{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Is \"Best New Music\" sampled independently?\n",
    "\n",
    "In in my [previous notebook](https://github.com/nolanbconaway/pitchfork-data/blob/master/notebooks/review-score-exploration.ipynb), I observed that about 5% of reviews are given \"Best New Music\" status. In reality that value is a little low because i was analyzing data from before best new music came about. \n",
    "\n",
    "My question is about whether best new music reviews occur independently of one another. My hunch is that Pitchfork would try to spread these reviews out over time in order to better highlight each one.\n",
    "\n",
    "I can think of couple ways to test this.\n",
    "\n",
    "1. There are usually 4 reviews posted every day, Monday-Saturday. So, taking that 5% estimate at face value, if best new music reviews are distributed independently, then the probability of $k$ best new music reviews per day should be binomially distributed: $p(k) \\sim Binomial(p=0.05, N=4)$.\n",
    "2. Usually there is only one best new music on a given day, and not every day has a best new music. If best new music reviews are distributed independently, then the presence of best new music on day $n-1$ should not affect the probability of best new music on day $n$ (or $n+1$, and so on)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sqlite3, datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.stats import binom\n",
    "from scipy.signal import savgol_filter\n",
    "\n",
    "pd.set_option('precision', 2)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "con = sqlite3.connect('../pitchfork.db')\n",
    "all_reviews = pd.read_sql('SELECT * FROM reviews WHERE pub_year < 2017', con)\n",
    "years = pd.read_sql('SELECT * FROM years', con)\n",
    "con.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning...\n",
    "\n",
    "The \"Best New Music\" label wasn't around until 2003, so I first need to get rid of data from before then. I'm also going to get rid of Sunday reviews, which are commonly high scoring but not always labeled as best new music. Finally, I'll eliminate any review that is associated with multiple years (these are most likely reissues). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First best new music: January 14, 2003\n",
      "Global p(bnm): 0.0511534603811\n"
     ]
    }
   ],
   "source": [
    "# convert pub_date to datetime object, get unix timestamps\n",
    "reviews = all_reviews.copy(deep = True) # slice to new object\n",
    "reviews['pub_date'] = pd.to_datetime(reviews.pub_date, format = '%Y-%m-%d')\n",
    "reviews['unix_time'] = reviews.pub_date.astype(np.int64, copy = True) // 10**9\n",
    "\n",
    "# find the first best new music, get rid of everything before it\n",
    "first_bnm = reviews.loc[reviews.best_new_music == True, 'unix_time'].min()\n",
    "reviews = reviews.loc[reviews.unix_time >= first_bnm]\n",
    "\n",
    "# print out date of first bnm\n",
    "idx = (reviews.unix_time == first_bnm) & (reviews.best_new_music == True)\n",
    "first_bnm_str = datetime.datetime.fromtimestamp(first_bnm)\n",
    "print('First best new music: ' + first_bnm_str.strftime('%B %d, %Y'))\n",
    "\n",
    "# remove sunday reviews\n",
    "reviews = reviews.loc[reviews.pub_weekday < 6]\n",
    "\n",
    "# remove multi-year reviews\n",
    "year_counts = years.groupby('reviewid').count().reset_index()\n",
    "keepers = year_counts.loc[year_counts.year == 1, 'reviewid']\n",
