{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temporal Analysis\n",
    "\n",
    "What happens over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bookgender.config import db_uri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorting amazon ratings\n",
      "timestampifying Amazon actions\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 22464284 entries, 6941990 to 21090668\n",
      "Data columns (total 4 columns):\n",
      "user         int64\n",
      "item         int64\n",
      "rating       float64\n",
      "timestamp    datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2)\n",
      "memory usage: 856.9 MB\n"
     ]
    }
   ],
   "source": [
    "az_rates = pd.read_parquet('data/AZ/ratings.parquet')\n",
    "print('sorting amazon ratings')\n",
    "az_rates.sort_values('timestamp', inplace=True)\n",
    "print('timestampifying Amazon actions')\n",
    "az_rates['timestamp'] = pd.to_datetime(az_rates['timestamp'], unit='s')\n",
    "az_rates.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 188962927 entries, 0 to 188962926\n",
      "Data columns (total 5 columns):\n",
      "user          int64\n",
      "item          int64\n",
      "nactions      int64\n",
      "first_time    int64\n",
      "last_time     int64\n",
      "dtypes: int64(5)\n",
      "memory usage: 7.0 GB\n"
     ]
    }
   ],
   "source": [
    "gr_rates = pd.read_parquet('data/GR-I/ratings.parquet')\n",
    "gr_rates.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorting GoodReads actions\n",
      "timestampifying GoodReads actions\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 188962927 entries, 26747886 to 184734437\n",
      "Data columns (total 6 columns):\n",
      "user          int64\n",
      "item          int64\n",
      "nactions      int64\n",
      "first_time    int64\n",
      "last_time     int64\n",
      "timestamp     datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(5)\n",
      "memory usage: 9.9 GB\n"
     ]
    }
   ],
   "source": [
    "print('sorting GoodReads actions')\n",
    "gr_rates.sort_values('first_time', inplace=True)\n",
    "print('timestampifying GoodReads actions')\n",
    "gr_rates['timestamp'] = pd.to_datetime(gr_rates['first_time'], unit='s')\n",
    "gr_rates.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the gender info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     10735373\n",
       "unique           5\n",
       "top       unlinked\n",
       "freq       4505196\n",
       "Name: gender, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender = pd.read_parquet('data/author-gender.parquet')\n",
    "gender = gender.set_index('item')['gender']\n",
    "gender[gender.str.startswith('no-')] = 'unlinked'\n",
    "gender = gender.astype('category')\n",
    "gender.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f049d775d10>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "az_j = az_rates.set_index('timestamp')[['user', 'item']].join(gender, on='item', how='left')\n",
    "az_j['gender'].fillna('unlinked', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gr_j = gr_rates.set_index('timestamp')[['user', 'item']].join(gender, on='item', how='left')\n",
    "gr_j['gender'].fillna('unlinked', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evolution over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "1996-05-31    2\n",
       "1996-06-30    3\n",
       "1996-07-31    0\n",
       "1996-08-31    1\n",
       "1996-09-30    1\n",
       "Freq: M, Name: item, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az_counts = az_j.resample('1M')['item'].count()\n",
    "az_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2006-08-31     18\n",
       "2006-09-30     12\n",
       "2006-10-31     12\n",
       "2006-11-30     13\n",
       "2006-12-31    299\n",
       "Freq: M, Name: item, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr_counts = gr_j.resample('1M')['item'].count()\n",
    "gr_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0570000750>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az_counts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f443ae4f350>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az_counts.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f05600646d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gr_counts.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>unknown</th>\n",
       "      <th>unlinked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <td>1996-05-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-06-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-07-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-08-31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-09-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender      ambiguous  female  male  unknown  unlinked\n",
       "timestamp                                             \n",
       "1996-05-31          0       0     2        0         0\n",
       "1996-06-30          0       0     3        0         0\n",
       "1996-07-31          0       0     0        0         0\n",
       "1996-08-31          0       1     0        0         0\n",
       "1996-09-30          0       0     1        0         0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az_gcounts = az_j.resample('1M').apply(lambda df: df.groupby('gender')['item'].count())\n",
    "az_gcounts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>unknown</th>\n",
       "      <th>unlinked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <td>2006-08-31</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-10-31</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-11-30</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-12-31</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender      ambiguous  female  male  unknown  unlinked\n",
       "timestamp                                             \n",
       "2006-08-31          3       1    12        2         0\n",
       "2006-09-30          2       1     9        0         0\n",
       "2006-10-31          3       0     9        0         0\n",
       "2006-11-30          0       4     7        2         0\n",
       "2006-12-31         28      36   230        1         4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr_gcounts = gr_j.resample('1M').apply(lambda df: df.groupby('gender')['item'].count())\n",
