ch14.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.USA.gov Data from Bitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "np.random.seed(123)\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)\n",
    "pd.options.display.max_rows = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'datasets/bitly_usagov/example.txt'\n",
    "open(path).readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
       " 'c': 'US',\n",
       " 'nk': 1,\n",
       " 'tz': 'America/New_York',\n",
       " 'gr': 'MA',\n",
       " 'g': 'A6qOVH',\n",
       " 'h': 'wfLQtf',\n",
       " 'l': 'orofrog',\n",
       " 'al': 'en-US,en;q=0.8',\n",
       " 'hh': '1.usa.gov',\n",
       " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
       " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991',\n",
       " 't': 1331923247,\n",
       " 'hc': 1331822918,\n",
       " 'cy': 'Danvers',\n",
       " 'll': [42.576698, -70.954903]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "path = 'datasets/bitly_usagov/example.txt'\n",
    "records = [json.loads(line) for line in open(path)]\n",
    "records[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting Time Zones in Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'tz'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-f3fbbc37f129>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtime_zones\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tz'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-5-f3fbbc37f129>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtime_zones\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tz'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 'tz'"
     ]
    }
   ],
   "source": [
    "time_zones = [rec['tz'] for rec in records]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/New_York',\n",
       " 'America/Denver',\n",
       " 'America/New_York',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/New_York',\n",
       " 'America/New_York',\n",
       " 'Europe/Warsaw',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec]\n",
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_counts(sequence):\n",
    "    counts = {}\n",
    "    for x in sequence:\n",
    "        if x in counts:\n",
    "            counts[x] += 1\n",
    "        else:\n",
    "            counts[x] = 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def get_counts2(sequence):\n",
    "    counts = defaultdict(int) # values will initialize to 0\n",
    "    for x in sequence:\n",
    "        counts[x] += 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = get_counts(time_zones)\n",
    "counts['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'America/New_York': 1251,\n",
       "         'America/Denver': 191,\n",
       "         'America/Sao_Paulo': 33,\n",
       "         'Europe/Warsaw': 16,\n",
       "         '': 521,\n",
       "         'America/Los_Angeles': 382,\n",
       "         'Asia/Hong_Kong': 10,\n",
       "         'Europe/Rome': 27,\n",
       "         'Africa/Ceuta': 2,\n",
       "         'Europe/Madrid': 35,\n",
       "         'Asia/Kuala_Lumpur': 3,\n",
       "         'Asia/Nicosia': 1,\n",
       "         'Europe/London': 74,\n",
       "         'Pacific/Honolulu': 36,\n",
       "         'America/Chicago': 400,\n",
       "         'Europe/Malta': 2,\n",
       "         'Europe/Lisbon': 8,\n",
       "         'Europe/Paris': 14,\n",
       "         'Europe/Copenhagen': 5,\n",
       "         'America/Mazatlan': 1,\n",
       "         'Europe/Dublin': 3,\n",
       "         'Europe/Brussels': 4,\n",
       "         'America/Vancouver': 12,\n",
       "         'Europe/Amsterdam': 22,\n",
       "         'Europe/Prague': 10,\n",
       "         'Europe/Stockholm': 14,\n",
       "         'America/Anchorage': 5,\n",
       "         'Asia/Bangkok': 6,\n",
       "         'Europe/Berlin': 28,\n",
       "         'America/Rainy_River': 25,\n",
       "         'Europe/Budapest': 5,\n",
       "         'Asia/Tokyo': 37,\n",
       "         'Europe/Vienna': 6,\n",
       "         'America/Phoenix': 20,\n",
       "         'Asia/Jerusalem': 3,\n",
       "         'Asia/Karachi': 3,\n",
       "         'America/Bogota': 3,\n",
       "         'America/Indianapolis': 20,\n",
       "         'America/Montreal': 9,\n",
       "         'Asia/Calcutta': 9,\n",
       "         'Europe/Skopje': 1,\n",
       "         'Asia/Beirut': 4,\n",
       "         'Australia/NSW': 6,\n",
       "         'Chile/Continental': 6,\n",
       "         'America/Halifax': 4,\n",
       "         'America/Edmonton': 6,\n",
       "         'Europe/Bratislava': 3,\n",
       "         'America/Recife': 2,\n",
       "         'Africa/Cairo': 3,\n",
       "         'Asia/Istanbul': 9,\n",
       "         'Asia/Novosibirsk': 1,\n",
       "         'Europe/Moscow': 10,\n",
       "         'Europe/Sofia': 1,\n",
       "         'Europe/Ljubljana': 1,\n",
       "         'America/Mexico_City': 15,\n",
       "         'Europe/Helsinki': 10,\n",
       "         'Europe/Bucharest': 4,\n",
       "         'Europe/Zurich': 4,\n",
       "         'America/Puerto_Rico': 10,\n",
       "         'America/Monterrey': 1,\n",
       "         'Europe/Athens': 6,\n",
       "         'America/Winnipeg': 4,\n",
       "         'Europe/Riga': 2,\n",
       "         'America/Argentina/Buenos_Aires': 1,\n",
       "         'Asia/Dubai': 4,\n",
       "         'Europe/Oslo': 10,\n",
       "         'Asia/Yekaterinburg': 1,\n",
       "         'Asia/Manila': 1,\n",
       "         'America/Caracas': 1,\n",
       "         'Asia/Riyadh': 1,\n",
       "         'America/Montevideo': 1,\n",
       "         'America/Argentina/Mendoza': 1,\n",
       "         'Asia/Seoul': 5,\n",
       "         'Europe/Uzhgorod': 1,\n",
       "         'Australia/Queensland': 1,\n",
       "         'Europe/Belgrade': 2,\n",
       "         'America/Costa_Rica': 1,\n",
       "         'America/Lima': 1,\n",
       "         'Asia/Pontianak': 1,\n",
       "         'America/Chihuahua': 2,\n",
       "         'Europe/Vilnius': 2,\n",
       "         'America/Managua': 3,\n",
       "         'Africa/Lusaka': 1,\n",
       "         'America/Guayaquil': 2,\n",
       "         'Asia/Harbin': 3,\n",
       "         'Asia/Amman': 2,\n",
       "         'Africa/Johannesburg': 1,\n",
       "         'America/St_Kitts': 1,\n",
       "         'Pacific/Auckland': 11,\n",
       "         'America/Santo_Domingo': 1,\n",
       "         'America/Argentina/Cordoba': 1,\n",
       "         'Asia/Kuching': 1,\n",
       "         'Europe/Volgograd': 1,\n",
       "         'America/La_Paz': 1,\n",
       "         'Africa/Casablanca': 1,\n",
       "         'Asia/Jakarta': 3,\n",
       "         'America/Tegucigalpa': 1})"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33, 'America/Sao_Paulo'),\n",
       " (35, 'Europe/Madrid'),\n",
       " (36, 'Pacific/Honolulu'),\n",
       " (37, 'Asia/Tokyo'),\n",
       " (74, 'Europe/London'),\n",
       " (191, 'America/Denver'),\n",
       " (382, 'America/Los_Angeles'),\n",
       " (400, 'America/Chicago'),\n",
       " (521, ''),\n",
       " (1251, 'America/New_York')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "counts = Counter(time_zones)\n",
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting Time Zones with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "      <th>nk</th>\n",
       "      <th>tz</th>\n",
       "      <th>gr</th>\n",
       "      <th>g</th>\n",
       "      <th>h</th>\n",
       "      <th>l</th>\n",
       "      <th>al</th>\n",
       "      <th>hh</th>\n",
       "      <th>r</th>\n",
       "      <th>u</th>\n",
       "      <th>t</th>\n",
       "      <th>hc</th>\n",
       "      <th>cy</th>\n",
       "      <th>ll</th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>kw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>MA</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331823e+09</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>bitly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>Provo</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>bitly</td>\n",
       "      <td>en-US</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331920e+09</td>\n",
       "      <td>Washington</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>BR</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>27</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>direct</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Braz</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>bitly</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.273672e+09</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>NJ</td>\n",
       "      <td>e5SvKE</td>\n",
       "      <td>fqPSr9</td>\n",
       "      <td>tweetdeckapi</td>\n",
       "      <td>en</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>direct</td>\n",
       "      <td>http://www.fda.gov/AdvisoryCommittees/Committe...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>1.301298e+09</td>\n",
       "      <td>Paramus</td>\n",
       "      <td>[40.9445, -74.07]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>OK</td>\n",
       "      <td>jQLtP4</td>\n",
       "      <td>jQLtP4</td>\n",
       "      <td>bitly</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>http://www.okc.gov/PublicNotificationSystem/Fo...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>1.307530e+09</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>[35.4715, -97.518997]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>bitly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>Provo</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3558</th>\n",
       "      <td>GoogleProducer</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>zjtI4X</td>\n",
       "      <td>zjtI4X</td>\n",
       "      <td>bitly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>direct</td>\n",
       "      <td>http://www.ahrq.gov/qual/qitoolkit/</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>1.327529e+09</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>[37.419201, -122.057404]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>VA</td>\n",
       "      <td>qxKrTK</td>\n",
       "      <td>qxKrTK</td>\n",
       "      <td>bitly</td>\n",
       "      <td>en-US</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>http://t.co/OEEEvwjU</td>\n",
       "      <td>http://herndon-va.gov/Content/public_safety/Pu...</td>\n",
       "      <td>1.331927e+09</td>\n",
       "      <td>1.312898e+09</td>\n",
       "      <td>Mc Lean</td>\n",
       "      <td>[38.935799, -77.162102]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3560 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      a   c   nk  \\\n",
       "0     Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...  US  1.0   \n",
       "1                                GoogleMaps/RochesterNY  US  0.0   \n",
       "2     Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...  US  1.0   \n",
       "3     Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...  BR  0.0   \n",
       "4     Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...  US  0.0   \n",
       "...                                                 ...  ..  ...   \n",
       "3555  Mozilla/4.0 (compatible; MSIE 9.0; Windows NT ...  US  1.0   \n",
       "3556  Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...  US  0.0   \n",
       "3557                             GoogleMaps/RochesterNY  US  0.0   \n",
       "3558                                     GoogleProducer  US  0.0   \n",
       "3559  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...  US  0.0   \n",
       "\n",
       "                       tz  gr       g       h             l              al  \\\n",
       "0        America/New_York  MA  A6qOVH  wfLQtf       orofrog  en-US,en;q=0.8   \n",
       "1          America/Denver  UT  mwszkS  mwszkS         bitly             NaN   \n",
       "2        America/New_York  DC  xxr3Qb  xxr3Qb         bitly           en-US   \n",
       "3       America/Sao_Paulo  27  zCaLwp  zUtuOu      alelex88           pt-br   \n",
       "4        America/New_York  MA  9b6kNl  9b6kNl         bitly  en-US,en;q=0.8   \n",
       "...                   ...  ..     ...     ...           ...             ...   \n",
       "3555     America/New_York  NJ  e5SvKE  fqPSr9  tweetdeckapi              en   \n",
       "3556      America/Chicago  OK  jQLtP4  jQLtP4         bitly  en-US,en;q=0.8   \n",
       "3557       America/Denver  UT  mwszkS  mwszkS         bitly             NaN   \n",
       "3558  America/Los_Angeles  CA  zjtI4X  zjtI4X         bitly             NaN   \n",
       "3559     America/New_York  VA  qxKrTK  qxKrTK         bitly           en-US   \n",
       "\n",
       "             hh                                                  r  \\\n",
       "0     1.usa.gov  http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...   \n",
       "1          j.mp                           http://www.AwareMap.com/   \n",
       "2     1.usa.gov                               http://t.co/03elZC4Q   \n",
       "3     1.usa.gov                                             direct   \n",
       "4        bit.ly                http://www.shrewsbury-ma.gov/selco/   \n",
       "...         ...                                                ...   \n",
       "3555  1.usa.gov                                             direct   \n",
       "3556  1.usa.gov  http://www.facebook.com/l.php?u=http%3A%2F%2F1...   \n",
       "3557       j.mp                           http://www.AwareMap.com/   \n",
       "3558  1.usa.gov                                             direct   \n",
       "3559  1.usa.gov                               http://t.co/OEEEvwjU   \n",
       "\n",
       "                                                      u             t  \\\n",
       "0           http://www.ncbi.nlm.nih.gov/pubmed/22415991  1.331923e+09   \n",
       "1           http://www.monroecounty.gov/etc/911/rss.php  1.331923e+09   \n",
       "2     http://boxer.senate.gov/en/press/releases/0316...  1.331923e+09   \n",
       "3               http://apod.nasa.gov/apod/ap120312.html  1.331923e+09   \n",
       "4     http://www.shrewsbury-ma.gov/egov/gallery/1341...  1.331923e+09   \n",
       "...                                                 ...           ...   \n",
       "3555  http://www.fda.gov/AdvisoryCommittees/Committe...  1.331927e+09   \n",
       "3556  http://www.okc.gov/PublicNotificationSystem/Fo...  1.331927e+09   \n",
       "3557        http://www.monroecounty.gov/etc/911/rss.php  1.331927e+09   \n",
       "3558                http://www.ahrq.gov/qual/qitoolkit/  1.331927e+09   \n",
       "3559  http://herndon-va.gov/Content/public_safety/Pu...  1.331927e+09   \n",
       "\n",
       "                hc             cy                        ll  _heartbeat_   kw  \n",
       "0     1.331823e+09        Danvers   [42.576698, -70.954903]          NaN  NaN  \n",
       "1     1.308262e+09          Provo  [40.218102, -111.613297]          NaN  NaN  \n",
       "2     1.331920e+09     Washington     [38.9007, -77.043098]          NaN  NaN  \n",
       "3     1.331923e+09           Braz  [-23.549999, -46.616699]          NaN  NaN  \n",
       "4     1.273672e+09     Shrewsbury   [42.286499, -71.714699]          NaN  NaN  \n",
       "...            ...            ...                       ...          ...  ...  \n",
       "3555  1.301298e+09        Paramus         [40.9445, -74.07]          NaN  NaN  \n",
       "3556  1.307530e+09  Oklahoma City     [35.4715, -97.518997]          NaN  NaN  \n",
       "3557  1.308262e+09          Provo  [40.218102, -111.613297]          NaN  NaN  \n",
       "3558  1.327529e+09  Mountain View  [37.419201, -122.057404]          NaN  NaN  \n",
       "3559  1.312898e+09        Mc Lean   [38.935799, -77.162102]          NaN  NaN  \n",
       "\n",
       "[3560 rows x 18 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "frame = pd.DataFrame(records)\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3560 entries, 0 to 3559\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   a            3440 non-null   object \n",
      " 1   c            2919 non-null   object \n",
      " 2   nk           3440 non-null   float64\n",
      " 3   tz           3440 non-null   object \n",
      " 4   gr           2919 non-null   object \n",
      " 5   g            3440 non-null   object \n",
      " 6   h            3440 non-null   object \n",
      " 7   l            3440 non-null   object \n",
      " 8   al           3094 non-null   object \n",
      " 9   hh           3440 non-null   object \n",
      " 10  r            3440 non-null   object \n",
      " 11  u            3440 non-null   object \n",
      " 12  t            3440 non-null   float64\n",
      " 13  hc           3440 non-null   float64\n",
      " 14  cy           2919 non-null   object \n",
      " 15  ll           2919 non-null   object \n",
      " 16  _heartbeat_  120 non-null    float64\n",
      " 17  kw           93 non-null     object \n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 500.8+ KB\n"
     ]
    }
   ],
   "source": [
    "frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['tz'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = frame['tz'].value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "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                 Unknown\n",
       "8                 Unknown\n",
       "9                 Unknown\n",
       "10    America/Los_Angeles\n",
       "11       America/New_York\n",
       "12       America/New_York\n",
       "13                Missing\n",
       "14       America/New_York\n",
       "15         Asia/Hong_Kong\n",
       "16         Asia/Hong_Kong\n",
       "17       America/New_York\n",
       "18         America/Denver\n",
       "19            Europe/Rome\n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')\n",
    "clean_tz[clean_tz == ''] = 'Unknown'\n",
    "clean_tz[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = clean_tz.value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "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",
       "                       ... \n",
       "America/St_Kitts          1\n",
       "Europe/Uzhgorod           1\n",
       "Europe/Sofia              1\n",
       "America/Monterrey         1\n",
       "America/Montevideo        1\n",
       "Name: tz, Length: 98, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ab9a0b1c48>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "subset = tz_counts[:10]\n",
    "sns.barplot(y=subset.index, x=subset.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P9'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][51][:50]  # long line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.Series([x.split()[0] for x in frame.a.dropna()])\n",
    "results[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe = frame[frame.a.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe = cframe.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Windows\n",
       "1    Not Windows\n",
       "2        Windows\n",
       "3    Not Windows\n",
       "4        Windows\n",
       "Name: os, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe['os'] = np.where(cframe['a'].str.contains('Windows'),\n",
    "                        'Windows', 'Not Windows')\n",
    "cframe['os'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz', 'os'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</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>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Lusaka</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Anchorage</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Buenos_Aires</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Cordoba</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Mendoza</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Bogota</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Caracas</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chihuahua</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Costa_Rica</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Edmonton</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Guayaquil</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Halifax</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Indianapolis</th>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                              Not Windows  Windows\n",
       "tz                                                  \n",
       "                                      245.0    276.0\n",
       "Africa/Cairo                            0.0      3.0\n",
       "Africa/Casablanca                       0.0      1.0\n",
       "Africa/Ceuta                            0.0      2.0\n",
       "Africa/Johannesburg                     0.0      1.0\n",
       "Africa/Lusaka                           0.0      1.0\n",
       "America/Anchorage                       4.0      1.0\n",
       "America/Argentina/Buenos_Aires          1.0      0.0\n",
       "America/Argentina/Cordoba               0.0      1.0\n",
       "America/Argentina/Mendoza               0.0      1.0\n",
       "America/Bogota                          1.0      2.0\n",
       "America/Caracas                         0.0      1.0\n",
       "America/Chicago                       115.0    285.0\n",
       "America/Chihuahua                       1.0      1.0\n",
       "America/Costa_Rica                      0.0      1.0\n",
       "America/Denver                        132.0     59.0\n",
       "America/Edmonton                        2.0      4.0\n",
       "America/Guayaquil                       2.0      0.0\n",
       "America/Halifax                         1.0      3.0\n",
       "America/Indianapolis                    8.0     12.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
    "agg_counts[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                       521.0\n",
       "Africa/Cairo             3.0\n",
       "Africa/Casablanca        1.0\n",
       "Africa/Ceuta             2.0\n",
       "Africa/Johannesburg      1.0\n",
       "                       ...  \n",
       "Europe/Volgograd         1.0\n",
       "Europe/Warsaw           16.0\n",
       "Europe/Zurich            4.0\n",
       "Pacific/Auckland        11.0\n",
       "Pacific/Honolulu        36.0\n",
       "Length: 97, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "Europe/Sofia        35\n",
       "Europe/Stockholm    78\n",
       "Europe/Uzhgorod     96\n",
       "Europe/Vienna       59\n",
