test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "Automatic pdb calling has been turned ON\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "%pdb\n",
    "import pandas as pd\n",
    "from datetime import datetime as dt\n",
    "import os\n",
    "import glob\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "fname = \"./data/date-hour-soo-dest-2015.csv\"\n",
    "bart_df = pd.read_csv(fname, names = [\"Date\", \"Hour\", \"Origin\", \"Destination\", \"Count\"],\n",
    "                     parse_dates = [\"Date\"], index_col = \"Date\" )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3], dtype=int32)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hour_df = bart_df.groupby(\"Hour\")\n",
    "#hour_df[\"Count\"].mean().plot(kind=\"bar\")\n",
    "#piv = bart_df.pivot_table(index=bart_df.index.dayofweek, values=\"Count\", aggfunc=np.sum, columns=\"Hour\")\n",
    "#piv/sum(piv)\n",
    "#resamp = bart_df.resample(\"d\").mean()\n",
    "#resamp.dropna(how=\"any\", inplace=True)\n",
    "#resamp = resamp[\"Count\"]\n",
    "#resamp = resamp.diff().dropna()\n",
    "#sm.graphics.tsa.plot_acf(resamp, lags=30)\n",
    "#resamp\n",
    "#pd.tools.plotting.autocorrelation_plot(resamp[\"Count\"],)\n",
    "#resamp\n",
    "bart_df[\"weekday\"].values[-20:-10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Hour'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-109-6af06ed86ad3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpiv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"m\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"t\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"tr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"f\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"s\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"su\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpiv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpiv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpiv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Hour\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpiv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpiv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"bar\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1995\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1997\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1998\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1999\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2002\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2003\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2004\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1348\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3289\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3290\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3291\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3292\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/indexes/base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   1945\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1946\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1949\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4154)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4084)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Hour'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/Users/ahoffer/Projects/git/bart/pandas/index.pyx\u001b[0m(161)\u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4084)\u001b[0;34m()\u001b[0m\n",
      "\n",
      "ipdb> q\n"
     ]
    }
   ],
   "source": [
    "piv.index = [\"m\", \"t\", \"w\", \"tr\", \"f\",\"s\",\"su\"]\n",
    "piv = piv[piv[\"Hour\"] < 10]\n",
    "(piv.T/sum(piv.T)).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t = bart_df.apply(lambda row: row[\"Hour\"]+1, axis = 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2015-01-01    1\n",
       "2015-01-01    1\n",
       "2015-01-01    1\n",
       "2015-01-01    1\n",
       "2015-01-01    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/ipykernel/__main__.py:13: FutureWarning: \n",
      "The default value for 'return_type' will change to 'axes' in a future release.\n",
      " To use the future behavior now, set return_type='axes'.\n",
      " To keep the previous behavior and silence this warning, set return_type='dict'.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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       "  <matplotlib.lines.Line2D at 0x11d3b15d0>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3b1c10>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3d5210>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3d5850>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3efe10>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3fa490>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x11baf9890>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb04750>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb2b390>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb44f90>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb67bd0>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb8b810>,\n",
       "  <matplotlib.lines.Line2D at 0x11c67c450>,\n",
       "  <matplotlib.lines.Line2D at 0x11c69f090>,\n",
       "  <matplotlib.lines.Line2D at 0x11d399c90>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3bc8d0>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3e0510>,\n",
       "  <matplotlib.lines.Line2D at 0x11d407150>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x11baf9250>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb04110>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb21d10>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb44950>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb67590>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb8b1d0>,\n",