    "reviews = reviews.loc[reviews.reviewid.isin(keepers)]\n",
    "\n",
    "# find overall proportion of best new music\n",
    "proportion_bnm = np.mean(reviews.best_new_music)\n",
    "print('Global p(bnm): ' + str(proportion_bnm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So about 5.1% of releases are best new music since 2003. But that percentage assumes a steady rate over time. Below i explore how best new music has changed over the years.\n",
    "\n",
    "## Best New Music from 2003-Present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ERCQJVWIdVyIiIqKKolAokJAQV2nHs7OzF29NT5rFwpWIiIjeKQkJcZizJgJG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c4bcf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute oberved and expected number of best new music\n",
    "table = reviews.groupby('pub_year')['best_new_music'].agg(['sum', 'count'])\n",
    "table['expected'] = table['count'] * proportion_bnm\n",
    "table.rename(columns = {'sum': 'observed'}, inplace=True)\n",
    "table.plot(y = ['expected', 'observed'], kind = 'bar')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('')\n",
    "plt.title('Observed vs. Expected Best New Music Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The observed and expected frequencies are almost never in sync.\n",
    "\n",
    "Between 2003 and 2007 the expected frequency steadily increases, indicating that more and more reviews were being written. The distribution flattens out in 2007 and is stable as of 2016. \n",
    "\n",
    "Interestingly, the rate of best new music releases essentially doubles in 2008. There's a decline starting in 2011, but in 2016 the observed frequencies are still above their values prior to 2009.\n",
    "\n",
    "Maybe an executive decision was made to have more best new music? Here's a plot showing the probability of best new music, as a function of score and year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GVqdj8KixRPV7hKh+j7BszRfo9Xo69X+UabM/Nmr724GD+Hp5EtLynjqdh/ZaLpTpsWnq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\nOp+SKO/rXJb9//zrITZt3YYyKhpnZyfGjRrJiy/00anz95WrfPr5Wv6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oNBCrvEdMZBzZ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TP5849ThH4fVqRsBXmlaV3e9uuBW40q3Lt9nz2b9rMjdE+B9RVmCroN6USbbi2p4FiOxLhbbAna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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10681fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_bmn = reviews.copy(deep = True)\n",
    "p_bmn['score'] = np.round(p_bmn['score'])\n",
    "p_bmn = pd.pivot_table(p_bmn, \n",
    "                columns = 'score', \n",
    "                index = 'pub_year', \n",
    "                values = 'best_new_music',\n",
    "                aggfunc = 'mean',\n",
    "                fill_value = 0.0)\n",
    "\n",
    "ax = sns.heatmap(p_bmn, vmin = 0, vmax = 1, annot = True)\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prior to 2009, less than 40% of releases in the 8.5 - 9.5 range (rounded to 9.0 above) were best new music, but afterwards nearly all are.\n",
    "\n",
    "## Is Best New Music binomially distributed?\n",
    "\n",
    "If Pitchfork is independently sampling best new music reviews, then the frequency of best new music on a given day should follow from the binomial distribution, given the probability of best new music that year, and the number of reviews that day:\n",
    "\n",
    "$$\n",