       "Europe/Vilnius      77\n",
       "Europe/Volgograd    15\n",
       "Europe/Warsaw       22\n",
       "Europe/Zurich       12\n",
       "Pacific/Auckland     0\n",
       "Pacific/Honolulu    29\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use to sort in ascending order\n",
    "indexer = agg_counts.sum(1).argsort()\n",
    "indexer[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</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>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.take(indexer)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</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>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = agg_counts.take(indexer[-10:])\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "America/New_York       1251.0\n",
       "                        521.0\n",
       "America/Chicago         400.0\n",
       "America/Los_Angeles     382.0\n",
       "America/Denver          191.0\n",
       "Europe/London            74.0\n",
       "Asia/Tokyo               37.0\n",
       "Pacific/Honolulu         36.0\n",
       "Europe/Madrid            35.0\n",
       "America/Sao_Paulo        33.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.sum(1).nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tz</th>\n",
       "      <th>os</th>\n",
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       "      <th>0</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Not Windows</td>\n",
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       "      <th>1</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>16.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>Europe/Madrid</td>\n",
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       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
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       "      <th>5</th>\n",
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       "      <td>36.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Windows</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>America/Denver</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>America/Denver</td>\n",
       "      <td>Windows</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>Windows</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>Windows</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td></td>\n",
       "      <td>Windows</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>America/New_York</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>America/New_York</td>\n",
       "      <td>Windows</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tz           os  total\n",
       "0     America/Sao_Paulo  Not Windows   13.0\n",
       "1     America/Sao_Paulo      Windows   20.0\n",
       "2         Europe/Madrid  Not Windows   16.0\n",
       "3         Europe/Madrid      Windows   19.0\n",
       "4      Pacific/Honolulu  Not Windows    0.0\n",
       "5      Pacific/Honolulu      Windows   36.0\n",
       "6            Asia/Tokyo  Not Windows    2.0\n",
       "7            Asia/Tokyo      Windows   35.0\n",
       "8         Europe/London  Not Windows   43.0\n",
       "9         Europe/London      Windows   31.0\n",
       "10       America/Denver  Not Windows  132.0\n",
       "11       America/Denver      Windows   59.0\n",
       "12  America/Los_Angeles  Not Windows  130.0\n",
       "13  America/Los_Angeles      Windows  252.0\n",
       "14      America/Chicago  Not Windows  115.0\n",
       "15      America/Chicago      Windows  285.0\n",
       "16                       Not Windows  245.0\n",
       "17                           Windows  276.0\n",
       "18     America/New_York  Not Windows  339.0\n",
       "19     America/New_York      Windows  912.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rearrange the data for plotting\n",
    "count_subset = count_subset.stack()\n",
    "count_subset.name = 'total'\n",
    "count_subset = count_subset.reset_index()\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ab9787e348>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='total', y='tz', hue='os',  data=count_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_total(group):\n",
    "    group['normed_total'] = group.total / group.total.sum()\n",
    "    return group\n",
    "\n",
    "results = count_subset.groupby('tz').apply(norm_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ab977ee108>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='normed_total', y='tz', hue='os',  data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "16    0.470250\n",
       "17    0.529750\n",
       "18    0.270983\n",
       "19    0.729017\n",
       "Name: total, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "g = count_subset.groupby('tz')\n",
    "results2 = count_subset.total / g.total.transform('sum')\n",
    "results2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MovieLens 1M Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  \n",
      "D:\\python\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  if sys.path[0] == '':\n",
      "D:\\python\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Make display smaller\n",
    "pd.options.display.max_rows = 10\n",
    "\n",
    "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
    "users = pd.read_table('datasets/movielens/users.dat', sep='::',\n",
    "                      header=None, names=unames)\n",
    "\n",
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_table('datasets/movielens/ratings.dat', sep='::',\n",
    "                        header=None, names=rnames)\n",
    "\n",
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table('datasets/movielens/movies.dat', sep='::',\n",
    "                       header=None, names=mnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
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       "   user_id gender  age  occupation    zip\n",
       "0        1      F    1          10  48067\n",
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   "source": [
    "users[:5]"
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   "source": [
    "ratings[:5]"
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  },
  {
   "cell_type": "code",
   "execution_count": 117,
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   "outputs": [
    {
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       "   movie_id                               title                        genres\n",
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       "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"
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    "movies[:5]"
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       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>30030</td>\n",
       "      <td>Broken Vessels (1998)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000206</th>\n",
       "      <td>5780</td>\n",
       "      <td>2845</td>\n",
       "      <td>1</td>\n",
       "      <td>958153068</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>92886</td>\n",
       "      <td>White Boys (1999)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000207</th>\n",
       "      <td>5851</td>\n",
       "      <td>3607</td>\n",
       "      <td>5</td>\n",
       "      <td>957756608</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>55410</td>\n",
       "      <td>One Little Indian (1973)</td>\n",
       "      <td>Comedy|Drama|Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000208</th>\n",
       "      <td>5938</td>\n",
       "      <td>2909</td>\n",
       "      <td>4</td>\n",
       "      <td>957273353</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>35401</td>\n",
       "      <td>Five Wives, Three Secretaries and Me (1998)</td>\n",
       "      <td>Documentary</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  occupation    zip  \\\n",
       "0              1      1193       5  978300760      F    1          10  48067   \n",
       "1              2      1193       5  978298413      M   56          16  70072   \n",
       "2             12      1193       4  978220179      M   25          12  32793   \n",
       "3             15      1193       4  978199279      M   25           7  22903   \n",
       "4             17      1193       5  978158471      M   50           1  95350   \n",
       "...          ...       ...     ...        ...    ...  ...         ...    ...   \n",
       "1000204     5949      2198       5  958846401      M   18          17  47901   \n",
       "1000205     5675      2703       3  976029116      M   35          14  30030   \n",
       "1000206     5780      2845       1  958153068      M   18          17  92886   \n",
       "1000207     5851      3607       5  957756608      F   18          20  55410   \n",
       "1000208     5938      2909       4  957273353      M   25           1  35401   \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",
       "...                                              ...                   ...  \n",
       "1000204                           Modulations (1998)           Documentary  \n",
       "1000205                        Broken Vessels (1998)                 Drama  \n",
       "1000206                            White Boys (1999)                 Drama  \n",
       "1000207                     One Little Indian (1973)  Comedy|Drama|Western  \n",
       "1000208  Five Wives, Three Secretaries and Me (1998)           Documentary  \n",
       "\n",
       "[1000209 rows x 10 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                                            1\n",
       "movie_id                                        1193\n",
       "rating                                             5\n",
       "timestamp                                  978300760\n",
       "gender                                             F\n",
       "age                                                1\n",
       "occupation                                        10\n",
       "zip                                            48067\n",
       "title         One Flew Over the Cuckoo's Nest (1975)\n",
       "genres                                         Drama\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3706\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>3.375000</td>\n",
       "      <td>2.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>3.388889</td>\n",
       "      <td>3.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.675676</td>\n",
       "      <td>2.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.828571</td>\n",
       "      <td>3.689024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings = data.pivot_table('rating', index='title',\n",
    "                                columns='gender', aggfunc='mean')\n",
    "print(len(mean_ratings))\n",
    "mean_ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "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": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_by_title = data.groupby('title').size()\n",
    "ratings_by_title[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([''burbs, The (1989)', '10 Things I Hate About You (1999)',\n",
       "       '101 Dalmatians (1961)', '101 Dalmatians (1996)', '12 Angry Men (1957)',\n",
       "       '13th Warrior, The (1999)', '2 Days in the Valley (1996)',\n",
       "       '20,000 Leagues Under the Sea (1954)', '2001: A Space Odyssey (1968)',\n",
       "       '2010 (1984)',\n",
       "       ...\n",
       "       'X-Men (2000)', 'Year of Living Dangerously (1982)',\n",
       "       'Yellow Submarine (1968)', 'You've Got Mail (1998)',\n",
       "       'Young Frankenstein (1974)', 'Young Guns (1988)',\n",
       "       'Young Guns II (1990)', 'Young Sherlock Holmes (1985)',\n",
       "       'Zero Effect (1998)', 'eXistenZ (1999)'],\n",
       "      dtype='object', name='title', length=1216)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_titles = ratings_by_title.index[ratings_by_title >= 250]\n",
    "active_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns (1988)</th>\n",
       "      <td>3.371795</td>\n",
       "      <td>3.425620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <td>2.934783</td>\n",
       "      <td>2.904025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Sherlock Holmes (1985)</th>\n",
       "      <td>3.514706</td>\n",
       "      <td>3.363344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Effect (1998)</th>\n",
       "      <td>3.864407</td>\n",
       "      <td>3.723140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <td>3.098592</td>\n",
       "      <td>3.289086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1216 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                    F         M\n",
       "title                                                \n",
       "'burbs, The (1989)                 2.793478  2.962085\n",
       "10 Things I Hate About You (1999)  3.646552  3.311966\n",
       "101 Dalmatians (1961)              3.791444  3.500000\n",
       "101 Dalmatians (1996)              3.240000  2.911215\n",
       "12 Angry Men (1957)                4.184397  4.328421\n",
       "...                                     ...       ...\n",
       "Young Guns (1988)                  3.371795  3.425620\n",
       "Young Guns II (1990)               2.934783  2.904025\n",
       "Young Sherlock Holmes (1985)       3.514706  3.363344\n",
       "Zero Effect (1998)                 3.864407  3.723140\n",
       "eXistenZ (1999)                    3.098592  3.289086\n",
       "\n",
       "[1216 rows x 2 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select rows on the index\n",
    "mean_ratings = mean_ratings.loc[active_titles]\n",
    "mean_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings = mean_ratings.rename(index={'Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)':\n",
    "                           'Seven Samurai (Shichinin no samurai) (1954)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Close Shave, A (1995)</th>\n",
       "      <td>4.644444</td>\n",
       "      <td>4.473795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>4.588235</td>\n",
       "      <td>4.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</th>\n",
       "      <td>4.572650</td>\n",
       "      <td>4.464589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallace &amp; Gromit: The Best of Aardman Animation (1996)</th>\n",
       "      <td>4.563107</td>\n",
       "      <td>4.385075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>4.562602</td>\n",
       "      <td>4.491415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.539075</td>\n",
       "      <td>4.560625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grand Day Out, A (1992)</th>\n",
       "      <td>4.537879</td>\n",
       "      <td>4.293255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>To Kill a Mockingbird (1962)</th>\n",
       "      <td>4.536667</td>\n",
       "      <td>4.372611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creature Comforts (1990)</th>\n",
       "      <td>4.513889</td>\n",
       "      <td>4.272277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>4.513317</td>\n",
       "      <td>4.518248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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...  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"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_female_ratings = mean_ratings.sort_values(by='F', ascending=False)\n",
    "top_female_ratings[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring Rating Disagreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\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>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "      <td>0.168607</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",
       "      <td>-0.334586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1961)</th>\n",
       "      <td>3.791444</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>-0.291444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>3.240000</td>\n",
       "      <td>2.911215</td>\n",
       "      <td>-0.328785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.184397</td>\n",
       "      <td>4.328421</td>\n",
       "      <td>0.144024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns (1988)</th>\n",
       "      <td>3.371795</td>\n",
       "      <td>3.425620</td>\n",
       "      <td>0.053825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <td>2.934783</td>\n",
       "      <td>2.904025</td>\n",
       "      <td>-0.030758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Sherlock Holmes (1985)</th>\n",
       "      <td>3.514706</td>\n",
       "      <td>3.363344</td>\n",
       "      <td>-0.151362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Effect (1998)</th>\n",
       "      <td>3.864407</td>\n",
       "      <td>3.723140</td>\n",
       "      <td>-0.141266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <td>3.098592</td>\n",
       "      <td>3.289086</td>\n",
       "      <td>0.190494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1216 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                    F         M      diff\n",
       "title                                                          \n",
       "'burbs, The (1989)                 2.793478  2.962085  0.168607\n",
       "10 Things I Hate About You (1999)  3.646552  3.311966 -0.334586\n",
       "101 Dalmatians (1961)              3.791444  3.500000 -0.291444\n",
       "101 Dalmatians (1996)              3.240000  2.911215 -0.328785\n",
       "12 Angry Men (1957)                4.184397  4.328421  0.144024\n",
       "...                                     ...       ...       ...\n",
       "Young Guns (1988)                  3.371795  3.425620  0.053825\n",
       "Young Guns II (1990)               2.934783  2.904025 -0.030758\n",
       "Young Sherlock Holmes (1985)       3.514706  3.363344 -0.151362\n",
       "Zero Effect (1998)                 3.864407  3.723140 -0.141266\n",
       "eXistenZ (1999)                    3.098592  3.289086  0.190494\n",
       "\n",
       "[1216 rows x 3 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']\n",
    "mean_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff = mean_ratings.sort_values(by='diff')\n",
    "sorted_by_diff[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reverse order of rows, take first 10 rows\n",
    "sorted_by_diff[::-1][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "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": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard deviation of rating grouped by title\n",
    "rating_std_by_title = data.groupby('title')['rating'].std()\n",
    "# Filter down to active_titles\n",
    "rating_std_by_title = rating_std_by_title.loc[active_titles]\n",
    "# Order Series by value in descending order\n",
    "rating_std_by_title.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## US Baby Names 1880–2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In [4]: names.head(10)\n",
    "Out[4]:\n",
    "        name sex  births  year\n",
    "0       Mary   F    7065  1880\n",
    "1       Anna   F    2604  1880\n",
    "2       Emma   F    2003  1880\n",
    "3  Elizabeth   F    1939  1880\n",
    "4     Minnie   F    1746  1880\n",
    "5   Margaret   F    1578  1880\n",
    "6        Ida   F    1472  1880\n",
    "7      Alice   F    1414  1880\n",
    "8     Bertha   F    1320  1880\n",
    "9      Sarah   F    1288  1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'head' 不是内部或外部命令，也不是可运行的程序\n",
      "或批处理文件。\n"
     ]
    }
   ],
   "source": [
    "!head -n 10 datasets/babynames/yob1880.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Woodie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Worthy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>York</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Zachariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births\n",
       "0          Mary   F    7065\n",
       "1          Anna   F    2604\n",
       "2          Emma   F    2003\n",
       "3     Elizabeth   F    1939\n",
       "4        Minnie   F    1746\n",
       "...         ...  ..     ...\n",
       "1995     Woodie   M       5\n",
       "1996     Worthy   M       5\n",
       "1997     Wright   M       5\n",
       "1998       York   M       5\n",
       "1999  Zachariah   M       5\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names1880 = pd.read_csv('datasets/babynames/yob1880.txt',\n",
    "                        names=['name', 'sex', 'births'])\n",
    "names1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = range(1880, 2011)\n",
    "\n",
    "pieces = []\n",
    "columns = ['name', 'sex', 'births']\n",
    "\n",
    "for year in years:\n",
    "    path = 'datasets/babynames/yob{}.txt'.format(year)\n",
    "    frame = pd.read_csv(path, names=columns)\n",
    "\n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "\n",
    "# Concatenate everything into a single DataFrame\n",
    "names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year\n",
       "0             Mary   F    7065  1880\n",
       "1             Anna   F    2604  1880\n",
       "2             Emma   F    2003  1880\n",
       "3        Elizabeth   F    1939  1880\n",
       "4           Minnie   F    1746  1880\n",
       "...            ...  ..     ...   ...\n",
       "1690779    Zymaire   M       5  2010\n",
       "1690780     Zyonne   M       5  2010\n",
       "1690781  Zyquarius   M       5  2010\n",
       "1690782      Zyran   M       5  2010\n",
       "1690783      Zzyzx   M       5  2010\n",
       "\n",
       "[1690784 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = names.pivot_table('births', index='year',\n",
    "                                 columns='sex', aggfunc=sum)\n",