       "  <matplotlib.lines.Line2D at 0x11bba3dd0>,\n",
       "  <matplotlib.lines.Line2D at 0x11c691a10>,\n",
       "  <matplotlib.lines.Line2D at 0x11d399650>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3bc290>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3d5e90>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3faad0>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x11c19ba90>,\n",
       "  <matplotlib.lines.Line2D at 0x1188b4f10>,\n",
       "  <matplotlib.lines.Line2D at 0x11c63b310>,\n",
       "  <matplotlib.lines.Line2D at 0x11c646a90>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb143d0>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb14a10>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb2bfd0>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb37650>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb4fc10>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb5b290>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb73850>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb73e90>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb97490>,\n",
       "  <matplotlib.lines.Line2D at 0x11bb97ad0>,\n",
       "  <matplotlib.lines.Line2D at 0x11c6870d0>,\n",
       "  <matplotlib.lines.Line2D at 0x11c687710>,\n",
       "  <matplotlib.lines.Line2D at 0x11c69fcd0>,\n",
       "  <matplotlib.lines.Line2D at 0x11c6aa350>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3a3910>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3a3f50>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3c9550>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3c9b90>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3ef190>,\n",
       "  <matplotlib.lines.Line2D at 0x11d3ef7d0>]}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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axpSE554qljY7jDZgp5HLRKv1PPdUcXjuKevNUbHRenYaNcFzTzVXz5i50PT+6ZxGDm2P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\nmL75//GBzaT6812mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118857210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bart_df[\"weekday\"] = bart_df.index.dayofweek \n",
    "weekday_filter = bart_df[\"weekday\"] < 5\n",
    "hour_filter = bart_df[\"Hour\"] < 12\n",
    "bart_df = bart_df[weekday_filter & hour_filter  & (bart_df[\"Count\"] > 5)]\n",
    "#mon = bart_df.resample(\"M\", how=np.mean)\n",
    "weekdays = [\"Mon\", \"Tues\", \"Wed\", \"Thurs\", \"Fri\"]\n",
    "bart_df[\"weekday\"].unique = weekdays\n",
    "mon = bart_df.pivot_table(aggfunc=np.sum, index=\"Hour\", columns=bart_df.index.month, values=\"Count\")\n",
    "mon = mon.dropna(axis=0, how=\"any\")\n",
    "#mon = mon/sum(mon)\n",
    "#mon = bart_df.groupby(\"Origin\")\n",
    "#mon[\"Count\"].mean().plot(kind = \"bar\")\n",
    "mon.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "append() takes exactly one argument (0 given)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-2299e2a913d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0mmy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: append() takes exactly one argument (0 given)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m<ipython-input-3-2299e2a913d5>\u001b[0m(6)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m      2 \u001b[0;31m\u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m      3 \u001b[0;31m\u001b[0;32mfor\u001b[0m \u001b[0mf_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcsv_files\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m      4 \u001b[0;31m    \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m      5 \u001b[0;31m        \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m----> 6 \u001b[0;31m        \u001b[0mmy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> \n",
      "ipdb> q\n"
     ]
    }
   ],
   "source": [
    "csv_files = glob.glob(\"./data/*.csv\")\n",
    "my_list = []\n",
    "for f_name in csv_files:\n",
    "    with open(f_name, 'r') as f:\n",
    "        f.read()\n",
    "        my_list.append()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bart_df = bart_df[(bart_df[\"Count\"] > 2) & (bart_df[\"Hour\"] > 6) & (bart_df[\"Hour\"] < 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('Date', u'occurred at index 2015-01-01 00:00:00')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-113-87c42514a541>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbart_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"DateTime\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbart_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Date\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"%Y-%m-%d\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mbart_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"DayOfWeek\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbart_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweekday\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"DateTime\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, broadcast, raw, reduce, args, **kwds)\u001b[0m\n\u001b[1;32m   4059\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4060\u001b[0m                         \u001b[0mreduce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4061\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4062\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4063\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_broadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_apply_standard\u001b[0;34m(self, func, axis, ignore_failures, reduce)\u001b[0m\n\u001b[1;32m   