    "gr_gcounts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>unknown</th>\n",
       "      <th>unlinked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <td>1996-05-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-06-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-07-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-08-31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-09-30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender      ambiguous  female  male  unknown  unlinked\n",
       "timestamp                                             \n",
       "1996-05-31          0       0     2        0         0\n",
       "1996-06-30          0       0     5        0         0\n",
       "1996-07-31          0       0     5        0         0\n",
       "1996-08-31          0       1     5        0         0\n",
       "1996-09-30          0       1     6        0         0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az_gcc = az_gcounts.cumsum()\n",
    "az_gcc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>unknown</th>\n",
       "      <th>unlinked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <td>2006-08-31</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-10-31</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-11-30</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-12-31</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>267</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender      ambiguous  female  male  unknown  unlinked\n",
       "timestamp                                             \n",
       "2006-08-31          3       1    12        2         0\n",
       "2006-09-30          5       2    21        2         0\n",
       "2006-10-31          8       2    30        2         0\n",
       "2006-11-30          8       6    37        4         0\n",
       "2006-12-31         36      42   267        5         4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr_gcc = gr_gcounts.cumsum()\n",
    "gr_gcc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "az_cc = az_counts.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at # of ratings over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stack(df, ax=plt):\n",
    "    ax.stackplot(df.index, df.T, labels=df.columns)\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack(az_gcounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack(az_gcc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack(gr_gcc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And fraction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>unknown</th>\n",
       "      <th>unlinked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
       "      <td>1996-05-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-06-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-09-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-11-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender      ambiguous    female      male  unknown  unlinked\n",
       "timestamp                                                   \n",
       "1996-05-31        0.0  0.000000  1.000000      0.0       0.0\n",
       "1996-06-30        0.0  0.000000  1.000000      0.0       0.0\n",
       "1996-08-31        0.0  1.000000  0.000000      0.0       0.0\n",
       "1996-09-30        0.0  0.000000  1.000000      0.0       0.0\n",
       "1996-11-30        0.0  0.166667  0.833333      0.0       0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az_gfrac = az_gcounts.divide(az_counts, axis=0).dropna()\n",
    "az_gfrac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack(az_gfrac)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fraction of Known:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1996-05-31</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-06-30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-08-31</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-09-30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-11-30</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender        female      male\n",
       "timestamp                     \n",
       "1996-05-31  0.000000  1.000000\n",
       "1996-06-30  0.000000  1.000000\n",
       "1996-08-31  1.000000  0.000000\n",
       "1996-09-30  0.000000  1.000000\n",
       "1996-11-30  0.166667  0.833333"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az_k = az_gcounts[['female', 'male']]\n",
    "az_kfrac = az_k.divide(az_k.sum(axis=1), axis=0).dropna()\n",
    "az_kfrac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack(az_kfrac)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cumulative fractions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az_cf = az_gcc.divide(az_cc, axis=0)\n",
    "stack(az_cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gr_cf = gr_gcc.divide(gr_counts.cumsum(), axis=0)\n",
    "stack(gr_cf)"
   ]
  },
  {
   "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>gender</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1996-05-31</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-06-30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-07-31</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-08-31</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996-09-30</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender        female      male\n",
       "timestamp                     \n",
       "1996-05-31  0.000000  1.000000\n",
       "1996-06-30  0.000000  1.000000\n",
       "1996-07-31  0.000000  1.000000\n",
       "1996-08-31  0.166667  0.833333\n",
       "1996-09-30  0.142857  0.857143"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az_kcf = az_k.cumsum().divide(az_k.sum(axis=1).cumsum(), axis=0).dropna()\n",
    "az_kcf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack(az_kcf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2006-08-31</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-09-30</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.913043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-10-31</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-11-30</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>0.860465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006-12-31</td>\n",
       "      <td>0.135922</td>\n",
       "      <td>0.864078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender        female      male\n",
       "timestamp                     \n",