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138bc7ad588>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JcVtg/OvGeJ2KfDsag0B3zoErnjF60tlQvdpAlFIRwEBgCxB05oZtPgeau4UCFctP8WZaTenxVaTTgGucn98HlVLblVLb09LS6vNRm4yDgwMDBgw4+4iNjbV3lkRLtvcbUA7Q5/qzSfnFZczZFMvVvYIYWCFQPDuxB59MG1wpeAA4WBRTB4TSM8Sb/66Lodxaobpm0hvg5A7Ln6XVO/kb5CZWHoCplNEl+srnYe88owdUU7Fa4eAS+OFRcPKAgXdUvd+Q+yA/zeg5ZmN1DiBKKU9gAfCE1jqnpl2rSNMNSK8xO3U5Rmv9kdZ6sNZ6cEBAQC2ntA83Nzd279599hEREWHvLImWymo12j+6jq00Hfu8bXHkFJXx0BVdaji4MqUUj13ZleNp+fy0v0LB3jMQBtwBJzcagxRbs2OrwcEZOo+unK4UXPEnGHinURKJ22bbfOSmwG/vwvvDYf4dUFYI138Erj5V7995jDFlzbZPbJsv6hhAlFJOGMHjK63192Zyilk1hfl8pl9gPBBe4fAwILGW9LAq0htyDSHaruNrISce+t18Nqm03MqnvxxnaKTfBSWN2kzoE0yXAA/eWxODtWIpJLivcRNLP9ZYOW+eYtZAx+Hg7FH19vF/B68O8MPDthsjE/0DvN0LVv0VXLzg+o/hsR1Go351LBYYfJ8x23LqIdvky1Rr5Z3ZI+pT4KDW+t8VNi0GpgH/MJ8XVUh/TCk1D6PBPFtrnaSUWgH8vULD+dXAc1rrDKVUrlJqOEbV2N3Afxpyjfp//HP+9mM0BxJrKljVX68O3rx4Te8a9yksLGTAgAEAREZGsnDhwkbNg2gjUg4YYxV8OkL3SWeTF+9OJDG7iFev61PvUzpYFH8Y040n5u/mu53x3DzY/M0WbJ4reW/rbQfJSYLU6JpH8Lv6wLXvwRdTjdH6v3uzYdc6stIY8Dn5baOh/oyME7DoMQgZYHTRrc933fcmWPk8HPgBAm1X3ViXEsgo4C5gjFJqt/mYhHFTH6eUOgqMM98DLAOOAzHAx8CjAGbj+SvANvPx8pkGdeAR4BPzmGMYDejU9xotUcUqLAkeokFOH4U51xoNptMWgbM7abnFvLQ4mme/30uPYC9GRwXWfp4qTOnfgcGd2vH6soNk5JcYif7dweJk9Mhqrc5MINl1bM37dbkSRjwG2z42Fumqr7JiWDYTTh+BOVNhz3wjvbzUWHrYYjGmJ6lvoPYKMlaWtPHI9Lr0wvqVqtscAC6YYc3sGfX7as71GXDBt6y13g5c8BNJa51e32s0VG0lBSGare8fALSx0qBfZzLyS7j67fXkFJVx8+AwnhwbhcXSsOnYLRbF36/vy6R3fuG1pQd56+b+xoR9AT0geV/jfo7m5Nhq8AyGoDrcF8a9bASApTONFRy7jKn7dbb8D7JOGmNMtn0KCx+E1S+DxcFIv3mO0bOqIXpOgeXPGD8w/Ls17By1kJHoQrRkKQcgcZexoqD5K3XBjngyC0r59uERvH59PwK9XS/qElFBXjxweWcW7IxnY8xpIzG4LyS30hKItdwogXQZU7d1UCwOcONnRlD9Zlrd2x3yT8OGN6DbeGMixDu/hzF/NUb+B/Uxuun2urbhn6PnNcazDUshEkCEaMn2fA0WR2PRJ0BrzdxtpxjUqV29G81r8viYbkS0d+epb/YYVVnBfSAv2ZgnqrVJ3G1MCVKfKexdvOD2+cZ68l/fbASH6pQVw6GlRrApyT+3SqSjM1w+E677AG77GkbUrWa+oKSMfy4/xM5TmZU3+IRC2BBj1LqNSACxs7y8PHtnQbRU5WXGuI9u48922916IoPjafncNrSB1R7VcHN24L3bLyEjv4SnvtmNNdCscU5phdVY0d+DskDnK+t3nG843DYP8lJg3u3GRJbnSz0E/9fP2J52ECa8DgHdG5zVpOxCbvpwEx+sO8aDc3aQlltceYeeUyBpjzGlvw1IABGipTq+1rhZ9b/1bNLcrafwcnXkd31DGv1yfUJ9+Ovknqw7nMYXsV5GYmtrB8lJMsZP9LsFPNqfTX5/XQz/WX0UXdscWGGD4Lr/GSPF3+ph9KI6tdmYOyvjhNHZAQ23fwt/PGxMhNhAJ9PzmfLeb5xML+DFa3qRW1RqBPeKXa57TTGeD9imFCILSgnRUu3+GtzaQZSxuFlmfgnL9idz65Bw3JwdbHLJO4d3YtXBVN7fksE077DW1w6y4Q2jDWT0ua6vcRkFvLniMFYNCVmFvHZdXxxq6pTQeyp4r4Ids2D/97DrC2jf1ai6KiuCe3+CoF4XndV3Vh8lr6iMH34/iu7BXrg4OvDnhfv46JfjPHxm0Gi7CGNMiF/ni75eVaQEIkRLVJRj1KP3uREcXdBa8/pPBykps3LrkMatvqpIKcXl3fxJySmmxL9X6yqBZJyAnbPhkruNG6/ps99OYFGKu4Z3Yt62OJ6cf96v/KqED4Wp78PMI8Z8WR6Bxhxady5olOCRkFXI4t2J3DIknO7BRmnwtqHhXN0riHd+Pkp2QYUZkyf/u+aBhxdBAogQLdGJ9VBeDL2norXm5SUH+GZ7PH8Y05VeHbxteuleIcb5k927Gd1XS4vqdqC13Gi3aY60hp9fMjokXP6ns8nZBaXM3xbHNf078MrUPvxpfHcW70nkndVH63ZeF09jypPpP8HTJyBscKNk99NfTgBw/2WRZ9OUUswY243C0nLmbz/VKNepjQQQIVqimNXg7AVhQ3l/3TE+/y2We0dF8NQ4248MPxOgDhAJuhwS67jI1Io/w0ejbZexhtLamCrkwA9Gd2jvc+1HX289RUFJ+dkb9aOju3DDJWG8s/ooaw/VcVXHM+rSJbgOsgpKmLftFFP6dyCsnXulbb07+DC8sx+zN548NxW/DUkAEaKl0doIIJGXk1UC762JYULvYF6Y3AvVSDepmvi6OxPq68aaop7GZIOHltZ+UGkR7J5r9NrKOG7zPNZq77fw/khjCdhlf4KN/zHWf7985tldSsqszNp4gku7+tO7gzFxoVKK167rQ68Qb2bM20VcRtOuE5+eV8wrSw5SUFJe7eSY946KJCGrkJUHUmyeHwkgdqaU4q677jr7vqysjICAACZPtk2dpWghalr1Lz0Gsk9B16uYty2OwtJyZozt1iTB44yeId7sTC03Zqo9tKT2FfpiVkFxtvH62FpbZ69mRdnGCO2CdCOobfvYmGF44r8qlRJWH0whJaeY6ZdGVDrc1cmBD+8chAaemL+78nT3NmC1ajYdS+fp7/Yw4h9rWLAznntGRpxt+zjf2J5BhPu58flvJ2yaL5AAYnceHh7s37+fwsJCAFatWkVo6AVLm4i2ZNdX8FZ3o6G8KjE/A1AaOYbZG2MZ2aU9PUNs2+5xvt4dvDmelkdJ14nGGIOU6JoP2PcteASAT/i5eabs5Ze3oCDDGPj3zAl4bDtMee+CxZm+2R5HsLcrV1Qxj1jH9u68cm0fdpzM5MP1tpuVeHtsBqP+uYbbPt7Mkr1J3HBJGD8/dQUvTal+ihUHi+KekZFsi80kOjHbZnkDCSDNwsSJE1m61KgGmDt3LrfddpudcyTsKmm38ev4yPKqt8eshvZdWZHoQlJ2EfeOiqx6Pxvq1cEbq4bDvpcCyiiFVKcoBw4vh97XG5MPnvilaRrTj/5szEhbWnguLTMWNn8AA26HDgOMCSj9u10QPFJyilh/JI0bBoVW22X32gEdmNwvhLdXHWFffMNv1Hvisrjn861si82olB6fWcBDX+zAxdHCu7cNZPvzY3n9+r50DfSs9ZzXDwzF0aL4YVdCg/NVFzIO5Iyfnm38LonBfWHiP2rd7dZbb+Xll19m8uTJ7N27l+nTp/PLL780bl5Ey5FlLrZ5YFGltT0Aoy0h9le45G4+/y2WTu3dGdOjYTPtXowzPbH2ZrnQt+NwY6W80dVMG35oidFjrN/NkHXKWG41cReED7FdBvd+CwsfMhr547bBbXON6Ul+nGH0tBrzfI2HL9gZj1XDTYPCq91HKcVrU/uyPTaTB7/Yzlf3D6NzQO0394rKyq08s2Avh5JzWXc4jTuHd+TWIR0J8XHlwTk7KCm38uk9Q+hSz/O283BmdPdAFu9J5NmJPWset3IRpATSDPTr14/Y2Fjmzp3LpEmTaj9AtG7ZZgA5ugqKcytvi/0FygpJDBjFjpOZ3DW8k81uDjUJa+eGt6sj0Yk5xlrpKfuqny5j37fGuIrQQebqfsoYRW8LOYmw4U1jhuKOI+Da941eYu+PgPcGQ/w2Y/oQ7w7VnkJrzbfb4xka6UeEfzWLSZl83J34/N4hlJRZufl/mzmUXL81heZuPcWh5Fzeuqk/910ayddbTjH5P78y6NWfOZicw7u3Dax38Dhj6sAOpOQUs+V4eoOOrwspgZxRh5KCLU2ZMoWZM2eybt060tNt9wcXLUB2nDEba8p+OLLi7ESJxPxsrBHhEcjK/K5ALBNtMGVJXSil6NXB21iE7YrJRlXRvu8q9WICjKnEj62FK542Gqjd/SCk/7m0xpKfDnNvMQIEQNREYx0NJzdjOvSlTxnf48jHjbUyarD9ZCYnTufz6Oi6LQHcM8Sb+Q+N4I5PNnPrR5v57uERdA2suoG7osz8Et5ceYSRXdpz/SWh3KDCuGdkBPsTsjmamkfvDt5c2b3hpcuxPYPwdHHkh90JjOzqX/sBDSAlkGZi+vTpvPDCC/Tt29feWRH2VJQDRdksLh+B9gw2xiZYy42G369uMhqh71/FzzF5dAv0JNTXzW5Z7RXiw6HkHMp9I6DrOPj1bcg+r87917eNGWqHPngurcuVEL/1wtLVxdjxuRE8xvwVHtlkVFk5md9N5GXw2DYY/1qtwUNrzTs/H8Xb1ZFJ9QjOXQM9+eahEThaLNz96VaSsgur3Tc+s4A5m2KZPnsbecVlvHhN77M96ML93JnYN4THr+rGVT1rzmttXJ0cGN87mJ/2JVNUapv16yWANBNhYWHMmDHD3tkQ9pYdD8DKRFeSO4wzqrE+n2QsMtTrWpi+gnz3MLaeyGB09wC7ZrV3B2+KSq0cTs6FSW+AtczoHntG5knYMw8G3XN2tmDAWO/CWgbx2xsnI1ar0a4SeblRAgrq1eBBeyuiU/g15jRPjYvCw6V+FTSd2nsw694h5BSVMe2zrew6lUlJWeXBfOsOp3Llm+t4YVG0sWrkNb2q7Y7bGK4bGEpucVn9Bz3WkQQQO6tqOvfRo0ezZEkNvVpE62W2fyRof5ZahxqT76UehOs+ghs/BxdPNh5Lp6TcyuiLqN5oDJdF+aMUrIhOBr9Io0rq4I9w2FyR+rd3jMWWRv4BgGNpecaCVIFmF9S0w42TkRPrjNX7Bt1zUacpKi3n1aUH6B7kxZ3DOzXoHH1CffjorkHEphdw3fsb6fvSCh76YjvRidlsPZHBw1/uICrIizV/vIJfnr6Su0ZEXFSeazOiS3uCvV05mFS/tpm6kjYQIZqTCgHkf7FBTL/+UyydRhiLA5nWHU7Fw9mBwRGNt2BUQwR6uTI0wo+l+5J4clwUjPiDsT7J3FuNddMzTxjdZc28P/3dXg4m5bDrr2NxcWtnrIfRGHbMAjc/6HFxg2/fX3eM+MxC5j4wHEeHhv+2HtnVn9+eGcO22Ay2nsjg+53xrIhOwdnRQng7N+ZMH0p7T5eLymtdOVgUa2ZegbuzbW71UgIRojnJiqMUR6zuAaTllbDda0yl4KG1Zt3hNEZ29cfF0TZTttfH5H4hxKTmcSQl11hR78yyrO06gV8XuPQpAA4n57LjZCYFJeVsP5kFAT3rvvRrTfJSjalUBtxujOlooC82xfLu6qNcO6ADI7q0r3X/2gR4uTCpbwgvTenNL8+M4Ymx3Rga4ceX9w9rsuBxhq2CB0gJBK11k04BcTFqXcxGtHjlmadItPpx47BOfP7bCZbtS2JopN/Z7YdTcknIKuTRK+vWQ8jWxvcJ5oXF0SzZm8RT47yMYHd+TyyM7qrO5q/6tYdSGRXYA/YvMKZAuZj/f3vmGe0pl0yr12Hrj6QxZ2MsvUN9KCu38v66Y4ztGcg/b+jX8LxUw8fNiSfG2n6SS3to0wHE1dWV9PR02rdv3+yDiNaa9PR0XF1d7Z0VYR67gaEAACAASURBVEMlGadI0P5EBXlyRVQAy/cnc8MlYXy4/hhbYzPOLllq7/aPMwK9XBkW6ceyfUk8Wc18XIUl5Xy/M54JfYLJLChh7eFUnr+0pzEnVW5ypdlv6+3AIugwEALqfoPOyC/hqfm7KSmzsvZwKlYNv+sXwv/dMgCni6i6aovadAAJCwsjPj6etLQ0e2elTlxdXQkLC7N3NoQNWbLjSKQ74b5uTOobwsoDKVzz3q94uToyvncwkf4e9A/ztWv33fP9rm8If10UzZGUvCp7FC3dl0ROURm3D+vIgcQcXl5ygBSXCILAaAdpaADJTYGE7XBl1aPKtdYUlJRf0JvqpcXR5BSV8uMfLiW8nTvxmYV0C/TEYocBmS1dmw4gTk5OREY2/TxCQlSpvBTnwlQS9CiGt3Ojd6gPV/UIZGBHX+4eGYG3q5O9c1ilCX1CeHFxNHO3nqpykr+vt5yks78HwyL9CPJ25eUlB1if1Z6bwWgH6TKmYRc+Yvb26nHh7A0lZVZ+//VONsac5qO7BzPKHEi3MjqZxXsSeXJsFD2CjelYbNmNtrVr0wFEiGYlJwGFJgl/gr1dcXSw8Ok9NpwvqpEEeLlw8+Bwvth8ktuHdSQq6NwNeWPMaXaeyuLFa4y1SiL9PYho787yE+Xc7OZ3cT2xDv8Evp0gsPISsWXlVp6Yv4tVB1II8XHl3s+38erUPhxIyuGrLSfpGeLdbNqQWjqp8BOiuTAHERa6dbiobqT28PSEHni6OPLS4uiznT201ryx8jAhPq7cNvTcOu2juwey8Xg61oAeDR8LUpIPx9dB90lnG+HzistYujeJe2dtY9m+ZJ7/XU+Wz7icPqHePL1gL19sPsmNg8KZPX2ItHU0EimBCNFcmLPwap/qZ4Btrvw8nJl5dRR/XRTN0n1JTO7XgdUHU9l1Kot/XN8XV6dzXY6viApg1sZYUl0jCT65pGE9sY6tMQZZmtVXMam5TP3vRvKKy/DzcOaFyb2YfqlRPf3l/cOYuzWOK7sH1Hu2XFEzCSBCNBfmIEKX9i0vgADcPqwT87bF8eT83aw5mMq+hGwi/T24YVDljh+9zTXVYy0dCS7OhtykGmfHPctabszsqyzGs6uPMeMu8NGG45RZrcx9YDhDI/0qzVDs7uzIfZdKW6ctSAARopmwZsWRrn0I8vO1d1YaxMGi+HTaED5cf4xvt8eRX1LOO7de2DU2wMsFX3cn9pWGMByMqVrqEkAOLTHW+Dij/+3g4ERqbhE/7ErkliHhjTIIUNSdBBAhmomS9FgSdHtC2zWfLrr1FezjyktTevPU1VHsT8hmROcLb+hKKaKCvNiUU8YDAGmHoOtVtZ88eiG4+8O9Pxkz+QZ0B+DLTScptVrPVlmJpiMtSUI0Ezo7kSTdvlmN8Wgob1cnRnbxr3aAbvcgL7alOaC9OhhL3NampMBYG6XnNcagwbBB4OJJYUk5X2w+ydieQUTWsviTaHwSQIRoJiyFpzmtfVp0CaSuooK9yC0qI7/79XB0pTEivSYxq6C0AHpPrZS8cFcCmQWl3C+lD7uQACJEc1BeiktJlhFAWkEJpDbdzbEi0UHXGOuW75lX8wHRPxjVV50urZS8cFc83YO8Ks0XJpqOBBAhmoP80wAUufhV6vLaWkUFGd1pdxcEGD2pdn1pdOetSmnhueorh3PNtik5RWw/mcnv+oU0+7nsWisJIEI0B/nmfGwe9l1lsKn4ujsT5O3C4ZRcGHgnpB+FuC1wciP8+AQcWmasNKi1sdZ6af4F1Vc/7UtCa+q19KxoXNILS4jmIN9YctTRO9jOGWk6UUFexjoi106Fn56Bb6ZBXjIoB2ONc78uYC2FrFPg2/GC6qul+5LoEexF10AZHGgvUgIRojnIM0ogTj5Bds5I0+ke5MXRlDzKnTyMBaGKc2D0n+GZWLjhU/AKNhaemvIfeHB9ldVXUvqwLymBCNEMlOWm4Ai4tqEAEhXsRXGZlVMZBUSOfx3G/g2c3Y2NfW80HtWQ6qvmQQKIEM1AYVYyTtoJX9+205voTE+sw8m5xhgOh5pvRwlZhdw3axtglECk+sr+aq3CUkp9ppRKVUrtr5D2klIqQSm123xMqrDtOaVUjFLqsFJqfIX0CWZajFLq2QrpkUqpLUqpo0qp+UopZzPdxXwfY26PqO0aQrRUpdkpnMaHAO+2s+JktyBPlIJDyTl12v+bbXEcTskl3M+dXh28efyqbjbOoahNXdpAZgETqkh/W2s9wHwsA1BK9QJuBXqbx7yvlHJQSjkA/wUmAr2A28x9Af5pnqsbkAncZ6bfB2RqrbsCb5v7VXuN+n1sIZoXa14ap7U3/p4u9s5Kk3F3dqRvqA/L9yefnQK+OlprftidwKgu/nx892C+un+4VF81A7UGEK31BiCjjue7FpintS7WWp8AYoCh5iNGa31ca10CzAOuVUbn7THAd+bxs4GpFc4123z9HXCVuX911xCixXIoSOO09iHAq+0EEICbBodzKDmXfQnZNe6381QWJ9MLmDowtIlyJuriYnphPaaU2mtWcbUz00KBuAr7xJtp1aW3B7K01mXnpVc6l7k929y/unNdQCn1oFJqu1Jqe0tZ91y0TU5Fp0nXPrT3dLZ3VprUlP4dcHWyMH9bXI37LdwVj6uThQl92k4355agoQHkA6ALMABIAt4y06saDqobkN6Qc12YqPVHWuvBWuvBAQFtY4CWaIGsVtxKMslzbIeLY9uqjfVxc2JSnxAW706ksKS8yn1Kyqws2ZvE1b2C8XSRfj/NSYMCiNY6RWtdrrW2Ah9zrgopHqi4Gk4YkFhD+mnAVynleF56pXOZ230wqtKqO5cQLVNRFg6UU+TSNteyuHlIOLnFZfy4N5G1h1P5+7KD/Hr0NFarxmrVLNwVT1ZBKdddItVXzU2DwrlSKkRrnWS+vQ4400NrMfC1UurfQAegG7AVo9TQTSkVCSRgNILfrrXWSqm1wI0Y7SLTgEUVzjUN2GRuX2PuX901hGiZzGlMrG7+ds6IfQyL9COivTtPf7f3bNpHG47T0c+dwtJy0nKL6ejnzmVd2+b305zVGkCUUnOB0YC/UioeeBEYrZQagFF1FAs8BKC1jlZKfQMcAMqA32uty83zPAasAByAz7TW0eYlngHmKaVeBXYBn5rpnwJfKKViMEoet9Z2DSFapDxjGhM8A+2bDztRSvH0hB4s2ZvIlP4dGNXVn9UHU1mwMx4vV0fG9QpiTI8gHB1k4ozmRtXWfa61GDx4sN6+fbu9syHEhfYvgO+m82Hvr3j4psn2zo0QlSildmitB1e1TUK6EE1p03/hwKJKScXZKQC4+koPI9GySJcGIZpK0l5Y8WfwDoMek8Fi9LgqykzGQVvwbNc2q7BEyyUlECGayppXAAU58XB83dnkkuxkMvAmwLv1r0QoWhcJIEI0hdjf4OhKdnZ5hBJnX2MFPpM1L4107U1AG5rGRLQOEkCEsDWt4eeXKHUP4o4DQ1hsHYU+tBQKjBmCLAVppLXBaUxEyycBRAhbS9kP8Vv50vkmCrULn+WPQpUXG72vAOei06Tjg59H25rGRLR8EkCEsLXUgwB8ndKRP43vzgnHLiS6RcH2z6EkH7eSTPId2+FgqWqWHiGaLwkgQtiYNfUw5VjQ7SJ54LLOXN07iHcLJ0BqNLw/HGddRLFr25zGRLRsEkCEsLHsuP3EWoN45KpeODtauG5gKPOKhrN19BdgLmVT6t52lrIVrYcEECFszJJ+hGO6A0MijOVqL+3qj7+nM5/Hh8Ijv/E3pxmcCBhr51wKUX8SQISwpfJSPPNOEqvCCGtnjPNwdLBw7YBQVh5I4cudp/mqYAS+Pl52zqgQ9Scj0YWwpYwTOFBOgXcXLBUayWeM7caxtDye/8GYyFrGgIiWSEogQtjS6cMAqMDulZK9XZ34dNoQHhndBYBuQVICES2PlECEsKGCxAO4A77hvS/Y5mBRPDOhBw9c1lnGgIgWSQKIEDaUn3CALO1HZGj1M+1K8BAtlVRhCWFD6vQRYqyhdA+WKirR+kgAEcJWrFa88k4Q5xBGoMxzJVohCSCixUrIKuSlxdFk5pfYOytVy0nAxVpIvncXlJJpSkTrIwFENHs5RaVVpr+4aD+zNsbyxPzdWK3Nb2lmnWb0wLIERNk5J0LYhgQQ0ax98stxBr68itUHUyql/3I0jZ8PpjIkoh3rj6Tx3toYO+Wwernx0QB4h/exc06EsA0JIKLZik7M5p/LDwHw1Dd7iMsoAKCs3MorSw4Q7ufGF/cN47qBobz98xF+PXrantm9QNmRn4nX/nQM72TvrAhhExJARLNUVFrOjHm7aefuzIJHRmLVmse+3snaQ6k89/0+jqTk8ZdJPXF1cuC16/rQLdCTx+ftIim70N5ZN+Sl4Zv8G4vLRxIV7G3v3AhhExJARLP09s9HiEnN462b+zMg3Jc3b+rPnvhs7p21jQU747l+YCjjextjK9ydHXn/jkEUl5bz2Ne7KC23kltUyr74bPt9gOiFWHQ5O33GyTgP0WrJQELR7JSUWZm/LY7f9Qvhsm4BAIzvHcyc6UNxdFD0D/PFw6XyP92ugZ7844Z+/GHuLsb/3wbiMgooLdf83y0DmDowtMk/Q9HOuZywdmTw0FFNfm0hmoqUQESzs/5IGlkFpdw4KKxS+uVRAYzs4n9B8Djjmv4deOzKrrg6OnDfpZ3pG+rDq0sPkF1QdS8um8k4jmvKThZbR3G9HYKXEE1FAohodn7YlUB7D2cu6+pf72Nnju/OshmX8ezEHvzzhn5kFpTyD7MhvqlY934HQFrEZAK9XZv02kI0JQkgwu52nMxg/Nsb2J+QTU5RKasOpnBN/w44OlzcP89eHbyZPiqCuVtPsT02o5FyWwurlaIdX7HF2oOrhg1qmmsKYScSQITdfbX5FIdTcrl31jY+2XCckjJro7VbPDE2ilBfN55esJei0vJGOWeNDi/FPTeWhQ4TuKqnLFMrWjcJIMKuSsut/HwwheGd/SguLefdNTFE+nvQP8yn9oPz02HDm5CXWu0uHi6O/OOGvhxPy+ffq440Ys6roDVFa9/ipA7Ee9ANODvKfy/Rusm/cGFXm4+nk1NUxn2XdubTe4bg6mTh9qEda587SmtY/AdY8wq8PwIOLT23rawE1v8L3u4D+77jsm4B3Da0Ix//cpzvdsTzwqL9/O7dX84OTGw0sb/gmrqLWXoKD47uXvv+QrRw0o1X2NXy/cm4OztwWTd/XJ0c2PH8ONydHWo/cO98OLwUhj8KJ3+DebdDYC8IGwLx2yE1GrzDYMF9EL+NP49/kQ1H0pj57R6cHS2UWzVfbD7Jnyf1vLgPUJIPyfvAvT15P/+LQu2D14i78ZclakUbIAFE2E25VbMiOoUruwfi6mQEjQu66OalwvbPof8t0C7CSMtJhJ+ehvDhcPWrYC2HbR/DsTVw4Adw9oLb5kHXsbDqBdj8Pl4F6Xw27d/sic9mfO9gnv9uO9/tiOePV0fh4liHgFWNlC/uJyhuGQCewCfqdu67oleDzydESyIBRNjNrlOZnM4rZnyfalbr0xp+eBRiVsGGN2DQNCgtMkoeZSUw9X2wOBiPEb83HlYrKGU8ACa8Dm7tYO1rdO80ku6D74W1r/FO7P9xY+FfWBndm2v6d2hQ/nMPrycobhlfWcexrawbHqqI8Cvvw8fdqYHfiBAtiwQQYTfL9iXj7GDhyu4BVe+w+2sjeFz+NOSbJRFnD4iaAIOnQ/suFx5jqaJZ77KZcGoz/PQMxKyGQ0tQKB5zX8nHW0Y0LIBYy8lZ+EdydXsG3f9fLvXwZH9CDuN6Sc8r0XZIABF2EZOax5dbTjKhTzBerlX8Ys9JhOXPQceRMPo5IzBc9SI4e4JjPeeWsljg+o/hf5fBoSVwxTOo0gKu2PQ+zx0/yvbYKBwsCl93ZyL9Pep0yhM/f0Rk0VF+6PoKUzsaQaNT+7odK0RrIQFENLlyq+bp7/bg7uzA85OracRe/TKUl8C1750rVbj7NfyiHu1h2o+QcQK6jYWME1g2vscdTmu48UPjvF6ujmx+7qpqp0o5I/3UIfw2vsYe1YOrb36k4XkSooWTACKa3Oe/nWDnqSzevqU/gV5VTPVRXgaHl0Gf66uupmqo9l3Onc8vEhU1nodObcD/8r9QYrXw0pIDLNqdyO3DOgLGlPKHk3M5nJxLSbmVsT2D0EXZFH9+A75aw3Uf4O4i7R2i7ZIAIppUel4xb648zFU9Apk6oJrR5vHboCgbul1t28wMfQCXI8u5fd996KxTXOnuwWOb/sNtQ8PJLS5jyru/4Ju5j2sdfiNSJbNsSQh9HU4xgCSOT/yS/v0vsW3+hGjmJICIJvXdjniKSq08M7FH9YMFj64AiyN0udK2mek8BiKvgMIMVMfhdDq8jM6pK9kVN4D5m4/zr7znGOpyCO3gQolvZ0ZmrsfBWkzSpX+n+/BJts2bEC1ArQFEKfUZMBlI1Vr3MdP8gPlABBAL3Ky1zlTGHeEdYBJQANyjtd5pHjMNeN487ata69lm+iBgFuAGLANmaK11Q64hmjerVTN36ymGRLQjKsjr3AatIT8NPAON90dXQccR4FqH6UwuhsUC0xafyRzW94fzaNpSHv9uAiPTv2Wo0yEY+xJq8HRcXH2MLsKlBYS5eNo2X0K0EHWZymQWMOG8tGeB1VrrbsBq8z3ARKCb+XgQ+ADOBpwXgWHAUOBFpVQ785gPzH3PHDehIdcQzd/GY+nEphdwx7Dz1gjfvwDe6g4xP0N2AqTsh27jmjZzFguWUTPork7S+/QyZjotwNp5DIx64lwgs1hAgocQZ9UaQLTWG4Dz58K+Fphtvp4NTK2QPkcbNgO+SqkQYDywSmudobXOBFYBE8xt3lrrTVprDcw571z1uYZo5r7eepJ27k5MOH/g4KGloK2w8GFj7AfYvv2jKn1voswjhDedP8LNUopl0r/ODUgUQlygoZMpBmmtkwDMZ7PugVAgrsJ+8WZaTenxVaQ35BoXUEo9qJTarpTanpaWVq8PKBpXak4RK6NTuHFQ2NlpSwBjGpLja40qq+JcWPsq+IRDQI+mz6SjM46jfo8FK5YRj4J/t6bPgxAtSGM3olf1c003IL0h17gwUeuPgI8ABg8eXNt5hQ2tPpRKmVVz0+DwyhsSd0NhJgy+D0pyYcmTRvWVvX75D7kfHJxh4F32ub4QLUhDA0iKUipEa51kVh+dWZAhHqh4hwgDEs300eelrzPTw6rYvyHXEM1YYlYhFgWdzx/pfWw1oIweV+7tweIEnUfbIYcmJzcY9pD9ri9EC9LQKqzFwDTz9TRgUYX0u5VhOJBtVj+tAK5WSrUzG8+vBlaY23KVUsPN3lV3n3eu+lxDNGNJ2UUEebteuExtzGoI6Q8e/kap45K7wDe86pMIIZqVunTjnYtRevBXSsVj9Kb6B/CNUuo+4BRwk7n7MozutTEYXWzvBdBaZyilXgG2mfu9rLU+0zD/COe68f5kPqjvNUTzlpxdRLDPeaPOi7KNQYOXPmGfTAkhLkqtAURrfVs1m66qYl8N/L6a83wGfFZF+nagTxXp6fW9hmi+ErML6RHsVTnx+HrQ5dDlgj+zEKIFkCVthc1prY0SiLdb5Q1HVhiLP4UPtU/GhBAXRQKIsLmcojIKSsoJqViFdWoL7Pka+t4IDjIhoRAtkQQQYXPJ2UUA59pAivNg4UPgEwbjXrZjzoQQF0MmUxQ2l5RdCHCuBLLyeciMhXuWgqu3/TImhLgoUgIRNlepBHJyI+z43Fi/PGKUnXMmhLgYEkCEzSVlF6EUBHk6wbKnwTsMrvyLvbMlhLhIUoUlbC4pu5AATxecds+GlH1w4+fg7G7vbAkhLpKUQITNJWUXEeVVAmtehYjLoPd19s6SEKIRSAARNpecXcREyxZj0sTxr8kU6UK0EhJAhM0lZxcRYUkDBxcI6mvv7AghGokEEGFTuUWl5BaXEaxTjUkSLfJPTojWQv43C5tKyTG68PqVJoFvRzvnRgjRmCSACJtKzDICiGdhIvh2qmVvIURLIgFE2FRydhHuFOFUnCElECFaGQkgwqaSsosIU+Z69BJAhGhVJIAIm0rOKaSXW6bxpl2EXfMihGhcEkCETSVkFdHD1QwgUgIRolWRACJsKiYll+4umeDoCh4B9s6OEKIRSQARNpNdWEpidhGdHE4bpQ8ZgS5EqyIBRNjM4eRcAALKU6QLrxCtkAQQYTOHknMA8ChMlPYPIVohCSDCZg4l59LBtRRLUaYEECFaIQkgwmYOJeUw0j/feNNOqrCEaG0kgAibsFo1h5NzucTbqMaSEogQrY8EEGETCVmF5JeUG114QRrRhWiFJIAImziYZJQ8wi1p4OQO7u3tnCMhRGOTACJs4pDZhdevNNkofcgYECFaHQkgwiYOJ+fSqb07jhkx4NfZ3tkRQtiABBBhEweTc+gT6ALpxyCol72zI4SwAQkgotHlF5cRezqfYT4ZoMshsKe9sySEsAEJIKLRrYhOxqphlFeqkRAoJRAhWiMJIKLRfb8zgXA/NzpbT4HFCfy62DtLQggbkAAiGlVSdiG/HTvN9QPDUGkHwb8bODrbO1tCCBuQACIa1cJdCWgN118SCqkHpP1DiFZMAohoNFprvt+ZwOBO7ejkaYWsUxJAhGjFJICIRrM3PpuY1DyuvyQM0g4bidKALkSrJQFENJpfY04DMKlvsFF9BVICEaIVkwAiGs2h5FxCfd3wdXeG1IPg6Aa+EfbOlhDCRiSAiEZzJDmXHsFexpvUAxDYAyzyT0yI1uqi/ncrpWKVUvuUUruVUtvNND+l1Cql1FHzuZ2ZrpRS7yqlYpRSe5VSl1Q4zzRz/6NKqWkV0geZ548xj1U1XUPYT0mZlWNpeXQ/G0AOSvuHEK1cY/w8vFJrPUBrPdh8/yywWmvdDVhtvgeYCHQzHw8CH4ARDIAXgWHAUODFCgHhA3PfM8dNqOUawk6On86jzKqNAJKbDHkpEkCEaOVsUb9wLTDbfD0bmFohfY42bAZ8lVIhwHhgldY6Q2udCawCJpjbvLXWm7TWGphz3rmquoawk8Pm9O09gr3h6CojsfNou+VHCGF7FxtANLBSKbVDKfWgmRaktU4CMJ8DzfRQIK7CsfFmWk3p8VWk13SNSpRSDyqltiultqelpTXwI4q6OJSci6NFEenvAUeWg3cYBPW2d7aEEDbkeJHHj9JaJyqlAoFVSqlDNexb1YpCugHpdaa1/gj4CGDw4MH1OlbUz+HkXLoEeOJMKRxbC/1vlUWkhGjlLqoEorVONJ9TgYUYbRgpZvUT5rM5JSvxQHiFw8OAxFrSw6pIp4ZrCDs5nJxrtH+c/A1K8yFqvL2zJISwsQYHEKWUh1LK68xr4GpgP7AYONOTahqwyHy9GLjb7I01HMg2q59WAFcrpdqZjedXAyvMbblKqeFm76u7zztXVdcQdpBbVEpCVqERQI6sMMZ/RF5u72wJIWzsYqqwgoCFZs9aR+BrrfVypdQ24Bul1H3AKeAmc/9lwCQgBigA7gXQWmcopV4Btpn7vay1zjBfPwLMAtyAn8wHwD+quYawgyMpZgN6kCes/Ak6XwFObnbOlRDC1hocQLTWx4H+VaSnA1dVka6B31dzrs+Az6pI3w70qes1hH0cMntg9XZJhqyTcOkTds6REKIpyDBhcdEOJ+fi6eJIULpZiOw61r4ZEkI0CQkg4qLEpOayfH8yvUK8UVknwdEVfMJrP1AI0eJJABENti02gxs+2IRVwwvX9IKsOPAJk+67QrQRFzsORLRR8ZkF3PXpFjr4uDF7+lDC/dwhO05KH0K0IVICEQ3y/rpjWK3wxf3DjOABkB1vlECEEG2CBBBRbwlZhXy7PY6bh4QR6mt21y0tMiZQ9O1o38wJIZqMBBBRb++vjQHgkdFdzyXmJBjPUoUlRJshAUTUy6n0Ar7ZHsdNg8PPlT4Ask4Zz1KFJUSbIY3ook5yikr5ZMNxPvn1BI4WC4+O7lJ5h2xz4mRfKYEI0VZIABE10lrz0/5kXlgUzem8Yn7XL4Q/josirJ175R2z4wAF3qFVnkcI0fpIABHVyisuY+Y3e1genUyfUG8+u2cw/cJ8q945Kw68QsDBqWkzKYSwGwkgokpZBSVM+3wb+xOyeXZiD+6/NBJHhxqazLLjpPpKiDZGGtFbsTWHUvj9Vzspt9ZtLa1yqyb2dD5rD6Vy60ebOZiYwwd3XMLDV3SpOXiADCIUog2SEkgr9t6aGHaeyuLGwWFc2b3KVX/P2nI8nWcW7CU2vQAAd2cHPr1nMJd1C6j9QlYrZCdAL1maXoi2RAJIKxV7Op+dp7IA+GrzqWoDSHZhKf9eeZjZm07S0c+d16/vS7dAT6KCvfB2rWN7Rl4KWEulCkuINkYCSCv1/a4ElILrBobyw64EkrILCfFxY198NkdTc2nv6cLRlFzeWxtDdmEp94yM4OkJ3XF3bsA/iew449lHRqEL0ZZIAGkldsdl8cKi/Tw5LorRUQEs3BXPqC7+PDk2ioW7Epi3NY5eHbz5/Vc7KavQJnJZN3+em9iTXh28G35xGUQoRJskAaQV2B2XxV2fbCG3uIyHv9jBH8Z0JS6jkCfHRhHu587l3QKYtTGWgpIy+oT68M8b+pFbVIqzo6X6brn1IYMIhWiTJIC0IKfSC1i0O4HoxByOpObi7GChc4AHvxw9TTsPZ+Y+OJwn5+/mzZVHcHd2YHzvYABuH9aR9UfS6B/mw5z7hta9baOusuPA1RdcvBr3vEKIZk0CSDNntWp+jTnNnE2xrD6UCkBkew+6BXlSWq45mJRLpL8HH9w5iFBfN768fxh3fbqFUV398XAx/rzjegbx4Z2XMLKrf+MHD4DTR6ULrxBtkASQZqaotJwFO+PJKiiloKSMn/YnczwtUfL1wgAADPxJREFUH39PZx67sit3DOtEsI9rtccHebuy4onLK6VZLIoJfUJsk+E98+DEerhspm3OL4RotiSANCPlVs2MebtYEZ1yNm1AuC9v39KfSX1DcHF0qNN5VFMtKZu0B36cAZ0uhdHPNs01hRDNhgQQO9Jas+NkJhroFeLNq0sPsCI6hb9O7sVdwzvh5KCaLhjUV0EGzL8T3PzgplkyB5YQbZAEkCZUUmZlf2I2CkjJKeKD9cfZE5dVaZ9HR3fhvksj7ZPBurKWw4L7ITcZ7v0JPOswWl0I0epIAGkiZeVW7vxkC1tjM86mhbVz4+/X9SXI24W98dn4eThz94hOdsxlHa39OxxbDde8A2GD7Z0bIYSdSABpIv9edYStsRk8O7EH3YO8cHKwMKyz3/+3d+dBUpRnHMe/v704lhsVQW4vQOMiIKJRIZIoouVRaoJIeWCV0TLlUWo8q+IREzVWkrJMKloe0RyaeB8xIB6E8kKWaxGQG2FXFGQ5BARk98kf77thwF1YRnZ6Ojyfqql55+3u2Wff3eln3re736Y4TlI4vG+nhCNshJVzofwJ+OhhGHARDLwk6YiccwnyBNLEtmyrYdL8L/njxEWMOqYbVww9ePcb7S1rl8Hyj+B752X/HivnwtzX4JPXYMUMKCiGsgtg5AN7L07nXCp5AmkCn67eyCOTFvPS9Co2bq0BoM+BrbnjzCNyF4QZPHcZVH4EpftB72F7tv2apfDmHTD7xfC66zFwyi9D8ijdb+/G6pxLJU8ge0FtrfHC9CrKl1Yz/4uvmLF8LUUFBZxR1pne+5XStkUxI47sTPPixp2Gu1fMeTkkj8ISeON2uHwSFNRzT4+v18C8cfD5LFg5BzZ+Cdu+Dr0XFcLQm2DQZdA6BUNszrmc8gTyHS2v3sT1z85k+pKVtGnZnEM6teHKYQdz8XE9OaDNThf8VfwTPpsBZT+BzmUNvOEUePuu0GPoPwZK94f1VWG+qU2rtz++roY2XeGQ4VDcAqY9BYvehqPHQNno0HvYvy+ccB28eDlU/AN6HA/jbgnv16E31GyFBW+E56IWsP/hYT6rouZw2Ag47ipo06WJW9A5l1Yya9zd6tJu0KBBVl5evucbfjEHxt8aDhgfPhKWvc/GcXeybW0V7zU7iVfX9mJEwWTOKPyQghZtUO9hYee8rhI2r4PDToU+Z8Bbd8HUJwABBp37Q4t24ZTYLv1hwCXweQW8dCUUNoMt66CgKPQCarZ8O67CZjvVC9r3CENPHXpD9WK48Dk4eDg8OhzWfgrfbAYpDEetWRoSR98z4agfh4RWkMMeknMuFSRNNbN6T7f0BLI7C94MV1uvr8RKStHWjVRZR+Zbd04sqKCIGmpLWlFw5Lnwzdew+J0wDNT6wHDAed0y/pc0TrgOjr86TP8x91WwmnCsompqKAN0GwKj/h6GlmY+HXbyHXpDu+7h2EPLjuHivZKWUL0knE67aU04UN6uB0x5FN78BXQfAmNeCAnj0/fhz6dDr6Fw5oPhvZxzrhE8gZB9Apk4byX3vDqLsi3TOHHrJGZu60HtwEu4fmQZrWvWQdW0MDTUrFXYoLYWardBUUlIDpXlMOcl6PF96DOy/h/y1ecw/S+weT384DYobniuq0bZVB2GoUpabq/bsCokoHy9st05l5c8gZB9Apm2bA2PvbuEFsWFtCwp5IyjujC4V4cmiNA55/LPrhKIH0TfjQHd2zNgdPukw3DOubxTz3mdzjnn3O55AnHOOZcVTyDOOeeykuoEImmEpHmSFkryOxo551wOpTaBSCoE/gCcBvQDLpDUL9monHNu35HaBAIMBhaa2WIz2wo8A5yVcEzOObfPSHMCOQhYnvG6MtY555zLgTQnkPouqd7hqkhJl0sql1S+atWqHIXlnHP7hjRfSFgJdMt43RX4LHMFM3sEeARA0ipJn2b5s/YDvsxy26R57Mnw2JOR1tjzOe4G77Od2qlMJBUB84HhQBUwBRhtZrOb4GeVN3Qpf77z2JPhsScjrbGnNe7U9kDMbJuknwHjgULg8aZIHs455+qX2gQCYGavA68nHYdzzu2L0nwQPZceSTqA78BjT4bHnoy0xp7KuFN7DMQ551yyvAfinHMuK55AnHPOZWWfTSCSHpe0UtLHGXX9JX0oaUa8AHFwrG8r6VVJMyXNlnRpxjYXS1oQHxcnGHuZpA8kzYqxtslYdkuccHKepFMz6nM+GeWexC7pR5Kmxvqpkk7O2GZgrF8o6UGpae/Vu6dtHpd3l7RB0g0ZdXnd5nHZUXHZ7Li8eazPaZvvaeySiiU9GevnSrolY5sk2r2bpHdiLLMlXRPrO0iaEPcZEyS1j/WK7bpQUoWkARnvlfP9TKOY2T75AE4CBgAfZ9S9AZwWyyOBibF8K3BfLO8PVAMlQAdgcXxuH8vtE4p9CjA0lscCd8dyP2Am0AzoBSwinPZcGMu94+8yE+iXZ7EfDXSJ5SOBqoxtPgKOI8xI8O+6v1s+xJ2x/HngWeCG+DoNbV4EVABl8XVHoDCJNs8i9tHAM7HcElgK9Eyw3TsDA2K5NeG6tX7A/cDNsf5mtu9bRsZ2FTAEmBzrE9nPNOaxz/ZAzGwSIRHsUA3UfRNry/Yr2w1oHb9xtYrbbQNOBSaYWbWZrQEmACMSiv1wYFIsTwDOjeWzCB+qLWa2BFhImIgykcko9yR2M5tuZnV/g9lAc0nNJHUG2pjZBxY+YU8BZ+dL3ACSziZ80DOvTcr7NgdOASrMbGbcdrWZ1STR5lnEbkCpwkXGLYCtwHqSa/cVZjYtlr8C5hLm6zsLeDKu9iTb2/Es4CkLPgTaxXZPZD/TGPtsAmnAtcBvJC0HHgDqusAPAX0JCWUWcI2Z1ZJfEzp+DJwZy+ezfZqXhmJMQ+yZzgWmm9kWQpyVGcuSir3euCWVAjcBd+60fhra/DDAJI2XNE3Sz2N9vrQ5NBz7c8BGYAWwDHjAzKrJg3aX1JPQo54MdDKzFRCSDHBAXC0Nn9UdeALZ0ZXAdWbWDbgOeCzWnwrMALoA/YGH4rjrbid0zKGxwFWSphK6y1tjfUMxpiF2ACQdAdwH/LSuqp73SCL2huK+E/idmW3Yaf18iRsajr0IOAG4MD6fI2k46Yh9MFBD+Jz2Aq6X1JuEY5fUijCcea2Zrd/VqvXU5dtndQepvhK9CVwMXBPLzwKPxvKlwL2x675Q0hKgD+GbwLCM7bsCE3MS6U7M7BPC8AOSDgNOj4t2NenkLiejzJVdxI6krsCLwEVmtihWVxLirZNI7LuI+1jgPEn3A+2AWkmbgankf5tXAv8xsy/jstcJxyD+Sh60Oewy9tHAODP7Blgp6T1gEOHbeyLtLqmYkDz+ZmYvxOovJHU2sxVxiGplrG/os5o3+5mdeQ9kR58BQ2P5ZGBBLC8jTNqIpE6EMdjFhHm4TpHUPp5JcUqsyzlJB8TnAuB24E9x0SvAqHjsoBdwKOFg6BTgUEm9JJUAo+K6OddQ7JLaAf8CbjGz9+rWj93+ryQNicelLgJezpe4zexEM+tpZj2B3wO/MrOHSEGbE/5/j5LUMh5LGArMyZc2jzE3FPsy4OR4NlMp4UD0JyTU7rGdHgPmmtlvMxa9QviySnx+OaP+ohj/EGBdbPe82c98S9JH8ZN6AE8Txkq/IWT4ywhd9qmEszQmAwPjul0IZ2jNIoy/jsl4n7GEA9MLgUsTjP0awlke84F7ibMMxPVvI5yFMo+MM2cIZ33Mj8tuy7fYCTuHjYThw7rHAXHZoPi3WEQ4RqV8iXun7e4gnoWVhjaP648hHPz/GLg/oz6nbZ7F/0srwsjBbGAOcGPC7X4CYaipIuP/dyThzLa3CF9Q3wI6xPVFuE33IsK+ZlDGe+V8P9OYh09l4pxzLis+hOWccy4rnkCcc85lxROIc865rHgCcc45lxVPIM4557LiCcQ551xWPIE4lyKSCpOOwbk6nkCcayKS7q67B0R8fY+kqyXdKGlKvOfDnRnLX1K478lsSZdn1G+QdJekyYTp1J3LC55AnGs6jxGnrIjTbowCviBMJzOYMDHnQEknxfXHmtlAwhXfV0vqGOtLCffDONbM3s3lL+Dcrvhkis41ETNbKmm1pKOBTsB04BjCXEbT42qtCAllEiFpnBPru8X61YQZZp/PZezONYYnEOea1qPAJcCBwOOESTl/bWYPZ64kaRjwQ+A4M9skaSLQPC7ebGY1uQrYucbyISznmtaLhLvHHUOYQXU8MDbeIwJJB8XZZdsCa2Ly6EOYSda5vOY9EOeakJltlfQOsDb2It6Q1Bf4IMz2zQbC7LfjgCskVRBmTf4wqZidayyfjde5JhQPnk8DzjezBbtb37k08SEs55qIpH6E+ze85cnD/T/yHohzzrmseA/EOedcVjyBOOecy4onEOecc1nxBOKccy4rnkCcc85l5b88Oll0yQ4+FAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    group['prop'] = group.births / group.births.sum()\n",
    "    return group\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year      prop\n",
       "0             Mary   F    7065  1880  0.077643\n",
       "1             Anna   F    2604  1880  0.028618\n",
       "2             Emma   F    2003  1880  0.022013\n",
       "3        Elizabeth   F    1939  1880  0.021309\n",
       "4           Minnie   F    1746  1880  0.019188\n",
       "...            ...  ..     ...   ...       ...\n",
       "1690779    Zymaire   M       5  2010  0.000003\n",
       "1690780     Zyonne   M       5  2010  0.000003\n",
       "1690781  Zyquarius   M       5  2010  0.000003\n",
       "1690782      Zyran   M       5  2010  0.000003\n",
       "1690783      Zzyzx   M       5  2010  0.000003\n",
       "\n",
       "[1690784 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "            ... \n",
       "2008  M      1.0\n",
       "2009  F      1.0\n",
       "      M      1.0\n",
       "2010  F      1.0\n",
       "      M      1.0\n",
       "Name: prop, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby(['year', 'sex']).prop.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <th rowspan=\"5\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>1677639</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677640</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677641</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677642</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677645</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name sex  births  year      prop\n",
       "year sex                                               \n",
       "1880 F   0             Mary   F    7065  1880  0.077643\n",
       "         1             Anna   F    2604  1880  0.028618\n",
       "         2             Emma   F    2003  1880  0.022013\n",
