ch14.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" "
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "In [5]: path = 'datasets/bitly_usagov/example.txt'\n",
    "\n",
    "In [6]: open(path).readline()\n",
    "Out[6]: '{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11\n",
    "(KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1,\n",
    "\"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\":\n",
    "\"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\":\n",
    "\"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\":\n",
    "\"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\":\n",
    "1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "path = 'datasets/bitly_usagov/example.txt'\n",
    "records = [json.loads(line) for line in open(path)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In [18]: records[0]\n",
    "Out[18]:\n",
    "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko)\n",
    "Chrome/17.0.963.78 Safari/535.11',\n",
    " 'al': 'en-US,en;q=0.8',\n",
    " 'c': 'US',\n",
    " 'cy': 'Danvers',\n",
    " 'g': 'A6qOVH',\n",
    " 'gr': 'MA',\n",
    " 'h': 'wfLQtf',\n",
    " 'hc': 1331822918,\n",
    " 'hh': '1.usa.gov',\n",
    " 'l': 'orofrog',\n",
    " 'll': [42.576698, -70.954903],\n",
    " 'nk': 1,\n",
    " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
    " 't': 1331923247,\n",
    " 'tz': 'America/New_York',\n",
    " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting Time Zones in Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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-4-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-4-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": 5,
   "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": 5,
     "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": 6,
   "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": 7,
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = get_counts(time_zones)\n",
    "counts['America/New_York']\n",
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "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": 13,
   "metadata": {
    "scrolled": true
   },
   "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"
     ]
    },
    {
     "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "frame = pd.DataFrame(records)\n",
    "frame.info()\n",
    "frame['tz'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = frame['tz'].value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')\n",
    "clean_tz[clean_tz == ''] = 'Unknown'\n",
    "tz_counts = clean_tz.value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x288 with 0 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P9'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]\n",
    "frame['a'][50]\n",
    "frame['a'][51][:50]  # long line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.Series([x.split()[0] for x in frame.a.dropna()])\n",
    "results[:5]\n",
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe = frame[frame.a.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe = cframe.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz', 'os'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "  </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"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
    "agg_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  24\n",
       "Africa/Cairo                      20\n",
       "Africa/Casablanca                 21\n",
       "Africa/Ceuta                      92\n",
       "Africa/Johannesburg               87\n",
       "Africa/Lusaka                     53\n",
       "America/Anchorage                 54\n",
       "America/Argentina/Buenos_Aires    57\n",
       "America/Argentina/Cordoba         26\n",
       "America/Argentina/Mendoza         55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "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>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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = agg_counts.take(indexer[-10:])\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.sum(1).nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "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>tz</th>\n",
       "      <th>os</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Windows</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Windows</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <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",
       "  </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"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total', ylabel='tz'>"
      ]
     },
     "execution_count": 29,
     "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": [
    "# 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[:10]\n",
    "sns.barplot(x='total', y='tz', hue='os',  data=count_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='normed_total', ylabel='tz'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = count_subset.groupby('tz')\n",
    "results2 = count_subset.total / g.total.transform('sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MovieLens 1M Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py:765: 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",
      "  return read_csv(**locals())\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": 35,
   "metadata": {},
   "outputs": [
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       "   user_id gender  age  occupation    zip\n",
       "0        1      F    1          10  48067\n",
       "1        2      M   56          16  70072\n",
       "2        3      M   25          15  55117\n",
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       "4        5      M   25          20  55455"
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       "   user_id  movie_id  rating  timestamp\n",
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       "   movie_id                               title                        genres\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy"
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       "         user_id  movie_id  rating  timestamp\n",
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       "\n",
       "[1000209 rows x 4 columns]"
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     "execution_count": 35,
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   ],
   "source": [
    "users[:5]\n",
    "ratings[:5]\n",
    "movies[:5]\n",
    "ratings"
   ]
  },
  {
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   "execution_count": 36,
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       "         user_id  movie_id  rating  timestamp gender  age  occupation    zip  \\\n",
       "0              1      1193       5  978300760      F    1          10  48067   \n",
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       "3             15      1193       4  978199279      M   25           7  22903   \n",
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       "...          ...       ...     ...        ...    ...  ...         ...    ...   \n",
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       "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "data\n",
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings = data.pivot_table('rating', index='title',\n",
    "                                columns='gender', aggfunc='mean')\n",
    "mean_ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_by_title = data.groupby('title').size()\n",
    "ratings_by_title[:10]\n",
    "active_titles = ratings_by_title.index[ratings_by_title >= 250]\n",
    "active_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
       "        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",
       "    </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": 39,
     "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": 40,
   "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": 41,
   "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>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": 41,
