        1         1       5  978824268\n",
       "41        1      1961       5  978301590\n",
       "42        1      1962       4  978301753\n",
       "43        1      2692       4  978301570\n",
       "44        1       260       4  978300760\n",
       "45        1      1028       5  978301777\n",
       "46        1      1029       5  978302205\n",
       "47        1      1207       4  978300719\n",
       "48        1      2028       5  978301619\n",
       "49        1       531       4  978302149\n",
       "50        1      3114       4  978302174\n",
       "51        1       608       4  978301398\n",
       "52        1      1246       4  978302091\n",
       "53        2      1357       5  978298709\n",
       "54        2      3068       4  978299000\n",
       "55        2      1537       4  978299620\n",
       "56        2       647       3  978299351\n",
       "57        2      2194       4  978299297\n",
       "58        2       648       4  978299913\n",
       "59        2      2268       5  978299297\n",
       "        ...       ...     ...        ...\n",
       "\n",
       "[1000209 rows x 4 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <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>accupation</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0 </th>\n",
       "      <td>   1</td>\n",
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       "      <td>  978300760</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>   2</td>\n",
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       "      <td>  978298413</td>\n",
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       "      <th>2 </th>\n",
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       "      <td>  978220179</td>\n",
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       "      <td> 12</td>\n",
       "      <td> 32793</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>  1</td>\n",
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       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
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       "      <th>5 </th>\n",
       "      <td>  18</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  978156168</td>\n",
       "      <td> F</td>\n",
       "      <td> 18</td>\n",
       "      <td>  3</td>\n",
       "      <td> 95825</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td>  19</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  982730936</td>\n",
       "      <td> M</td>\n",
       "      <td>  1</td>\n",
       "      <td> 10</td>\n",
       "      <td> 48073</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>  24</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  978136709</td>\n",
       "      <td> F</td>\n",
       "      <td> 25</td>\n",
       "      <td>  7</td>\n",
       "      <td> 10023</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td>  28</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 3</td>\n",
       "      <td>  978125194</td>\n",
       "      <td> F</td>\n",
       "      <td> 25</td>\n",
       "      <td>  1</td>\n",
       "      <td> 14607</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>  33</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  978557765</td>\n",
       "      <td> M</td>\n",
       "      <td> 45</td>\n",
       "      <td>  3</td>\n",
       "      <td> 55421</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>  39</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  978043535</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td>  4</td>\n",
       "      <td> 61820</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>  42</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 3</td>\n",
       "      <td>  978038981</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td>  8</td>\n",
       "      <td> 24502</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>  44</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  978018995</td>\n",
       "      <td> M</td>\n",
       "      <td> 45</td>\n",
       "      <td> 17</td>\n",
       "      <td> 98052</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>  47</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977978345</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td>  4</td>\n",
       "      <td> 94305</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>  48</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977975061</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td>  4</td>\n",
       "      <td> 92107</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>  49</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  978813972</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td> 12</td>\n",
       "      <td> 77084</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>  53</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977946400</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td>  0</td>\n",
       "      <td> 96931</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>  54</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977944039</td>\n",
       "      <td> M</td>\n",
       "      <td> 50</td>\n",
       "      <td>  1</td>\n",
       "      <td> 56723</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>  58</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977933866</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td>  2</td>\n",
       "      <td> 30303</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>  59</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977934292</td>\n",
       "      <td> F</td>\n",
       "      <td> 50</td>\n",
       "      <td>  1</td>\n",
       "      <td> 55413</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>  62</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977968584</td>\n",
       "      <td> F</td>\n",
       "      <td> 35</td>\n",
       "      <td>  3</td>\n",
       "      <td> 98105</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>  80</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977786172</td>\n",
       "      <td> M</td>\n",
       "      <td> 56</td>\n",
       "      <td>  1</td>\n",
       "      <td> 49327</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>  81</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977785864</td>\n",
       "      <td> F</td>\n",
       "      <td> 25</td>\n",
       "      <td>  0</td>\n",
       "      <td> 60640</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>  88</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977694161</td>\n",
       "      <td> F</td>\n",
       "      <td> 45</td>\n",
       "      <td>  1</td>\n",
       "      <td> 02476</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>  89</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977683596</td>\n",
       "      <td> F</td>\n",
       "      <td> 56</td>\n",
       "      <td>  9</td>\n",
       "      <td> 85749</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>  95</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977626632</td>\n",
       "      <td> M</td>\n",
       "      <td> 45</td>\n",
       "      <td>  0</td>\n",
       "      <td> 98201</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>  96</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 3</td>\n",
       "      <td>  977621789</td>\n",
       "      <td> F</td>\n",
       "      <td> 25</td>\n",
       "      <td> 16</td>\n",
       "      <td> 78028</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>  99</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 2</td>\n",
       "      <td>  982791053</td>\n",
       "      <td> F</td>\n",
       "      <td>  1</td>\n",
       "      <td> 10</td>\n",
       "      <td> 19390</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td> 102</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td> 1040737607</td>\n",
       "      <td> M</td>\n",
       "      <td> 35</td>\n",
       "      <td> 19</td>\n",
       "      <td> 20871</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td> 104</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 2</td>\n",
       "      <td>  977546620</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td> 12</td>\n",
       "      <td> 00926</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> 107</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977533908</td>\n",
       "      <td> M</td>\n",
       "      <td> 45</td>\n",
       "      <td> 18</td>\n",
       "      <td> 63129</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td> 109</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 2</td>\n",
       "      <td>  977516253</td>\n",
       "      <td> M</td>\n",
       "      <td> 45</td>\n",
       "      <td> 15</td>\n",
       "      <td> 92028</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td> 120</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977458103</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td> 11</td>\n",
       "      <td> 27106</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td> 123</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977448350</td>\n",
       "      <td> M</td>\n",
       "      <td> 35</td>\n",
       "      <td>  9</td>\n",
       "      <td> 67208</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td> 131</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977431822</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td>  4</td>\n",
       "      <td> 06520</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td> 136</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977421091</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td>  2</td>\n",
       "      <td> 21202</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td> 139</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977359402</td>\n",
       "      <td> F</td>\n",
       "      <td> 25</td>\n",
       "      <td> 20</td>\n",
       "      <td> 45409</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td> 146</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  979940868</td>\n",
       "      <td> F</td>\n",
       "      <td> 35</td>\n",
       "      <td> 20</td>\n",
       "      <td> 10954</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td> 150</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  978163667</td>\n",
       "      <td> M</td>\n",
       "      <td> 35</td>\n",
       "      <td>  7</td>\n",
       "      <td> 98144</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td> 151</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  993121122</td>\n",
       "      <td> F</td>\n",
       "      <td> 25</td>\n",
       "      <td> 20</td>\n",
       "      <td> 85013</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td> 155</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977253254</td>\n",
       "      <td> M</td>\n",
       "      <td> 35</td>\n",
       "      <td> 12</td>\n",
       "      <td> 07470</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td> 166</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977299851</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td>  4</td>\n",
       "      <td> 92802</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td> 169</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977197066</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td>  7</td>\n",
       "      <td> 55439</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td> 175</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  978051929</td>\n",
       "      <td> F</td>\n",
       "      <td> 25</td>\n",
       "      <td>  2</td>\n",
       "      <td> 95123</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td> 178</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977098352</td>\n",
       "      <td> M</td>\n",
       "      <td> 56</td>\n",
       "      <td> 17</td>\n",
       "      <td> 53705</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td> 180</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977089623</td>\n",
       "      <td> M</td>\n",
       "      <td> 45</td>\n",
       "      <td> 12</td>\n",
       "      <td> 01603</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td> 190</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977026946</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td> 17</td>\n",
       "      <td> 55125</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td> 191</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 3</td>\n",
       "      <td>  977026629</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td>  4</td>\n",
       "      <td> 04915</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td> 192</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977089850</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td>  1</td>\n",
       "      <td> 10977</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td> 195</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  977064554</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td> 12</td>\n",
       "      <td> 10458</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td> 196</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  977004875</td>\n",
       "      <td> F</td>\n",
       "      <td> 35</td>\n",
       "      <td>  9</td>\n",
       "      <td> 94587</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td> 202</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  976937045</td>\n",
       "      <td> M</td>\n",
       "      <td> 18</td>\n",
       "      <td>  4</td>\n",
       "      <td> 53706</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td> 206</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 1</td>\n",
       "      <td>  976921007</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td> 17</td>\n",
       "      <td> 20194</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td> 208</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 5</td>\n",
       "      <td>  984510357</td>\n",
       "      <td> M</td>\n",
       "      <td> 35</td>\n",
       "      <td> 17</td>\n",
       "      <td> 55432</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td> 216</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  976868526</td>\n",
       "      <td> M</td>\n",
       "      <td> 45</td>\n",
       "      <td> 13</td>\n",
       "      <td> 52761</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td> 224</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  976833559</td>\n",
       "      <td> F</td>\n",
       "      <td> 18</td>\n",
       "      <td>  4</td>\n",
       "      <td> 14850</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td> 225</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  976832915</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td>  7</td>\n",
       "      <td> 11215</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td> 227</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  976829587</td>\n",
       "      <td> M</td>\n",
       "      <td> 35</td>\n",
       "      <td> 20</td>\n",
       "      <td> 90291</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td> 230</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  976824488</td>\n",
       "      <td> M</td>\n",
       "      <td> 45</td>\n",
       "      <td>  1</td>\n",
       "      <td> 43210</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td> 231</td>\n",
       "      <td> 1193</td>\n",
       "      <td> 4</td>\n",
       "      <td>  997057648</td>\n",
       "      <td> M</td>\n",
       "      <td> 25</td>\n",
       "      <td>  3</td>\n",
       "      <td> 55455</td>\n",
       "      <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td> Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000209 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  movie_id  rating   timestamp gender  age  accupation    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",
       "5        18      1193       4   978156168      F   18           3  95825   \n",
       "6        19      1193       5   982730936      M    1          10  48073   \n",
       "7        24      1193       5   978136709      F   25           7  10023   \n",
       "8        28      1193       3   978125194      F   25           1  14607   \n",
       "9        33      1193       5   978557765      M   45           3  55421   \n",
       "10       39      1193       5   978043535      M   18           4  61820   \n",
       "11       42      1193       3   978038981      M   25           8  24502   \n",
       "12       44      1193       4   978018995      M   45          17  98052   \n",
       "13       47      1193       4   977978345      M   18           4  94305   \n",
       "14       48      1193       4   977975061      M   25           4  92107   \n",
       "15       49      1193       4   978813972      M   18          12  77084   \n",