       "         3        Elizabeth   F    1939  1880  0.021309\n",
       "         4           Minnie   F    1746  1880  0.019188\n",
       "...                     ...  ..     ...   ...       ...\n",
       "2010 M   1677639     Camilo   M     194  2010  0.000102\n",
       "         1677640     Destin   M     194  2010  0.000102\n",
       "         1677641     Jaquan   M     194  2010  0.000102\n",
       "         1677642     Jaydan   M     194  2010  0.000102\n",
       "         1677645     Maxton   M     193  2010  0.000102\n",
       "\n",
       "[261877 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)\n",
    "top1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Mary</td>\n",
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       "      <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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name sex  births  year      prop\n",
       "0            Mary   F    7065  1880  0.077643\n",
       "1            Anna   F    2604  1880  0.028618\n",
       "2            Emma   F    2003  1880  0.022013\n",
       "3       Elizabeth   F    1939  1880  0.021309\n",
       "4          Minnie   F    1746  1880  0.019188\n",
       "...           ...  ..     ...   ...       ...\n",
       "261872     Camilo   M     194  2010  0.000102\n",
       "261873     Destin   M     194  2010  0.000102\n",
       "261874     Jaquan   M     194  2010  0.000102\n",
       "261875     Jaydan   M     194  2010  0.000102\n",
       "261876     Maxton   M     193  2010  0.000102\n",
       "\n",
       "[261877 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the group index, not needed\n",
    "top1000.reset_index(inplace=True, drop=True)\n",
    "top1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pieces = []\n",
    "# for year, group in names.groupby(['year', 'sex']):\n",
    "#     pieces.append(group.sort_values(by='births', ascending=False)[:1000])\n",
    "# top1000 = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Naming Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']\n",
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8279.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>297.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>404.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5145.0</td>\n",
       "      <td>2839.0</td>\n",
       "      <td>530.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3941.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8914.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4925.0</td>\n",
       "      <td>3028.0</td>\n",
       "      <td>526.0</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",
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       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>955.0</td>\n",
       "      <td>4028.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>8511.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4764.0</td>\n",
       "      <td>3438.0</td>\n",
       "      <td>492.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1265.0</td>\n",
       "      <td>4352.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>7936.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5120.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>496.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>448.0</td>\n",
       "      <td>4628.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>7374.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>295.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>5164.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 6868 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name   Aaden  Aaliyah  Aarav   Aaron  Aarush  Ab  Abagail  Abb  Abbey  Abbie  \\\n",
       "year                                                                           \n",
       "1880     NaN      NaN    NaN   102.0     NaN NaN      NaN  NaN    NaN   71.0   \n",
       "1881     NaN      NaN    NaN    94.0     NaN NaN      NaN  NaN    NaN   81.0   \n",
       "1882     NaN      NaN    NaN    85.0     NaN NaN      NaN  NaN    NaN   80.0   \n",
       "1883     NaN      NaN    NaN   105.0     NaN NaN      NaN  NaN    NaN   79.0   \n",
       "1884     NaN      NaN    NaN    97.0     NaN NaN      NaN  NaN    NaN   98.0   \n",
       "...      ...      ...    ...     ...     ...  ..      ...  ...    ...    ...   \n",
       "2006     NaN   3737.0    NaN  8279.0     NaN NaN    297.0  NaN  404.0  440.0   \n",
       "2007     NaN   3941.0    NaN  8914.0     NaN NaN    313.0  NaN  349.0  468.0   \n",
       "2008   955.0   4028.0  219.0  8511.0     NaN NaN    317.0  NaN  344.0  400.0   \n",
       "2009  1265.0   4352.0  270.0  7936.0     NaN NaN    296.0  NaN  307.0  369.0   \n",
       "2010   448.0   4628.0  438.0  7374.0   226.0 NaN    277.0  NaN  295.0  324.0   \n",
       "\n",
       "name  ...   Zoa     Zoe    Zoey   Zoie  Zola  Zollie  Zona  Zora  Zula   Zuri  \n",
       "year  ...                                                                      \n",
       "1880  ...   8.0    23.0     NaN    NaN   7.0     NaN   8.0  28.0  27.0    NaN  \n",
       "1881  ...   NaN    22.0     NaN    NaN  10.0     NaN   9.0  21.0  27.0    NaN  \n",
       "1882  ...   8.0    25.0     NaN    NaN   9.0     NaN  17.0  32.0  21.0    NaN  \n",
       "1883  ...   NaN    23.0     NaN    NaN  10.0     NaN  11.0  35.0  25.0    NaN  \n",
       "1884  ...  13.0    31.0     NaN    NaN  14.0     6.0   8.0  58.0  27.0    NaN  \n",
       "...   ...   ...     ...     ...    ...   ...     ...   ...   ...   ...    ...  \n",
       "2006  ...   NaN  5145.0  2839.0  530.0   NaN     NaN   NaN   NaN   NaN    NaN  \n",
       "2007  ...   NaN  4925.0  3028.0  526.0   NaN     NaN   NaN   NaN   NaN    NaN  \n",
       "2008  ...   NaN  4764.0  3438.0  492.0   NaN     NaN   NaN   NaN   NaN    NaN  \n",
       "2009  ...   NaN  5120.0  3981.0  496.0   NaN     NaN   NaN   NaN   NaN    NaN  \n",
       "2010  ...   NaN  6200.0  5164.0  504.0   NaN     NaN   NaN   NaN   NaN  258.0  \n",
       "\n",
       "[131 rows x 6868 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = top1000.pivot_table('births', index='year',\n",
    "                                   columns='name',\n",
    "                                   aggfunc=sum)\n",
    "total_births"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131 entries, 1880 to 2010\n",
      "Columns: 6868 entries, Aaden to Zuri\n",
      "dtypes: float64(6868)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "total_births.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000138C7C28308>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000138C9086D08>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000138C7F43FC8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000138C85E51C8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]\n",
    "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
    "            title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring the increase in naming diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138c7f11c48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = top1000.pivot_table('prop', index='year',\n",
    "                            columns='sex', aggfunc=sum)\n",
    "table.plot(title='Sum of table1000.prop by year and sex',\n",
    "           yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name 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",
       "...         ...  ..     ...   ...       ...\n",
       "261872   Camilo   M     194  2010  0.000102\n",
       "261873   Destin   M     194  2010  0.000102\n",
       "261874   Jaquan   M     194  2010  0.000102\n",
       "261875   Jaydan   M     194  2010  0.000102\n",
       "261876   Maxton   M     193  2010  0.000102\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 2010]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 1900]\n",
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "in1900.values.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>209</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>223</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>234</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>241</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>246</td>\n",
       "      <td>117</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   38   14\n",
       "1881   38   14\n",
       "1882   38   15\n",
       "1883   39   15\n",
       "1884   39   16\n",
       "...   ...  ...\n",
       "2006  209   99\n",
       "2007  223  103\n",
       "2008  234  109\n",
       "2009  241  114\n",
       "2010  246  117\n",
       "\n",
       "[131 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "</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"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138c9411c88>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7YLtec0z/SB68YGyjx5SXlzNx4kQABg8ezDvvvNOuNYiIiEjzWWt5ZvlOhsREcOaIWLfLaZBfBCu3qCtQRETEd6zdfYANWcU8fNFYgoKM2+U0yC+CVVMtSyIiIhL4XlyZSa/wEC6bnOh2KcelMVYiIiLi82pqPXzyzT5mjo0nopvvtgspWImIiIjP25BVzMGKGp8dW1VHwaoRpaWlbpcgIiIiwGdb8zEGThvmG4stH4+ClYiIiPi8z7blMz4xyudmWj+agpWIiIj4tOJD1WzYc4Azh/t2axUoWImIiIiPW55RgMfCGT4+vgoUrERERMTHfbY1n17hIUxMinK7lCYpWImIiIjPstby2bZ8pg2NISTY92OL71coIiIiXVbGvlJyiiv8ohsQFKwaZYzhuuuuO/xzTU0NsbGxzJ0718WqREREuo7/bM0H4IwRvj9wHRSsGhUREUF6ejrl5eUALF26lAEDBrhclYiISNfx2bYChsRGkNinh9ulNIuCVRNmz57N+++/D8Arr7zC1Vdf7XJFIiIiXUNFdS1f7SjkjOH+0Q0IfrIIM4vvg9y09r1mfArMfrTJw6666ioefvhh5s6dS2pqKjfffDOff/55+9YiIiIix1i1s4jKGo/PL2NTn1qsmjB+/HgyMzN55ZVXmDNnjtvliIiIdBmfb8snLDiIk4ZEu11Ks/lHi1UzWpY60oUXXsjdd9/NsmXLKCwsdLUWERGRrmL1rv1MSOpNjzD/iCvgL8HKZTfffDO9e/cmJSWFZcuWuV2OiIhIwKusqWXj3oPcNC3Z7VJaRF2BzZCYmMidd97pdhkiIiJdxqbsg1TVepg00PdnW69PLVaNKC0tPWbb9OnTmT59eucXIyIi0oWs230AgEkD+7hcScuoxUpERER8zro9B0joHU5cZLjbpbSIgpWIiIj4nHW79/tdNyAoWImIiIiPyS+pJGt/OZOS/KsbEHw8WFlr3S6h2fypVhEREV+2fk/d+Cq1WLWb8PBwCgsL/SKwWGspLCwkPNy/+oFFRER80brd+wkJMowb0NvtUlrMZ+8KTExMJCsri/z8fLdLaZbw8HASExPdLkNERMTvrdt9gDH9IwkPDXa7lBbz2WAVGhrK4MGD3S5DREREOlGtx7Ih6wCXn+CfjRU+2xUoIiIiXc/WvBIOVdX63fxVdRSsRERExGfUTQw6Mcn/Bq6DgpWIiIj4kHW799OnRyiD+vZwu5RWUbASERERn7F+zwEmDeyDMcbtUlpFwUpERER8QnF5Ndv2lTLJT7sBQcFKREREfERqln8uvFyfgpWIiIj4hDW79mMMjE/yv4lB6yhYiYiIiE/49Jt9TEiMIjI81O1SWk3BSkRERFyXd7CCDVnFnDsmzu1S2kTBSkRERFz38eZ9AJwzWsFKREREpE0+2pxHUnR3RsT1dLuUNlGwEhEREVcdqqpheUYB546O99v5q+ooWImIiIirPt9WQFWNh3PG9HO7lDZTsBIRERFXfbQpj8jwEKYmR7tdSpspWImIiIhraj2WT77Zx1mj+hEa7P+xpMlnYIxJMsZ8aozZbIzZaIy507s92hiz1BizzftnH+92Y4x5whiTYYxJNcZM7ugnISIiIv5p3e79FJZV+f3dgHWaEw1rgJ9aa0cDJwO3G2PGAPcBH1trhwMfe38GmA0M937dBjzV7lWLiIhIQFi6OY+QIMOZI2PdLqVdNBmsrLU51tq13u9LgM3AAOAi4HnvYc8DF3u/vwh4wTq+BKKMMQntXrmIiIj4vY825XHykL5+Pdt6fS3qzDTGJAOTgK+AOGttDjjhC6gbyj8A2FPvtCzvtqOvdZsxZrUxZnV+fn7LKxcRERG/tiO/lO35ZX4/23p9zQ5WxpiewFvAXdbag40d2sA2e8wGa/9hrZ1irZ0SGxsYzX8iIiLSfEs25gIwY7T/T7NQp1nByhgTihOqXrLWvu3dnFfXxef9c593exaQVO/0RCC7fcoVERGRQGCt5c3VWZyYHE1inx5ul9NumnNXoAH+BWy21j5Wb9d7wA3e728A5tfbfr337sCTgeK6LkMRERERgDW79rOjoIzLpyS6XUq7CmnGMdOA64A0Y8x677afA48CrxtjvgvsBi737lsEzAEygEPATe1asYiIiPi9N1Zn0SMsmDkpgXV/W5PBylq7nIbHTQHMaOB4C9zexrpEREQkQJVV1rAwNZu54xOI6NacNh7/4f9TnIqIiIhfWZSWQ1lVLVdMSWr6YD+jYCUiIiKd6o3VWQyJieCEQX3cLqXdKViJiIhIp9lZUMaqzCLmTUnEuT8usChYiYiISKd5c80eggxcNjmw7gaso2AlIiIinaLWY3lrzV6mj+xHXGS42+V0CAUrERER6RQrtxeSe7CCy08IzNYqULASERGRTvLF9gJCggzTRwbOEjZHU7ASERGRTrE6cz/jBvSme1iw26V0GAUrERER6XCVNbWszzrA1OTAm2KhPgUrERER6XDpe4upqvEwJTna7VI6lIKViIiIdLivM/cDMCUAJwWtT8FKREREOtzqzCKGxEbQt2c3t0vpUApWIiIi0qE8HsvqXfuZOiiwuwFBwUpEREQ62Pb8Ug4cqmbqYAUrERERkTapG18V6HcEgoKViIiIdLCvM4uI7dWNgdE93C6lwylYiYiISIf6OrOIqcl9MMa4XUqHU7ASERGRDpNTXE7W/nKmdIGB66BgJSIiIh1o9eHxVQpWIiIiIm2yOrOIiLBgRif0cruUTqFgJSIiIh3m68z9TB7Uh5DgrhE5usazFBERkU53sKKab3IPdpnxVaBgJSIiIh3k860FeCyc2AUmBq2jYCUiIiId4o01e0joHa5gJSIiItIWOcXlfLY1n3knJBIcFPjzV9VRsBIREZF29/bavXgszDsh0e1SOpWClYiIiLQray1vrN7DyUOiGdQ3wu1yOpWClYiIiLSrrzP3k1l4iMtPSHK7lE6nYCUiIiLt6vXVe+jZLYTZKfFul9LpFKxERESk3ZRW1vB+ag4XTEigR1iI2+V0OgUrERERaTeLUnMor65lXhfsBgQFKxEREWlHr6/ew9DYCCYPjHK7FFcoWImIiEi72JR9kNW79nP5lCSM6TpzV9WnYCUiIiLt4s8fbaVXeAhXTx3odimuUbASERGRNkvLKubDTXncctoQevcIdbsc1yhYiYiISJv9+aOt9O4eyk2nJbtdiqsUrERERKRN1u85wMff7OO2M4YQGd51W6tAwUpERETa6LGlW4mOCOOGU5PdLsV1ClYiIiLSaqszi/hsaz7fO2MIPbt1vQlBj6ZgJSIiIq32+EdbiekZxnWnDHK7FJ+gYCUiIiKtkrGvlBUZhdx6+pAuuXxNQxSsREREpFUWpmZjDFw8aYDbpfgMBSsRERFpMWstC1NzODE5mrjIcLfL8RkKViIiItJiW/JKyNhXytwJ/d0uxacoWImIiEiLLdyQQ5CBWWPj3S7FpyhYiYiISIs43YDZnDK0L7G9urldjk9RsBIREZEW2Zh9kMzCQ8wdr27AoylYiYiISIssTM0hJMioG7ABClYiIiLSbHXdgNOGxdAnIsztcnyOgpWIiIg024asYrL2lzN3fILbpfgkBSsRERFptoUbsgkLDuI8dQM2SMFKREREmsXjsbyflsMZI2Lo3T3U7XJ8koKViIiINMva3fvJKa7gfHUDHpeClYiIiDTLwtQcwkKCOGd0nNul+CwFKxEREWlSrceyKC2Hs0bG0itc3YDH02SwMsY8Y4zZZ4xJr7ft18aYvcaY9d6vOfX23W+MyTDGbDHGzOyowkVERKTzfJ1ZxL6SSk0K2oTmtFg9B8xqYPvj1tqJ3q9FAMaYMcBVwFjvOX83xgS3V7EiIiLijoWp2XQPDWbG6H5ul+LTmgxW1trPgKJmXu8i4FVrbaW1dieQAZzYhvpERETEZTW1Hhan5XL26H70CAtxuxyf1pYxVj8yxqR6uwr7eLcNAPbUOybLu+0YxpjbjDGrjTGr8/Pz21CGiIiIdKQvdxRRWFbFBbobsEmtDVZPAUOBiUAO8D/e7aaBY21DF7DW/sNaO8VaOyU2NraVZYiIiEhHW5iaTURYMNNHqhuwKa0KVtbaPGttrbXWAzzNt919WUBSvUMTgey2lSgiIiJuqa71sGRjLueMiSM8VMOmm9KqYGWMqd8WeAlQd8fge8BVxphuxpjBwHBgVdtKFBEREbesyCjgwKFq3Q3YTE2OQDPGvAJMB2KMMVnAg8B0Y8xEnG6+TOB7ANbajcaY14FNQA1wu7W2tmNKFxERkY62MDWHXuEhnDEixu1S/EKTwcpae3UDm//VyPGPAI+0pSgRERFxX3lVLR+k5zJzXDzdQtQN2ByaeV1EREQatDg9h5LKGi4/IdHtUvyGgpWIiIg06I3VWQzq24MTB0e7XYrfULASERGRY+wuPMTKHYVcfkIixjQ0m5I0RMFKREREjvHmmj0YA5dOVjdgSyhYiYiIyBFqPZY312Rx+vBY+kd1d7scv6JgJSIiIkf4YnsB2cUVXDFFrVUtpWAlIiIiR3h9dRa9u4dyzug4t0vxOwpWIiIicljxoWo+2JjLxRP7awmbVlCwEhERkcPe27CXqhoPl09JavpgOYaClYiIiBz25tq9jIrvxdj+kW6X4pcUrERERASAPUWH2LDnABdNHKC5q1pJwUpEREQAZ8FlgLnjE1yuxH8pWImIiAgAC1OzmZAURVJ0D7dL8VsKViIiIsLOgjI2Zh/kArVWtYmClYiIiPB+ajYAc1IUrNpCwUpERERYmJrDlEF9tIRNGylYiYiIdHEZ+0r4JrdEg9bbgYKViIhIF7dgQw7GwGx1A7aZgpWIiEgXZq1lYWo2JyZHExcZ7nY5fk/BSkREpAvbklfC9vwy5k7o73YpAUHBSkREpAtbsCGbIAOzx8W7XUpAULASERHpoiprannt6yzOHBFLTM9ubpcTEBSsREREuqiFG3IoKK3k5tMGu11KwFCwEhER6YKstTyzYifD+/XktGExbpcTMBSsREREuqCvM/ezMfsgN00bjDHG7XIChoKViIhIF/TM8p1E9QjlkkkD3C4loChYiYiIdDF7ig7x4aZcrj5xIN3Dgt0uJ6AoWImIiHQxL6zMxBjD9acMcruUgKNgJSIi0oWUVtbw6td7mJOSQEJvLbjc3hSsREREupC31mRRUlHDTdOS3S4lIClYiYiIdBEej+W5LzKZmBTF5IF93C4nIClYiYiIdBHLtu5jZ0GZJgTtQApWIiIiXcQzyzOJjwzXuoAdSMFKRESkC9iaV8LyjAKuO2UQocH6+O8oemVFRES6gGdX7KRbSBDXnDjQ7VICmoKViIhIgCsqq+LttXu5dPIA+kSEuV1Ox1n/MuRvcbUEBSsREZEA98qq3VTWeLhpWgAPWl/1NLz7A1jxF1fLULASEREJYNW1Hl5Ymclpw2IYEdfL7XI6xpf/C4vuhhGzYe7jrpaiYCUiIhLA3l6bRd7BSm4+LdntUjrGF3+DJffCqLlwxQsQ0s3VckJcfXQRERHpMBn7SnhowSamDOrD9BH93C6n/a18Ej58AMZcBJf9C4JD3a5IwUpERCQQHaqq4Qf/Xkt4aDB/vWYSQUHG7ZLaV94mWPorp6Xqsmcg2DcijW9UISIiIu3GWssD76STkV/KCzefGHiLLVsL7/8EuvWCC57wmVAFClYiIiIB55VVe3hn3V7uOmc4pw+Pdbuc9rfhFdi90glVEX3druYIGrwuIiISQNL3FvPrBRs5fXgMPz57uNvltL9DRfDhLyDxRJh0ndvVHEPBSkREJEAUl1fzw5fWEt0jjD9fOZHgQBtXBfDxw1C+H+Y+BkG+F2N8ryIRERFpMWst97yxgewD5Tx57ST69nR32oEOkbUa1jwHJ30f4lPcrqZBClYiIiIB4J+f7+TDTXncN3sUJwyKdruc9mctLL4XesbB9Pvdrua4FKxERET83NeZRTy65BtmjY3nu6cF6LI12z6EvavhrPshPNLtao5LwUpERMSPFZVV8aOX15LUpzt/uHw8xgTguCpr4dNHIGoQTLzW7WoapWAlIiLix36/aDOFpVX87ZrJRIa7P/N4h9iyCHI2wJn3+sTs6o1RsG1q48UAACAASURBVBIREfFTX2cW8caaLG45fQjjBvR2u5yO4fHAp7+H6KEw/kq3q2mSJggVERHxQ9W1Hn7xTjr9e4dzx4xhbpfTcTa/B3lpcOnTPjXD+vH4foUiIiJyjOdWZLIlr4T/u+4EeoQF6Me5pxaW/R5iRsC4y9yuplkC9J0QEREJXDnF5Tz+0VZmjOrHeWPi3C6n42x8B/K/gXnPQFCw29U0i8ZYiYiI+JmHF2yi1mP59YVjA/MuQIDivbD4Z85EoGMucbuaZmsyWBljnjHG7DPGpNfbFm2MWWqM2eb9s493uzHGPGGMyTDGpBpjJndk8SIiIl3N2t37WZyey4/OGkZSdA+3y+kYtdXw5k1QUwnznvXJpWuOpzmVPgfMOmrbfcDH1trhwMfenwFmA8O9X7cBT7VPmSIiIgLwzPKd9AoP4eZAnQgU4KNfw56v4MInIMa/FpJuMlhZaz8Dio7afBHwvPf754GL621/wTq+BKKMMQntVayIiEhXln2gnMXpuVw1NYmIbgE6THrTe7Dyb3DibX4zYL2+1ratxVlrcwC8f/bzbh8A7Kl3XJZ32zGMMbcZY1YbY1bn5+e3sgwREZGu48Uvd2Gt5fpTkt0upWMUbof5t0P/yXDeb92uplXau9OyoRF0tqEDrbX/sNZOsdZOiY2NbecyREREAkt5VS0vf7WbmWPjA3NsVdFOeOEi5+6/K56HkG5uV9QqrQ1WeXVdfN4/93m3ZwFJ9Y5LBLJbX56IiIgAvLNuL8Xl1YE5tqpwOzx3PlSVwvXzIWqg2xW1WmuD1XvADd7vbwDm19t+vffuwJOB4rouQxEREWkday3PrNjJuAGRTBnUx+1y2lfBNidU1VTADQsgYYLbFbVJc6ZbeAVYCYw0xmQZY74LPAqca4zZBpzr/RlgEbADyACeBn7YIVWLiIh0IZ9vKyBjXyk3TxscWPNW1bVU1VbDDQudOav8XJO3FFhrrz7OrhkNHGuB29talIiIiHzr2RU7ie3VjfPHB9CN9lVl8Oq14KmBGxdBv1FuV9Qu/GfGLRERkS5o7e79fLoln+tOHkS3EP9Y1qVJ1sLCnzjL1Vz2r4AJVaBgJSIi4rNqaj088E468ZHhfDeQBq2vfR5SX4Xp98PQs9yupl0pWImIiPioF1buYnPOQR68YEzgTAiavR4W/QyGng1n3ON2Ne1OwUpERMQH5R2s4LGlW5k+MpZZ4+LdLqd9VBTDGzdARAxc+rRfrQHYXAESf0VERALLb9/fTFWth4cuHBs4dwJ+9BAc2A03LXHCVQAKvKgoIiLi5z7fls+CDdncPn0Yg/pGuF1O+8haA6ufgZO+DwNPcruaDqNgJSIi4kNqPZYH39vI4JgIvnfmELfLaR+eWnj/v6BXvDNgPYApWImIiPiQpZvy2JFfxj0zRxIeGiDTK6x+BnI2wMzfQXik29V0KAUrERERH/Lsip0MiOrOeWPi3C6lfZTkwccPw5CzYOwlblfT4RSsREREfMTG7GK+2lnEjacmExIcIB/RH/7CWQdwzp8gUAbhNyJA3jURERH/9+yKTHqEBXPF1CS3S2kfW5ZA2utw2n9BzDC3q+kUClYiIiI+IL+kkvfWZzPvhER6dw91u5y2278L3vkexI+H037idjWdRsFKRETEB7z81W6qaj3ceGqy26W0XU2lMxGotXDF8xAa7nZFnUYThIqIiLissqaWF7/cxVkjYxkS29Ptctrugwcgex1c+RJEB8iUEc2kFisRERGXLdyQQ0FpJTcHwkLLaW/C10/DqT+G0XPdrqbTqcVKRER8Wm5xBUvSc1iUnktRWRX3zRrFOUdNRbC78BC/mJ/Oqp2FjV7rkkkD+M1F43zqjrtaj+Xpz3cwvF9PThvmx8u8WAvrX3IWWB54Csx40O2KXKFgJSIiPmfvgXIWp+WwOD2XNbv2AzAyrhcGuOWF1Vw0sT8PXjCWqO6hPL8ykz8s2UJwkOGqqQPpFtJwaMovqeSVVXsorazl8Ssm+Ey4+veXu/gmt4S/XTPJf9cEPLAbFtwJ2z9xQtXlz0FwAAzAbwUFKxER8QkV1bW8sDKT99Ny2bDnAABjEiK5+7wRzBqXwLB+Pamq8fD3ZRn87ZMMlm8rIDG6Bxv2HGD6yFh+d0kK/aO6N/oYI+J78ejib6j1ePjLVZMIdTlc7TtYwZ8+2MLpw2M4PyXB1VpabfWzzlxV1jpzVU35LgT5Rmh1g4KViIi4rryqllte+JoVGYWMT+zNvbNGMXtcPMkxRy5AHBYSxF3njGDWuHh+9mYqmQVlPHbFBC6ZNKBZrT3fP3MoIUGG376/mVrPWv569WTCjtPC1RkeWbSZyhoPD180zj9bq1LfgIV3wZDpcMET0GeQ2xW5TsFKRERcVVZZw3ef/5pVO4v4n8sncNkJiU2eMyo+kvm3T6Oq1kO3kJatp3fL6UMIDjI8tGATl//vF/xh3gRGxvdqbfmttiKjgPnrs7ljxnAGHxUg/UL+Fqf7b+ApcO2bXbbr72hdt61ORERcV1pZw43PrmLVziIev3Jis0JVHWNMi0NVnZumDebv105mz/5y5v71c574eBvVtZ5WXas1Kmtq+eX8dAb17cEPpw/ttMdtN1Vl8Pr1ENod5j2jUFWPWqxERKRTVNbU8vRnOygorTq8bfWuIjbnlPDE1ZOYO75/p9YzJyWBkwZH89CCTTy2dCuL0nJ48trJDO2EeaSeWradHfllPHvTVMJDWxcOXWMtLLjLabG6/l2I7Nz3zdcpWImISIerqK7ley+u4T9b849YrqV7aDBPXjOJWePcGbjdt2c3b6hL4P6307jy/1by8q0nMyKu47oGX1iZyZ8/2saFE/pz1sh+HfY4HWbNs876f2c94IytkiMoWImISIcqr6rl1hdWs2J7Af99WQpXTh3odknHOG9sPENie3LN019y1T++5KVbTmJ0QmS7P84zy3fy8MJNnDsmjj9ePr7dr9/hstfB4nth6Aw4/W63q/FJGmMlIiId5lBVDTc/9zUrthfwx3kTfDJU1RnWryevfe8UwoKDuObpL9mYXdyu13/6sx08vHATs8bG8+Q1k1s9Psw15fvh9RsgIhYufbpLT6nQGL0qIiLSIapqPNz83Nd8tbOQx66YwLwWDEx3y+CYCF773sl0Dw3mmqe/YmteSZuuV1Pr4YuMAu55YwOPLNrM+SkJ/PWaSa5O8dAq1sK7P4SDe+Hy5yGir9sV+Sx1BYqISIf43aLNfLmjyDvPlO+HqjqD+kbw2vdO4dKnvuD7/17Dez86jZ7dWvZx+U3uQZ7/IpMPNuZRVFZF99BgbjhlEL+cO8ZnZnxvkS+egC2LYNajkDTV7Wp8moKViIi0u/dTc3jui0xumpbMpZP9J1TVSYruwRNXTeLaf37J/W+n8cRVE5s1gWdlTS1PfpLB35dtp1tIEDNGxzEnJZ4zR/Sje5ifdf3V2fUFfPQQjL4QTvq+29X4PAUrERFpVzvyS7n3rVQmDYzi/tmj3S6n1U4Z2pefnjeSP36whanJfbj+lORGj1+/5wA/e3MDW/NKuWTSAH41dwx9IsI6p9iOkpsOr30H+iTDRU+CP84O38kUrEREpN2UV9Xyw5fWEhpsePIad5eLaQ8/OHMoa3bt5zcLNzE+MYqJSVFH7C8ur+bjzXksTs/l48159OsVzjM3TuHsUXEuVdyOcjbACxdBSHe49g0Ib/+7JAORgpWIiLQLj8fywDtpbMkr4dkbpza5ILI/CAoyPHbFBM5/Yjnff3EN04bFHN6XX1rJyu0FVNda4iPDufX0Idx+9jAiwwNgFvLsdfDCxRDWE25cANFD3K7IbyhYiYhIm3k8lgfeTePtdXv5ybkjmO6PE18eR1SPMJ76zmR+9mYqX+4oPLy9e1gwN56azOyUBCYmRhEUFCDdZFlr4MVLoHtvuGGB0w0ozaZgJSIibVLrsdz3VipvrMni9rOG8uOzh7ldUrsbnxjFkrvOcLuMjrdnFfz7MugR7YSqKN+dd8xXKViJiEir1Xos97yxgbfX7eXOGcO565zhzbp7TnzQrpXw0jzo2Q9uWAi9B7hdkV9SsBIRkVYpKqvivrdS+XBTHj85dwR3zBjudknSWpnL4aUrIDLBCVWR7qzdGAgUrEREpEWstbyflsOD8zdysKKaX5w/mltO1+Bmv7VjGbx8ldPtd8MC6BUAdzS6SMFKRESabV9JBb94J50PN+UxIbE3f5h3MiPje7ldlrRWxsfw6jXOXX/Xvwc9Y92uyO8pWImISLPsKizjmqe/oqC0kvtnj+K7pw32z+VZxLH1Q2fyz5jhcP18iIhp+hxpkoKViIg0aWdBGdc8/SXl1bW89YNTGTegt9slSVtsWQyvXw+xo5xQ1SPa7YoChoKViEgXlbX/EEvSc9maV8Lpw2M5e1Q/IhpYbHh7filX/+NLajyWV249mdEJmoHbr239EF67DuLHwXXvQPc+blcUUBSsRES6kF2FZSxOz2VxWg4bsooB6NUthNdXZ9EtJIgzR8QybVgMod4uvlqPh798nAFYXr3tZEbEaTyVXyvaCW/dAv1Gw3XvQveops+RFlGwEhEJcNvzS1mclsOitFw25RwEYEJib+6bPYrZ4+JJ7NODNbv2sygthyXpuXy4Ke+I8+Mjw/n3LScxrJ9ClV+rroA3bgADXPmiQlUHUbASEQkw1lq27StlUVoOi9Ny2ZJXAsAJg/rwi/NHM8sbpuo7cXA0Jw6O5ldzx1BQWomtty+qRyjdQoI78RlIh1hyn7Ow8tWvapmaDqRgJSISAKy1bMo5yOK0XBal57AjvwxjYGpyNL++YAyzxiUQ3zu8yesEBRn6RTZ9nPiZ1NdhzbMw7U4YOdvtagKagpWIiB8rq6zh/z7bwfz1e9lVeIggAycP6ctN0wYzc2wc/XopJHV5eRthwZ0w8FQ4+1duVxPwFKxERPzUiowC7n0rlaz95Zw+PIbvnzmU88bE0bdnN7dLE1+RtxGevxC6RcK8ZyBYH/sdTa+wiIifOVhRze8XbeaVVXtI7tuD1247mZOG9HW7LPE1OanwwkUQEg43av2/zqJgJSLiRz75Jo+fv53OvpIKvnfGEP7r3BGEh2pguRwlex28cDGE9YQbFzhL1kinULASEfED+8uqeHjhJt5Zt5cRcT353+umMTFJt8tLA7LWwIuXQHhvJ1TpDsBOpWAlIuLjFqfl8Mv56Rw4VM0dM4Zz+1lDNf2BNGzPKvj3Zc4SNTcsgKiBblfU5ShYiYj4qPySSn41P53F6bmM7R/JCzefxJj+Wk5GjmPXSnhpHvTs54Sq3oluV9QlKViJiPgYay3vrt/LQws2caiylntmjuS2M4YcXmZG5BiZy+GlK5wB6jcsgMj+blfUZSlYiYj4kJzicn7+dhqfbsln8sAo/jBvvJaSkcbt+A+8fKXT7XfDAugV53ZFXZqClYiID7DW8sqqPfx+0WaqPR5+OXcMN56aTHCQcbs08WXbP4FXrnbu+rv+PegZ63ZFXZ6ClYgPq1vzbUtuSYvOGx7Xk5FxvTBGH8r+YHfhIe57O5UvthdyypC+PHpZCoP6Rrhdlvi6bUvh1WshZjhcPx8iYtyuSGhjsDLGZAIlQC1QY62dYoyJBl4DkoFM4Apr7f62lSkSWMoqa6jx2OPuz9p/iCXpuSxKy2F7flmrHmNITASzU+KZPS6Bsf0jXQtZHo8lSK0uDfJ4LM+vzOQPS7YQHGR45JJxXD11oF4vadqWJfD6dRA7yglVPaLdrki8jLXH/8+9yZOdYDXFWltQb9sfgCJr7aPGmPuAPtbaexu7zpQpU+zq1atbXYeIP8gsKGNReg5L0nNJzSpu8vi6Nd9mj4tn6uBoQpr5YVvrga8zi1icnsOXO4qo9VgGRvc4HLImJPbulJC1cnshP38njQOHqjhvTDyzU+I5dWgMYSEagA2wPb+Ue99MZfWu/UwfGcvvLkmhf1R3t8sSf7B5IbxxI8SPg+vege593K6oyzHGrLHWTmlwXwcEqy3AdGttjjEmAVhmrR3Z2HUUrAJLda2Hr3YUsSg9h43ZB4/YF9uzGzPHxnHumDiieoQBUFxezceb8/hwYx49woKZNS6eM0bEHnc26d2Fh1iUnsOKjAJGJ0Qye1w8E5OiMMZgrWVj9kEWpeWQtrf4cDAZEtuzXZ5bUVkVH27MZemmPArKqpp9XmlF9eGWpwlJUZw9sh89w4/fYNy7eyhnjYxt85pvhaWVLN2Ux+L0XFZkFFDjsQyI6s6scfHMSYlnUlIfgoKc121zTgmL03NYub2Q6kZa0yLDQ5gxqh+zxiUQ3/vYBX5LKqp5dPE3vPTVbgZG92BiUhSffLOP0soaIsND+M7Jg7hjxvAuO1t4Ta2Hpz/fyeMfbaV7aDC/mjuGSycPULetNM+m+fDmzZAwEb7zFnTXJLFu6MhgtRPYD1jg/6y1/zDGHLDWRtU7Zr+19pg4bYy5DbgNYODAgSfs2rWr1XVIxyipqObVVXsoqahu9jk5xRV8tDmP/Yeq6REWzKSBUYQEfdtCkbGvlL0HygkJMpw6LIZgA8szCqiutcRHhlNRU8uBQ9VEhAVz1qh+DIn5dpxJZa2H5dsKDoe1IbER7Ck6RHWtpX/vcE4e0pevdxWxp6ic4CDD4JgIMvaVAjAqvpcT1uq1lvTuEcY5o/sdM5al+FA1H23OY1fht11wHgvr9uw/3AKUFN2dITHND2uhwYZThsYwa1w8A1xqlSg+VM3SzXksTsvh820FVNV6iIvsxrShMazdvZ/MwkMEGZiYFEWv8NDjXif7QDnbvK/rCYP6cFK91jSPhbfXZpFzsIKbpw3mp+eNoEdYCBXVtazIKODtdXt5PzWHITER/GHeeKYkd63ui805B/nZm6mk7S1m5tg4fnPxOPr1OjacijQo/S1461ZInALXvgnhmtPMLR0ZrPpba7ONMf2ApcCPgfeaE6zqU4uV71m2ZR8/fzuN7OIKWvKLdM+wEGaM7sfslATObKDVyVpLalbx4S4xj7XMGhvP7JQEJiZGUWstX+4oZFFaLks35VJYr1XI4LT2zBmXwKxx8SRF9zgcghan57BqZxGTB/VhzrgEzh0TR5+IMHKKy1mSnsvitFzW7N6Pp97f97pvxyREMiclnuiIbizZmMsX3pYd4IjnPrivb4xZag8HK6r5ZPO+wy1UE5KimD0ugfPGxhHTjFayjH2lLEnP4f20XL7JPbJVckS/Xvzu0hROGNTwP/vPt+Vz31tpZBeXc8Mpyfxs1kh6hAX2fTRVNR6e/DSDJz/NoHf3UB6+aBxzUuL9+u+QdLLUN+Cd2yDpZLj2deimKTjc1GHB6qgH+TVQCtyKugL91oFDVfxm4WbeWpvFsH49+cO88UweGJj99/UHiK/dfQDg8FikOeMSGN9JY5G6orLKGv6w5BueX7mLpOjuPHrpeKYNC8w7mlKzDvCzN1P5JreEiyb258ELxhIdEeZ2WeJP1r8C838Ig6bBNa9BmO4YdVuHBCtjTAQQZK0t8X6/FHgYmAEU1hu8Hm2t/Vlj11Kw6ly1HsuaXftZlJbD0k157D/0batQda0Hj4UfnDmUH88Y1mXWI8spLudgeQ0j4noqTHWiVTuLuPetVHYWlHH1iUncP2c0kY10Q/qTiupaHv9oK09/toPYXt145OIUzhmjiRulhda+CO/9GIacCVe9AmE93K5I6LhgNQR4x/tjCPCytfYRY0xf4HVgILAbuNxaW9TYtRSs2q5u0Pbi9Bx2NHJ7fq3Hsm7PAfJLKgkLCeLMEbEk9/32H2pQkOGC8f0ZN6B3Z5Qt4gSQpVt5+vMd9OsVzpPXTj5uN6K/+DqziHvfTGVHQRlXTkni5+ePpnf3wAiM0olWPwsL74KhM+CqlyBUd436ik7pCmwLBavWsdayIauYxWk5LErPOWLQdmN35g/r15PZ4xI4a1Q/enYL7LEt4j/W7znAna+uI7+kkmdvnMpJQ/q6XVKLlVXW8McPtvD8ykz69+7Oo5elcPpwzYQtLWQtrPoHLP4ZDD8PrngRQnWTgy9RsPJxhaWVVNR4mn18zoFyFqfnsiQ99/AddtOGxTAnJZ5zx8Rr/Ib4rX0HK7j66S/JPlDBv26cwqlD/Wfc1fJtBdz3dipZ+8u54ZRB/GzWKCL0i4u01MFsWPgT2LoYRs6By5+DkLZNuyLtT8HKB+3IL2Vxei6L03NI33uw6ROOEhYcxBkjYpg9LoFzRsfRu4e6GSQw5JdUcu0/v2R30SH+ef1UThvum+HKWsvWvFIWpeWwOD2HrXmlDI6J4L8vG8+Jg7vWNBLSDqyFdS/CB7+A2iqY8Us46fsQ1DXGufobBSsfsS2vhEVpTpj6xrv22+SBUZw7Jp6+LWhl6hUewmnDYxqda0jEnxWWVnLtP79iR0EZD104lqumJrl2U0HGvlIWp+Xw8Tf7jpjTrbyq9vB0JFOTozk/JYErpyZ12YlPpQ2qDsGbN8HWJZB8OlzwF+g71O2qpBEKVi6pP5v14vRcMvaVOv8JD4pmdko8s8bFk9BbgxFFGrK/rIofvrSWlTsKmTasL49eOp6k6I6/I6qhlihwfglKqDe5a7AxTB0czcyxcZrkU1qvqgxevhIyl8OsR+HE2yBIyz75OgWrDmStPTybeB2PtXyduZ/FaTmHZ7OuW1pl5th4+kXqP2GR5vB4LK98vZvfL/qGWo/lnpkjufrEgXQPO7ZVqKisirS9xTT2f1q/XuGMTuh1TOtX/btqF6flsqOgDGPgxORo5qQkMHNsfIPL94i0SWWJE6p2r4RL/g/GX+F2RdJMClbtzFrLppyDLE7LZdFxpjcIDjKcOrQvc1ISOG9MXJvXfBPpyrIPlPPzd9JYtiWf7qHBnD2qH7PGxTMhMYrPtuUfseB0U5KiuzNnXAKzUxIwwCJvmNpddIjgIMMpQ/oyOyWe88bEE9tL/26lg1QchJcuh6yv4bKnYdxlblckLaBg1Q6staTtLT48RmrXUS1RY/r3Pmb5kz66O0+k3VhrWbmjkMVpuSxOz6WgtPLwviExEcxJSWDasBi6hTbcjWItZOxzxjmuqLdske6qlU7j8cCer2Dze7DxXSjbB5f9C8Ze7HZl0kIKVs1UvzsgNav4iH078suOWDx49rh4tUSJuKRu9YCN2cWcOjSmxTPmFx+q5pMteVgLM0bprlrpYDVV8MVfYNU/oTQXgrvBsBlw0vdgyHS3q5NW6PLBqqC0knfW7m30mPzSSpakO90BQQbG9I8kLPjb33yjI7oxc2wc546JI6qHfqMVEZFmyF4H838EeekwfKYzjmr4eRAe6XZl0gaNBasuMXtd3sEKHlm0udFj6lqifjh9KOeqJUpERNqi6hB89kdY8ReIiHXW+Rs1x+2qpBN0iWA1Kj6S9IdmNnpMaLDpMgsOi4hIB6gqg20fwqb5sPVDqC6DSdfBeb+F7lFuVyedpEsEq+AgozXxRET8WXU5fPo7SH8LbL0lwCJiYNRcGHMRxI6Czp5ItuKgN0y9C9s+gppyp4Vq/BUw4SoYeHLn1iOuU9oQERHftusLZ5xS0XYnRPWot2RQQQYsexSW/R5iRkDi1PYLV0EhMGgajJgJ4b2/3V5+ALYsdlqmtn/sLEHTMx4mX+cEvIGnaCmaLkzBSkREfENZgTNZZu23SweRuRxW/wuiBsH178GQM489ryQPvlngBJ0dy9qvnqoyWPMcBIfB0LOdwJS53HkMTzVEJsLUW2HMhZB4omZMF6CL3BUoAjizHNfvQmhKaAQE63cPkQ5VPxRlLm/g36hxFiOe8UsIi+jc2jweZwLPTfOduaeK9zgBb8xFMOZiGDC587sexSd0+bsCpYuyFvZtdv5T3DQf8hu/M/QY4VEw6nznP9Eh0yFEd4qKtIuD2bDZG6Z2fQFY6DscTvsJjJgF3Xp9e2x4JET2d6fOoCAYeJLzNfMRKMmFXvEKU9IoBSsJLNZCbqo3TL0HhdsA44yTOPuXENrMRa+tdead2bwQ1r8E3SIhZrhzrYYYA/0naXyFSGNyNsCS+2HXCufnfmNg+n3uDTxvCWMgMsHtKsQPKFiJ/6lridr2IVQf+nZ7xUHYuhj2Z4IJhuTT4OQfOINde8W17rFqqmDnf5ygdjD7+MfVVsHaF2DVP5w7gkZf4HxYDDpN3Yki1RXw2R9g+Z+du/jO/gWMvghiR7hdmUi70xgr8R0leXBg1/H311TCjk+dkFOYcez+oFAYfIYTaEad7/wH3pkqSyFj6ZFz2HSPhtFzYeT5R97JFBwG8Slq2ZLWKc5qPOibIIgb2/wW2o5irTMYfcFdULAFJl7rdKl17+NuXSJt1OWXtBEf56mFL5+CT37rzAHTGBMMg0/3hqe50LNf59TYUlWHnNuwN73n3JZdVXLsMRGx386/k3y6WrakaTVV8Pmf4PP/AU9N48eG9XSmCRhzEQw7p/MGflsLOeu/HdtYtMO5e+7Cvzh1iAQABSvxXfu+gfm3w97VzqDVqbc0Ms7CQMJEiOjbqSW2WXWF8/xqKr7ddmg/bFkEWz9wWraCuzmtWHXCe8PIWc6dR4NOVcuWQNYa599K/mZIuQLGX3ncIX9Ul0PGR84YwUMFznxMIY20XoVFwPBznb9vg8+AkBauh+rxwN41ziSZm9+DA7udX4KGnAmjL4SUeUcOSBfxcwpW0vkKtjm/rf5/e/cebGV13nH8++MuHkDwBggNyKBIHTGiiBbvDShmSjI2U5tarXSapNM2mEnSaJPO1DptE5ppOh0703aiM2baadq0VcilGmK1NqlQRcL9dpAIKHIRBaFyO+fpH2tt9nuAfYDTzdnn7Pf3mdlz3rPed2/e/bD2fp+z1nrXentF7WPa29I4qQEtcNf89OXbkwevng1HPoDW52Hr4nRxqnjvjVR+9AMYfEGavbmnJleDz09dr+Nvgb79G302zaW9H7gljwAADMBJREFUDbYshpX/nMbwtYyEj34jJd2no+0obPlv2PRC6kqv5f3tsHFRalkdNCzd7HG6/5fRDm++BvveTN3xE25PrWSX39Wx+9usiTixsrMvAnatqzb/71yTykdM6PwLetTVMPOxntul10iHD6SLXTGePdHebXB4f3V6ijHXpTE+FcPH5Qu1uzpPS9tReOPHee6k78KBXdBvEEz5VfjIox1nAK+nIwfzGMaF8NYy4AyuDSMmpGTqslleE89KwYmVnR0R8PbK6uR5uzcAStMNTJ6T7owbdkmjz9LOtiMHYdN/pDqw7gdwaO+Jx1QG8V8xBy68vGPL5JBRPbc1rru0Hanefbr2e/DBHug/GCbOTJ+liTNhYEujz9LMMk8QavUTkf6arbRMvbs5tU6MmwHTPpWSqSEjG32W1p36D4JJs9Pj6OHUwnJMob6sejp1Zx2vrNNTHD2UuujWLEjj7Q6+BwOG5LF1c2DCHTBgcKPP0szOUEm+weyUdm2AtQvSYOqD+2ofd3Av7H+7OjB1xkPpzrbuntrAeqZ+A05spRw2JiVORw6mVpn9O6r72o6kZUyW/xO8+mRq2Zp0d9cHUfd0lTF1axbAhmfh0L58o8Ldab25S29LiaqZ9VruCiyrWsu9jLkOhnbSfdd3QEqoLp/tgalWP8WEozI9xaBhMHFWatGq6Ns/1b9xN/WegfLFsXKVu0DPGZ6n2mjSBNKsyXmMVZnt3thx0s32dti6JE+yWVjuZfKcNAamUWtymVUUu8haf5SSrmP7DkL7kZyY3J3GHnU2P9M5I9INEn36nLjv+M9GvR3YDeu+Bxt/VL27s9LlOW5G70kMzewETqzK5FhL1DO5JWrdicdUlnupTLLZ1eVezLrbybrSTmXI6NTNNnlOagVbs7Bri3J3RcvI6r/tNSTNmoYTq2a0a331ArFj5UkOKLREjZrS8S6sERN63ySbZsc7egjeXgXRVvuYPZvT3YobF0FbZR4npUlXJ89JrVlna+60foPg4itP3lpmZr2a7wpsBhFpLqNjY6LWAYKx18NNn08T81UMHZXGQHluKGtm/QbCmKmdHzN2Gkz5FTj0fpqM9tD+NMO/W2nN7CxxYtXT7FwHm1+CtsPVsgM709w2ezalqQ0+9Atp6ZdJH01JlJl1buAQuPKeRp+FmZWAE6tGi4Adq6stUbvXn3hMZeHhG3+3Zy88bGZmVnJOrBohArYvL6z+XmiJmvZbqauiuGxF3wGe28bMzKwXcGLVXSKqq7+vWVBd/X38zXDj7+WWqAtP/TpmZmbWYzmxqqfKci/rvt9xdun2o7D5v2Dftrz6+21wy5c8yaaZmVmTcWJ1OnZvhB2rau8/1hq1EPbmlqiWwl1HEoy8Cu74w9TN59XfzczMmpITq4pD+yHaq7/v3XpmEwlWWqJudUuUmZlZWZU7sdq1oTqAvOYkmzfCXfPTzz6dhGvo6I4Dzs3MzKx0ypFY7W6FZz7TseyDd+Gd1rQ9djrc9hXof051/6ChaQFYTyRoZmZmp6kciVWfPmmCwKLBF8C0T3vhYTMzM6ubciRWIy6FX3+60WdhZmZmTc6rg5qZmZnViRMrMzMzszpxYmVmZmZWJ06szMzMzOrEiZWZmZlZnTixMjMzM6sTJ1ZmZmZmdeLEyszMzKxOnFiZmZmZ1YkTKzMzM7M6cWJlZmZmVidOrMzMzMzqxImVmZmZWZ0oIhp9DkjaBbzRDf/UBcDubvh3mo3j1jWOW9c4bl3juHWN49Y1ZY/bhyLiwpPt6BGJVXeR9GpEXNvo8+htHLeucdy6xnHrGsetaxy3rnHcanNXoJmZmVmdOLEyMzMzq5OyJVZ/1+gT6KUct65x3LrGcesax61rHLeucdxqKNUYKzMzM7OzqWwtVmZmZmZnTa9OrCQ9KWmnpFWFsqslLZb0U0mvSpqWy4dJ+q6k5ZJWS3qw8JwHJG3Mjwca8V66U424TZH0sqSVOU5DC/sekdQqab2kWYXyO3NZq6SHu/t9dLcziZukj0hamsuXSrq98JypubxV0l9JUiPeT3c50/qW9/+cpP2SvlAoc33r/HN6Vd63Ou8flMtd32p/TvtLeiqXr5X0SOE5ZatvYyW9kOOwWtK8XD5C0qJ8fVwkaXguV65PrZJWSLqm8FqluqaeICJ67QO4GbgGWFUo+yFwV96eDbyYt/8A+FrevhDYAwwARgCv55/D8/bwRr+3BsTtFeCWvD0XeCxvTwaWAwOB8cAmoG9+bAIuzXFcDkxu9HvrQXH7MDA6b18JvFl4zv8ANwAC/r1SX5v1cSZxK+z/V+A7wBfy765vnde3fsAKYEr+/Xygr+vbKeP2SeDbeXsw8DNgXEnr2yjgmrw9BNiQv//nAw/n8oepXkdn5/okYDqwJJeX7pp6/KNXt1hFxEukBKlDMVD5K24Y8FahfEj+a60lP+8oMAtYFBF7IuJdYBFw59k+90aqEbfLgZfy9iLgnrw9h/TFcygiNgOtwLT8aI2I1yPiMPDtfGzTOpO4RcSyiKjUvdXAIEkDJY0ChkbEy5G+hb4FfOzsn33jnGF9Q9LHSF/GqwvHu74lteI2E1gREcvzc9+JiDbXt2NqxS2AcyX1A84BDgP7KGd92x4Rr+Xt94G1wCWk9/1UPuwpqvVnDvCtSBYD5+X6Vrpr6vF6dWJVw0PAn0vaCnwdqDTtPg5cQUq0VgLzIqKdVHG2Fp6/LZeVzSrgl/L2J4CxebtWfBy3pFbciu4BlkXEIVKMthX2OW6FuEk6F/gS8Ohxx7u+JbXq22VASHpO0muSfj+Xu74lteL2L8ABYDuwBfh6ROyh5PVN0jhSq/sS4OKI2A4p+QIuyof52lBDMyZWvw18LiLGAp8Dnsjls4CfAqOBq4HHcz/7ycYblPFWybnA70haSmoGPpzLa8XHcUtqxQ0AST8PfA34dKXoJK/huFXj9ijwjYjYf9zxjltSK279gBnAr+WfH5d0B45bRa24TQPaSNeF8cDnJV1KieMmqYXUFf9QROzr7NCTlPnaQPowNpsHgHl5+zvAN/P2g8BXc3N4q6TNwCRSNn1r4fljgBe75Ux7kIhYR+pOQNJlwN151zY6tsKModq9Wqu8NDqJG5LGAE8D90fEply8jRSrCsetY9yuB35Z0nzgPKBd0kFgKa5vp/qc/mdE7M77fkAaZ/T3uL51FrdPAs9GxBFgp6SfANeSWlxKV98k9SclVf8QEf+Wi3dIGhUR23NX385cXuvaUPprajO2WL0F3JK3bwc25u0twB0Aki4m9bm/DjwHzJQ0PN/tMDOXlYqki/LPPsBXgL/JuxYC9+bxQeOBiaTBsK8AEyWNlzQAuDcfWyq14ibpPOD7wCMR8ZPK8bkp/X1J0/N4v/uBBd1+4g1WK24RcVNEjIuIccBfAn8aEY/j+gZ0+jl9DrhK0uA8XugWYI3rW9JJ3LYAt+c73M4lDcJeRwnrW64fTwBrI+IvCrsWkhosyD8XFMrvz7GbDuzN9c3X1EaPnv//PIB/JPWNHyFlyb9JagZfSrqLYwkwNR87mnTH4EpSf/t9hdeZSxqU3Qo82Oj31aC4zSPdBbIB+Cp58th8/JdJd8isp3BHEemukA1535cb/b56UtxIX94HSN3PlcdFed+1uQ5uIo39U6PeU0+L23HP+yPyXYGub6f1Ob2PNOB/FTC/UO76Vvtz2kLq2VgNrAG+WOL6NoPUZbei8J01m3SH6fOkRorngRH5eAF/neOzEri28FqluqYe//DM62ZmZmZ10oxdgWZmZmYN4cTKzMzMrE6cWJmZmZnViRMrMzMzszpxYmVmZmZWJ06szMzMzOrEiZWZlZ6kvo0+BzNrDk6szKxXkfSYpHmF3/9E0mclfVHSK5JWSHq0sP8ZSUslrZb0qUL5fkl/LGkJcEM3vw0za1JOrMyst3mCvMRGXqLkXmAHabmlaaRF1qdKujkfPzcippJmIP+spPNz+bnAqoi4PiJ+3J1vwMyaVzMuwmxmTSwifibpHUkfBi4GlgHXkdYkW5YPayElWi+RkqmP5/KxufwdoI204KyZWd04sTKz3uibwG8AI4EnSQus/1lE/G3xIEm3Ar8I3BAR/yvpRWBQ3n0wItq664TNrBzcFWhmvdHTwJ2klqrn8mOupBYASZdIuggYBrybk6pJwPRGnbCZlYNbrMys14mIw5JeAN7LrU4/lHQF8LIkgP3AfcCzwGckrQDWA4sbdc5mVg6KiEafg5nZGcmD1l8DPhERGxt9PmZmFe4KNLNeRdJkoBV43kmVmfU0brEyMzMzqxO3WJmZmZnViRMrMzMzszpxYmVmZmZWJ06szMzMzOrEiZWZmZlZnTixMjMzM6uT/wMr3RYALy8h2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year      prop\n",
       "0             Mary   F    7065  1880  0.077643\n",
       "1             Anna   F    2604  1880  0.028618\n",
       "2             Emma   F    2003  1880  0.022013\n",
       "3        Elizabeth   F    1939  1880  0.021309\n",
       "4           Minnie   F    1746  1880  0.019188\n",
       "...            ...  ..     ...   ...       ...\n",
       "1690779    Zymaire   M       5  2010  0.000003\n",
       "1690780     Zyonne   M       5  2010  0.000003\n",
       "1690781  Zyquarius   M       5  2010  0.000003\n",
       "1690782      Zyran   M       5  2010  0.000003\n",
       "1690783      Zzyzx   M       5  2010  0.000003\n",
       "\n",
       "[1690784 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          y\n",
       "1          a\n",
       "2          a\n",
       "3          h\n",
       "4          e\n",
       "          ..\n",
       "1690779    e\n",
       "1690780    e\n",
       "1690781    s\n",
       "1690782    n\n",
       "1690783    x\n",
       "Name: name, Length: 1690784, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract last letter from name column\n",
    "get_last_letter = lambda x: x[-1]\n",
    "last_letters = names.name.map(get_last_letter)\n",
    "last_letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          y\n",
       "1          a\n",
       "2          a\n",
       "3          h\n",
       "4          e\n",
       "          ..\n",
       "1690779    e\n",
       "1690780    e\n",
       "1690781    s\n",
       "1690782    n\n",
       "1690783    x\n",
       "Name: last_letter, Length: 1690784, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_letters.name = 'last_letter'\n",
    "last_letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"10\" halign=\"left\">F</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>31446.0</td>\n",
       "      <td>31581.0</td>\n",
       "      <td>36536.0</td>\n",
       "      <td>38330.0</td>\n",
       "      <td>43680.0</td>\n",
       "      <td>45408.0</td>\n",
       "      <td>49100.0</td>\n",
       "      <td>48942.0</td>\n",
       "      <td>59442.0</td>\n",
       "      <td>58631.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39124.0</td>\n",
       "      <td>38815.0</td>\n",
       "      <td>37825.0</td>\n",
       "      <td>38650.0</td>\n",
       "      <td>36838.0</td>\n",
       "      <td>36156.0</td>\n",
       "      <td>34654.0</td>\n",
       "      <td>32901.0</td>\n",
       "      <td>31430.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>50950.0</td>\n",
       "      <td>49284.0</td>\n",
       "      <td>48065.0</td>\n",
       "      <td>45914.0</td>\n",
       "      <td>43144.0</td>\n",
       "      <td>42600.0</td>\n",
       "      <td>42123.0</td>\n",
       "      <td>39945.0</td>\n",
       "      <td>38862.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>27113.0</td>\n",
       "      <td>27238.0</td>\n",
       "      <td>27697.0</td>\n",
       "      <td>26778.0</td>\n",
       "      <td>26078.0</td>\n",
       "      <td>26635.0</td>\n",
       "      <td>26864.0</td>\n",
       "      <td>25318.0</td>\n",
       "      <td>24048.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>609.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60838.0</td>\n",
       "      <td>55829.0</td>\n",
       "      <td>53391.0</td>\n",
       "      <td>51754.0</td>\n",
       "      <td>50670.0</td>\n",
       "      <td>51410.0</td>\n",
       "      <td>50595.0</td>\n",
       "      <td>47910.0</td>\n",
       "      <td>46172.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>33378.0</td>\n",
       "      <td>34080.0</td>\n",
       "      <td>40399.0</td>\n",
       "      <td>41914.0</td>\n",
       "      <td>48089.0</td>\n",
       "      <td>49616.0</td>\n",
       "      <td>53884.0</td>\n",
       "      <td>54353.0</td>\n",
       "      <td>66750.0</td>\n",
       "      <td>66663.0</td>\n",
       "      <td>...</td>\n",
       "      <td>145395.0</td>\n",
       "      <td>144651.0</td>\n",
       "      <td>144769.0</td>\n",
       "      <td>142098.0</td>\n",
       "      <td>141123.0</td>\n",
       "      <td>142999.0</td>\n",
       "      <td>143698.0</td>\n",
       "      <td>140966.0</td>\n",
       "      <td>135496.0</td>\n",
       "      <td>129012.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>2418.0</td>\n",
       "      <td>2589.0</td>\n",
       "      <td>2723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>52265.0</td>\n",
       "      <td>50103.0</td>\n",
       "      <td>49079.0</td>\n",
       "      <td>47556.0</td>\n",
       "      <td>45464.0</td>\n",
       "      <td>43217.0</td>\n",
       "      <td>40251.0</td>\n",
       "      <td>36937.0</td>\n",
       "      <td>33181.0</td>\n",
       "      <td>30656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10691.0</td>\n",
       "      <td>11009.0</td>\n",
       "      <td>11718.0</td>\n",
       "      <td>12399.0</td>\n",
       "      <td>13025.0</td>\n",
       "      <td>13992.0</td>\n",
       "      <td>14306.0</td>\n",
       "      <td>14834.0</td>\n",
       "      <td>16640.0</td>\n",
       "      <td>16352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>10469.0</td>\n",
       "      <td>10404.0</td>\n",
       "      <td>12145.0</td>\n",
       "      <td>12063.0</td>\n",
       "      <td>13917.0</td>\n",
       "      <td>13927.0</td>\n",
       "      <td>14936.0</td>\n",
       "      <td>14980.0</td>\n",
       "      <td>17931.0</td>\n",
       "      <td>17601.0</td>\n",
       "      <td>...</td>\n",
       "      <td>139109.0</td>\n",
       "      <td>134557.0</td>\n",
       "      <td>130569.0</td>\n",
       "      <td>128367.0</td>\n",
       "      <td>125190.0</td>\n",
       "      <td>123707.0</td>\n",
       "      <td>123397.0</td>\n",
       "      <td>122633.0</td>\n",
       "      <td>112922.0</td>\n",
       "      <td>110425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>106.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>2737.0</td>\n",
       "      <td>2722.0</td>\n",
       "      <td>2710.0</td>\n",
       "      <td>2903.0</td>\n",
       "      <td>3086.0</td>\n",
       "      <td>3301.0</td>\n",
       "      <td>3473.0</td>\n",
       "      <td>3633.0</td>\n",
       "      <td>3476.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                F                                                        \\\n",
       "year            1880     1881     1882     1883     1884     1885     1886   \n",
       "last_letter                                                                  \n",
       "a            31446.0  31581.0  36536.0  38330.0  43680.0  45408.0  49100.0   \n",
       "b                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "c                NaN      NaN      5.0      5.0      NaN      NaN      NaN   \n",
       "d              609.0    607.0    734.0    810.0    916.0    862.0   1007.0   \n",
       "e            33378.0  34080.0  40399.0  41914.0  48089.0  49616.0  53884.0   \n",
       "...              ...      ...      ...      ...      ...      ...      ...   \n",
       "v                NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "w                NaN      5.0      NaN      NaN      NaN      NaN      5.0   \n",
       "x                NaN      NaN      NaN      7.0      NaN      NaN      NaN   \n",
       "y            10469.0  10404.0  12145.0  12063.0  13917.0  13927.0  14936.0   \n",
       "z              106.0     95.0    106.0    141.0    148.0    150.0    202.0   \n",
       "\n",
       "sex                                     ...         M                      \\\n",
       "year            1887     1888     1889  ...      2001      2002      2003   \n",
       "last_letter                             ...                                 \n",
       "a            48942.0  59442.0  58631.0  ...   39124.0   38815.0   37825.0   \n",
       "b                NaN      NaN      NaN  ...   50950.0   49284.0   48065.0   \n",
       "c                NaN      NaN      NaN  ...   27113.0   27238.0   27697.0   \n",
       "d             1027.0   1298.0   1374.0  ...   60838.0   55829.0   53391.0   \n",
       "e            54353.0  66750.0  66663.0  ...  145395.0  144651.0  144769.0   \n",
       "...              ...      ...      ...  ...       ...       ...       ...   \n",
       "v                NaN      NaN      NaN  ...    1209.0    1332.0    1652.0   \n",
       "w                NaN      NaN      NaN  ...   52265.0   50103.0   49079.0   \n",
       "x                NaN      NaN      NaN  ...   10691.0   11009.0   11718.0   \n",
       "y            14980.0  17931.0  17601.0  ...  139109.0  134557.0  130569.0   \n",
       "z              188.0    238.0    277.0  ...    2840.0    2737.0    2722.0   \n",
       "\n",
       "sex                                                                      \\\n",
       "year             2004      2005      2006      2007      2008      2009   \n",
       "last_letter                                                               \n",
       "a             38650.0   36838.0   36156.0   34654.0   32901.0   31430.0   \n",
       "b             45914.0   43144.0   42600.0   42123.0   39945.0   38862.0   \n",
       "c             26778.0   26078.0   26635.0   26864.0   25318.0   24048.0   \n",
       "d             51754.0   50670.0   51410.0   50595.0   47910.0   46172.0   \n",
       "e            142098.0  141123.0  142999.0  143698.0  140966.0  135496.0   \n",
       "...               ...       ...       ...       ...       ...       ...   \n",
       "v              1823.0    1794.0    2010.0    2295.0    2418.0    2589.0   \n",
       "w             47556.0   45464.0   43217.0   40251.0   36937.0   33181.0   \n",
       "x             12399.0   13025.0   13992.0   14306.0   14834.0   16640.0   \n",
       "y            128367.0  125190.0  123707.0  123397.0  122633.0  112922.0   \n",
       "z              2710.0    2903.0    3086.0    3301.0    3473.0    3633.0   \n",
       "\n",
       "sex                    \n",
       "year             2010  \n",
       "last_letter            \n",
       "a             28438.0  \n",
       "b             38859.0  \n",
       "c             23125.0  \n",
       "d             44398.0  \n",
       "e            129012.0  \n",
       "...               ...  \n",
       "v              2723.0  \n",
       "w             30656.0  \n",
       "x             16352.0  \n",
       "y            110425.0  \n",
       "z              3476.0  \n",
       "\n",
       "[26 rows x 262 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = names.pivot_table('births', index=last_letters,\n",
    "                          columns=['sex', 'year'], aggfunc=sum)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.016148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.110972</td>\n",
       "      <td>0.152569</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.160987</td>\n",
       "      <td>0.058168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "...               ...       ...       ...       ...       ...       ...\n",
       "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
       "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
       "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
       "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
       "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831\n",
       "\n",
       "[26 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = subtable / subtable.sum()\n",
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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')\n",
    "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female',\n",
    "                      legend=False)\n",
    "plt.subplots_adjust(hspace=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = table / table.sum()\n",
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138d49c6f88>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boy names that became girl names (and vice versa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_names = pd.Series(top1000.name.unique())\n",
    "lesley_like = all_names[all_names.str.lower().str.contains('lesl')]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>79</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2462</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>11</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>92</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.000913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4512</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>9</td>\n",
       "      <td>1882</td>\n",
       "      <td>0.000083</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",
       "    <tr>\n",
       "      <th>256327</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>699</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.000371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258036</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>1975</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.001081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258378</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>598</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260073</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>1558</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260456</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>502</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name sex  births  year      prop\n",
       "632     Leslie   F       8  1880  0.000088\n",
       "1108    Leslie   M      79  1880  0.000715\n",
       "2462    Leslie   F      11  1881  0.000120\n",
       "3014    Leslie   M      92  1881  0.000913\n",
       "4512    Leslie   F       9  1882  0.000083\n",
       "...        ...  ..     ...   ...       ...\n",
       "256327   Lesly   F     699  2008  0.000371\n",
       "258036  Leslie   F    1975  2009  0.001081\n",
       "258378   Lesly   F     598  2009  0.000327\n",
       "260073  Leslie   F    1558  2010  0.000886\n",
       "260456   Lesly   F     502  2010  0.000285\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>8.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>11.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>9.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>7.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>15.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>4166.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3805.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>3022.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>2573.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2060.0</td>\n",
       "      <td>NaN</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     8.0   79.0\n",
       "1881    11.0   92.0\n",
       "1882     9.0  128.0\n",
       "1883     7.0  125.0\n",
       "1884    15.0  125.0\n",
       "...      ...    ...\n",
       "2006  4166.0    NaN\n",
       "2007  3805.0    NaN\n",
       "2008  3022.0    NaN\n",
       "2009  2573.0    NaN\n",
       "2010  2060.0    NaN\n",
       "\n",