     "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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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",
       "      <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": 43,
     "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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": 44,
     "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": 45,
   "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": 45,
     "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": 46,
   "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": 47,
   "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",
       "    </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": 47,
     "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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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%d.txt' % 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": 50,
   "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",
       "    </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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "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>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": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total births by sex and year'}, xlabel='year'>"
      ]
     },
     "execution_count": 51,
     "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": [
    "total_births = names.pivot_table('births', index='year',\n",
    "                                 columns='sex', aggfunc=sum)\n",
    "total_births.tail()\n",
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby(['year', 'sex']).prop.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "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",
    "# Drop the group index, not needed\n",
    "top1000.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "code",
   "execution_count": 56,
   "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>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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Naming Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']\n",
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_births = top1000.pivot_table('births', index='year',\n",
    "                                   columns='name',\n",
    "                                   aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='year'>, <AxesSubplot:xlabel='year'>,\n",
       "       <AxesSubplot:xlabel='year'>, <AxesSubplot:xlabel='year'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 59,
     "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": [
    "total_births.info()\n",
    "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sum of table1000.prop by year and sex'}, xlabel='year'>"
      ]
     },
     "execution_count": 61,
     "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": 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></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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 2010]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "prop_cumsum[:10]\n",
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 65,
     "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": 66,
   "metadata": {},
   "outputs": [],
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 68,
   "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Number of popular names in top 50%'}, xlabel='year'>"
      ]
     },
     "execution_count": 68,
     "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": [
    "diversity.head()\n",
    "diversity.plot(title=\"Number of popular names in top 50%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The “last letter” revolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "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.name = 'last_letter'\n",
    "\n",
    "table = names.pivot_table('births', index=last_letters,\n",
    "                          columns=['sex', 'year'], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()\n",
    "letter_prop = subtable / subtable.sum()\n",
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Male'}, xlabel='last_letter'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Female'}, xlabel='last_letter'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots_adjust(hspace=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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": 74,
     "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": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 77,
     "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": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "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": 79,
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex     F   M\n",
       "year         \n",
       "2006  1.0 NaN\n",
       "2007  1.0 NaN\n",
       "2008  1.0 NaN\n",
       "2009  1.0 NaN\n",
       "2010  1.0 NaN"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = filtered.pivot_table('births', index='year',\n",
    "                             columns='sex', aggfunc='sum')\n",
    "table = table.div(table.sum(1), axis=0)\n",
    "table.tail()"
   ]
  },
  {
   "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": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "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": [
    "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": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6636"
      ]
     },
     "execution_count": 83,
     "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": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['id', 'description', 'tags', 'manufacturer', 'group', 'portions', 'nutrients'])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "{'value': 25.18,\n",
       " 'units': 'g',\n",
       " 'description': 'Protein',\n",
       " 'group': 'Composition'}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>25.18</td>\n",
       "      <td>g</td>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.20</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>3.06</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>3.28</td>\n",
       "      <td>g</td>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>376.00</td>\n",
       "      <td>kcal</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39.28</td>\n",
       "      <td>g</td>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1573.00</td>\n",
       "      <td>kJ</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     value units                  description        group\n",
       "0    25.18     g                      Protein  Composition\n",
       "1    29.20     g            Total lipid (fat)  Composition\n",
       "2     3.06     g  Carbohydrate, by difference  Composition\n",
       "3     3.28     g                          Ash        Other\n",
       "4   376.00  kcal                       Energy       Energy\n",
       "5    39.28     g                        Water  Composition\n",
       "6  1573.00    kJ                       Energy       Energy"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0].keys()\n",
    "db[0]['nutrients'][0]\n",
    "nutrients = pd.DataFrame(db[0]['nutrients'])\n",
    "nutrients[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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></th>\n",
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       "      <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": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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_keys = ['description', 'group', 'id', 'manufacturer']\n",
    "info = pd.DataFrame(db, columns=info_keys)\n",
    "info[:5]\n",
    "info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vegetables and Vegetable Products    812\n",
       "Beef Products                        618\n",
       "Baked Products                       496\n",
       "Breakfast Cereals                    403\n",
       "Fast Foods                           365\n",
       "Legumes and Legume Products          365\n",
       "Lamb, Veal, and Game Products        345\n",