       "2006-08-31  0.076923  0.923077\n",
       "2006-09-30  0.086957  0.913043\n",
       "2006-10-31  0.062500  0.937500\n",
       "2006-11-30  0.139535  0.860465\n",
       "2006-12-31  0.135922  0.864078"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr_k = gr_gcounts[['female', 'male']]\n",
    "gr_kcf = gr_k.cumsum().divide(gr_k.sum(axis=1).cumsum(), axis=0).dropna()\n",
    "gr_kcf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Books over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item</th>\n",
       "      <th>pub_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>201761454</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>114293136</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>113908707</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>111276590</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>101891138</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        item pub_year\n",
       "0  201761454     2008\n",
       "1  114293136     1982\n",
       "2  113908707     1984\n",
       "3  111276590     2003\n",
       "4  101891138     2006"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_year = pd.read_sql('''\n",
    "    SELECT cluster AS item, min(pub_year) AS pub_year\n",
    "    FROM locmds.book_pub_year\n",
    "    JOIN locmds.book_rec_isbn USING (rec_id)\n",
    "    JOIN isbn_cluster USING (isbn_id)\n",
    "    GROUP BY cluster\n",
    "''', db_uri())\n",
    "book_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>item</th>\n",
       "      <th>pub_year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>201761454</td>\n",
       "      <td>2008</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>114293136</td>\n",
       "      <td>1982</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>113908707</td>\n",
       "      <td>1984</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>111276590</td>\n",
       "      <td>2003</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>101891138</td>\n",
       "      <td>2006</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        item  pub_year   gender\n",
       "0  201761454      2008     male\n",
       "1  114293136      1982     male\n",
       "2  113908707      1984  unknown\n",
       "3  111276590      2003  unknown\n",
       "4  101891138      2006   female"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_yg = book_year.join(gender, on='item', how='left')\n",
    "book_yg['gender'].fillna('unknown', inplace=True)\n",
    "book_yg['pub_year'] = book_yg['pub_year'].astype('i4')\n",
    "book_yg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_yg = book_yg[book_yg.pub_year < 2015]\n",
    "book_yg = book_yg[book_yg.pub_year > 1960]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff9aaecf50>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_counts = book_yg.groupby('pub_year').item.count()\n",
    "y_counts.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>unknown</th>\n",
       "      <th>unlinked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_year</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",
       "      <td>1961</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>123</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1962</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>142</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1963</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>180</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1964</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "      <td>165</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1965</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>216</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender    ambiguous  female  male  unknown  unlinked\n",
       "pub_year                                            \n",
       "1961             10      14   123       15        19\n",
       "1962              5      22   142       28        30\n",
       "1963             12      19   180       27        26\n",
       "1964              8      36   165       25        45\n",
       "1965             13      28   216       48        47"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_gc = book_yg.groupby(['pub_year', 'gender']).item.count().unstack()\n",
    "y_gc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>unknown</th>\n",
       "      <th>unlinked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_year</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",
       "      <td>1961</td>\n",
       "      <td>0.055249</td>\n",
       "      <td>0.077348</td>\n",
       "      <td>0.679558</td>\n",
       "      <td>0.082873</td>\n",
       "      <td>0.104972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1962</td>\n",
       "      <td>0.036765</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.649510</td>\n",
       "      <td>0.105392</td>\n",
       "      <td>0.120098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1963</td>\n",
       "      <td>0.040179</td>\n",
       "      <td>0.081845</td>\n",
       "      <td>0.662202</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.111607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1964</td>\n",
       "      <td>0.036803</td>\n",
       "      <td>0.095689</td>\n",
       "      <td>0.641430</td>\n",
       "      <td>0.099895</td>\n",
       "      <td>0.126183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1965</td>\n",
       "      <td>0.036838</td>\n",
       "      <td>0.091328</td>\n",
       "      <td>0.633922</td>\n",
       "      <td>0.109747</td>\n",
       "      <td>0.128166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender    ambiguous    female      male   unknown  unlinked\n",
       "pub_year                                                   \n",
       "1961       0.055249  0.077348  0.679558  0.082873  0.104972\n",
       "1962       0.036765  0.088235  0.649510  0.105392  0.120098\n",
       "1963       0.040179  0.081845  0.662202  0.104167  0.111607\n",
       "1964       0.036803  0.095689  0.641430  0.099895  0.126183\n",
       "1965       0.036838  0.091328  0.633922  0.109747  0.128166"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_gcc = y_gc.cumsum()\n",
    "y_gcf = y_gcc.divide(y_counts.cumsum(), axis=0)\n",
    "y_gcf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stack(y_gcf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}