       "[131 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = filtered.pivot_table('births', index='year',\n",
    "                             columns='sex', aggfunc='sum')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1880      87.0\n",
       "1881     103.0\n",
       "1882     137.0\n",
       "1883     132.0\n",
       "1884     140.0\n",
       "         ...  \n",
       "2006    4166.0\n",
       "2007    3805.0\n",
       "2008    3022.0\n",
       "2009    2573.0\n",
       "2010    2060.0\n",
       "Length: 131, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</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",
       "...        ...       ...\n",
       "2006  1.000000       NaN\n",
       "2007  1.000000       NaN\n",
       "2008  1.000000       NaN\n",
       "2009  1.000000       NaN\n",
       "2010  1.000000       NaN\n",
       "\n",
       "[131 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = table.div(table.sum(1), axis=0)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138d4b27a08>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={'M': 'k-', 'F': 'k--'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## USDA Food Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{\n",
    "  \"id\": 21441,\n",
    "  \"description\": \"KENTUCKY FRIED CHICKEN, Fried Chicken, EXTRA CRISPY,\n",
    "Wing, meat and skin with breading\",\n",
    "  \"tags\": [\"KFC\"],\n",
    "  \"manufacturer\": \"Kentucky Fried Chicken\",\n",
    "  \"group\": \"Fast Foods\",\n",
    "  \"portions\": [\n",
    "    {\n",
    "      \"amount\": 1,\n",
    "      \"unit\": \"wing, with skin\",\n",
    "      \"grams\": 68.0\n",
    "    },\n",
    "\n",
    "    ...\n",
    "  ],\n",
    "  \"nutrients\": [\n",
    "    {\n",
    "      \"value\": 20.8,\n",
    "      \"units\": \"g\",\n",
    "      \"description\": \"Protein\",\n",
    "      \"group\": \"Composition\"\n",
    "    },\n",
    "\n",
    "    ...\n",
    "  ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6636"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "db = json.load(open('datasets/usda_food/database.json'))\n",
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['id', 'description', 'tags', 'manufacturer', 'group', 'portions', 'nutrients'])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'value': 25.18,\n",
       " 'units': 'g',\n",
       " 'description': 'Protein',\n",
       " 'group': 'Composition'}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0]['nutrients'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.180</td>\n",
       "      <td>g</td>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.200</td>\n",
       "      <td>g</td>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.060</td>\n",
       "      <td>g</td>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.280</td>\n",
       "      <td>g</td>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>376.000</td>\n",
       "      <td>kcal</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>1.472</td>\n",
       "      <td>g</td>\n",
       "      <td>Serine</td>\n",
       "      <td>Amino Acids</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>93.000</td>\n",
       "      <td>mg</td>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>18.584</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>8.275</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.830</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       value units                         description        group\n",
       "0     25.180     g                             Protein  Composition\n",
       "1     29.200     g                   Total lipid (fat)  Composition\n",
       "2      3.060     g         Carbohydrate, by difference  Composition\n",
       "3      3.280     g                                 Ash        Other\n",
       "4    376.000  kcal                              Energy       Energy\n",
       "..       ...   ...                                 ...          ...\n",
       "157    1.472     g                              Serine  Amino Acids\n",
       "158   93.000    mg                         Cholesterol        Other\n",
       "159   18.584     g        Fatty acids, total saturated        Other\n",
       "160    8.275     g  Fatty acids, total monounsaturated        Other\n",
       "161    0.830     g  Fatty acids, total polyunsaturated        Other\n",
       "\n",
       "[162 rows x 4 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients = pd.DataFrame(db[0]['nutrients'])\n",
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1008</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheese, cheddar</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1009</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese, edam</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1018</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cheese, feta</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1019</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cheese, mozzarella, part skim milk</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1028</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          description                   group    id  \\\n",
       "0                     Cheese, caraway  Dairy and Egg Products  1008   \n",
       "1                     Cheese, cheddar  Dairy and Egg Products  1009   \n",
       "2                        Cheese, edam  Dairy and Egg Products  1018   \n",
       "3                        Cheese, feta  Dairy and Egg Products  1019   \n",
       "4  Cheese, mozzarella, part skim milk  Dairy and Egg Products  1028   \n",
       "\n",
       "  manufacturer  \n",
       "0               \n",
       "1               \n",
       "2               \n",
       "3               \n",
       "4               "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_keys = ['description', 'group', 'id', 'manufacturer']\n",
    "info = pd.DataFrame(db, columns=info_keys)\n",
    "info[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6636 entries, 0 to 6635\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   description   6636 non-null   object\n",
      " 1   group         6636 non-null   object\n",
      " 2   id            6636 non-null   int64 \n",
      " 3   manufacturer  5195 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 207.5+ KB\n"
     ]
    }
   ],
   "source": [
    "info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vegetables and Vegetable Products    812\n",
       "Beef Products                        618\n",
       "Baked Products                       496\n",
       "Breakfast Cereals                    403\n",
       "Legumes and Legume Products          365\n",
       "Fast Foods                           365\n",
       "Lamb, Veal, and Game Products        345\n",
       "Sweets                               341\n",
       "Fruits and Fruit Juices              328\n",
       "Pork Products                        328\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(info.group)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nutrients = []\n",
    "\n",
    "for rec in db:\n",
    "    fnuts = pd.DataFrame(rec['nutrients'])\n",
    "    fnuts['id'] = rec['id']\n",
    "    nutrients.append(fnuts)\n",
    "nutrients\n",
    "print(nutrients)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "nutrients = pd.concat(nutrients, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.180</td>\n",
       "      <td>g</td>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.200</td>\n",
       "      <td>g</td>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.060</td>\n",
       "      <td>g</td>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.280</td>\n",
       "      <td>g</td>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>376.000</td>\n",
       "      <td>kcal</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39.280</td>\n",
       "      <td>g</td>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1573.000</td>\n",
       "      <td>kJ</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000</td>\n",
       "      <td>g</td>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>673.000</td>\n",
       "      <td>mg</td>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.640</td>\n",
       "      <td>mg</td>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22.000</td>\n",
       "      <td>mg</td>\n",
       "      <td>Magnesium, Mg</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>490.000</td>\n",
       "      <td>mg</td>\n",
       "      <td>Phosphorus, P</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>93.000</td>\n",
       "      <td>mg</td>\n",
       "      <td>Potassium, K</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>690.000</td>\n",
       "      <td>mg</td>\n",
       "      <td>Sodium, Na</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.940</td>\n",
       "      <td>mg</td>\n",
       "      <td>Zinc, Zn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.024</td>\n",
       "      <td>mg</td>\n",
       "      <td>Copper, Cu</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.021</td>\n",
       "      <td>mg</td>\n",
       "      <td>Manganese, Mn</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14.500</td>\n",
       "      <td>mcg</td>\n",
       "      <td>Selenium, Se</td>\n",
       "      <td>Elements</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1054.000</td>\n",
       "      <td>IU</td>\n",
       "      <td>Vitamin A, IU</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>262.000</td>\n",
       "      <td>mcg</td>\n",
       "      <td>Retinol</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value units                  description        group    id\n",
       "0     25.180     g                      Protein  Composition  1008\n",
       "1     29.200     g            Total lipid (fat)  Composition  1008\n",
       "2      3.060     g  Carbohydrate, by difference  Composition  1008\n",
       "3      3.280     g                          Ash        Other  1008\n",
       "4    376.000  kcal                       Energy       Energy  1008\n",
       "5     39.280     g                        Water  Composition  1008\n",
       "6   1573.000    kJ                       Energy       Energy  1008\n",
       "7      0.000     g         Fiber, total dietary  Composition  1008\n",
       "8    673.000    mg                  Calcium, Ca     Elements  1008\n",
       "9      0.640    mg                     Iron, Fe     Elements  1008\n",
       "10    22.000    mg                Magnesium, Mg     Elements  1008\n",
       "11   490.000    mg                Phosphorus, P     Elements  1008\n",
       "12    93.000    mg                 Potassium, K     Elements  1008\n",
       "13   690.000    mg                   Sodium, Na     Elements  1008\n",
       "14     2.940    mg                     Zinc, Zn     Elements  1008\n",
       "15     0.024    mg                   Copper, Cu     Elements  1008\n",
       "16     0.021    mg                Manganese, Mn     Elements  1008\n",
       "17    14.500   mcg                 Selenium, Se     Elements  1008\n",
       "18  1054.000    IU                Vitamin A, IU     Vitamins  1008\n",
       "19   262.000   mcg                      Retinol     Vitamins  1008"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14179"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients.duplicated().sum()  # number of duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "nutrients = nutrients.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6636 entries, 0 to 6635\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   food          6636 non-null   object\n",
      " 1   fgroup        6636 non-null   object\n",
      " 2   id            6636 non-null   int64 \n",
      " 3   manufacturer  5195 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 207.5+ KB\n"
     ]
    }
   ],
   "source": [
    "col_mapping = {'description' : 'food',\n",
    "               'group'       : 'fgroup'}\n",
    "info = info.rename(columns=col_mapping, copy=False)\n",
    "info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>units</th>\n",
       "      <th>nutrient</th>\n",
       "      <th>nutgroup</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.180</td>\n",
       "      <td>g</td>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.200</td>\n",
       "      <td>g</td>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.060</td>\n",
       "      <td>g</td>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.280</td>\n",
       "      <td>g</td>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>376.000</td>\n",
       "      <td>kcal</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>1008</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",
       "    <tr>\n",
       "      <th>389350</th>\n",
       "      <td>0.000</td>\n",
       "      <td>mcg</td>\n",
       "      <td>Vitamin B-12, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389351</th>\n",
       "      <td>0.000</td>\n",
       "      <td>mg</td>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389352</th>\n",
       "      <td>0.072</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389353</th>\n",
       "      <td>0.028</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389354</th>\n",
       "      <td>0.041</td>\n",
       "      <td>g</td>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375176 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          value units                            nutrient     nutgroup     id\n",
       "0        25.180     g                             Protein  Composition   1008\n",
       "1        29.200     g                   Total lipid (fat)  Composition   1008\n",
       "2         3.060     g         Carbohydrate, by difference  Composition   1008\n",
       "3         3.280     g                                 Ash        Other   1008\n",
       "4       376.000  kcal                              Energy       Energy   1008\n",
       "...         ...   ...                                 ...          ...    ...\n",
       "389350    0.000   mcg                 Vitamin B-12, added     Vitamins  43546\n",
       "389351    0.000    mg                         Cholesterol        Other  43546\n",
       "389352    0.072     g        Fatty acids, total saturated        Other  43546\n",
       "389353    0.028     g  Fatty acids, total monounsaturated        Other  43546\n",
       "389354    0.041     g  Fatty acids, total polyunsaturated        Other  43546\n",
       "\n",
       "[375176 rows x 5 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_mapping = {'description' : 'nutrient',\n",
    "               'group' : 'nutgroup'}\n",
    "nutrients = nutrients.rename(columns=col_mapping, copy=False)\n",
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 375176 entries, 0 to 375175\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   value         375176 non-null  float64\n",
      " 1   units         375176 non-null  object \n",
      " 2   nutrient      375176 non-null  object \n",
      " 3   nutgroup      375176 non-null  object \n",
      " 4   id            375176 non-null  int64  \n",
      " 5   food          375176 non-null  object \n",
      " 6   fgroup        375176 non-null  object \n",
      " 7   manufacturer  293054 non-null  object \n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 25.8+ MB\n"
     ]
    }
   ],
   "source": [
    "ndata = pd.merge(nutrients, info, on='id', how='outer')\n",
    "ndata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value                                             0.04\n",
       "units                                                g\n",
       "nutrient                                       Glycine\n",
       "nutgroup                                   Amino Acids\n",
       "id                                                6158\n",
       "food            Soup, tomato bisque, canned, condensed\n",
       "fgroup                      Soups, Sauces, and Gravies\n",
       "manufacturer                                          \n",
       "Name: 30000, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata.iloc[30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138d0f33cc8>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)\n",
    "result['Zinc, Zn'].sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_nutrient = ndata.groupby(['nutgroup', 'nutrient'])\n",
    "\n",
    "get_maximum = lambda x: x.loc[x.value.idxmax()]\n",
    "get_minimum = lambda x: x.loc[x.value.idxmin()]\n",
    "\n",
    "max_foods = by_nutrient.apply(get_maximum)[['value', 'food']]\n",
    "\n",
    "# make the food a little smaller\n",
    "max_foods.food = max_foods.food.str[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>food</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nutgroup</th>\n",
       "      <th>nutrient</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Amino Acids</th>\n",
       "      <th>Alanine</th>\n",
       "      <td>8.009</td>\n",
       "      <td>Gelatins, dry powder, unsweetened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arginine</th>\n",
       "      <td>7.436</td>\n",
       "      <td>Seeds, sesame flour, low-fat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aspartic acid</th>\n",
       "      <td>10.203</td>\n",
       "      <td>Soy protein isolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cystine</th>\n",
       "      <td>1.307</td>\n",
       "      <td>Seeds, cottonseed flour, low fat (glandless)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glutamic acid</th>\n",
       "      <td>17.452</td>\n",
       "      <td>Soy protein isolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Vitamins</th>\n",
       "      <th>Vitamin D2 (ergocalciferol)</th>\n",
       "      <td>28.100</td>\n",
       "      <td>Mushrooms, maitake, raw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin D3 (cholecalciferol)</th>\n",
       "      <td>27.400</td>\n",
       "      <td>Fish, halibut, Greenland, raw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin E (alpha-tocopherol)</th>\n",
       "      <td>149.400</td>\n",
       "      <td>Oil, wheat germ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin E, added</th>\n",
       "      <td>46.550</td>\n",
       "      <td>Cereals ready-to-eat, GENERAL MILLS, Multi-Gra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vitamin K (phylloquinone)</th>\n",
       "      <td>1714.500</td>\n",
       "      <td>Spices, sage, ground</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             value  \\\n",
       "nutgroup    nutrient                                 \n",
       "Amino Acids Alanine                          8.009   \n",
       "            Arginine                         7.436   \n",
       "            Aspartic acid                   10.203   \n",
       "            Cystine                          1.307   \n",
       "            Glutamic acid                   17.452   \n",
       "...                                            ...   \n",
       "Vitamins    Vitamin D2 (ergocalciferol)     28.100   \n",
       "            Vitamin D3 (cholecalciferol)    27.400   \n",
       "            Vitamin E (alpha-tocopherol)   149.400   \n",
       "            Vitamin E, added                46.550   \n",
       "            Vitamin K (phylloquinone)     1714.500   \n",
       "\n",
       "                                                                                       food  \n",
       "nutgroup    nutrient                                                                         \n",
       "Amino Acids Alanine                                       Gelatins, dry powder, unsweetened  \n",
       "            Arginine                                           Seeds, sesame flour, low-fat  \n",
       "            Aspartic acid                                               Soy protein isolate  \n",
       "            Cystine                            Seeds, cottonseed flour, low fat (glandless)  \n",
       "            Glutamic acid                                               Soy protein isolate  \n",
       "...                                                                                     ...  \n",
       "Vitamins    Vitamin D2 (ergocalciferol)                             Mushrooms, maitake, raw  \n",
       "            Vitamin D3 (cholecalciferol)                      Fish, halibut, Greenland, raw  \n",
       "            Vitamin E (alpha-tocopherol)                                    Oil, wheat germ  \n",
       "            Vitamin E, added              Cereals ready-to-eat, GENERAL MILLS, Multi-Gra...  \n",
       "            Vitamin K (phylloquinone)                                  Spices, sage, ground  \n",
       "\n",
       "[94 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_foods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nutrient\n",
       "Alanine                           Gelatins, dry powder, unsweetened\n",
       "Arginine                               Seeds, sesame flour, low-fat\n",
       "Aspartic acid                                   Soy protein isolate\n",
       "Cystine                Seeds, cottonseed flour, low fat (glandless)\n",
       "Glutamic acid                                   Soy protein isolate\n",
       "Glycine                           Gelatins, dry powder, unsweetened\n",
       "Histidine                Whale, beluga, meat, dried (Alaska Native)\n",
       "Hydroxyproline    KENTUCKY FRIED CHICKEN, Fried Chicken, ORIGINA...\n",
       "Isoleucine        Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Leucine           Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Lysine            Seal, bearded (Oogruk), meat, dried (Alaska Na...\n",
       "Methionine                    Fish, cod, Atlantic, dried and salted\n",
       "Phenylalanine     Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Proline                           Gelatins, dry powder, unsweetened\n",
       "Serine            Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Threonine         Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Tryptophan         Sea lion, Steller, meat with fat (Alaska Native)\n",
       "Tyrosine          Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Valine            Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Name: food, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_foods.loc['Amino Acids']['food']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2012 Federal Election Commission Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3063: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count    Dtype  \n",