    "p(k_{day}) \\sim Binomial(p_{year}, N_{day})\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pub_date</th>\n",
       "      <th>N</th>\n",
       "      <th>observed</th>\n",
       "      <th>pub_year</th>\n",
       "      <th>p</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-01-15</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.30e-04</td>\n",
       "      <td>3.81e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-01-16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.30e-04</td>\n",
       "      <td>3.81e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-01-20</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.88e-04</td>\n",
       "      <td>1.82e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-01-21</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.88e-04</td>\n",
       "      <td>1.82e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-01-22</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.88e-04</td>\n",
       "      <td>1.82e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003-01-23</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.30e-04</td>\n",
       "      <td>3.81e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2003-01-27</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.88e-04</td>\n",
       "      <td>1.82e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003-01-28</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.30e-04</td>\n",
       "      <td>3.81e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2003-01-29</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.88e-04</td>\n",
       "      <td>1.82e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2003-01-30</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>7.88e-04</td>\n",
       "      <td>1.82e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pub_date  N  observed  pub_year     p     0     1     2         3  \\\n",
       "0 2003-01-15  4         1      2003  0.04  0.83  0.15  0.01  3.30e-04   \n",
       "1 2003-01-16  4         0      2003  0.04  0.83  0.15  0.01  3.30e-04   \n",
       "2 2003-01-20  5         2      2003  0.04  0.80  0.18  0.02  7.88e-04   \n",
       "3 2003-01-21  5         0      2003  0.04  0.80  0.18  0.02  7.88e-04   \n",
       "4 2003-01-22  5         0      2003  0.04  0.80  0.18  0.02  7.88e-04   \n",
       "5 2003-01-23  4         0      2003  0.04  0.83  0.15  0.01  3.30e-04   \n",
       "6 2003-01-27  5         0      2003  0.04  0.80  0.18  0.02  7.88e-04   \n",
       "7 2003-01-28  4         0      2003  0.04  0.83  0.15  0.01  3.30e-04   \n",
       "8 2003-01-29  5         0      2003  0.04  0.80  0.18  0.02  7.88e-04   \n",
       "9 2003-01-30  5         0      2003  0.04  0.80  0.18  0.02  7.88e-04   \n",
       "\n",
       "          4  \n",
       "0  3.81e-06  \n",
       "1  3.81e-06  \n",
       "2  1.82e-05  \n",
       "3  1.82e-05  \n",
       "4  1.82e-05  \n",
       "5  3.81e-06  \n",
       "6  1.82e-05  \n",
       "7  3.81e-06  \n",
       "8  1.82e-05  \n",
       "9  1.82e-05  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get counts / observed bnm for each day\n",
    "g = reviews.groupby('pub_date')\n",
    "day_counts = g['best_new_music'].agg(['count','sum']).reset_index()\n",
    "rename_cols = {'count':'N', 'sum':'observed'}\n",
    "day_counts.rename(columns = rename_cols, inplace = True)\n",
    "day_counts['pub_year'] = day_counts.pub_date.dt.year\n",
    "\n",
    "# get probabilities for each year\n",