4155\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4156\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4157\u001b[0;31m                     \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4158\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4159\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-113-87c42514a541>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(row)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbart_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"DateTime\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbart_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Date\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"%Y-%m-%d\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mbart_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"DayOfWeek\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbart_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweekday\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"DateTime\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    581\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 583\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/indexes/base.pyc\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   1992\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1993\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1994\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1995\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1996\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ('Date', u'occurred at index 2015-01-01 00:00:00')"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/indexes/base.py\u001b[0m(1994)\u001b[0;36mget_value\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m   1992 \u001b[0;31m                    \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1993 \u001b[0;31m                \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m-> 1994 \u001b[0;31m                    \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1995 \u001b[0;31m            \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0;32m   1996 \u001b[0;31m                \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "ipdb> q\n"
     ]
    }
   ],
   "source": [
    "bart_df[\"DateTime\"] = bart_df.apply(lambda row: dt.strptime(row[\"Date\"], \"%Y-%m-%d\") , axis=1)\n",
    "bart_df[\"DayOfWeek\"] = bart_df.apply(lambda row: datetime.date.weekday(row[\"DateTime\"]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'DayOfWeek'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-112-b7b55936476f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdaily_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbart_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbart_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"DayOfWeek\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Date\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m hourly_df = bart_df[(bart_df[\"Hour\"] > 6) & (bart_df[\"Hour\"] < 10) \n\u001b[1;32m      3\u001b[0m                    & (bart_df[\"DayOfWeek\"]) > 0 & (bart_df[\"DayOfWeek\"] < 6)].groupby(\"Hour\")\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1995\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1996\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1997\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1998\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1999\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2002\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2003\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2004\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1348\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3289\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3290\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3291\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3292\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ahoffer/.virtualenvs/test/lib/python2.7/site-packages/pandas/indexes/base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   1945\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1946\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1949\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4154)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4018)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'DayOfWeek'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> \u001b[0;32m/Users/ahoffer/Projects/git/bart/pandas/hashtable.pyx\u001b[0m(683)\u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)\u001b[0;34m()\u001b[0m\n",
      "\n",
      "ipdb> q\n"
     ]
    }
   ],
   "source": [
    "daily_df = bart_df[(bart_df[\"DayOfWeek\"] < 5)].groupby(\"Date\")\n",
    "hourly_df = bart_df[(bart_df[\"Hour\"] > 6) & (bart_df[\"Hour\"] < 10) \n",