      "---  ------             --------------    -----  \n",
      " 0   cmte_id            1001731 non-null  object \n",
      " 1   cand_id            1001731 non-null  object \n",
      " 2   cand_nm            1001731 non-null  object \n",
      " 3   contbr_nm          1001731 non-null  object \n",
      " 4   contbr_city        1001712 non-null  object \n",
      " 5   contbr_st          1001727 non-null  object \n",
      " 6   contbr_zip         1001620 non-null  object \n",
      " 7   contbr_employer    988002 non-null   object \n",
      " 8   contbr_occupation  993301 non-null   object \n",
      " 9   contb_receipt_amt  1001731 non-null  float64\n",
      " 10  contb_receipt_dt   1001731 non-null  object \n",
      " 11  receipt_desc       14166 non-null    object \n",
      " 12  memo_cd            92482 non-null    object \n",
      " 13  memo_text          97770 non-null    object \n",
      " 14  form_tp            1001731 non-null  object \n",
      " 15  file_num           1001731 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 122.3+ MB\n"
     ]
    }
   ],
   "source": [
    "fec = pd.read_csv('datasets/fec/P00000001-ALL.csv')\n",
    "fec.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cmte_id                             C00431445\n",
       "cand_id                             P80003338\n",
       "cand_nm                         Obama, Barack\n",
       "contbr_nm                         ELLMAN, IRA\n",
       "contbr_city                             TEMPE\n",
       "contbr_st                                  AZ\n",
       "contbr_zip                          852816719\n",
       "contbr_employer      ARIZONA STATE UNIVERSITY\n",
       "contbr_occupation                   PROFESSOR\n",
       "contb_receipt_amt                          50\n",
       "contb_receipt_dt                    01-DEC-11\n",
       "receipt_desc                              NaN\n",
       "memo_cd                                   NaN\n",
       "memo_text                                 NaN\n",
       "form_tp                                 SA17A\n",
       "file_num                               772372\n",
       "Name: 123456, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.iloc[123456]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick',\n",
       "       'Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G',\n",
       "       'Huntsman, Jon', 'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands = fec.cand_nm.unique()\n",
    "unique_cands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Obama, Barack'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "parties = {'Bachmann, Michelle': 'Republican',\n",
    "           'Cain, Herman': 'Republican',\n",
    "           'Gingrich, Newt': 'Republican',\n",
    "           'Huntsman, Jon': 'Republican',\n",
    "           'Johnson, Gary Earl': 'Republican',\n",
    "           'McCotter, Thaddeus G': 'Republican',\n",
    "           'Obama, Barack': 'Democrat',\n",
    "           'Paul, Ron': 'Republican',\n",
    "           'Pawlenty, Timothy': 'Republican',\n",
    "           'Perry, Rick': 'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
    "           'Romney, Mitt': 'Republican',\n",
    "           'Santorum, Rick': 'Republican'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Obama, Barack\n",
       "123457    Obama, Barack\n",
       "123458    Obama, Barack\n",
       "123459    Obama, Barack\n",
       "123460    Obama, Barack\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.cand_nm[123456:123461]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Democrat\n",
       "123457    Democrat\n",
       "123458    Democrat\n",
       "123459    Democrat\n",
       "123460    Democrat\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.cand_nm[123456:123461].map(parties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat      593746\n",
       "Republican    407985\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add it as a column\n",
    "fec['party'] = fec.cand_nm.map(parties)\n",
    "fec['party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "      <th>party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>HARVEY, WILLIAM</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.6601e+08</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>SMITH, LANIER</td>\n",
       "      <td>LANETT</td>\n",
       "      <td>AL</td>\n",
       "      <td>3.68633e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>01-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WARDENBURG, HAROLD</td>\n",
       "      <td>HOT SPRINGS NATION</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.19016e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BECKMAN, JAMES</td>\n",
       "      <td>SPRINGDALE</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.27647e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>500.0</td>\n",
       "      <td>23-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>21-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>BLEVINS, DARONDA</td>\n",
       "      <td>PIGGOTT</td>\n",
       "      <td>AR</td>\n",
       "      <td>7.24548e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>COLLINS, SARAH</td>\n",
       "      <td>MESA</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.52107e+08</td>\n",
       "      <td>ST. JOSEPH HOSPITAL</td>\n",
       "      <td>RN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>21-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>COLEMAN, RONALD</td>\n",
       "      <td>TUCSON</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.57499e+08</td>\n",
       "      <td>RAYTHEON</td>\n",
       "      <td>ELECTRICAL ENGINEER</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>ATCHLEY, JR, KEITH</td>\n",
       "      <td>MESA</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85215</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>22-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>FARNSWORTH, ROSS</td>\n",
       "      <td>MESA</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.52062e+08</td>\n",
       "      <td>FARNSWORTH COMPANIES</td>\n",
       "      <td>LAND DEVELOPER</td>\n",
       "      <td>500.0</td>\n",
       "      <td>22-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>PRESTON, CLIFFORD</td>\n",
       "      <td>ORO VALLEY</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.57379e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>21-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>PETERSEN, W</td>\n",
       "      <td>GILBERT</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85295</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>27-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>MILLER, WILLIAM</td>\n",
       "      <td>SCOTTSDALE</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.52513e+08</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>INFORMATION REQUESTED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>11-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>DOLAN, WILLIAM</td>\n",
       "      <td>PHOENIX</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.50145e+08</td>\n",
       "      <td>VA MEDICAL CENTER</td>\n",
       "      <td>PHYSICIAN</td>\n",
       "      <td>300.0</td>\n",
       "      <td>08-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>WILSON, RICHARD</td>\n",
       "      <td>FLAGSTAFF</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.60011e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>500.0</td>\n",
       "      <td>17-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>REULING, RICHARD</td>\n",
       "      <td>GREEN VALLEY</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8.56225e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>05-JUL-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>749073</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>ALLUMBAUGH, KATHY</td>\n",
       "      <td>SANTA ANA</td>\n",
       "      <td>CA</td>\n",
       "      <td>9.27051e+08</td>\n",
       "      <td>NONE</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>13-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>C00410118</td>\n",
       "      <td>P20002978</td>\n",
       "      <td>Bachmann, Michelle</td>\n",
       "      <td>DEL POZO, JOSE</td>\n",
       "      <td>DEL MAR</td>\n",
       "      <td>CA</td>\n",
       "      <td>9.20143e+08</td>\n",
       "      <td>RETIRES</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>300.0</td>\n",
       "      <td>16-JUN-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>736166</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
       "0   C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "1   C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
       "2   C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
       "3   C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
       "4   C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
       "5   C00410118  P20002978  Bachmann, Michelle      BECKMAN, JAMES   \n",
       "6   C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
       "7   C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
       "8   C00410118  P20002978  Bachmann, Michelle      COLLINS, SARAH   \n",
       "9   C00410118  P20002978  Bachmann, Michelle     COLEMAN, RONALD   \n",
       "10  C00410118  P20002978  Bachmann, Michelle  ATCHLEY, JR, KEITH   \n",
       "11  C00410118  P20002978  Bachmann, Michelle    FARNSWORTH, ROSS   \n",
       "12  C00410118  P20002978  Bachmann, Michelle   PRESTON, CLIFFORD   \n",
       "13  C00410118  P20002978  Bachmann, Michelle         PETERSEN, W   \n",
       "14  C00410118  P20002978  Bachmann, Michelle     MILLER, WILLIAM   \n",
       "15  C00410118  P20002978  Bachmann, Michelle      DOLAN, WILLIAM   \n",
       "16  C00410118  P20002978  Bachmann, Michelle     WILSON, RICHARD   \n",
       "17  C00410118  P20002978  Bachmann, Michelle    REULING, RICHARD   \n",
       "18  C00410118  P20002978  Bachmann, Michelle   ALLUMBAUGH, KATHY   \n",
       "19  C00410118  P20002978  Bachmann, Michelle      DEL POZO, JOSE   \n",
       "\n",
       "           contbr_city contbr_st   contbr_zip        contbr_employer  \\\n",
       "0               MOBILE        AL   3.6601e+08                RETIRED   \n",
       "1               MOBILE        AL   3.6601e+08                RETIRED   \n",
       "2               LANETT        AL  3.68633e+08  INFORMATION REQUESTED   \n",
       "3              PIGGOTT        AR  7.24548e+08                   NONE   \n",
       "4   HOT SPRINGS NATION        AR  7.19016e+08                   NONE   \n",
       "5           SPRINGDALE        AR  7.27647e+08                   NONE   \n",
       "6              PIGGOTT        AR  7.24548e+08  INFORMATION REQUESTED   \n",
       "7              PIGGOTT        AR  7.24548e+08                   NONE   \n",
       "8                 MESA        AZ  8.52107e+08    ST. JOSEPH HOSPITAL   \n",
       "9               TUCSON        AZ  8.57499e+08               RAYTHEON   \n",
       "10                MESA        AZ        85215                   NONE   \n",
       "11                MESA        AZ  8.52062e+08   FARNSWORTH COMPANIES   \n",
       "12          ORO VALLEY        AZ  8.57379e+08  INFORMATION REQUESTED   \n",
       "13             GILBERT        AZ        85295  INFORMATION REQUESTED   \n",
       "14          SCOTTSDALE        AZ  8.52513e+08  INFORMATION REQUESTED   \n",
       "15             PHOENIX        AZ  8.50145e+08      VA MEDICAL CENTER   \n",
       "16           FLAGSTAFF        AZ  8.60011e+08                   NONE   \n",
       "17        GREEN VALLEY        AZ  8.56225e+08                   NONE   \n",
       "18           SANTA ANA        CA  9.27051e+08                   NONE   \n",
       "19             DEL MAR        CA  9.20143e+08                RETIRES   \n",
       "\n",
       "        contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
       "0                 RETIRED              250.0        20-JUN-11          NaN   \n",
       "1                 RETIRED               50.0        23-JUN-11          NaN   \n",
       "2   INFORMATION REQUESTED              250.0        05-JUL-11          NaN   \n",
       "3                 RETIRED              250.0        01-AUG-11          NaN   \n",
       "4                 RETIRED              300.0        20-JUN-11          NaN   \n",
       "5                 RETIRED              500.0        23-JUN-11          NaN   \n",
       "6   INFORMATION REQUESTED              250.0        21-JUN-11          NaN   \n",
       "7                 RETIRED              250.0        05-JUL-11          NaN   \n",
       "8                      RN              250.0        21-JUN-11          NaN   \n",
       "9     ELECTRICAL ENGINEER              250.0        20-JUN-11          NaN   \n",
       "10                RETIRED              250.0        22-JUN-11          NaN   \n",
       "11         LAND DEVELOPER              500.0        22-JUN-11          NaN   \n",
       "12  INFORMATION REQUESTED              250.0        21-JUN-11          NaN   \n",
       "13  INFORMATION REQUESTED              250.0        27-JUN-11          NaN   \n",
       "14  INFORMATION REQUESTED              250.0        11-JUL-11          NaN   \n",
       "15              PHYSICIAN              300.0        08-JUL-11          NaN   \n",
       "16                RETIRED              500.0        17-JUN-11          NaN   \n",
       "17                RETIRED             1000.0        05-JUL-11          NaN   \n",
       "18                RETIRED              250.0        13-JUN-11          NaN   \n",
       "19                RETIRED              300.0        16-JUN-11          NaN   \n",
       "\n",
       "   memo_cd memo_text form_tp  file_num       party  \n",
       "0      NaN       NaN   SA17A    736166  Republican  \n",
       "1      NaN       NaN   SA17A    736166  Republican  \n",
       "2      NaN       NaN   SA17A    749073  Republican  \n",
       "3      NaN       NaN   SA17A    749073  Republican  \n",
       "4      NaN       NaN   SA17A    736166  Republican  \n",
       "5      NaN       NaN   SA17A    736166  Republican  \n",
       "6      NaN       NaN   SA17A    736166  Republican  \n",
       "7      NaN       NaN   SA17A    749073  Republican  \n",
       "8      NaN       NaN   SA17A    736166  Republican  \n",
       "9      NaN       NaN   SA17A    736166  Republican  \n",
       "10     NaN       NaN   SA17A    736166  Republican  \n",
       "11     NaN       NaN   SA17A    736166  Republican  \n",
       "12     NaN       NaN   SA17A    736166  Republican  \n",
       "13     NaN       NaN   SA17A    736166  Republican  \n",
       "14     NaN       NaN   SA17A    749073  Republican  \n",
       "15     NaN       NaN   SA17A    749073  Republican  \n",
       "16     NaN       NaN   SA17A    736166  Republican  \n",
       "17     NaN       NaN   SA17A    749073  Republican  \n",
       "18     NaN       NaN   SA17A    736166  Republican  \n",
       "19     NaN       NaN   SA17A    736166  Republican  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     991475\n",
       "False     10256\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fec.contb_receipt_amt > 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fec = fec[fec.contb_receipt_amt > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cmte_id</th>\n",
       "      <th>cand_id</th>\n",
       "      <th>cand_nm</th>\n",
       "      <th>contbr_nm</th>\n",
       "      <th>contbr_city</th>\n",
       "      <th>contbr_st</th>\n",
       "      <th>contbr_zip</th>\n",
       "      <th>contbr_employer</th>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th>contb_receipt_dt</th>\n",
       "      <th>receipt_desc</th>\n",
       "      <th>memo_cd</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>form_tp</th>\n",
       "      <th>file_num</th>\n",
       "      <th>party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>C00431171</td>\n",
       "      <td>P80003353</td>\n",
       "      <td>Romney, Mitt</td>\n",
       "      <td>ELDERBAUM, WILLIAM</td>\n",
       "      <td>DPO</td>\n",
       "      <td>AA</td>\n",
       "      <td>3.4023e+08</td>\n",
       "      <td>US GOVERNMENT</td>\n",
       "      <td>FOREIGN SERVICE OFFICER</td>\n",
       "      <td>25.0</td>\n",
       "      <td>01-FEB-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>780124</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>C00431171</td>\n",
       "      <td>P80003353</td>\n",
       "      <td>Romney, Mitt</td>\n",
       "      <td>ELDERBAUM, WILLIAM</td>\n",
       "      <td>DPO</td>\n",
       "      <td>AA</td>\n",
       "      <td>3.4023e+08</td>\n",
       "      <td>US GOVERNMENT</td>\n",
       "      <td>FOREIGN SERVICE OFFICER</td>\n",
       "      <td>110.0</td>\n",
       "      <td>01-FEB-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>780124</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>C00431171</td>\n",
       "      <td>P80003353</td>\n",
       "      <td>Romney, Mitt</td>\n",
       "      <td>CARLSEN, RICHARD</td>\n",
       "      <td>APO</td>\n",
       "      <td>AE</td>\n",
       "      <td>9.128e+07</td>\n",
       "      <td>DEFENSE INTELLIGENCE AGENCY</td>\n",
       "      <td>INTELLIGENCE ANALYST</td>\n",
       "      <td>250.0</td>\n",
       "      <td>13-APR-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>785689</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>C00431171</td>\n",
       "      <td>P80003353</td>\n",
       "      <td>Romney, Mitt</td>\n",
       "      <td>DELUCA, PIERRE</td>\n",
       "      <td>APO</td>\n",
       "      <td>AE</td>\n",
       "      <td>9.128e+07</td>\n",
       "      <td>CISCO</td>\n",
       "      <td>ENGINEER</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21-AUG-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>760261</td>\n",
       "      <td>Republican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>C00431171</td>\n",
       "      <td>P80003353</td>\n",
       "      <td>Romney, Mitt</td>\n",
       "      <td>SARGENT, MICHAEL</td>\n",
       "      <td>APO</td>\n",
       "      <td>AE</td>\n",
       "      <td>9.01201e+07</td>\n",
       "      <td>RAYTHEON TECHNICAL SERVICES CORP</td>\n",
       "      <td>COMPUTER SYSTEMS ENGINEER</td>\n",
       "      <td>100.0</td>\n",
       "      <td>07-MAR-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>780128</td>\n",
       "      <td>Republican</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701381</th>\n",
       "      <td>C00431445</td>\n",
       "      <td>P80003338</td>\n",
       "      <td>Obama, Barack</td>\n",
       "      <td>MOUNTS, ROBERT</td>\n",
       "      <td>APO</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>962043077</td>\n",
       "      <td>HQ USFK (FKDC-SA)</td>\n",
       "      <td>GS-15 INTERNATIONAL RELATIONS OFFICER</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26-FEB-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>787803</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701382</th>\n",
       "      <td>C00431445</td>\n",
       "      <td>P80003338</td>\n",
       "      <td>Obama, Barack</td>\n",
       "      <td>TAITANO, TYRONE</td>\n",
       "      <td>HAGATNA</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>969323373</td>\n",
       "      <td>NOT EMPLOYED</td>\n",
       "      <td>RETIRED</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20-JAN-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>775668</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701383</th>\n",
       "      <td>C00431445</td>\n",
       "      <td>P80003338</td>\n",
       "      <td>Obama, Barack</td>\n",
       "      <td>TUCKER, PAMELA</td>\n",
       "      <td>APO</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>963190030</td>\n",
       "      <td>DODEA</td>\n",
       "      <td>EDUCATOR</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20-JAN-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>775668</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701384</th>\n",
       "      <td>C00431445</td>\n",
       "      <td>P80003338</td>\n",
       "      <td>Obama, Barack</td>\n",
       "      <td>MOUNTS, ROBERT</td>\n",
       "      <td>APO</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>962043077</td>\n",
       "      <td>HQ USFK (FKDC-SA)</td>\n",
       "      <td>GS-15 INTERNATIONAL RELATIONS OFFICER</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26-APR-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SA17A</td>\n",
       "      <td>785239</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701385</th>\n",
       "      <td>C00431445</td>\n",
       "      <td>P80003338</td>\n",
       "      <td>Obama, Barack</td>\n",
       "      <td>NEAL, AMBER</td>\n",
       "      <td>APO</td>\n",
       "      <td>ZZ</td>\n",
       "      <td>091022065</td>\n",
       "      <td>THE DEPARTMENT OF DEFENSE EDUCATION AC</td>\n",
       "      <td>TEACHER</td>\n",
       "      <td>135.0</td>\n",
       "      <td>04-SEP-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>X</td>\n",
       "      <td>* OBAMA VICTORY FUND 2012</td>\n",
       "      <td>SA18</td>\n",
       "      <td>756218</td>\n",
       "      <td>Democrat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>694282 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cmte_id    cand_id        cand_nm           contbr_nm contbr_city  \\\n",
       "411     C00431171  P80003353   Romney, Mitt  ELDERBAUM, WILLIAM         DPO   \n",
       "412     C00431171  P80003353   Romney, Mitt  ELDERBAUM, WILLIAM         DPO   \n",
       "413     C00431171  P80003353   Romney, Mitt    CARLSEN, RICHARD         APO   \n",
       "414     C00431171  P80003353   Romney, Mitt      DELUCA, PIERRE         APO   \n",
       "415     C00431171  P80003353   Romney, Mitt    SARGENT, MICHAEL         APO   \n",
       "...           ...        ...            ...                 ...         ...   \n",
       "701381  C00431445  P80003338  Obama, Barack      MOUNTS, ROBERT         APO   \n",
       "701382  C00431445  P80003338  Obama, Barack     TAITANO, TYRONE     HAGATNA   \n",
       "701383  C00431445  P80003338  Obama, Barack      TUCKER, PAMELA         APO   \n",
       "701384  C00431445  P80003338  Obama, Barack      MOUNTS, ROBERT         APO   \n",
       "701385  C00431445  P80003338  Obama, Barack         NEAL, AMBER         APO   \n",
       "\n",
       "       contbr_st   contbr_zip                         contbr_employer  \\\n",