       "16       53      1193       5   977946400      M   25           0  96931   \n",
       "17       54      1193       5   977944039      M   50           1  56723   \n",
       "18       58      1193       5   977933866      M   25           2  30303   \n",
       "19       59      1193       4   977934292      F   50           1  55413   \n",
       "20       62      1193       4   977968584      F   35           3  98105   \n",
       "21       80      1193       4   977786172      M   56           1  49327   \n",
       "22       81      1193       5   977785864      F   25           0  60640   \n",
       "23       88      1193       5   977694161      F   45           1  02476   \n",
       "24       89      1193       5   977683596      F   56           9  85749   \n",
       "25       95      1193       5   977626632      M   45           0  98201   \n",
       "26       96      1193       3   977621789      F   25          16  78028   \n",
       "27       99      1193       2   982791053      F    1          10  19390   \n",
       "28      102      1193       5  1040737607      M   35          19  20871   \n",
       "29      104      1193       2   977546620      M   25          12  00926   \n",
       "30      107      1193       5   977533908      M   45          18  63129   \n",
       "31      109      1193       2   977516253      M   45          15  92028   \n",
       "32      120      1193       5   977458103      M   25          11  27106   \n",
       "33      123      1193       5   977448350      M   35           9  67208   \n",
       "34      131      1193       5   977431822      M   18           4  06520   \n",
       "35      136      1193       4   977421091      M   18           2  21202   \n",
       "36      139      1193       4   977359402      F   25          20  45409   \n",
       "37      146      1193       4   979940868      F   35          20  10954   \n",
       "38      150      1193       5   978163667      M   35           7  98144   \n",
       "39      151      1193       4   993121122      F   25          20  85013   \n",
       "40      155      1193       5   977253254      M   35          12  07470   \n",
       "41      166      1193       5   977299851      M   18           4  92802   \n",
       "42      169      1193       5   977197066      M   25           7  55439   \n",
       "43      175      1193       5   978051929      F   25           2  95123   \n",
       "44      178      1193       4   977098352      M   56          17  53705   \n",
       "45      180      1193       5   977089623      M   45          12  01603   \n",
       "46      190      1193       5   977026946      M   25          17  55125   \n",
       "47      191      1193       3   977026629      M   18           4  04915   \n",
       "48      192      1193       4   977089850      M   18           1  10977   \n",
       "49      195      1193       5   977064554      M   25          12  10458   \n",
       "50      196      1193       4   977004875      F   35           9  94587   \n",
       "51      202      1193       4   976937045      M   18           4  53706   \n",
       "52      206      1193       1   976921007      M   25          17  20194   \n",
       "53      208      1193       5   984510357      M   35          17  55432   \n",
       "54      216      1193       4   976868526      M   45          13  52761   \n",
       "55      224      1193       4   976833559      F   18           4  14850   \n",
       "56      225      1193       4   976832915      M   25           7  11215   \n",
       "57      227      1193       4   976829587      M   35          20  90291   \n",
       "58      230      1193       4   976824488      M   45           1  43210   \n",
       "59      231      1193       4   997057648      M   25           3  55455   \n",
       "        ...       ...     ...         ...    ...  ...         ...    ...   \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  \n",
       "5   One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "6   One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "7   One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "8   One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "9   One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "10  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "11  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "12  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "13  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "14  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "15  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "16  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "17  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "18  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "19  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "20  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "21  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "22  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "23  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "24  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "25  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "26  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "27  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "28  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "29  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "30  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "31  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "32  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "33  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "34  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "35  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "36  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "37  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "38  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "39  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "40  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "41  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "42  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "43  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "44  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "45  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "46  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "47  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "48  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "49  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "50  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "51  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "52  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "53  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "54  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "55  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "56  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "57  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "58  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "59  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "                                       ...    ...  \n",
       "\n",
       "[1000209 rows x 10 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                                            1\n",
       "movie_id                                        1193\n",
       "rating                                             5\n",
       "timestamp                                  978300760\n",
       "gender                                             F\n",
       "age                                                1\n",
       "accupation                                        10\n",
       "zip                                            48067\n",
       "title         One Flew Over the Cuckoo's Nest (1975)\n",
       "genres                                         Drama\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "    <tr>\n",
       "      <th>1-900 (1994)</th>\n",
       "      <td> 2.000000</td>\n",
       "      <td> 3.000000</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",
       "<p>10 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "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\n",
       "1-900 (1994)                       2.000000  3.000000\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\n",
       "\n",
       "[10 rows x 2 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_rating = data.pivot_table('rating', rows = 'title', cols = 'gender', aggfunc = 'mean')\n",
    "mean_rating[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rating_by_title = data.groupby('title').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_by_title[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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)'], dtype='object')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_titles = rating_by_title.index[rating_by_title >= 250]\n",
    "active_titles[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "    <tr>\n",
       "      <th>13th Warrior, The (1999)</th>\n",
       "      <td> 3.112000</td>\n",
       "      <td> 3.168000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <td> 3.488889</td>\n",
       "      <td> 3.244813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
       "      <td> 3.670103</td>\n",
       "      <td> 3.709205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001: A Space Odyssey (1968)</th>\n",
       "      <td> 3.825581</td>\n",
       "      <td> 4.129738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 (1984)</th>\n",
       "      <td> 3.446809</td>\n",
       "      <td> 3.413712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28 Days (2000)</th>\n",
       "      <td> 3.209424</td>\n",
       "      <td> 2.977707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39 Steps, The (1935)</th>\n",
       "      <td> 3.965517</td>\n",
       "      <td> 4.107692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54 (1998)</th>\n",
       "      <td> 2.701754</td>\n",
       "      <td> 2.782178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7th Voyage of Sinbad, The (1958)</th>\n",
       "      <td> 3.409091</td>\n",
       "      <td> 3.658879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8MM (1999)</th>\n",
       "      <td> 2.906250</td>\n",
       "      <td> 2.850962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>About Last Night... (1986)</th>\n",
       "      <td> 3.188679</td>\n",
       "      <td> 3.140909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Absent Minded Professor, The (1961)</th>\n",
       "      <td> 3.469388</td>\n",
       "      <td> 3.446809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Absolute Power (1997)</th>\n",
       "      <td> 3.469136</td>\n",
       "      <td> 3.327759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abyss, The (1989)</th>\n",
       "      <td> 3.659236</td>\n",
       "      <td> 3.689507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ace Ventura: Pet Detective (1994)</th>\n",
       "      <td> 3.000000</td>\n",
       "      <td> 3.197917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ace Ventura: When Nature Calls (1995)</th>\n",
       "      <td> 2.269663</td>\n",
       "      <td> 2.543333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addams Family Values (1993)</th>\n",
       "      <td> 3.000000</td>\n",
       "      <td> 2.878531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addams Family, The (1991)</th>\n",
       "      <td> 3.186170</td>\n",
       "      <td> 3.163498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventures in Babysitting (1987)</th>\n",
       "      <td> 3.455782</td>\n",
       "      <td> 3.208122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventures of Buckaroo Bonzai Across the 8th Dimension, The (1984)</th>\n",
       "      <td> 3.308511</td>\n",
       "      <td> 3.402321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventures of Priscilla, Queen of the Desert, The (1994)</th>\n",
       "      <td> 3.989071</td>\n",
       "      <td> 3.688811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventures of Robin Hood, The (1938)</th>\n",
       "      <td> 4.166667</td>\n",
       "      <td> 3.918367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>African Queen, The (1951)</th>\n",
       "      <td> 4.324232</td>\n",
       "      <td> 4.223822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age of Innocence, The (1993)</th>\n",
       "      <td> 3.827068</td>\n",
       "      <td> 3.339506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agnes of God (1985)</th>\n",
       "      <td> 3.534884</td>\n",
       "      <td> 3.244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air America (1990)</th>\n",
       "      <td> 2.823529</td>\n",
       "      <td> 2.741176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Air Force One (1997)</th>\n",
       "      <td> 3.699588</td>\n",
       "      <td> 3.555822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airplane II: The Sequel (1982)</th>\n",
       "      <td> 2.906250</td>\n",
       "      <td> 2.995671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airplane! (1980)</th>\n",
       "      <td> 3.656566</td>\n",
       "      <td> 4.064419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akira (1988)</th>\n",
       "      <td> 3.511111</td>\n",
       "      <td> 3.980344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aladdin (1992)</th>\n",
       "      <td> 3.857143</td>\n",
       "      <td> 3.756494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alice in Wonderland (1951)</th>\n",
       "      <td> 3.705882</td>\n",
       "      <td> 3.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alien (1979)</th>\n",
       "      <td> 3.888252</td>\n",
       "      <td> 4.216119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alien Nation (1988)</th>\n",
       "      <td> 3.433333</td>\n",
       "      <td> 3.195946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alien: Resurrection (1997)</th>\n",
       "      <td> 2.708738</td>\n",
       "      <td> 2.997041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aliens (1986)</th>\n",
       "      <td> 3.802083</td>\n",
       "      <td> 4.186684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alien� (1992)</th>\n",
       "      <td> 3.008264</td>\n",
       "      <td> 3.042289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alive (1993)</th>\n",
       "      <td> 3.469388</td>\n",
       "      <td> 3.376866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All About Eve (1950)</th>\n",
       "      <td> 4.363057</td>\n",
       "      <td> 4.186992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All About My Mother (Todo Sobre Mi Madre) (1999)</th>\n",
       "      <td> 4.333333</td>\n",
       "      <td> 3.944000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All Quiet on the Western Front (1930)</th>\n",
       "      <td> 3.957447</td>\n",
       "      <td> 4.244344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All That Jazz (1979)</th>\n",
       "      <td> 4.061856</td>\n",
       "      <td> 3.718182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almost Famous (2000)</th>\n",
       "      <td> 4.220217</td>\n",
       "      <td> 4.228731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amadeus (1984)</th>\n",
       "      <td> 4.346734</td>\n",
       "      <td> 4.213415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Beauty (1999)</th>\n",
       "      <td> 4.238901</td>\n",
       "      <td> 4.347301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Gigolo (1980)</th>\n",
       "      <td> 3.337500</td>\n",
       "      <td> 3.106977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Graffiti (1973)</th>\n",
       "      <td> 3.940476</td>\n",
       "      <td> 4.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American History X (1998)</th>\n",
       "      <td> 4.181818</td>\n",
       "      <td> 4.240741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Movie (1999)</th>\n",
       "      <td> 4.028986</td>\n",
       "      <td> 4.008850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Pie (1999)</th>\n",
       "      <td> 3.539792</td>\n",
       "      <td> 3.754545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American President, The (1995)</th>\n",
       "      <td> 3.923483</td>\n",
       "      <td> 3.718654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Psycho (2000)</th>\n",
       "      <td> 3.085271</td>\n",
       "      <td> 3.253493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Tail, An (1986)</th>\n",
       "      <td> 3.649254</td>\n",
       "      <td> 3.318519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Werewolf in London, An (1981)</th>\n",