    "year_ps = reviews.groupby('pub_year').best_new_music.mean().reset_index()\n",
    "year_ps.rename(columns = {'best_new_music':'p'}, inplace = True)\n",
    "\n",
    "# merge into table\n",
    "data = pd.merge(day_counts, year_ps, on = 'pub_year')\n",
    "\n",
    "# empty out columns for expected k probabilities\n",
    "for k in range(5):\n",
    "    data[k] = pd.Series(0, index = table.index)\n",
    "\n",
    "# fill in data\n",
    "for i, row in data.iterrows():\n",
    "    if row.N < 5: xs = range(row.N + 1)\n",
    "    else: xs = range(5)\n",
    "    data.loc[i,xs] = binom.pmf(xs, row.N, row.p)\n",
    "data.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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9e3Xv3l1PPPG0/vGPhfr++/+VXfvuu//U559v1L59e5WRMV4JCTeqUaPGuvXWO1RaWqIp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c84fe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks = np.arange(5)\n",
    "\n",
    "# get observed and expected probs\n",
    "observed_ps = np.array([sum(data.observed == k) for k in ks])\n",
    "observed_ps = observed_ps / float(sum(observed_ps))\n",
    "expected_ps = data[ks].mean().as_matrix()\n",
    "\n",
    "df = pd.DataFrame(\n",
    "                index = ks,\n",
    "                data = dict(Expected = expected_ps,\n",
    "                            Observed = observed_ps)\n",
    "            )\n",
    "df.plot(y = ['Observed','Expected'], kind = 'bar')\n",
    "plt.ylim([0,1])\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel('# BNM')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result\n",
    "\n",
    "The number of best new music reviews per day is pretty much what you'd expect if you had independently sampled from the probability distribution of each year. We could probably get en even tighter fit by setting the probabilities per month (or week, etc), but this is enough to satisfy me.\n",
    "\n",
    "So Pitchfork probably isn't *trying* to prevent multiple best new music reviews from being published on a single day. It's simply very unlikely that this would happen, given the base rate of best new music reviews.\n",
    "\n",
    "## Does Best New Music on day $n$ change the probability on day $n+1$?\n",
    "\n",
    "If Pitchfork is trying to spread out best new music reviews, then publishing a best new music on day $n$ might lower the probability of best new music on day $n+1$, compared to if it had not published best new music on day $n$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unix_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1042588800</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042675200</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043020800</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043107200</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043193600</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0      1      2      3     4      5      6      7      8   \\\n",
       "unix_time                                                                  \n",
       "1042588800   True  False    NaN    NaN   NaN   True  False  False  False   \n",
       "1042675200  False    NaN    NaN    NaN  True  False  False  False    NaN   \n",
       "1043020800   True  False  False  False   NaN    NaN    NaN  False  False   \n",
       "1043107200  False  False  False    NaN   NaN    NaN  False  False  False   \n",
       "1043193600  False  False    NaN    NaN   NaN  False  False  False  False   \n",