       "411           AA   3.4023e+08                           US GOVERNMENT   \n",
       "412           AA   3.4023e+08                           US GOVERNMENT   \n",
       "413           AE    9.128e+07             DEFENSE INTELLIGENCE AGENCY   \n",
       "414           AE    9.128e+07                                   CISCO   \n",
       "415           AE  9.01201e+07        RAYTHEON TECHNICAL SERVICES CORP   \n",
       "...          ...          ...                                     ...   \n",
       "701381        ZZ    962043077                       HQ USFK (FKDC-SA)   \n",
       "701382        ZZ    969323373                            NOT EMPLOYED   \n",
       "701383        ZZ    963190030                                   DODEA   \n",
       "701384        ZZ    962043077                       HQ USFK (FKDC-SA)   \n",
       "701385        ZZ    091022065  THE DEPARTMENT OF DEFENSE EDUCATION AC   \n",
       "\n",
       "                            contbr_occupation  contb_receipt_amt  \\\n",
       "411                   FOREIGN SERVICE OFFICER               25.0   \n",
       "412                   FOREIGN SERVICE OFFICER              110.0   \n",
       "413                      INTELLIGENCE ANALYST              250.0   \n",
       "414                                  ENGINEER               30.0   \n",
       "415                 COMPUTER SYSTEMS ENGINEER              100.0   \n",
       "...                                       ...                ...   \n",
       "701381  GS-15 INTERNATIONAL RELATIONS OFFICER               25.0   \n",
       "701382                                RETIRED              250.0   \n",
       "701383                               EDUCATOR                3.0   \n",
       "701384  GS-15 INTERNATIONAL RELATIONS OFFICER               25.0   \n",
       "701385                                TEACHER              135.0   \n",
       "\n",
       "       contb_receipt_dt receipt_desc memo_cd                  memo_text  \\\n",
       "411           01-FEB-12          NaN     NaN                        NaN   \n",
       "412           01-FEB-12          NaN     NaN                        NaN   \n",
       "413           13-APR-12          NaN     NaN                        NaN   \n",
       "414           21-AUG-11          NaN     NaN                        NaN   \n",
       "415           07-MAR-12          NaN     NaN                        NaN   \n",
       "...                 ...          ...     ...                        ...   \n",
       "701381        26-FEB-12          NaN     NaN                        NaN   \n",
       "701382        20-JAN-12          NaN     NaN                        NaN   \n",
       "701383        20-JAN-12          NaN     NaN                        NaN   \n",
       "701384        26-APR-12          NaN     NaN                        NaN   \n",
       "701385        04-SEP-11          NaN       X  * OBAMA VICTORY FUND 2012   \n",
       "\n",
       "       form_tp  file_num       party  \n",
       "411      SA17A    780124  Republican  \n",
       "412      SA17A    780124  Republican  \n",
       "413      SA17A    785689  Republican  \n",
       "414      SA17A    760261  Republican  \n",
       "415      SA17A    780128  Republican  \n",
       "...        ...       ...         ...  \n",
       "701381   SA17A    787803    Democrat  \n",
       "701382   SA17A    775668    Democrat  \n",
       "701383   SA17A    775668    Democrat  \n",
       "701384   SA17A    785239    Democrat  \n",
       "701385    SA18    756218    Democrat  \n",
       "\n",
       "[694282 rows x 17 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack', 'Romney, Mitt'])]\n",
    "fec_mrbo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donation Statistics by Occupation and Employer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIRED                                   233990\n",
       "INFORMATION REQUESTED                      35107\n",
       "ATTORNEY                                   34286\n",
       "HOMEMAKER                                  29931\n",
       "PHYSICIAN                                  23432\n",
       "INFORMATION REQUESTED PER BEST EFFORTS     21138\n",
       "ENGINEER                                   14334\n",
       "TEACHER                                    13990\n",
       "CONSULTANT                                 13273\n",
       "PROFESSOR                                  12555\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.contbr_occupation.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5303: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "occ_mapping = {\n",
    "   'INFORMATION REQUESTED PER BEST EFFORTS' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED (BEST EFFORTS)' : 'NOT PROVIDED',\n",
    "   'C.E.O.': 'CEO'\n",
    "}\n",
    "\n",
    "# If no mapping provided, return x\n",
    "f = lambda x: occ_mapping.get(x, x)\n",
    "fec.contbr_occupation = fec.contbr_occupation.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_mapping = {\n",
    "   'INFORMATION REQUESTED PER BEST EFFORTS' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED' : 'NOT PROVIDED',\n",
    "   'SELF' : 'SELF-EMPLOYED',\n",
    "   'SELF EMPLOYED' : 'SELF-EMPLOYED',\n",
    "}\n",
    "\n",
    "# If no mapping provided, return x\n",
    "f = lambda x: emp_mapping.get(x, x)\n",
    "fec.contbr_employer = fec.contbr_employer.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATTORNEY</th>\n",
       "      <td>11141982.97</td>\n",
       "      <td>7.477194e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEO</th>\n",
       "      <td>2074974.79</td>\n",
       "      <td>4.211041e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSULTANT</th>\n",
       "      <td>2459912.71</td>\n",
       "      <td>2.544725e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGINEER</th>\n",
       "      <td>951525.55</td>\n",
       "      <td>1.818374e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXECUTIVE</th>\n",
       "      <td>1355161.05</td>\n",
       "      <td>4.138850e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOMEMAKER</th>\n",
       "      <td>4248875.80</td>\n",
       "      <td>1.363428e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVESTOR</th>\n",
       "      <td>884133.00</td>\n",
       "      <td>2.431769e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAWYER</th>\n",
       "      <td>3160478.87</td>\n",
       "      <td>3.912243e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGER</th>\n",
       "      <td>762883.22</td>\n",
       "      <td>1.444532e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOT PROVIDED</th>\n",
       "      <td>4866973.96</td>\n",
       "      <td>2.056547e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWNER</th>\n",
       "      <td>1001567.36</td>\n",
       "      <td>2.408287e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHYSICIAN</th>\n",
       "      <td>3735124.94</td>\n",
       "      <td>3.594320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESIDENT</th>\n",
       "      <td>1878509.95</td>\n",
       "      <td>4.720924e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROFESSOR</th>\n",
       "      <td>2165071.08</td>\n",
       "      <td>2.967027e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REAL ESTATE</th>\n",
       "      <td>528902.09</td>\n",
       "      <td>1.625902e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIRED</th>\n",
       "      <td>25305116.38</td>\n",
       "      <td>2.356124e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <td>672393.40</td>\n",
       "      <td>1.640253e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "party                 Democrat    Republican\n",
       "contbr_occupation                           \n",
       "ATTORNEY           11141982.97  7.477194e+06\n",
       "CEO                 2074974.79  4.211041e+06\n",
       "CONSULTANT          2459912.71  2.544725e+06\n",
       "ENGINEER             951525.55  1.818374e+06\n",
       "EXECUTIVE           1355161.05  4.138850e+06\n",
       "HOMEMAKER           4248875.80  1.363428e+07\n",
       "INVESTOR             884133.00  2.431769e+06\n",
       "LAWYER              3160478.87  3.912243e+05\n",
       "MANAGER              762883.22  1.444532e+06\n",
       "NOT PROVIDED        4866973.96  2.056547e+07\n",
       "OWNER               1001567.36  2.408287e+06\n",
       "PHYSICIAN           3735124.94  3.594320e+06\n",
       "PRESIDENT           1878509.95  4.720924e+06\n",
       "PROFESSOR           2165071.08  2.967027e+05\n",
       "REAL ESTATE          528902.09  1.625902e+06\n",
       "RETIRED            25305116.38  2.356124e+07\n",
       "SELF-EMPLOYED        672393.40  1.640253e+06"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_occupation = fec.pivot_table('contb_receipt_amt',\n",
    "                                index='contbr_occupation',\n",
    "                                columns='party', aggfunc='sum')\n",
    "over_2mm = by_occupation[by_occupation.sum(1) > 2000000]\n",
    "over_2mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c3db164548>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "over_2mm.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_amounts(group, key, n=5):\n",
    "    totals = group.groupby(key)['contb_receipt_amt'].sum()\n",
    "    return totals.nlargest(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_occupation                     \n",
       "Obama, Barack  RETIRED                                   25305116.38\n",
       "               ATTORNEY                                  11141982.97\n",
       "               INFORMATION REQUESTED                      4866973.96\n",
       "               HOMEMAKER                                  4248875.80\n",
       "               PHYSICIAN                                  3735124.94\n",
       "               LAWYER                                     3160478.87\n",
       "               CONSULTANT                                 2459912.71\n",
       "Romney, Mitt   RETIRED                                   11508473.59\n",
       "               INFORMATION REQUESTED PER BEST EFFORTS    11396894.84\n",
       "               HOMEMAKER                                  8147446.22\n",
       "               ATTORNEY                                   5364718.82\n",
       "               PRESIDENT                                  2491244.89\n",
       "               EXECUTIVE                                  2300947.03\n",
       "               C.E.O.                                     1968386.11\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby('cand_nm')\n",
    "grouped.apply(get_top_amounts, 'contbr_occupation', n=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_employer                       \n",
       "Obama, Barack  RETIRED                                   22694358.85\n",
       "               SELF-EMPLOYED                             17080985.96\n",
       "               NOT EMPLOYED                               8586308.70\n",
       "               INFORMATION REQUESTED                      5053480.37\n",
       "               HOMEMAKER                                  2605408.54\n",
       "               SELF                                       1076531.20\n",
       "               SELF EMPLOYED                               469290.00\n",
       "               STUDENT                                     318831.45\n",
       "               VOLUNTEER                                   257104.00\n",
       "               MICROSOFT                                   215585.36\n",
       "Romney, Mitt   INFORMATION REQUESTED PER BEST EFFORTS    12059527.24\n",
       "               RETIRED                                   11506225.71\n",
       "               HOMEMAKER                                  8147196.22\n",
       "               SELF-EMPLOYED                              7409860.98\n",
       "               STUDENT                                     496490.94\n",
       "               CREDIT SUISSE                               281150.00\n",
       "               MORGAN STANLEY                              267266.00\n",
       "               GOLDMAN SACH & CO.                          238250.00\n",
       "               BARCLAYS CAPITAL                            162750.00\n",
       "               H.I.G. CAPITAL                              139500.00\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_top_amounts, 'contbr_employer', n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bucketing Donation Amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411         (10, 100]\n",
       "412       (100, 1000]\n",
       "413       (100, 1000]\n",
       "414         (10, 100]\n",
       "415         (10, 100]\n",
       "             ...     \n",
       "701381      (10, 100]\n",
       "701382    (100, 1000]\n",
       "701383        (1, 10]\n",
       "701384      (10, 100]\n",
       "701385    (100, 1000]\n",
       "Name: contb_receipt_amt, Length: 694282, dtype: category\n",
       "Categories (8, interval[int64]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.array([0, 1, 10, 100, 1000, 10000,\n",
    "                 100000, 1000000, 10000000])\n",
    "labels = pd.cut(fec_mrbo.contb_receipt_amt, bins)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>493</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>40070</td>\n",
       "      <td>3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>372280</td>\n",
       "      <td>31853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>153991</td>\n",
       "      <td>43357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>22284</td>\n",
       "      <td>26186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                         493            77\n",
       "(1, 10]                      40070          3681\n",
       "(10, 100]                   372280         31853\n",
       "(100, 1000]                 153991         43357\n",
       "(1000, 10000]                22284         26186\n",
       "(10000, 100000]                  2             1\n",
       "(100000, 1000000]                3             0\n",
       "(1000000, 10000000]              4             0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', labels])\n",
    "grouped.size().unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>318.24</td>\n",
       "      <td>77.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>337267.62</td>\n",
       "      <td>29819.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>20288981.41</td>\n",
       "      <td>1987783.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>54798531.46</td>\n",
       "      <td>22363381.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>51753705.67</td>\n",
       "      <td>63942145.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>59100.00</td>\n",
       "      <td>12700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>1490683.08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>7148839.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                      318.24         77.00\n",
       "(1, 10]                  337267.62      29819.66\n",
       "(10, 100]              20288981.41    1987783.76\n",
       "(100, 1000]            54798531.46   22363381.69\n",
       "(1000, 10000]          51753705.67   63942145.42\n",
       "(10000, 100000]           59100.00      12700.00\n",
       "(100000, 1000000]       1490683.08           NaN\n",
       "(1000000, 10000000]     7148839.76           NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)\n",
    "bucket_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>0.805182</td>\n",
       "      <td>0.194818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>0.918767</td>\n",
       "      <td>0.081233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>0.910769</td>\n",
       "      <td>0.089231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>0.710176</td>\n",
       "      <td>0.289824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>0.447326</td>\n",
       "      <td>0.552674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>0.823120</td>\n",
       "      <td>0.176880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                    0.805182      0.194818\n",
       "(1, 10]                   0.918767      0.081233\n",
       "(10, 100]                 0.910769      0.089231\n",
       "(100, 1000]               0.710176      0.289824\n",
       "(1000, 10000]             0.447326      0.552674\n",
       "(10000, 100000]           0.823120      0.176880\n",
       "(100000, 1000000]         1.000000           NaN\n",
       "(1000000, 10000000]       1.000000           NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_sums = bucket_sums.div(bucket_sums.sum(axis=1), axis=0)\n",
    "normed_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c3ce8a0b08>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_sums[:-2].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donation Statistics by State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>56405.00</td>\n",
       "      <td>135.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB</th>\n",
       "      <td>2048.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AE</th>\n",
       "      <td>42973.75</td>\n",
       "      <td>5680.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>281840.15</td>\n",
       "      <td>86204.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>543123.48</td>\n",
       "      <td>527303.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>1130155.46</td>\n",
       "      <td>270316.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WV</th>\n",
       "      <td>169154.47</td>\n",
       "      <td>126725.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>194046.74</td>\n",
       "      <td>252595.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XX</th>\n",
       "      <td>0.00</td>\n",
       "      <td>400250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZ</th>\n",
       "      <td>5963.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AA              56405.00        135.00\n",
       "AB               2048.00          0.00\n",
       "AE              42973.75       5680.00\n",
       "AK             281840.15      86204.24\n",
       "AL             543123.48     527303.51\n",
       "...                  ...           ...\n",
       "WI            1130155.46     270316.32\n",
       "WV             169154.47     126725.12\n",
       "WY             194046.74     252595.84\n",
       "XX                  0.00     400250.00\n",
       "ZZ               5963.00          0.00\n",
       "\n",
       "[67 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', 'contbr_st'])\n",
    "totals = grouped.contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm\n",
       "Obama, Barack    1.355699e+08\n",
       "Romney, Mitt     8.817892e+07\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals.sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>281840.15</td>\n",
       "      <td>86204.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>543123.48</td>\n",
       "      <td>527303.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>359247.28</td>\n",
       "      <td>105556.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>1506476.98</td>\n",
       "      <td>1888436.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>23824984.24</td>\n",
       "      <td>11237636.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>4250933.16</td>\n",
       "      <td>1341521.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>1130155.46</td>\n",
       "      <td>270316.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WV</th>\n",
       "      <td>169154.47</td>\n",
       "      <td>126725.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>194046.74</td>\n",
       "      <td>252595.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XX</th>\n",
       "      <td>0.00</td>\n",
       "      <td>400250.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK             281840.15      86204.24\n",
       "AL             543123.48     527303.51\n",
       "AR             359247.28     105556.00\n",
       "AZ            1506476.98    1888436.23\n",
       "CA           23824984.24   11237636.60\n",
       "...                  ...           ...\n",
       "WA            4250933.16    1341521.56\n",
       "WI            1130155.46     270316.32\n",
       "WV             169154.47     126725.12\n",
       "WY             194046.74     252595.84\n",
       "XX                  0.00     400250.00\n",
       "\n",
       "[52 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals = totals[totals.sum(1) > 100000]\n",
    "totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
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       "    <tr>\n",
       "      <th>contbr_st</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>0.765778</td>\n",
       "      <td>0.234222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>0.507390</td>\n",
       "      <td>0.492610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>0.772902</td>\n",
       "      <td>0.227098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>0.443745</td>\n",
       "      <td>0.556255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.679498</td>\n",
       "      <td>0.320502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>0.585970</td>\n",
       "      <td>0.414030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>0.371476</td>\n",
       "      <td>0.628524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>0.810113</td>\n",
       "      <td>0.189887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.802776</td>\n",
       "      <td>0.197224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>0.467417</td>\n",
       "      <td>0.532583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK              0.765778      0.234222\n",
       "AL              0.507390      0.492610\n",
       "AR              0.772902      0.227098\n",
       "AZ              0.443745      0.556255\n",
       "CA              0.679498      0.320502\n",
       "CO              0.585970      0.414030\n",
       "CT              0.371476      0.628524\n",
       "DC              0.810113      0.189887\n",
       "DE              0.802776      0.197224\n",
       "FL              0.467417      0.532583"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent = totals.div(totals.sum(1), axis=0)\n",
    "percent[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  }
 ],
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