       "      <td> 3.548077</td>\n",
       "      <td> 3.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Werewolf in Paris, An (1997)</th>\n",
       "      <td> 2.814815</td>\n",
       "      <td> 2.739336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1216 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                                                     F  \\\n",
       "title                                                                          \n",
       "'burbs, The (1989)                                                  2.793478   \n",
       "10 Things I Hate About You (1999)                                   3.646552   \n",
       "101 Dalmatians (1961)                                               3.791444   \n",
       "101 Dalmatians (1996)                                               3.240000   \n",
       "12 Angry Men (1957)                                                 4.184397   \n",
       "13th Warrior, The (1999)                                            3.112000   \n",
       "2 Days in the Valley (1996)                                         3.488889   \n",
       "20,000 Leagues Under the Sea (1954)                                 3.670103   \n",
       "2001: A Space Odyssey (1968)                                        3.825581   \n",
       "2010 (1984)                                                         3.446809   \n",
       "28 Days (2000)                                                      3.209424   \n",
       "39 Steps, The (1935)                                                3.965517   \n",
       "54 (1998)                                                           2.701754   \n",
       "7th Voyage of Sinbad, The (1958)                                    3.409091   \n",
       "8MM (1999)                                                          2.906250   \n",
       "About Last Night... (1986)                                          3.188679   \n",
       "Absent Minded Professor, The (1961)                                 3.469388   \n",
       "Absolute Power (1997)                                               3.469136   \n",
       "Abyss, The (1989)                                                   3.659236   \n",
       "Ace Ventura: Pet Detective (1994)                                   3.000000   \n",
       "Ace Ventura: When Nature Calls (1995)                               2.269663   \n",
       "Addams Family Values (1993)                                         3.000000   \n",
       "Addams Family, The (1991)                                           3.186170   \n",
       "Adventures in Babysitting (1987)                                    3.455782   \n",
       "Adventures of Buckaroo Bonzai Across the 8th Dimension, The (1984)  3.308511   \n",
       "Adventures of Priscilla, Queen of the Desert, The (1994)            3.989071   \n",
       "Adventures of Robin Hood, The (1938)                                4.166667   \n",
       "African Queen, The (1951)                                           4.324232   \n",
       "Age of Innocence, The (1993)                                        3.827068   \n",
       "Agnes of God (1985)                                                 3.534884   \n",
       "Air America (1990)                                                  2.823529   \n",
       "Air Force One (1997)                                                3.699588   \n",
       "Airplane II: The Sequel (1982)                                      2.906250   \n",
       "Airplane! (1980)                                                    3.656566   \n",
       "Akira (1988)                                                        3.511111   \n",
       "Aladdin (1992)                                                      3.857143   \n",
       "Alice in Wonderland (1951)                                          3.705882   \n",
       "Alien (1979)                                                        3.888252   \n",
       "Alien Nation (1988)                                                 3.433333   \n",
       "Alien: Resurrection (1997)                                          2.708738   \n",
       "Aliens (1986)                                                       3.802083   \n",
       "Alien� (1992)                                                       3.008264   \n",
       "Alive (1993)                                                        3.469388   \n",
       "All About Eve (1950)                                                4.363057   \n",
       "All About My Mother (Todo Sobre Mi Madre) (1999)                    4.333333   \n",
       "All Quiet on the Western Front (1930)                               3.957447   \n",
       "All That Jazz (1979)                                                4.061856   \n",
       "Almost Famous (2000)                                                4.220217   \n",
       "Amadeus (1984)                                                      4.346734   \n",
       "American Beauty (1999)                                              4.238901   \n",
       "American Gigolo (1980)                                              3.337500   \n",
       "American Graffiti (1973)                                            3.940476   \n",
       "American History X (1998)                                           4.181818   \n",
       "American Movie (1999)                                               4.028986   \n",
       "American Pie (1999)                                                 3.539792   \n",
       "American President, The (1995)                                      3.923483   \n",
       "American Psycho (2000)                                              3.085271   \n",
       "American Tail, An (1986)                                            3.649254   \n",
       "American Werewolf in London, An (1981)                              3.548077   \n",
       "American Werewolf in Paris, An (1997)                               2.814815   \n",
       "                                                                         ...   \n",
       "\n",
       "gender                                                                     M  \n",
       "title                                                                         \n",
       "'burbs, The (1989)                                                  2.962085  \n",
       "10 Things I Hate About You (1999)                                   3.311966  \n",
       "101 Dalmatians (1961)                                               3.500000  \n",
       "101 Dalmatians (1996)                                               2.911215  \n",
       "12 Angry Men (1957)                                                 4.328421  \n",
       "13th Warrior, The (1999)                                            3.168000  \n",
       "2 Days in the Valley (1996)                                         3.244813  \n",
       "20,000 Leagues Under the Sea (1954)                                 3.709205  \n",
       "2001: A Space Odyssey (1968)                                        4.129738  \n",
       "2010 (1984)                                                         3.413712  \n",
       "28 Days (2000)                                                      2.977707  \n",
       "39 Steps, The (1935)                                                4.107692  \n",
       "54 (1998)                                                           2.782178  \n",
       "7th Voyage of Sinbad, The (1958)                                    3.658879  \n",
       "8MM (1999)                                                          2.850962  \n",
       "About Last Night... (1986)                                          3.140909  \n",
       "Absent Minded Professor, The (1961)                                 3.446809  \n",
       "Absolute Power (1997)                                               3.327759  \n",
       "Abyss, The (1989)                                                   3.689507  \n",
       "Ace Ventura: Pet Detective (1994)                                   3.197917  \n",
       "Ace Ventura: When Nature Calls (1995)                               2.543333  \n",
       "Addams Family Values (1993)                                         2.878531  \n",
       "Addams Family, The (1991)                                           3.163498  \n",
       "Adventures in Babysitting (1987)                                    3.208122  \n",
       "Adventures of Buckaroo Bonzai Across the 8th Dimension, The (1984)  3.402321  \n",
       "Adventures of Priscilla, Queen of the Desert, The (1994)            3.688811  \n",
       "Adventures of Robin Hood, The (1938)                                3.918367  \n",
       "African Queen, The (1951)                                           4.223822  \n",
       "Age of Innocence, The (1993)                                        3.339506  \n",
       "Agnes of God (1985)                                                 3.244898  \n",
       "Air America (1990)                                                  2.741176  \n",
       "Air Force One (1997)                                                3.555822  \n",
       "Airplane II: The Sequel (1982)                                      2.995671  \n",
       "Airplane! (1980)                                                    4.064419  \n",
       "Akira (1988)                                                        3.980344  \n",
       "Aladdin (1992)                                                      3.756494  \n",
       "Alice in Wonderland (1951)                                          3.692308  \n",
       "Alien (1979)                                                        4.216119  \n",
       "Alien Nation (1988)                                                 3.195946  \n",
       "Alien: Resurrection (1997)                                          2.997041  \n",
       "Aliens (1986)                                                       4.186684  \n",
       "Alien� (1992)                                                       3.042289  \n",
       "Alive (1993)                                                        3.376866  \n",
       "All About Eve (1950)                                                4.186992  \n",
       "All About My Mother (Todo Sobre Mi Madre) (1999)                    3.944000  \n",
       "All Quiet on the Western Front (1930)                               4.244344  \n",
       "All That Jazz (1979)                                                3.718182  \n",
       "Almost Famous (2000)                                                4.228731  \n",
       "Amadeus (1984)                                                      4.213415  \n",
       "American Beauty (1999)                                              4.347301  \n",
       "American Gigolo (1980)                                              3.106977  \n",
       "American Graffiti (1973)                                            4.055556  \n",
       "American History X (1998)                                           4.240741  \n",
       "American Movie (1999)                                               4.008850  \n",
       "American Pie (1999)                                                 3.754545  \n",
       "American President, The (1995)                                      3.718654  \n",
       "American Psycho (2000)                                              3.253493  \n",
       "American Tail, An (1986)                                            3.318519  \n",
       "American Werewolf in London, An (1981)                              3.800000  \n",
       "American Werewolf in Paris, An (1997)                               2.739336  \n",
       "                                                                         ...  \n",
       "\n",
       "[1216 rows x 2 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_rating = mean_rating.ix[active_titles]\n",
    "mean_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean_rating = mean_rating.rename(index = {'Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)':\n",
    "                                          'Seven Samurai (Shichinin no samurai)(1954)'})                                          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "<p>10 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "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\n",
       "\n",
       "[10 rows x 2 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_female_rating = mean_rating.sort_index(by = 'F', ascending = False)\n",
    "top_female_rating[:10]                                            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measure rating disaggreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean_rating['diff'] = mean_rating['M'] - mean_rating['F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sorted_by_diff = mean_rating.sort_index(by = 'diff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "<p>15 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "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\n",
       "\n",
       "[15 rows x 3 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "<p>15 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "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\n",
       "\n",
       "[15 rows x 3 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reverse order of rows, taking first 15 rows\n",
    "sorted_by_diff[::-1][:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Standard deviation of rating grouped by title\n",
    "rating_sd_by_title = data.groupby('title')['rating'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Filter down to active titles\n",
    "rating_sd_by_title = rating_sd_by_title.ix[active_titles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Order series by values in ascending order\n",
    "rating_sd_by_title.order(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US baby name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "plt.rc('figure', figsize = (12, 5))\n",
    "np.set_printoptions(precision = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://www.ssa.gov/oact/babynames/limits.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>names</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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>     Annie</td>\n",
       "      <td> F</td>\n",
       "      <td> 1258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>     Clara</td>\n",
       "      <td> F</td>\n",
       "      <td> 1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>      Ella</td>\n",
       "      <td> F</td>\n",
       "      <td> 1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>  Florence</td>\n",
       "      <td> F</td>\n",
       "      <td> 1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>      Cora</td>\n",
       "      <td> F</td>\n",
       "      <td> 1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>    Martha</td>\n",
       "      <td> F</td>\n",
       "      <td> 1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>     Laura</td>\n",
       "      <td> F</td>\n",
       "      <td> 1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>    Nellie</td>\n",
       "      <td> F</td>\n",
       "      <td>  995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>     Grace</td>\n",
       "      <td> F</td>\n",
       "      <td>  982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>    Carrie</td>\n",
       "      <td> F</td>\n",
       "      <td>  949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>     Maude</td>\n",
       "      <td> F</td>\n",
       "      <td>  858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>     Mabel</td>\n",
       "      <td> F</td>\n",
       "      <td>  808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>    Bessie</td>\n",
       "      <td> F</td>\n",
       "      <td>  794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>    Jennie</td>\n",
       "      <td> F</td>\n",
       "      <td>  793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>  Gertrude</td>\n",
       "      <td> F</td>\n",
       "      <td>  787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>     Julia</td>\n",
       "      <td> F</td>\n",
       "      <td>  783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>    Hattie</td>\n",
       "      <td> F</td>\n",
       "      <td>  769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>     Edith</td>\n",
       "      <td> F</td>\n",
       "      <td>  768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>    Mattie</td>\n",
       "      <td> F</td>\n",
       "      <td>  704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>      Rose</td>\n",
       "      <td> F</td>\n",
       "      <td>  700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> Catherine</td>\n",
       "      <td> F</td>\n",