    "                   & (bart_df[\"DayOfWeek\"]) > 0 & (bart_df[\"DayOfWeek\"] < 6)].groupby(\"Hour\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bart_df_pivot = bart_df.head(10000).pivot_table(values=\"Count\", index=\"Origin\", columns=\"Destination\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11e5ecc90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1168ea390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11e63c310>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11e69fb90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11e722bd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11e6c34d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x120457b90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1204d8a10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x12054a110>]], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QbHDDhg0sXRreOZIYMyYzzZczMG8Cfy+pEJ5+T4KVXcEKywgL5r+On88kPPNn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8c4490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bart_df[bart_df[\"Count\"] > 50].hist(column=\"Count\", by=\"DayOfWeek\", bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>DayOfWeek</td>    <th>  R-squared:         </th> <td>   0.036</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.036</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   374.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 10 Oct 2016</td> <th>  Prob (F-statistic):</th> <td>5.74e-82</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:29:36</td>     <th>  Log-Likelihood:    </th> <td> -26596.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 10000</td>      <th>  AIC:               </th> <td>5.319e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  9999</td>      <th>  BIC:               </th> <td>5.320e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Count</th> <td>    0.0128</td> <td>    0.001</td> <td>   19.355</td> <td> 0.000</td> <td>    0.012     0.014</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>602.844</td> <th>  Durbin-Watson:     </th> <td>   0.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 402.527</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.376</td>  <th>  Prob(JB):          </th> <td>3.91e-88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.366</td>  <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              DayOfWeek   R-squared:                       0.036\n",
       "Model:                            OLS   Adj. R-squared:                  0.036\n",
       "Method:                 Least Squares   F-statistic:                     374.6\n",
       "Date:                Mon, 10 Oct 2016   Prob (F-statistic):           5.74e-82\n",
       "Time:                        23:29:36   Log-Likelihood:                -26596.\n",
       "No. Observations:               10000   AIC:                         5.319e+04\n",
       "Df Residuals:                    9999   BIC:                         5.320e+04\n",
       "Df Model:                           1                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Count          0.0128      0.001     19.355      0.000         0.012     0.014\n",
       "==============================================================================\n",
       "Omnibus:                      602.844   Durbin-Watson:                   0.065\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              402.527\n",
       "Skew:                          -0.376   Prob(JB):                     3.91e-88\n",
       "Kurtosis:                       2.366   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = sm.OLS(bart_df.head(10000)[\"DayOfWeek\"], bart_df.head(10000)[\"Count\"])\n",
    "res = a.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x120cea8d0>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P8rfAVmAN8HUDX5JWzorO9CVJ47WqTklM8ge3knw9yXSSh8Zdy3JIsjbJXUkeSfJwksvG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\nUkMMfUlqiKEvSQ0x9CWpIYa+JDXk/wH6HLeyYZIbIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1201e5790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(bart_df.head(10000)[\"DayOfWeek\"], bart_df.head(10000)[\"Count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>30.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>20.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count\n",
       "DateTime         \n",
       "2015-01-01    NaN\n",
       "2015-01-01    NaN\n",
       "2015-01-01    NaN\n",
       "2015-01-01    NaN\n",
       "2015-01-01    NaN\n",
       "2015-01-01    NaN\n",
       "2015-01-01    NaN\n",
       "2015-01-01    NaN\n",
       "2015-01-01    NaN\n",
       "2015-01-01    5.9\n",
       "2015-01-01    5.9\n",
       "2015-01-01    5.8\n",
       "2015-01-01    4.9\n",
       "2015-01-01    4.9\n",
       "2015-01-01    5.3\n",
       "2015-01-01    5.7\n",
       "2015-01-01    5.2\n",
       "2015-01-01    5.7\n",
       "2015-01-01    4.7\n",
       "2015-01-01    4.7\n",
       "2015-01-01    5.2\n",
       "2015-01-01    7.3\n",
       "2015-01-01    7.4\n",
       "2015-01-01    7.9\n",
       "2015-01-01    7.6\n",
       "2015-01-01    7.2\n",
       "2015-01-01    7.5\n",
       "2015-01-01    7.0\n",
       "2015-01-01    7.3\n",
       "2015-01-01    7.7\n",
       "...           ...\n",
       "2015-01-02    8.9\n",
       "2015-01-02    8.8\n",
       "2015-01-02    8.2\n",
       "2015-01-02    7.7\n",
       "2015-01-02    7.5\n",
       "2015-01-02    8.5\n",
       "2015-01-02    9.8\n",
       "2015-01-02    9.9\n",
       "2015-01-02    9.8\n",
       "2015-01-02    6.9\n",
       "2015-01-02   13.8\n",
       "2015-01-02   13.9\n",
       "2015-01-02   13.5\n",
       "2015-01-02   13.6\n",
       "2015-01-02   21.2\n",
       "2015-01-02   20.1\n",
       "2015-01-02   19.2\n",
       "2015-01-02   27.1\n",
       "2015-01-02   30.2\n",
       "2015-01-02   30.8\n",
       "2015-01-02   24.4\n",
       "2015-01-02   25.7\n",
       "2015-01-02   26.4\n",
       "2015-01-02   27.1\n",
       "2015-01-02   20.0\n",
       "2015-01-02   20.4\n",
       "2015-01-02   22.5\n",
       "2015-01-02   14.9\n",
       "2015-01-02   11.2\n",
       "2015-01-02   10.7\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bart_df[0:1000][[\"DateTime\",\"Count\"]].set_index(\"DateTime\").rolling(window=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rm.plot()"
   ]
  }
 ],
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