       "Sweets                               341\n",
       "Pork Products                        328\n",
       "Fruits and Fruit Juices              328\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(info.group)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "nutrients = []\n",
    "\n",
    "for rec in db:\n",
    "    fnuts = pd.DataFrame(rec['nutrients'])\n",
    "    fnuts['id'] = rec['id']\n",
    "    nutrients.append(fnuts)\n",
    "\n",
    "nutrients = pd.concat(nutrients, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>...</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>389355 rows × 5 columns</p>\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",
       "...         ...   ...                                 ...          ...    ...\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",
       "[389355 rows x 5 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14179"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients.duplicated().sum()  # number of duplicates\n",
    "nutrients = nutrients.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_mapping = {'description' : 'food',\n",
    "               'group'       : 'fgroup'}\n",
    "info = info.rename(columns=col_mapping, copy=False)\n",
    "info.info()\n",
    "col_mapping = {'description' : 'nutrient',\n",
    "               'group' : 'nutgroup'}\n",
    "nutrients = nutrients.rename(columns=col_mapping, copy=False)\n",
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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"
     ]
    },
    {
     "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": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata = pd.merge(nutrients, info, on='id', how='outer')\n",
    "ndata.info()\n",
    "ndata.iloc[30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='fgroup'>"
      ]
     },
     "execution_count": 93,
     "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": 94,
   "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": 95,
   "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",
       "                                       ...                        \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, Length: 19, dtype: object"
      ]
     },
     "execution_count": 95,
     "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": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\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": 97,
   "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",
       "                    ...      \n",
       "receipt_desc              NaN\n",
       "memo_cd                   NaN\n",
       "memo_text                 NaN\n",
       "form_tp                 SA17A\n",
       "file_num               772372\n",
       "Name: 123456, Length: 16, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.iloc[123456]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "'Obama, Barack'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands = fec.cand_nm.unique()\n",
    "unique_cands\n",
    "unique_cands[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": 100,
   "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": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Democrat      593746\n",
       "Republican    407985\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.cand_nm[123456:123461]\n",
    "fec.cand_nm[123456:123461].map(parties)\n",
    "# Add it as a column\n",
    "fec['party'] = fec.cand_nm.map(parties)\n",
    "fec['party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     991475\n",
       "False     10256\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fec.contb_receipt_amt > 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "fec = fec[fec.contb_receipt_amt > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack', 'Romney, Mitt'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donation Statistics by Occupation and Employer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.contbr_occupation.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "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": 106,
   "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": 107,
   "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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "<p>17 rows × 2 columns</p>\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",
       "...                        ...           ...\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\n",
       "\n",
       "[17 rows x 2 columns]"
      ]
     },
     "execution_count": 107,
     "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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 108,
     "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": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='contbr_occupation'>"
      ]
     },
     "execution_count": 109,
     "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": 110,
   "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": 111,
   "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",
       "                                           ...     \n",
       "Romney, Mitt   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, Length: 14, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
       "                                           ...     \n",
       "Romney, Mitt   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, Length: 20, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby('cand_nm')\n",
    "grouped.apply(get_top_amounts, 'contbr_occupation', n=7)\n",
    "grouped.apply(get_top_amounts, 'contbr_employer', n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bucketing Donation Amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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": 112,
     "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": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', labels])\n",
    "grouped.size().unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "execution_count": 114,
     "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": 115,
   "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": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='contb_receipt_amt'>"
      ]
     },
     "execution_count": 115,
     "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": [
    "bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)\n",
    "normed_sums = bucket_sums.div(bucket_sums.sum(axis=1), axis=0)\n",
    "normed_sums\n",
    "normed_sums[:-2].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donation Statistics by State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>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>CO</th>\n",
       "      <td>2132429.49</td>\n",
       "      <td>1506714.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>2068291.26</td>\n",
       "      <td>3499475.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>4373538.80</td>\n",
       "      <td>1025137.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>336669.14</td>\n",
       "      <td>82712.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>7318178.58</td>\n",
       "      <td>8338458.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "CO            2132429.49    1506714.12\n",
       "CT            2068291.26    3499475.45\n",
       "DC            4373538.80    1025137.50\n",
       "DE             336669.14      82712.00\n",
       "FL            7318178.58    8338458.81"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', 'contbr_st'])\n",
    "totals = grouped.contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "totals = totals[totals.sum(1) > 100000]\n",
    "totals[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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",
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       "        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>contbr_st</th>\n",
       "      <th></th>\n",
       "      <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": 117,
     "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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