       "      <td>  688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>   Lillian</td>\n",
       "      <td> F</td>\n",
       "      <td>  672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>       Ada</td>\n",
       "      <td> F</td>\n",
       "      <td>  652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>    Lillie</td>\n",
       "      <td> F</td>\n",
       "      <td>  647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>     Helen</td>\n",
       "      <td> F</td>\n",
       "      <td>  636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>    Jessie</td>\n",
       "      <td> F</td>\n",
       "      <td>  635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>    Louise</td>\n",
       "      <td> F</td>\n",
       "      <td>  635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>     Ethel</td>\n",
       "      <td> F</td>\n",
       "      <td>  633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>      Lula</td>\n",
       "      <td> F</td>\n",
       "      <td>  621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>    Myrtle</td>\n",
       "      <td> F</td>\n",
       "      <td>  615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>       Eva</td>\n",
       "      <td> F</td>\n",
       "      <td>  614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>   Frances</td>\n",
       "      <td> F</td>\n",
       "      <td>  605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>      Lena</td>\n",
       "      <td> F</td>\n",
       "      <td>  603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>      Lucy</td>\n",
       "      <td> F</td>\n",
       "      <td>  591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>      Edna</td>\n",
       "      <td> F</td>\n",
       "      <td>  588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>    Maggie</td>\n",
       "      <td> F</td>\n",
       "      <td>  582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>     Pearl</td>\n",
       "      <td> F</td>\n",
       "      <td>  569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>     Daisy</td>\n",
       "      <td> F</td>\n",
       "      <td>  564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>    Fannie</td>\n",
       "      <td> F</td>\n",
       "      <td>  560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td> Josephine</td>\n",
       "      <td> F</td>\n",
       "      <td>  544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>      Dora</td>\n",
       "      <td> F</td>\n",
       "      <td>  524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>      Rosa</td>\n",
       "      <td> F</td>\n",
       "      <td>  507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td> Katherine</td>\n",
       "      <td> F</td>\n",
       "      <td>  502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>     Agnes</td>\n",
       "      <td> F</td>\n",
       "      <td>  473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>     Marie</td>\n",
       "      <td> F</td>\n",
       "      <td>  471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>      Nora</td>\n",
       "      <td> F</td>\n",
       "      <td>  471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>       May</td>\n",
       "      <td> F</td>\n",
       "      <td>  462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>     Mamie</td>\n",
       "      <td> F</td>\n",
       "      <td>  436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>   Blanche</td>\n",
       "      <td> F</td>\n",
       "      <td>  427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>    Stella</td>\n",
       "      <td> F</td>\n",
       "      <td>  414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        names 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\n",
       "10      Annie   F    1258\n",
       "11      Clara   F    1226\n",
       "12       Ella   F    1156\n",
       "13   Florence   F    1063\n",
       "14       Cora   F    1045\n",
       "15     Martha   F    1040\n",
       "16      Laura   F    1012\n",
       "17     Nellie   F     995\n",
       "18      Grace   F     982\n",
       "19     Carrie   F     949\n",
       "20      Maude   F     858\n",
       "21      Mabel   F     808\n",
       "22     Bessie   F     794\n",
       "23     Jennie   F     793\n",
       "24   Gertrude   F     787\n",
       "25      Julia   F     783\n",
       "26     Hattie   F     769\n",
       "27      Edith   F     768\n",
       "28     Mattie   F     704\n",
       "29       Rose   F     700\n",
       "30  Catherine   F     688\n",
       "31    Lillian   F     672\n",
       "32        Ada   F     652\n",
       "33     Lillie   F     647\n",
       "34      Helen   F     636\n",
       "35     Jessie   F     635\n",
       "36     Louise   F     635\n",
       "37      Ethel   F     633\n",
       "38       Lula   F     621\n",
       "39     Myrtle   F     615\n",
       "40        Eva   F     614\n",
       "41    Frances   F     605\n",
       "42       Lena   F     603\n",
       "43       Lucy   F     591\n",
       "44       Edna   F     588\n",
       "45     Maggie   F     582\n",
       "46      Pearl   F     569\n",
       "47      Daisy   F     564\n",
       "48     Fannie   F     560\n",
       "49  Josephine   F     544\n",
       "50       Dora   F     524\n",
       "51       Rosa   F     507\n",
       "52  Katherine   F     502\n",
       "53      Agnes   F     473\n",
       "54      Marie   F     471\n",
       "55       Nora   F     471\n",
       "56        May   F     462\n",
       "57      Mamie   F     436\n",
       "58    Blanche   F     427\n",
       "59     Stella   F     414\n",
       "          ... ...     ...\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names1880 = pd.read_csv('ch02/names/yob1880.txt', names = ['names', 'sex', 'births'])\n",
    "names1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F       90993\n",
       "M      110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# year 2010 is the last year available right now\n",
    "years = range(1880, 2011)\n",
    "\n",
    "pieces = []\n",
    "columns = ['names', 'sex', 'births']\n",
    "\n",
    "for year in years:\n",
    "    path = 'ch02/names/yob%d.txt' %year\n",
    "    frame = pd.read_csv(path, names = columns)\n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "    \n",
    "# concatenate them into a single dataframe\n",
    "names = pd.concat(pieces, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'ch02/all_names'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-112-af1154dd18f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ch02/all_names'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/io/pickle.pyc\u001b[0m in \u001b[0;36mread_pickle\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtry_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m     \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mPY3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/dist-packages/pandas/io/pickle.pyc\u001b[0m in \u001b[0;36mtry_read\u001b[0;34m(path, encoding)\u001b[0m\n\u001b[1;32m     43\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mpc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mpc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'ch02/all_names'"
     ]
    }
   ],
   "source": [
    "# names = pd.read_pickle('ch02/all_names')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "<p>5 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "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\n",
       "\n",
       "[5 rows x 2 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = names.pivot_table('births', rows = 'year', cols = 'sex', aggfunc = sum)\n",
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fb871688890>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FSEpKwq5duzB16lQMGTIEK1asQK1atcTuutJxXZq4OP7i4viLh2MvLo6/uDj+qo+T8Hwa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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb871688f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title = 'Total birth by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>names</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",
       "    <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",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>    Martha</td>\n",
       "      <td> F</td>\n",
       "      <td> 1040</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>     Laura</td>\n",
       "      <td> F</td>\n",
       "      <td> 1012</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.011122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>    Nellie</td>\n",
       "      <td> F</td>\n",
       "      <td>  995</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.010935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>     Grace</td>\n",
       "      <td> F</td>\n",
       "      <td>  982</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.010792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>    Carrie</td>\n",
       "      <td> F</td>\n",
       "      <td>  949</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.010429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>     Maude</td>\n",
       "      <td> F</td>\n",
       "      <td>  858</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.009429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>     Mabel</td>\n",
       "      <td> F</td>\n",
       "      <td>  808</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>    Bessie</td>\n",
       "      <td> F</td>\n",
       "      <td>  794</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>    Jennie</td>\n",
       "      <td> F</td>\n",
       "      <td>  793</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>  Gertrude</td>\n",
       "      <td> F</td>\n",
       "      <td>  787</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>     Julia</td>\n",
       "      <td> F</td>\n",
       "      <td>  783</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>    Hattie</td>\n",
       "      <td> F</td>\n",
       "      <td>  769</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>     Edith</td>\n",
       "      <td> F</td>\n",
       "      <td>  768</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>    Mattie</td>\n",
       "      <td> F</td>\n",
       "      <td>  704</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>      Rose</td>\n",
       "      <td> F</td>\n",
       "      <td>  700</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> Catherine</td>\n",
       "      <td> F</td>\n",
       "      <td>  688</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>   Lillian</td>\n",
       "      <td> F</td>\n",
       "      <td>  672</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>       Ada</td>\n",
       "      <td> F</td>\n",
       "      <td>  652</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>    Lillie</td>\n",
       "      <td> F</td>\n",
       "      <td>  647</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>     Helen</td>\n",
       "      <td> F</td>\n",
       "      <td>  636</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>    Jessie</td>\n",
       "      <td> F</td>\n",
       "      <td>  635</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>    Louise</td>\n",
       "      <td> F</td>\n",
       "      <td>  635</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>     Ethel</td>\n",
       "      <td> F</td>\n",
       "      <td>  633</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>      Lula</td>\n",
       "      <td> F</td>\n",
       "      <td>  621</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>    Myrtle</td>\n",
       "      <td> F</td>\n",
       "      <td>  615</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>       Eva</td>\n",
       "      <td> F</td>\n",
       "      <td>  614</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>   Frances</td>\n",
       "      <td> F</td>\n",
       "      <td>  605</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>      Lena</td>\n",
       "      <td> F</td>\n",
       "      <td>  603</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>      Lucy</td>\n",
       "      <td> F</td>\n",
       "      <td>  591</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>      Edna</td>\n",
       "      <td> F</td>\n",
       "      <td>  588</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>    Maggie</td>\n",
       "      <td> F</td>\n",
       "      <td>  582</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>     Pearl</td>\n",
       "      <td> F</td>\n",
       "      <td>  569</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>     Daisy</td>\n",
       "      <td> F</td>\n",
       "      <td>  564</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>    Fannie</td>\n",
       "      <td> F</td>\n",
       "      <td>  560</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td> Josephine</td>\n",
       "      <td> F</td>\n",
       "      <td>  544</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>      Dora</td>\n",
       "      <td> F</td>\n",
       "      <td>  524</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>      Rosa</td>\n",
       "      <td> F</td>\n",
       "      <td>  507</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td> Katherine</td>\n",
       "      <td> F</td>\n",
       "      <td>  502</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>     Agnes</td>\n",
       "      <td> F</td>\n",
       "      <td>  473</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>     Marie</td>\n",
       "      <td> F</td>\n",
       "      <td>  471</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>      Nora</td>\n",
       "      <td> F</td>\n",
       "      <td>  471</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>       May</td>\n",
       "      <td> F</td>\n",
       "      <td>  462</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>     Mamie</td>\n",
       "      <td> F</td>\n",
       "      <td>  436</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.004792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>   Blanche</td>\n",
       "      <td> F</td>\n",
       "      <td>  427</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.004693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>    Stella</td>\n",
       "      <td> F</td>\n",
       "      <td>  414</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.004550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        names 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\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\n",
       "15     Martha   F    1040  1880  0.011429\n",
       "16      Laura   F    1012  1880  0.011122\n",
       "17     Nellie   F     995  1880  0.010935\n",
       "18      Grace   F     982  1880  0.010792\n",
       "19     Carrie   F     949  1880  0.010429\n",
       "20      Maude   F     858  1880  0.009429\n",
       "21      Mabel   F     808  1880  0.008880\n",
       "22     Bessie   F     794  1880  0.008726\n",
       "23     Jennie   F     793  1880  0.008715\n",
       "24   Gertrude   F     787  1880  0.008649\n",
       "25      Julia   F     783  1880  0.008605\n",
       "26     Hattie   F     769  1880  0.008451\n",
       "27      Edith   F     768  1880  0.008440\n",
       "28     Mattie   F     704  1880  0.007737\n",
       "29       Rose   F     700  1880  0.007693\n",
       "30  Catherine   F     688  1880  0.007561\n",
       "31    Lillian   F     672  1880  0.007385\n",
       "32        Ada   F     652  1880  0.007165\n",
       "33     Lillie   F     647  1880  0.007110\n",
       "34      Helen   F     636  1880  0.006990\n",
       "35     Jessie   F     635  1880  0.006979\n",
       "36     Louise   F     635  1880  0.006979\n",
       "37      Ethel   F     633  1880  0.006957\n",
       "38       Lula   F     621  1880  0.006825\n",
       "39     Myrtle   F     615  1880  0.006759\n",
       "40        Eva   F     614  1880  0.006748\n",
       "41    Frances   F     605  1880  0.006649\n",
       "42       Lena   F     603  1880  0.006627\n",
       "43       Lucy   F     591  1880  0.006495\n",
       "44       Edna   F     588  1880  0.006462\n",
       "45     Maggie   F     582  1880  0.006396\n",
       "46      Pearl   F     569  1880  0.006253\n",
       "47      Daisy   F     564  1880  0.006198\n",
       "48     Fannie   F     560  1880  0.006154\n",
       "49  Josephine   F     544  1880  0.005978\n",
       "50       Dora   F     524  1880  0.005759\n",
       "51       Rosa   F     507  1880  0.005572\n",
       "52  Katherine   F     502  1880  0.005517\n",
       "53      Agnes   F     473  1880  0.005198\n",
       "54      Marie   F     471  1880  0.005176\n",
       "55       Nora   F     471  1880  0.005176\n",