       "\n",
       "               9   ...       21     22     23     24     25     26     27  \\\n",
       "unix_time          ...                                                      \n",
       "1042588800    NaN  ...    False  False   True    NaN    NaN  False   True   \n",
       "1042675200    NaN  ...    False   True    NaN    NaN  False   True  False   \n",
       "1043020800  False  ...    False   True  False  False    NaN    NaN    NaN   \n",
       "1043107200  False  ...     True  False  False    NaN    NaN    NaN  False   \n",
       "1043193600    NaN  ...    False  False    NaN    NaN    NaN  False  False   \n",
       "\n",
       "               28     29     30  \n",
       "unix_time                        \n",
       "1042588800  False  False    NaN  \n",
       "1042675200  False    NaN    NaN  \n",
       "1043020800  False  False  False  \n",
       "1043107200  False  False  False  \n",
       "1043193600  False  False  False  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create table where best new music looks ahead to see if there are future bnms\n",
    "bnm_lookahead = reviews.groupby('unix_time')['best_new_music'].agg('any').to_frame()\n",
    "\n",
    "lookahead  = 30     # number of days to look ahead\n",
    "day_length = 86400  # seconds per day\n",
    "\n",
    "# add lookahead columns\n",
    "for i in range(lookahead):\n",
    "   bnm_lookahead[i+1] = pd.Series(index = bnm_lookahead.index) \n",
    "\n",
    "# get all pub_dates\n",
    "all_dates = bnm_lookahead.index.values\n",
    "\n",
    "# fill out table\n",
    "for i in all_dates:\n",
    "    for j in range(1, lookahead+1):\n",
    "        date = i + j*day_length\n",
    "        if date not in all_dates: continue\n",
    "        bnm_lookahead.loc[i, j] = bnm_lookahead.loc[date,'best_new_music']\n",
    "\n",
    "bnm_lookahead.rename(columns = {'best_new_music':0}, inplace = True)\n",
    "bnm_lookahead.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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fb893331zq+UCkJaWxn//u+ya7/fu3Y1nnnnc/ujdTCdOHCciIoQTJ07kSR1Xk3mNZs0a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\niIIaUHxyFRQUFBQUFBRSlRmL3OzsbM6ePTvh+Jk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z6O2kEjEpxq7WvbzbuDMh5t16J48v\n+eKM3WHafZ28evFtagbrEsfyLbk8Wvogi+xFc9ruK6GI3FkwF4PBgY4jvFD9CgB/s+WvbkiB0+xt\n5VfnfkNfoB+ANIOLby79alLkbywWY3jYy9CQJ/Hwej0MDw/jdLpYvLiMvLxC1OrZ+eveaAN1OBbm\n387+J2f7qwEosObx/eXf+swsOdYO1vNizasJv0ZRJXL3oq2sdC4ny5A55aROlmV6erpoamqgqame\n/v7kZd2YrCKkdhHVZhGMimgiHVjpRi3EuyGXy83q1espKVmMSpU8yeoLDPBOw4dUdZ9IWBpMopG7\nCm9ja87NVxUc10I4FuZk71kOdlZRO67Th3hQ3dqMldyUtZZ8S+68THS7/b0c6DjCoc6qidZdVzmb\ncjZQmV6Gy2m5Ye6dyQiEotR2tvFy42/xyAMACEE9qvpyIn47YVmDPEUVerUALu0AaWI3BnV8uTUr\nK4fVq9eRn1804bwMhbx80rafve0Hk8qyZxjT2JJ7M52+Lo52n0gSOTq1lvWZa7glZ+OU/sCTMZ1+\nrXrgIi/WvJboh7VqLQ8V38PW3JtvuKXyhcQXGYm7JLQdTAoCFFUi6zJWsTV3E3mW7MTxWCxGLBYh\nO9uNx+NPOj/hWJjTfec52nWC8wM1SX7XAgKL7EWsy1zFqrTlGDXXZ5I9FU3eFn5T/WpiMqYSVNyZ\nv5V7C+9Ae42uBmPBwK/VvZPk274qrZKHF92H2zD/OZ8VkTsL5mIwONFzhn89+x8A/OjmP0u5md2V\nkGSJXa17ebP+/cTNu8JRwVbregLDfoaGhsaJWS9Xu0T0ej0lJUtYvLiMzMzsaxrsbzSRC/HZ8wvV\nr3C46xgQt3A+ufLbN9S1cDkjET9v1L3LgXHWpcWORXy94lHKcgsnPT+RSIT29hYaG+tpbm7E7x9J\nej4oaYlqs/DhpGsIorHk60mvEUjT9uMQOtGq4lYIm83OqlXrWLKkYsIEqt3XyVv17ycmGBBfVru/\n6C42ZK5GEISEL3UoEXAYjm8nHL+0fykw8dI2EA0mWbHHfI83Zq1lhXvpvAjryYhIUU73nmVfx5EJ\nYtupt/NA2R3clLYBKTbFByww0ZhEQ4eXvqEAA94QA94g/d4gvYN+BoaDRA0DaBedRNDEhWVsMI1w\nw3KITf37Wo0avP7kFIRO7TBpYhdm9QiCAC5X2uikqXTCpCkYDbK/4wi7Wvcm/OwvJ9eczS05G1mb\nsRL9NWRnmG6/Fo6Fea/xIz5u3ZPokwuseXyt7LEZierPMtGYRL83iE6jTjxUKoG24Q52t+6jqvsk\nUXmcO4nKSJmmmAIpEzkUIxgMjD6CBAIBIpH4tWaz2SgrW8rixUuxWCa6XPnCIxzvOcXR7hOJDBlj\niIKape5y1mesYqmr7Lr1BxBfbXi7YQd72g4mJv1F1ny+UvbonF0zMSnGnvaDvNe4E380AFzKznB3\n4W3zuoqmiNxZMBdCqnrgIn938l8A+LP1/3XOLqoWbxu1nnosGjNOvQOXwYFdZ7vmGb0ky7y5t5GW\nbi9qQUISfLSbDuPT9gIgSCrsHW6sQ2ZEYqiF+EMlTH5ZmExmbDY7RqOJ9vYWAoFA0vMWi5XS0jIW\nLy7H6Zz+bO9GFLkQnzC8Xvcuu1r3AnGx9eTKb5M5RaBRqiLLMsd6TvFK7VsJvzSTxsijix5kfeZq\nNBp10vkZGfHR3NxAU1MDbW0tRKOXBhdJFhiRLER12QyEjAyOTDyfRp2I1aSla2B8ZgFI0/twqtox\nqePXldlsYeXKtZSXL0OjSR5A4jl230saeERBPSEP5Fzg1jvZmLWODVmrceoXdim5x9/LgY6jHOw8\nmmTdfWjRPdydP73CPdeTPk+AZ189Q1vvZEVhZNQZLWjyqhFU8T4n0l6C0FWCw6wj3WnGbTPgtOpx\nWnW4rHocVj0Osw6NqKK918cHR1s5dK4rafJk0QRJEztxiEMIwvhJUzlqdbL/fEyKUdV9ko9aPqVj\npAuNSmRN+ko252yk0DrRV3wmzLRfax1u54XqV2gdzViiElTclX8r9xTecV0FVKrR1Onl5y+dwBcc\nu7clVI4eNBnNqKzJrkMMWxF7M1F77IiCjEqQUCHFxzUkVIKEevSYSpAwqgOIQgxBEMjLK6SiopKC\ngqJJVyf7AgNUdZ/gSNeJREquMQyinlVplazJWEmuJRuTaJw3N8aTvWf5Xe2bicmZXq3nCyX3sDln\n47xY/0cift5v/IhP2w8kJmFmjYkHiu/i5qz185J5SRG5s2AuhFSLt42fVT0LwH9d/b1Z+6q0+zp5\np+HDCcEREO/oHDo7Lr0Dl8GJS+8YFcDxfZvOOuWF/dbei7yxvzX+OdY+tMWnEbTxGaw0YiVcvwI5\nOPFCUgkyWhH0GjVGvYjJoMVi0mPSazDoREx6keJsCybBy8WL1TQ21iUJHQC3O43S0nJKS5dgNl85\nKOlGFbkQF4g7Wz7hzfr3gbg4/P6Kb1FozV/glk2P/sAAv619nfP9NYlj6zNX88VFDyT8S9VqgUhk\nhJMnz9DQUEdPT3fSZ4QlDSO4CIpp9AyLRGITu5W8dDPLS1xUFrsoybGiEgSqWzzsPNrKqbq+pBhn\nuy6ES9WBfVSkGAwGVqxYw7JlK5IyMsiyzJm+87zVsIPOke4J3zkVl+dA1iWKhWjH7eswqPUU2fIp\nsRel3NJx3Lp7jvebdtI50oOAwB8t/32WucsXumkJaloG+YfXz+ILXLK4CkhoVRE0qgAUXCTmii/T\nq2WRbe67uWPJzRj1MxN0Hl+Ij4+1sft4O/7QpX5Ir46QrunGpRlALUiYTCZWrFjL0qWVaDTJS7my\nLNMb6MOsMc/ZEvS19GsxKcbutn280/Bhwr8y3ejmq0sepdRRMiftupFo6Bjif75QRTgmgBhGTGtD\nnd6CSnfJ1USWVMT6sol25yMHZhaQqhZkbOpB3NoBLGofggBGo4mysqWUly/DZpu4MifLMq2+do52\nneBY90mGLksZCHF3pjSDi3SjmzSDizSDm7TRfbPGdE0CeDDo4Xe1b3JqnE5YmVbJ7y1+6Lqk/Ooe\n6eH1+vc403c+cSzLlMGjix6c81R4isidBXMhpPoC/fyPgz8D4I+W/z6V7opr+pyukW7ebdzJ8Z7T\n19wWtaDGoR8VwXoHTr0Tl8HB8CD85r1O5KgOXW41qqxLKY6iXQVEWpeAPLuB223Tc/OyTDaUpzE8\n0EZt7QVaW5snuDjk5OSxeHE5xcWl6HQT/ZdvZJE7xoGOI/ym+lVkZLRqLd9dtj2lc2DGpBiftO3n\nnYYPEpXn3Honj5d9kXLnpXZ3dLSxe/eHDA1d8nOTZBiJmQiK6XhjNgZHJnw8Oq2apYVOlpe4WFbk\nxGmderm3e8DPR1Vt7DvTSShyyRJrEKO41N24tf2IgoRWq6OyciXLl6/GYLgkRCRZ4mTvWXr8fZeJ\nV21Spbux45+lbBjeyBA/PvILhkM+9Go9T617cspo/+vJJyfbeeHDWmKSDMjk6DpxawYQhSiCUUVb\nvpchIW7dzTCm84eV28mYYVqvywmGo+w93cnOo630DV0SQRpVDLfYS7q2D40qik6nZ/nyVVRWrkSv\nn/8l12vp1/oC/bxY/VpS6etN2et5uOT+BfcDvV5cbB3gr188QVTnQ8xoQnR3gOrS2CLG9DgCvjcR\nAQAAIABJREFUhdiChagkI5KsIiYLRCWBSEwmHJUIhWOEIvFHOHLlc6BTR3CJfbg0A+hGXadyc/Mp\nL6+kuLhkwioAXF7C/EwiDdeVMIj6CcI3zeAm3eieVABLssSetoO83bAj8fkOnZ0vL3n4mrXHbKge\nuMhrde8kBWUudZXxpcUP456jglGKyJ0FcyGkRiJ+ntr7lwBsL/8yG7LWzOj9vf5+3mvaydGuS0E0\nGpWGW3M3cWf+VmRk+oMD9AcGGQgO0h8cpD84wEAgvh+ZQTlcWYaxe8YkGvl6+ZeodJcTisQIhGL4\ng5H4NhTBH4oSCEbj21BsdBvFHxzdjv497I8QjSX/hmX5djZVZlGRb6atpY7a2mq6u5Mjk9VqNQUF\nxSxeXDa6JBTvND4LIhfgVO9ZfnXuN0SlKGpBzRMVX2ZNClZHa/G28ZuaV5OWRe/I28J9RXcmBSu0\ntDSxY8dbRKNRIpKIN2YlJKbTF9ATnqT4XJbLSGWxi+UlLkpz7WjEmU2i/MEIe0518vGxVvq9lwYL\nUSXhFPtJ1/aiV4URRZGlS5ezcuVaTKbrk+YuVRFFFR2Rdv7qk18gyRKZpgz+7zV/fE0+pHNBNCbx\n248vsuv46LVFjGJDM+mmIOXllZCm4c3unYyMulqscC/lGxVfntOKZDFJ4lhNL+8fbqG565KVTSXI\nOMV+MrS9GNQhRFEzeh2tmZfraLb9mizLHOk6zqsX32YkGnftsWkt8fyl44OdLxvFL8/9OnGQTz4i\nIFBgzU8KzFpoztV18uxHnyJkNKO29Sc9V2ovZmvupgl5ia+GJMtEIhLBUdHrC0Q4UdfP7mOt+IPj\nOzQZqziMWzOAXRxCJcjo9QaWLKmgoqISh2NyIReORWj2ttIb6Kc30Eevv4/eQD89gb6kstNXQq/W\nxUWv0U2awY1Tb+dA51GavfEVWQGBW/M28UDRXQt2j0NceB/sPMrbDR8kqgzq1XoeX/II6zJXzfrz\nFZE7C+ZCSMWkGH/yyf8DwGOlD3Fb3uZpvW8gOMj7jR9zqKsq4dsiCmpuybmJbQW3TaukpyzL+CIj\nSSK4b5wA7g8OTFr+drG9hCeWPj4nyxrhSIwTF/vYd6aT840DSV2mTqtmXVk6myuzSLfI1NXVUFt7\nAY8n2XdKp9NRUrKY0tIy8vPzcTrNN7zIhXhmgn8+/RzBWAgBgS8t/gJ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7L+2Hw9NL/3M5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\nIiIip8OQS0RERERO5/8B1px+/aSVcSQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106836828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the table\n",
    "\n",
    "x = range(lookahead + 1)\n",
    "y = bnm_lookahead.loc[:,x].mean().as_matrix()\n",
    "plt.plot(x, y, color='gray', label = 'Global Average')\n",
    "\n",
    "\n",
    "labels = ['No Best New Music', 'Best New Music']\n",
    "for tf, rows in bnm_lookahead.groupby(0):\n",
    "\n",
    "    y = rows.loc[:,x].mean()\n",
    "    plt.plot(x, y, label = labels[tf])\n",
    "    \n",
    "\n",
    "plt.xlabel('Days after $n$')\n",
    "plt.ylabel('Proportion Best New Music')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The obvious pattern, that $n+1$ should be less likely if there was best new music on day $n$, does not show up. If anything, there is slightly *more* best new music in the 30 days following a best new music, not less.\n",
    "\n",
    "But that overall difference is perplexing. If anything, the two distributions should be the same. Best new music shouldn't predict *more* best new music, right?\n",
    "\n",
    "Maybe that's just because the probability of best new music varies systematically over time, and so *in general* the presence of best new music predicts itself. If that were true, then things should level out if only the data following the 2008 increase is considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pWbIkkNWScL1f1xsMBvr3H0DPnl1YvXoVDRs2yfH+woXzaNPmPzz0UHMAHn/8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xYFp0dyO7YLU0AAi84AAACaG0JuLVzTFZiPCwAA0DwRcs9xtKxCpWV2SVI8IRcAAKBZIuSew7V1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\nww+VlJTk8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c538cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = bnm_lookahead.copy(deep = True)\n",
    "subset = subset[subset.index.values > 1230768000] # after Jan 1 2009\n",
    "\n",
    "\n",
    "x = range(lookahead + 1)\n",
    "y = subset.loc[:,x].mean().as_matrix()\n",
    "plt.plot(x, y, color='gray', label = 'Global Average')\n",
    "\n",
    "\n",
    "labels = ['No Best New Music', 'Best New Music']\n",
    "for tf, rows in subset.groupby(0):\n",
    "\n",
    "    y = rows.loc[:,x].mean()\n",
    "    plt.plot(x, y, label = labels[tf])\n",
    "    \n",
    "\n",
    "plt.xlabel('Days after $n$')\n",
    "plt.ylabel('Proportion Best New Music')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That did the trick. Now, the best new music and no best new music time-lines overlap pretty cleanly.\n",
    "\n",
    "## Summary\n",
    "\n",
    "I haven't found any evidence indicating that Pitchfork is purposefully spreading best new music reviews out over time. The number of best new music reviews per day lines up nicely with an independent binomial sampling model, and best new music reviews are equally likely every day, regardless of whether there was a best new music the day before (or two days before, and so on)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}