       "56        May   F     462  1880  0.005077\n",
       "57      Mamie   F     436  1880  0.004792\n",
       "58    Blanche   F     427  1880  0.004693\n",
       "59     Stella   F     414  1880  0.004550\n",
       "          ... ...     ...   ...       ...\n",
       "\n",
       "[1690784 rows x 5 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_prop(group):\n",
    "    # integers division floors\n",
    "    births = group.births.astype(float)\n",
    "    group['prop'] = births/births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(names.groupby(['year', 'sex']).prop.sum(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_index(by = 'births', ascending = False)[:1000]\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pieces = []\n",
    "\n",
    "for year, group in names.groupby(['year', 'sex']):\n",
    "    pieces.append(group.sort_index(by = 'births', ascending = False)[:1000])\n",
    "top1000 = pd.concat(pieces, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>names</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",
       "    <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",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>    Martha</td>\n",
       "      <td> F</td>\n",
       "      <td> 1040</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>     Laura</td>\n",
       "      <td> F</td>\n",
       "      <td> 1012</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.011122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>    Nellie</td>\n",
       "      <td> F</td>\n",
       "      <td>  995</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.010935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>     Grace</td>\n",
       "      <td> F</td>\n",
       "      <td>  982</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.010792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>    Carrie</td>\n",
       "      <td> F</td>\n",
       "      <td>  949</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.010429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>     Maude</td>\n",
       "      <td> F</td>\n",
       "      <td>  858</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.009429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>     Mabel</td>\n",
       "      <td> F</td>\n",
       "      <td>  808</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>    Bessie</td>\n",
       "      <td> F</td>\n",
       "      <td>  794</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>    Jennie</td>\n",
       "      <td> F</td>\n",
       "      <td>  793</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>  Gertrude</td>\n",
       "      <td> F</td>\n",
       "      <td>  787</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>     Julia</td>\n",
       "      <td> F</td>\n",
       "      <td>  783</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>    Hattie</td>\n",
       "      <td> F</td>\n",
       "      <td>  769</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>     Edith</td>\n",
       "      <td> F</td>\n",
       "      <td>  768</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.008440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>    Mattie</td>\n",
       "      <td> F</td>\n",
       "      <td>  704</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>      Rose</td>\n",
       "      <td> F</td>\n",
       "      <td>  700</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td> Catherine</td>\n",
       "      <td> F</td>\n",
       "      <td>  688</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>   Lillian</td>\n",
       "      <td> F</td>\n",
       "      <td>  672</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>       Ada</td>\n",
       "      <td> F</td>\n",
       "      <td>  652</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>    Lillie</td>\n",
       "      <td> F</td>\n",
       "      <td>  647</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.007110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>     Helen</td>\n",
       "      <td> F</td>\n",
       "      <td>  636</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>    Jessie</td>\n",
       "      <td> F</td>\n",
       "      <td>  635</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>    Louise</td>\n",
       "      <td> F</td>\n",
       "      <td>  635</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>     Ethel</td>\n",
       "      <td> F</td>\n",
       "      <td>  633</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>      Lula</td>\n",
       "      <td> F</td>\n",
       "      <td>  621</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>    Myrtle</td>\n",
       "      <td> F</td>\n",
       "      <td>  615</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>       Eva</td>\n",
       "      <td> F</td>\n",
       "      <td>  614</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>   Frances</td>\n",
       "      <td> F</td>\n",
       "      <td>  605</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>      Lena</td>\n",
       "      <td> F</td>\n",
       "      <td>  603</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>      Lucy</td>\n",
       "      <td> F</td>\n",
       "      <td>  591</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>      Edna</td>\n",
       "      <td> F</td>\n",
       "      <td>  588</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>    Maggie</td>\n",
       "      <td> F</td>\n",
       "      <td>  582</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>     Pearl</td>\n",
       "      <td> F</td>\n",
       "      <td>  569</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>     Daisy</td>\n",
       "      <td> F</td>\n",
       "      <td>  564</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>    Fannie</td>\n",
       "      <td> F</td>\n",
       "      <td>  560</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.006154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td> Josephine</td>\n",
       "      <td> F</td>\n",
       "      <td>  544</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>      Dora</td>\n",
       "      <td> F</td>\n",
       "      <td>  524</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>      Rosa</td>\n",
       "      <td> F</td>\n",
       "      <td>  507</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td> Katherine</td>\n",
       "      <td> F</td>\n",
       "      <td>  502</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>     Agnes</td>\n",
       "      <td> F</td>\n",
       "      <td>  473</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>     Marie</td>\n",
       "      <td> F</td>\n",
       "      <td>  471</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>      Nora</td>\n",
       "      <td> F</td>\n",
       "      <td>  471</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>       May</td>\n",
       "      <td> F</td>\n",
       "      <td>  462</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.005077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>     Mamie</td>\n",
       "      <td> F</td>\n",
       "      <td>  436</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.004792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>   Blanche</td>\n",
       "      <td> F</td>\n",
       "      <td>  427</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.004693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>    Stella</td>\n",
       "      <td> F</td>\n",
       "      <td>  414</td>\n",
       "      <td> 1880</td>\n",
       "      <td> 0.004550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        names 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\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\n",
       "15     Martha   F    1040  1880  0.011429\n",
       "16      Laura   F    1012  1880  0.011122\n",
       "17     Nellie   F     995  1880  0.010935\n",
       "18      Grace   F     982  1880  0.010792\n",
       "19     Carrie   F     949  1880  0.010429\n",
       "20      Maude   F     858  1880  0.009429\n",
       "21      Mabel   F     808  1880  0.008880\n",
       "22     Bessie   F     794  1880  0.008726\n",
       "23     Jennie   F     793  1880  0.008715\n",
       "24   Gertrude   F     787  1880  0.008649\n",
       "25      Julia   F     783  1880  0.008605\n",
       "26     Hattie   F     769  1880  0.008451\n",
       "27      Edith   F     768  1880  0.008440\n",
       "28     Mattie   F     704  1880  0.007737\n",
       "29       Rose   F     700  1880  0.007693\n",
       "30  Catherine   F     688  1880  0.007561\n",
       "31    Lillian   F     672  1880  0.007385\n",
       "32        Ada   F     652  1880  0.007165\n",
       "33     Lillie   F     647  1880  0.007110\n",
       "34      Helen   F     636  1880  0.006990\n",
       "35     Jessie   F     635  1880  0.006979\n",
       "36     Louise   F     635  1880  0.006979\n",
       "37      Ethel   F     633  1880  0.006957\n",
       "38       Lula   F     621  1880  0.006825\n",
       "39     Myrtle   F     615  1880  0.006759\n",
       "40        Eva   F     614  1880  0.006748\n",
       "41    Frances   F     605  1880  0.006649\n",
       "42       Lena   F     603  1880  0.006627\n",
       "43       Lucy   F     591  1880  0.006495\n",
       "44       Edna   F     588  1880  0.006462\n",
       "45     Maggie   F     582  1880  0.006396\n",
       "46      Pearl   F     569  1880  0.006253\n",
       "47      Daisy   F     564  1880  0.006198\n",
       "48     Fannie   F     560  1880  0.006154\n",
       "49  Josephine   F     544  1880  0.005978\n",
       "50       Dora   F     524  1880  0.005759\n",
       "51       Rosa   F     507  1880  0.005572\n",
       "52  Katherine   F     502  1880  0.005517\n",
       "53      Agnes   F     473  1880  0.005198\n",
       "54      Marie   F     471  1880  0.005176\n",
       "55       Nora   F     471  1880  0.005176\n",
       "56        May   F     462  1880  0.005077\n",
       "57      Mamie   F     436  1880  0.004792\n",
       "58    Blanche   F     427  1880  0.004693\n",
       "59     Stella   F     414  1880  0.004550\n",
       "          ... ...     ...   ...       ...\n",
       "\n",
       "[261877 rows x 5 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000.index = np.arange(len(top1000))\n",
    "top1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing naming trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']\n",
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_births = top1000.pivot_table('births', rows = 'year', cols = 'names', aggfunc = sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>names</th>\n",
       "      <th>Aaden</th>\n",
       "      <th>Aaliyah</th>\n",
       "      <th>Aarav</th>\n",
       "      <th>Aaron</th>\n",
       "      <th>Aarush</th>\n",
       "      <th>Ab</th>\n",
       "      <th>Abagail</th>\n",
       "      <th>Abb</th>\n",
       "      <th>Abbey</th>\n",
       "      <th>Abbie</th>\n",
       "      <th>Abbigail</th>\n",
       "      <th>Abbott</th>\n",
       "      <th>Abby</th>\n",
       "      <th>Abdiel</th>\n",
       "      <th>Abdul</th>\n",
       "      <th>Abdullah</th>\n",
       "      <th>Abe</th>\n",
       "      <th>Abel</th>\n",
       "      <th>Abelardo</th>\n",
       "      <th>Abigail</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  50</td>\n",
       "      <td>  10</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 14</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  79</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>  43</td>\n",
       "      <td>  12</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 11</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  45</td>\n",
       "      <td>  14</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 13</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  88</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>  47</td>\n",
       "      <td>   6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  9</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  50</td>\n",
       "      <td>  16</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 15</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 104</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>  37</td>\n",
       "      <td>  11</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 13</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  6</td>\n",
       "      <td>  9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  46</td>\n",
       "      <td>   8</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 18</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 107</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>  39</td>\n",
       "      <td>   9</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 20</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  6</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 140</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>  49</td>\n",
       "      <td>  14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  40</td>\n",
       "      <td>  10</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 11</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 119</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>  53</td>\n",
       "      <td>   8</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 11</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 115</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>  43</td>\n",
       "      <td>  13</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 21</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 118</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>  50</td>\n",
       "      <td>  15</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 13</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  92</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>  54</td>\n",
       "      <td> NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 15</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 121</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>  51</td>\n",
       "      <td>  15</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 15</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  97</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>  58</td>\n",
       "      <td>  12</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 15</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 120</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>  67</td>\n",
       "      <td>  10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  87</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>  39</td>\n",
       "      <td>  15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 112</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>  56</td>\n",
       "      <td>  15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  87</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>  48</td>\n",
       "      <td>  13</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 13</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  91</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>  54</td>\n",
       "      <td>  11</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 18</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  91</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>  58</td>\n",
       "      <td>  14</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 20</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  80</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>  52</td>\n",
       "      <td>  10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  73</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>  65</td>\n",
       "      <td> NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  72</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>  50</td>\n",
       "      <td>  11</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 15</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  79</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>  65</td>\n",
       "      <td>  10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  84</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>  57</td>\n",
       "      <td>  15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  57</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>  67</td>\n",
       "      <td>  16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  79</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>  74</td>\n",
       "      <td> NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  77</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>  94</td>\n",
       "      <td>  30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  90</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> 172</td>\n",
       "      <td>  40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 121</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> 202</td>\n",
       "      <td>  48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  99</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> 225</td>\n",
       "      <td>  51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 124</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> 271</td>\n",
       "      <td>  64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 146</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> 261</td>\n",
       "      <td>  62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 136</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> 280</td>\n",
       "      <td>  86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 120</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> 256</td>\n",
       "      <td>  76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 147</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> 265</td>\n",
       "      <td>  91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  98</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> 206</td>\n",
       "      <td>  79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 134</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> 230</td>\n",
       "      <td>  84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 142</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> 176</td>\n",
       "      <td>  91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 129</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> 174</td>\n",
       "      <td>  73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  96</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> 174</td>\n",
       "      <td> 111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 128</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> 117</td>\n",
       "      <td>  83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 105</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> 134</td>\n",
       "      <td> 101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 103</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> 139</td>\n",
       "      <td>  78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  92</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>  90</td>\n",
       "      <td>  85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  78</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>  61</td>\n",
       "      <td>  96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  77</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>  85</td>\n",
       "      <td> 123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  71</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>  79</td>\n",
       "      <td>  93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  57</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>  83</td>\n",
       "      <td>  94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  64</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>  68</td>\n",
       "      <td>  96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  60</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>  75</td>\n",
       "      <td>  93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  60</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>  59</td>\n",
       "      <td>  92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 443</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  70</td>\n",
       "      <td>  74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  52</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>  64</td>\n",
       "      <td>  91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  57</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>  55</td>\n",
       "      <td>  87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  69</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>  56</td>\n",
       "      <td>  95</td>\n",
       "      <td>NaN</td>\n",
       "      <td> 53</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 6865 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "names  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",
       "1890     NaN      NaN    NaN     96     NaN NaN      NaN    6    NaN    140   \n",
       "1891     NaN      NaN    NaN     69     NaN NaN      NaN  NaN    NaN    124   \n",
       "1892     NaN      NaN    NaN     95     NaN NaN      NaN  NaN    NaN    119   \n",
       "1893     NaN      NaN    NaN     81     NaN NaN      NaN  NaN    NaN    115   \n",
       "1894     NaN      NaN    NaN     79     NaN NaN      NaN  NaN    NaN    118   \n",
       "1895     NaN      NaN    NaN     94     NaN NaN      NaN  NaN    NaN     92   \n",
       "1896     NaN      NaN    NaN     69     NaN NaN      NaN  NaN    NaN    121   \n",
       "1897     NaN      NaN    NaN     87     NaN NaN      NaN  NaN    NaN     97   \n",
       "1898     NaN      NaN    NaN     89     NaN NaN      NaN  NaN    NaN    120   \n",
       "1899     NaN      NaN    NaN     71     NaN NaN      NaN  NaN    NaN     87   \n",
       "1900     NaN      NaN    NaN    104     NaN NaN      NaN  NaN    NaN    112   \n",
       "1901     NaN      NaN    NaN     80     NaN NaN      NaN  NaN    NaN     87   \n",
       "1902     NaN      NaN    NaN     78     NaN NaN      NaN  NaN    NaN     91   \n",
       "1903     NaN      NaN    NaN     93     NaN NaN      NaN  NaN    NaN     91   \n",
       "1904     NaN      NaN    NaN    117     NaN NaN      NaN  NaN    NaN     80   \n",
       "1905     NaN      NaN    NaN     96     NaN NaN      NaN  NaN    NaN     73   \n",
       "1906     NaN      NaN    NaN     96     NaN NaN      NaN  NaN    NaN     72   \n",
       "1907     NaN      NaN    NaN    130     NaN NaN      NaN  NaN    NaN     79   \n",
       "1908     NaN      NaN    NaN    114     NaN NaN      NaN  NaN    NaN     84   \n",
       "1909     NaN      NaN    NaN    142     NaN NaN      NaN  NaN    NaN     57   \n",
       "1910     NaN      NaN    NaN    145     NaN NaN      NaN  NaN    NaN     79   \n",
       "1911     NaN      NaN    NaN    187     NaN NaN      NaN  NaN    NaN     77   \n",
       "1912     NaN      NaN    NaN    303     NaN NaN      NaN  NaN    NaN     90   \n",
       "1913     NaN      NaN    NaN    417     NaN NaN      NaN  NaN    NaN    121   \n",
       "1914     NaN      NaN    NaN    485     NaN NaN      NaN  NaN    NaN     99   \n",
       "1915     NaN      NaN    NaN    553     NaN NaN      NaN  NaN    NaN    124   \n",
       "1916     NaN      NaN    NaN    584     NaN NaN      NaN  NaN    NaN    146   \n",
       "1917     NaN      NaN    NaN    597     NaN NaN      NaN  NaN    NaN    136   \n",
       "1918     NaN      NaN    NaN    656     NaN NaN      NaN  NaN    NaN    120   \n",
       "1919     NaN      NaN    NaN    645     NaN NaN      NaN  NaN    NaN    147   \n",
       "1920     NaN      NaN    NaN    668     NaN NaN      NaN  NaN    NaN     98   \n",
       "1921     NaN      NaN    NaN    696     NaN NaN      NaN  NaN    NaN    134   \n",
       "1922     NaN      NaN    NaN    700     NaN NaN      NaN  NaN    NaN    142   \n",
       "1923     NaN      NaN    NaN    616     NaN NaN      NaN  NaN    NaN    129   \n",
       "1924     NaN      NaN    NaN    683     NaN NaN      NaN  NaN    NaN     96   \n",
       "1925     NaN      NaN    NaN    644     NaN NaN      NaN  NaN    NaN    128   \n",
       "1926     NaN      NaN    NaN    593     NaN NaN      NaN  NaN    NaN    105   \n",
       "1927     NaN      NaN    NaN    546     NaN NaN      NaN  NaN    NaN    103   \n",
       "1928     NaN      NaN    NaN    557     NaN NaN      NaN  NaN    NaN     92   \n",
       "1929     NaN      NaN    NaN    469     NaN NaN      NaN  NaN    NaN     78   \n",
       "1930     NaN      NaN    NaN    500     NaN NaN      NaN  NaN    NaN     77   \n",
       "1931     NaN      NaN    NaN    458     NaN NaN      NaN  NaN    NaN     71   \n",
       "1932     NaN      NaN    NaN    514     NaN NaN      NaN  NaN    NaN     57   \n",
       "1933     NaN      NaN    NaN    460     NaN NaN      NaN  NaN    NaN     64   \n",
       "1934     NaN      NaN    NaN    478     NaN NaN      NaN  NaN    NaN     60   \n",
       "1935     NaN      NaN    NaN    461     NaN NaN      NaN  NaN    NaN     60   \n",
       "1936     NaN      NaN    NaN    443     NaN NaN      NaN  NaN    NaN    NaN   \n",
       "1937     NaN      NaN    NaN    464     NaN NaN      NaN  NaN    NaN     52   \n",
       "1938     NaN      NaN    NaN    478     NaN NaN      NaN  NaN    NaN     57   \n",
       "1939     NaN      NaN    NaN    471     NaN NaN      NaN  NaN    NaN     69   \n",
       "         ...      ...    ...    ...     ... ...      ...  ...    ...    ...   \n",
       "\n",
       "names  Abbigail  Abbott  Abby  Abdiel  Abdul  Abdullah  Abe  Abel  Abelardo  \\\n",
       "year                                                                          \n",
       "1880        NaN     NaN     6     NaN    NaN       NaN   50     9       NaN   \n",
       "1881        NaN     NaN     7     NaN    NaN       NaN   36    12       NaN   \n",
       "1882        NaN     NaN    11     NaN    NaN       NaN   50    10       NaN   \n",
       "1883        NaN     NaN   NaN     NaN    NaN       NaN   43    12       NaN   \n",
       "1884        NaN     NaN     6     NaN    NaN       NaN   45    14       NaN   \n",
       "1885        NaN     NaN   NaN     NaN    NaN       NaN   47     6       NaN   \n",
       "1886        NaN     NaN     7     NaN    NaN       NaN   50    16       NaN   \n",
       "1887        NaN     NaN   NaN     NaN    NaN       NaN   37    11       NaN   \n",
       "1888        NaN       6     9     NaN    NaN       NaN   46     8       NaN   \n",
       "1889        NaN     NaN   NaN     NaN    NaN       NaN   39     9       NaN   \n",
       "1890        NaN     NaN   NaN     NaN    NaN       NaN   49    14       NaN   \n",
       "1891        NaN     NaN    12     NaN    NaN       NaN   40    10       NaN   \n",
       "1892        NaN     NaN   NaN     NaN    NaN       NaN   53     8       NaN   \n",
       "1893        NaN     NaN   NaN     NaN    NaN       NaN   43    13       NaN   \n",
       "1894        NaN     NaN   NaN     NaN    NaN       NaN   50    15       NaN   \n",
       "1895        NaN     NaN   NaN     NaN    NaN       NaN   54   NaN       NaN   \n",
       "1896        NaN     NaN   NaN     NaN    NaN       NaN   51    15       NaN   \n",
       "1897        NaN     NaN   NaN     NaN    NaN       NaN   58    12       NaN   \n",
       "1898        NaN     NaN   NaN     NaN    NaN       NaN   67    10       NaN   \n",
       "1899        NaN     NaN   NaN     NaN    NaN       NaN   39    15       NaN   \n",
       "1900        NaN     NaN   NaN     NaN    NaN       NaN   56    15       NaN   \n",
       "1901        NaN     NaN   NaN     NaN    NaN       NaN   48    13       NaN   \n",
       "1902        NaN     NaN   NaN     NaN    NaN       NaN   54    11       NaN   \n",
       "1903        NaN     NaN   NaN     NaN    NaN       NaN   58    14       NaN   \n",
       "1904        NaN     NaN   NaN     NaN    NaN       NaN   52    10       NaN   \n",
       "1905        NaN     NaN   NaN     NaN    NaN       NaN   65   NaN       NaN   \n",
       "1906        NaN     NaN   NaN     NaN    NaN       NaN   50    11       NaN   \n",
       "1907        NaN     NaN   NaN     NaN    NaN       NaN   65    10       NaN   \n",
       "1908        NaN     NaN   NaN     NaN    NaN       NaN   57    15       NaN   \n",
       "1909        NaN     NaN   NaN     NaN    NaN       NaN   67    16       NaN   \n",
       "1910        NaN     NaN   NaN     NaN    NaN       NaN   74   NaN       NaN   \n",
       "1911        NaN     NaN   NaN     NaN    NaN       NaN   94    30       NaN   \n",
       "1912        NaN     NaN   NaN     NaN    NaN       NaN  172    40       NaN   \n",
       "1913        NaN     NaN   NaN     NaN    NaN       NaN  202    48       NaN   \n",
       "1914        NaN     NaN   NaN     NaN    NaN       NaN  225    51       NaN   \n",
       "1915        NaN     NaN   NaN     NaN    NaN       NaN  271    64       NaN   \n",
       "1916        NaN     NaN   NaN     NaN    NaN       NaN  261    62       NaN   \n",
       "1917        NaN     NaN   NaN     NaN    NaN       NaN  280    86       NaN   \n",
       "1918        NaN     NaN   NaN     NaN    NaN       NaN  256    76       NaN   \n",
       "1919        NaN     NaN   NaN     NaN    NaN       NaN  265    91       NaN   \n",
       "1920        NaN     NaN   NaN     NaN    NaN       NaN  206    79       NaN   \n",
       "1921        NaN     NaN   NaN     NaN    NaN       NaN  230    84       NaN   \n",
       "1922        NaN     NaN   NaN     NaN    NaN       NaN  176    91       NaN   \n",
       "1923        NaN     NaN   NaN     NaN    NaN       NaN  174    73       NaN   \n",
       "1924        NaN     NaN   NaN     NaN    NaN       NaN  174   111       NaN   \n",
       "1925        NaN     NaN   NaN     NaN    NaN       NaN  117    83       NaN   \n",
       "1926        NaN     NaN   NaN     NaN    NaN       NaN  134   101       NaN   \n",
       "1927        NaN     NaN   NaN     NaN    NaN       NaN  139    78       NaN   \n",
       "1928        NaN     NaN   NaN     NaN    NaN       NaN   90    85       NaN   \n",
       "1929        NaN     NaN   NaN     NaN    NaN       NaN   61    96       NaN   \n",
       "1930        NaN     NaN   NaN     NaN    NaN       NaN   85   123       NaN   \n",
       "1931        NaN     NaN   NaN     NaN    NaN       NaN   79    93       NaN   \n",
       "1932        NaN     NaN   NaN     NaN    NaN       NaN   83    94       NaN   \n",
       "1933        NaN     NaN   NaN     NaN    NaN       NaN   68    96       NaN   \n",
       "1934        NaN     NaN   NaN     NaN    NaN       NaN   75    93       NaN   \n",
       "1935        NaN     NaN   NaN     NaN    NaN       NaN   59    92       NaN   \n",
       "1936        NaN     NaN   NaN     NaN    NaN       NaN   70    74       NaN   \n",
       "1937        NaN     NaN   NaN     NaN    NaN       NaN   64    91       NaN   \n",
       "1938        NaN     NaN   NaN     NaN    NaN       NaN   55    87       NaN   \n",
       "1939        NaN     NaN   NaN     NaN    NaN       NaN   56    95       NaN   \n",
       "            ...     ...   ...     ...    ...       ...  ...   ...       ...   \n",
       "\n",
       "names  Abigail      \n",
       "year                \n",
       "1880        12 ...  \n",
       "1881         8 ...  \n",
       "1882        14 ...  \n",
       "1883        11 ...  \n",
       "1884        13 ...  \n",
       "1885         9 ...  \n",
       "1886        15 ...  \n",
       "1887        13 ...  \n",
       "1888        18 ...  \n",
       "1889        20 ...  \n",
       "1890       NaN ...  \n",
       "1891        11 ...  \n",
       "1892        11 ...  \n",
       "1893        21 ...  \n",
       "1894        13 ...  \n",
       "1895        15 ...  \n",
       "1896        15 ...  \n",
       "1897        15 ...  \n",
       "1898       NaN ...  \n",
       "1899       NaN ...  \n",
       "1900       NaN ...  \n",
       "1901        13 ...  \n",
       "1902        18 ...  \n",
       "1903        20 ...  \n",
       "1904       NaN ...  \n",
       "1905       NaN ...  \n",
       "1906        15 ...  \n",
       "1907       NaN ...  \n",
       "1908       NaN ...  \n",
       "1909       NaN ...  \n",
       "1910       NaN ...  \n",
       "1911       NaN ...  \n",
       "1912       NaN ...  \n",
       "1913       NaN ...  \n",
       "1914       NaN ...  \n",
       "1915       NaN ...  \n",
       "1916       NaN ...  \n",
       "1917       NaN ...  \n",
       "1918       NaN ...  \n",
       "1919       NaN ...  \n",
       "1920       NaN ...  \n",
       "1921       NaN ...  \n",
       "1922       NaN ...  \n",
       "1923       NaN ...  \n",
       "1924       NaN ...  \n",
       "1925       NaN ...  \n",
       "1926       NaN ...  \n",
       "1927       NaN ...  \n",
       "1928       NaN ...  \n",
       "1929       NaN ...  \n",
       "1930       NaN ...  \n",
       "1931       NaN ...  \n",
       "1932       NaN ...  \n",
       "1933       NaN ...  \n",
       "1934       NaN ...  \n",
       "1935       NaN ...  \n",
       "1936       NaN ...  \n",
       "1937       NaN ...  \n",
       "1938       NaN ...  \n",
       "1939        53 ...  \n",
       "           ...      \n",
       "\n",
       "[131 rows x 6865 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes.AxesSubplot object at 0x7fb873f6c210>,\n",
       "       <matplotlib.axes.AxesSubplot object at 0x7fb87152c150>,\n",
       "       <matplotlib.axes.AxesSubplot object at 0x7fb8714f86d0>,\n",
       "       <matplotlib.axes.AxesSubplot object at 0x7fb87141aad0>], dtype=object)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UqRJt27Y1fDjS6/WsWrWKQYMG0bx5c7755hsaN25s5JRCiOehxGK5atWqNGnS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb873f6c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]\n",
    "subset.plot(subplots = True, figsize = (12, 10), grid = False, title = 'Number of births per year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measure the increase of naming density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb87175d050>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb87175d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fb8713d91d0>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RYY6TkZNT/7luN+TnQ16eOfbsMR8LCzNnjl0u83klJbB5s1lEFxXBgQOwc6eZ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb87152c610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = top1000.pivot_table('prop', rows = 'year', cols = 'sex', aggfunc = sum)\n",
    "table.plot(title = 'Sum of table.top1000.prop by sex and year', \n",
    "           yticks = np.linspace(0, 1.2, 13),\n",
    "           xticks = range(1880, 2010, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>names</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>260877</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>260878</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>260879</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>260880</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>260881</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>260882</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>260883</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>260884</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>260885</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>260886</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",
       "    <tr>\n",
       "      <th>260887</th>\n",
       "      <td>      Joshua</td>\n",
       "      <td> M</td>\n",
       "      <td> 15238</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.008027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260888</th>\n",
       "      <td>       Mason</td>\n",
       "      <td> M</td>\n",
       "      <td> 14728</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260889</th>\n",
       "      <td> Christopher</td>\n",
       "      <td> M</td>\n",
       "      <td> 14135</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260890</th>\n",
       "      <td>      Andrew</td>\n",
       "      <td> M</td>\n",
       "      <td> 14093</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260891</th>\n",
       "      <td>       David</td>\n",
       "      <td> M</td>\n",
       "      <td> 14042</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260892</th>\n",
       "      <td>     Matthew</td>\n",
       "      <td> M</td>\n",
       "      <td> 13954</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260893</th>\n",
       "      <td>       Logan</td>\n",
       "      <td> M</td>\n",
       "      <td> 13943</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260894</th>\n",
       "      <td>      Elijah</td>\n",
       "      <td> M</td>\n",
       "      <td> 13735</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260895</th>\n",
       "      <td>       James</td>\n",
       "      <td> M</td>\n",
       "      <td> 13714</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260896</th>\n",
       "      <td>      Joseph</td>\n",
       "      <td> M</td>\n",
       "      <td> 13657</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.007194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260897</th>\n",
       "      <td>     Gabriel</td>\n",
       "      <td> M</td>\n",
       "      <td> 12722</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.006701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260898</th>\n",
       "      <td>    Benjamin</td>\n",
       "      <td> M</td>\n",
       "      <td> 12280</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.006469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260899</th>\n",
       "      <td>        Ryan</td>\n",
       "      <td> M</td>\n",
       "      <td> 11886</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.006261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260900</th>\n",
       "      <td>      Samuel</td>\n",
       "      <td> M</td>\n",
       "      <td> 11776</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.006203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260901</th>\n",
       "      <td>     Jackson</td>\n",
       "      <td> M</td>\n",
       "      <td> 11693</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.006159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260902</th>\n",
       "      <td>        John</td>\n",
       "      <td> M</td>\n",
       "      <td> 11424</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.006018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260903</th>\n",
       "      <td>      Nathan</td>\n",
       "      <td> M</td>\n",
       "      <td> 11269</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260904</th>\n",
       "      <td>    Jonathan</td>\n",
       "      <td> M</td>\n",
       "      <td> 11028</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260905</th>\n",
       "      <td>   Christian</td>\n",
       "      <td> M</td>\n",
       "      <td> 10965</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260906</th>\n",
       "      <td>        Liam</td>\n",
       "      <td> M</td>\n",
       "      <td> 10852</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260907</th>\n",
       "      <td>       Dylan</td>\n",
       "      <td> M</td>\n",
       "      <td> 10455</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260908</th>\n",
       "      <td>      Landon</td>\n",
       "      <td> M</td>\n",
       "      <td> 10400</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260909</th>\n",
       "      <td>       Caleb</td>\n",
       "      <td> M</td>\n",
       "      <td> 10359</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260910</th>\n",
       "      <td>       Tyler</td>\n",
       "      <td> M</td>\n",
       "      <td> 10357</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260911</th>\n",
       "      <td>       Lucas</td>\n",
       "      <td> M</td>\n",
       "      <td> 10294</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260912</th>\n",
       "      <td>        Evan</td>\n",
       "      <td> M</td>\n",
       "      <td>  9655</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260913</th>\n",
       "      <td>       Gavin</td>\n",
       "      <td> M</td>\n",
       "      <td>  9551</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260914</th>\n",
       "      <td>    Nicholas</td>\n",
       "      <td> M</td>\n",
       "      <td>  9549</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.005030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260915</th>\n",
       "      <td>       Isaac</td>\n",
       "      <td> M</td>\n",
       "      <td>  9254</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260916</th>\n",
       "      <td>     Brayden</td>\n",
       "      <td> M</td>\n",
       "      <td>  9046</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260917</th>\n",
       "      <td>        Luke</td>\n",
       "      <td> M</td>\n",
       "      <td>  8767</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260918</th>\n",
       "      <td>       Angel</td>\n",
       "      <td> M</td>\n",
       "      <td>  8716</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260919</th>\n",
       "      <td>     Brandon</td>\n",
       "      <td> M</td>\n",
       "      <td>  8473</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260920</th>\n",
       "      <td>        Jack</td>\n",
       "      <td> M</td>\n",
       "      <td>  8457</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260921</th>\n",
       "      <td>      Isaiah</td>\n",
       "      <td> M</td>\n",
       "      <td>  8443</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260922</th>\n",
       "      <td>      Jordan</td>\n",
       "      <td> M</td>\n",
       "      <td>  8156</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260923</th>\n",
       "      <td>        Owen</td>\n",
       "      <td> M</td>\n",
       "      <td>  8136</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260924</th>\n",
       "      <td>      Carter</td>\n",
       "      <td> M</td>\n",
       "      <td>  8101</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260925</th>\n",
       "      <td>      Connor</td>\n",
       "      <td> M</td>\n",
       "      <td>  7991</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260926</th>\n",
       "      <td>      Justin</td>\n",
       "      <td> M</td>\n",
       "      <td>  7792</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260927</th>\n",
       "      <td>        Jose</td>\n",
       "      <td> M</td>\n",
       "      <td>  7656</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260928</th>\n",
       "      <td>    Jeremiah</td>\n",
       "      <td> M</td>\n",
       "      <td>  7645</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.004027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260929</th>\n",
       "      <td>      Julian</td>\n",
       "      <td> M</td>\n",
       "      <td>  7534</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.003969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260930</th>\n",
       "      <td>      Robert</td>\n",
       "      <td> M</td>\n",
       "      <td>  7471</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.003935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260931</th>\n",
       "      <td>       Aaron</td>\n",
       "      <td> M</td>\n",
       "      <td>  7374</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.003884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260932</th>\n",
       "      <td>      Adrian</td>\n",
       "      <td> M</td>\n",
       "      <td>  7354</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.003874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260933</th>\n",
       "      <td>       Wyatt</td>\n",
       "      <td> M</td>\n",
       "      <td>  7319</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.003855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260934</th>\n",
       "      <td>       Kevin</td>\n",
       "      <td> M</td>\n",
       "      <td>  7278</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.003834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260935</th>\n",
       "      <td>      Hunter</td>\n",
       "      <td> M</td>\n",
       "      <td>  7271</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.003830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260936</th>\n",
       "      <td>     Cameron</td>\n",
       "      <td> M</td>\n",
       "      <td>  7137</td>\n",
       "      <td> 2010</td>\n",
       "      <td> 0.003760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              names sex  births  year      prop\n",
       "260877        Jacob   M   21875  2010  0.011523\n",
       "260878        Ethan   M   17866  2010  0.009411\n",
       "260879      Michael   M   17133  2010  0.009025\n",
       "260880       Jayden   M   17030  2010  0.008971\n",
       "260881      William   M   16870  2010  0.008887\n",
       "260882    Alexander   M   16634  2010  0.008762\n",
       "260883         Noah   M   16281  2010  0.008576\n",
       "260884       Daniel   M   15679  2010  0.008259\n",
       "260885        Aiden   M   15403  2010  0.008114\n",
       "260886      Anthony   M   15364  2010  0.008093\n",
       "260887       Joshua   M   15238  2010  0.008027\n",
       "260888        Mason   M   14728  2010  0.007758\n",
       "260889  Christopher   M   14135  2010  0.007446\n",
       "260890       Andrew   M   14093  2010  0.007424\n",
       "260891        David   M   14042  2010  0.007397\n",
       "260892      Matthew   M   13954  2010  0.007350\n",
       "260893        Logan   M   13943  2010  0.007345\n",
       "260894       Elijah   M   13735  2010  0.007235\n",
       "260895        James   M   13714  2010  0.007224\n",
       "260896       Joseph   M   13657  2010  0.007194\n",
       "260897      Gabriel   M   12722  2010  0.006701\n",
       "260898     Benjamin   M   12280  2010  0.006469\n",
       "260899         Ryan   M   11886  2010  0.006261\n",
       "260900       Samuel   M   11776  2010  0.006203\n",
       "260901      Jackson   M   11693  2010  0.006159\n",
       "260902         John   M   11424  2010  0.006018\n",
       "260903       Nathan   M   11269  2010  0.005936\n",
       "260904     Jonathan   M   11028  2010  0.005809\n",
       "260905    Christian   M   10965  2010  0.005776\n",
       "260906         Liam   M   10852  2010  0.005716\n",
       "260907        Dylan   M   10455  2010  0.005507\n",
       "260908       Landon   M   10400  2010  0.005478\n",
       "260909        Caleb   M   10359  2010  0.005457\n",
       "260910        Tyler   M   10357  2010  0.005456\n",
       "260911        Lucas   M   10294  2010  0.005423\n",
       "260912         Evan   M    9655  2010  0.005086\n",
       "260913        Gavin   M    9551  2010  0.005031\n",
       "260914     Nicholas   M    9549  2010  0.005030\n",
       "260915        Isaac   M    9254  2010  0.004875\n",
       "260916      Brayden   M    9046  2010  0.004765\n",
       "260917         Luke   M    8767  2010  0.004618\n",
       "260918        Angel   M    8716  2010  0.004591\n",
       "260919      Brandon   M    8473  2010  0.004463\n",
       "260920         Jack   M    8457  2010  0.004455\n",
       "260921       Isaiah   M    8443  2010  0.004447\n",
       "260922       Jordan   M    8156  2010  0.004296\n",
       "260923         Owen   M    8136  2010  0.004286\n",
       "260924       Carter   M    8101  2010  0.004267\n",
       "260925       Connor   M    7991  2010  0.004209\n",
       "260926       Justin   M    7792  2010  0.004105\n",
       "260927         Jose   M    7656  2010  0.004033\n",
       "260928     Jeremiah   M    7645  2010  0.004027\n",
       "260929       Julian   M    7534  2010  0.003969\n",
       "260930       Robert   M    7471  2010  0.003935\n",
       "260931        Aaron   M    7374  2010  0.003884\n",
       "260932       Adrian   M    7354  2010  0.003874\n",
       "260933        Wyatt   M    7319  2010  0.003855\n",
       "260934        Kevin   M    7278  2010  0.003834\n",
       "260935       Hunter   M    7271  2010  0.003830\n",
       "260936      Cameron   M    7137  2010  0.003760\n",
       "                ... ...     ...   ...       ...\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 2010]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum = df.sort_index(by = 'prop', ascending = False).prop.cumsum()\n",
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 1900]\n",
    "in1900 = df.sort_index(by = 'prop', ascending = False).prop.cumsum()\n",
    "in1900.values.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td> 38</td>\n",
       "      <td> 15</td>\n",
       "    </tr>\n",
       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "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\n",
       "\n",
       "[5 rows x 2 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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\n",
    "\n",
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
    "diversity = diversity.unstack('sex')\n",
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fb87124ffd0>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gli4VHY1+KHM8nC48ePDAqFaaucUqERERGZqgIOD0aeCffwBzc9HR6Ae9aL0wtpYMY3s9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\n+slYKD+VWi2vsJWlYEWvoI80MlJe5X2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noAXU+fwuGie1U53fj+/x+0OejztEDx+eQz7v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb87128da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title = 'Number of Popular names in top 50%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The last letter revolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Extract the last letter from the names\n",
    "get_last_letter = lambda x: x[-1]\n",
    "\n",
    "last_letters = names.names.apply(get_last_letter)\n",
    "last_letters.names = 'last_letter'\n",
    "\n",
    "table = names.pivot_table('births', rows = last_letters, cols = ['sex', 'year'], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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=\"20\" halign=\"left\">F</th>\n",
       "      <th></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>1890</th>\n",
       "      <th>1891</th>\n",
       "      <th>1892</th>\n",
       "      <th>1893</th>\n",
       "      <th>1894</th>\n",
       "      <th>1895</th>\n",
       "      <th>1896</th>\n",
       "      <th>1897</th>\n",
       "      <th>1898</th>\n",
       "      <th>1899</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>names</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> 62313</td>\n",
       "      <td> 60582</td>\n",
       "      <td> 68331</td>\n",
       "      <td> 67821</td>\n",
       "      <td> 70631</td>\n",
       "      <td> 73002</td>\n",
       "      <td> 73584</td>\n",
       "      <td> 72148</td>\n",
       "      <td> 79150</td>\n",
       "      <td> 70712</td>\n",
       "      <td>...</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>   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",
       "    </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>   NaN</td>\n",
       "      <td>   NaN</td>\n",
       "      <td>   NaN</td>\n",
       "      <td>   NaN</td>\n",
       "      <td>   NaN</td>\n",
       "      <td>   NaN</td>\n",
       "      <td>     5</td>\n",
       "      <td>   NaN</td>\n",
       "      <td>   NaN</td>\n",
       "      <td>   NaN</td>\n",
       "      <td>...</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>  1438</td>\n",
       "      <td>  1512</td>\n",
       "      <td>  1775</td>\n",
       "      <td>  1821</td>\n",
       "      <td>  1985</td>\n",
       "      <td>  2268</td>\n",
       "      <td>  2372</td>\n",
       "      <td>  2455</td>\n",
       "      <td>  2953</td>\n",
       "      <td>  3028</td>\n",
       "      <td>...</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> 70948</td>\n",
       "      <td> 67750</td>\n",
       "      <td> 77186</td>\n",
       "      <td> 76455</td>\n",
       "      <td> 79938</td>\n",
       "      <td> 83875</td>\n",
       "      <td> 84355</td>\n",
       "      <td> 82783</td>\n",
       "      <td> 91151</td>\n",
       "      <td> 81395</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex        F                                                                 \\\n",
       "year    1880   1881   1882   1883   1884   1885   1886   1887   1888   1889   \n",
       "names                                                                         \n",
       "a      31446  31581  36536  38330  43680  45408  49100  48942  59442  58631   \n",
       "b        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "c        NaN    NaN      5      5    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "d        609    607    734    810    916    862   1007   1027   1298   1374   \n",
       "e      33378  34080  40399  41914  48089  49616  53884  54353  66750  66663   \n",
       "\n",
       "sex                                                                          \\\n",
       "year    1890   1891   1892   1893   1894   1895   1896   1897   1898   1899   \n",
       "names                                                                         \n",
       "a      62313  60582  68331  67821  70631  73002  73584  72148  79150  70712   \n",
       "b        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "c        NaN    NaN    NaN    NaN    NaN    NaN      5    NaN    NaN    NaN   \n",
       "d       1438   1512   1775   1821   1985   2268   2372   2455   2953   3028   \n",
       "e      70948  67750  77186  76455  79938  83875  84355  82783  91151  81395   \n",
       "\n",
       "sex        \n",
       "year       \n",
       "names      \n",
       "a     ...  \n",
       "b     ...  \n",
       "c     ...  \n",
       "d     ...  \n",
       "e     ...  \n",
       "\n",
       "[5 rows x 262 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable = table.reindex(colums = [1910, 1960, 2010], levels = 'year')\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1880     90993\n",
       "     1881     91955\n",
       "     1882    107851\n",
       "     1883    112322\n",
       "     1884    129021\n",
       "     1885    133056\n",
       "     1886    144538\n",
       "     1887    145983\n",
       "     1888    178631\n",
       "     1889    178369\n",
       "     1890    190377\n",
       "     1891    185486\n",
       "     1892    212350\n",
       "     1893    212908\n",
       "     1894    222923\n",
       "...\n",
       "M    1996    1892700\n",
       "     1997    1883571\n",
       "     1998    1909676\n",
       "     1999    1918267\n",
       "     2000    1961702\n",
       "     2001    1940498\n",
       "     2002    1938941\n",
       "     2003    1972439\n",
       "     2004    1981557\n",
       "     2005    1993285\n",
       "     2006    2050234\n",
       "     2007    2069242\n",
       "     2008    2032310\n",
       "     2009    1973359\n",
       "     2010    1898382\n",
       "Length: 262, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "letter_prop = subtable/subtable.sum().astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fb87080edd0>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jWIBeVVWFiRMn4qabbgIAfPWrX8Vnn30WvF3TNDzwwAP49NNP8Yc//AGZmZnw+/3QNC14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\niEiG/wcHdEHBl0K5WAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb87112c690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize = (10, 8))\n",
    "letter_prop['M'].plot(kind = 'bar', rot = 0, ax = axes[0], title = 'Male', legend = False)\n",
    "letter_prop['F'].plot(kind = 'bar', rot = 0, ax = axes[1], title = 'Female', legend = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb870c82d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots_adjust(hspace = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "      <th>m</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></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",
       "      <td> 0.101907</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",
       "      <td> 0.101411</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",
       "      <td> 0.099000</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",
       "      <td> 0.096663</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",
       "      <td> 0.093774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             d         n         y         m\n",
       "year                                        \n",
       "1880  0.083055  0.153213  0.075760  0.101907\n",
       "1881  0.083247  0.153214  0.077451  0.101411\n",
       "1882  0.085340  0.149560  0.077537  0.099000\n",
       "1883  0.084066  0.151646  0.079144  0.096663\n",
       "1884  0.086120  0.149915  0.080405  0.093774\n",
       "\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = table/table.sum().astype(float)\n",
    "dny_ts = letter_prop.ix[['d', 'n', 'y', 'm'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x7fb868bd7490>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NeXl5cHC4c8WnqakpNBoNTExErm1iYoLIyEhMmzYN/fr1g62t7QOP0VfBwcFISkqq2J8/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\npFkIIYQQQohGSNIshBBCCCFEI/4/O/C/TVO7TowAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb85bf21f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### boy names that became girls' names (and vice versa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_names = top1000.names.unique()\n",
    "mask = np.array(['lesl' in x.lower() for x in all_names])\n",
    "lesley_like = all_names[mask]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "names\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.names.isin(lesley_like)]\n",
    "filtered.groupby('names').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>1880</th>\n",
       "      <td> 0.091954</td>\n",
       "      <td> 0.908046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td> 0.106796</td>\n",
       "      <td> 0.893204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td> 0.065693</td>\n",
       "      <td> 0.934307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td> 0.053030</td>\n",
       "      <td> 0.946970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td> 0.107143</td>\n",
       "      <td> 0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td> 0.075758</td>\n",
       "      <td> 0.924242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td> 0.055556</td>\n",
       "      <td> 0.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td> 0.067416</td>\n",
       "      <td> 0.932584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td> 0.116162</td>\n",
       "      <td> 0.883838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td> 0.129213</td>\n",
       "      <td> 0.870787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td> 0.099502</td>\n",
       "      <td> 0.900498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td> 0.145833</td>\n",
       "      <td> 0.854167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td> 0.096070</td>\n",
       "      <td> 0.903930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td> 0.123223</td>\n",
       "      <td> 0.876777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td> 0.138996</td>\n",
       "      <td> 0.861004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td> 0.085603</td>\n",
       "      <td> 0.914397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td> 0.102273</td>\n",
       "      <td> 0.897727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td> 0.132812</td>\n",
       "      <td> 0.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td> 0.092308</td>\n",
       "      <td> 0.907692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td> 0.090452</td>\n",
       "      <td> 0.909548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td> 0.095238</td>\n",
       "      <td> 0.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td> 0.124464</td>\n",
       "      <td> 0.875536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td> 0.128472</td>\n",
       "      <td> 0.871528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td> 0.089552</td>\n",
       "      <td> 0.910448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td> 0.109890</td>\n",
       "      <td> 0.890110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td> 0.124113</td>\n",
       "      <td> 0.875887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td> 0.099315</td>\n",
       "      <td> 0.900685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td> 0.110749</td>\n",
       "      <td> 0.889251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td> 0.123867</td>\n",
       "      <td> 0.876133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td> 0.107034</td>\n",
       "      <td> 0.892966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td> 0.137931</td>\n",
       "      <td> 0.862069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td> 0.097765</td>\n",
       "      <td> 0.902235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td> 0.066038</td>\n",
       "      <td> 0.933962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td> 0.058427</td>\n",
       "      <td> 0.941573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td> 0.059441</td>\n",
       "      <td> 0.940559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td> 0.058824</td>\n",
       "      <td> 0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td> 0.069368</td>\n",
       "      <td> 0.930632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td> 0.064286</td>\n",
       "      <td> 0.935714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td> 0.071630</td>\n",
       "      <td> 0.928370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td> 0.069049</td>\n",
       "      <td> 0.930951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td> 0.053026</td>\n",
       "      <td> 0.946974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td> 0.071106</td>\n",
       "      <td> 0.928894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td> 0.070267</td>\n",
       "      <td> 0.929733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td> 0.088252</td>\n",
       "      <td> 0.911748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td> 0.078246</td>\n",
       "      <td> 0.921754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td> 0.081382</td>\n",
       "      <td> 0.918618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td> 0.086497</td>\n",
       "      <td> 0.913503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td> 0.078541</td>\n",
       "      <td> 0.921459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td> 0.068780</td>\n",
       "      <td> 0.931220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td> 0.075166</td>\n",
       "      <td> 0.924834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td> 0.096140</td>\n",
       "      <td> 0.903860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td> 0.078234</td>\n",
       "      <td> 0.921766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td> 0.073841</td>\n",
       "      <td> 0.926159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td> 0.095423</td>\n",
       "      <td> 0.904577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td> 0.090514</td>\n",
       "      <td> 0.909486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td> 0.116968</td>\n",
       "      <td> 0.883032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td> 0.125526</td>\n",
       "      <td> 0.874474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td> 0.124736</td>\n",
       "      <td> 0.875264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td> 0.123856</td>\n",
       "      <td> 0.876144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td> 0.117841</td>\n",
       "      <td> 0.882159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F         M\n",
       "year                    \n",
       "1880  0.091954  0.908046\n",
       "1881  0.106796  0.893204\n",
       "1882  0.065693  0.934307\n",
       "1883  0.053030  0.946970\n",
       "1884  0.107143  0.892857\n",
       "1885  0.075758  0.924242\n",
       "1886  0.055556  0.944444\n",
       "1887  0.067416  0.932584\n",
       "1888  0.116162  0.883838\n",
       "1889  0.129213  0.870787\n",
       "1890  0.099502  0.900498\n",
       "1891  0.145833  0.854167\n",
       "1892  0.096070  0.903930\n",
       "1893  0.123223  0.876777\n",
       "1894  0.138996  0.861004\n",
       "1895  0.085603  0.914397\n",
       "1896  0.102273  0.897727\n",
       "1897  0.132812  0.867188\n",
       "1898  0.092308  0.907692\n",
       "1899  0.090452  0.909548\n",
       "1900  0.095238  0.904762\n",
       "1901  0.124464  0.875536\n",
       "1902  0.128472  0.871528\n",
       "1903  0.089552  0.910448\n",
       "1904  0.109890  0.890110\n",
       "1905  0.124113  0.875887\n",
       "1906  0.099315  0.900685\n",
       "1907  0.110749  0.889251\n",
       "1908  0.123867  0.876133\n",
       "1909  0.107034  0.892966\n",
       "1910  0.137931  0.862069\n",
       "1911  0.097765  0.902235\n",
       "1912  0.066038  0.933962\n",
       "1913  0.058427  0.941573\n",
       "1914  0.059441  0.940559\n",
       "1915  0.058824  0.941176\n",
       "1916  0.069368  0.930632\n",
       "1917  0.064286  0.935714\n",
       "1918  0.071630  0.928370\n",
       "1919  0.069049  0.930951\n",
       "1920  0.053026  0.946974\n",
       "1921  0.071106  0.928894\n",
       "1922  0.070267  0.929733\n",
       "1923  0.088252  0.911748\n",
       "1924  0.078246  0.921754\n",
       "1925  0.081382  0.918618\n",
       "1926  0.086497  0.913503\n",
       "1927  0.078541  0.921459\n",
       "1928  0.068780  0.931220\n",
       "1929  0.075166  0.924834\n",
       "1930  0.096140  0.903860\n",
       "1931  0.078234  0.921766\n",
       "1932  0.073841  0.926159\n",
       "1933  0.095423  0.904577\n",
       "1934  0.090514  0.909486\n",
       "1935  0.116968  0.883032\n",
       "1936  0.125526  0.874474\n",
       "1937  0.124736  0.875264\n",
       "1938  0.123856  0.876144\n",
       "1939  0.117841  0.882159\n",
       "           ...       ...\n",
       "\n",
       "[131 rows x 2 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = filtered.pivot_table('births', rows = 'year', cols = 'sex', aggfunc = sum)\n",
    "table = table.div(table.sum(1), axis = 0)\n",
    "table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-166-e72db582d4c7>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-166-e72db582d4c7>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    table.plot(style = {'M', 'k-', 'F': 'k--'})\u001b[0m\n\u001b[0m                                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "table.plot(style = {'M', 'k-', 'F': 'k--'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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