Jesse's Test Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jesse's Test Code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some random filler text here explaining what our overall goals are. Let's get some data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from project.store import fread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = fread(\"examplerow\")"
   ]
  },
  {
   "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>artist_discovery</th>\n",
       "      <th>artist_discovery_rank</th>\n",
       "      <th>artist_familiarity</th>\n",
       "      <th>artist_familiarity_rank</th>\n",
       "      <th>artist_hotttnesss</th>\n",
       "      <th>artist_hotttnesss_rank</th>\n",
       "      <th>artist_id</th>\n",
       "      <th>artist_location</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>audio_summary</th>\n",
       "      <th>id</th>\n",
       "      <th>song_currency</th>\n",
       "      <th>song_hotttnesss</th>\n",
       "      <th>song_type</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.618004</td>\n",
       "      <td>966</td>\n",
       "      <td>0.447882</td>\n",
       "      <td>21048</td>\n",
       "      <td>0.689561</td>\n",
       "      <td>1519</td>\n",
       "      <td>AR0T77X1187FB46E52</td>\n",
       "      <td>{u'latitude': 42.9869, u'location': u'London, ...</td>\n",
       "      <td>The Weekend</td>\n",
       "      <td>{u'time_signature': 4, u'analysis_url': u'http...</td>\n",
       "      <td>SOBWZNS12A8C13A053</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[studio, electric, vocal]</td>\n",
       "      <td>Bring It On</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   artist_discovery  artist_discovery_rank  artist_familiarity  artist_familiarity_rank  artist_hotttnesss  artist_hotttnesss_rank           artist_id                                    artist_location  artist_name                                      audio_summary                  id  song_currency  song_hotttnesss                  song_type        title\n",
       "0          0.618004                    966            0.447882                    21048           0.689561                    1519  AR0T77X1187FB46E52  {u'latitude': 42.9869, u'location': u'London, ...  The Weekend  {u'time_signature': 4, u'analysis_url': u'http...  SOBWZNS12A8C13A053              0                0  [studio, electric, vocal]  Bring It On"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acousticness': 0.026718,\n",
       " 'analysis_url': 'http://echonest-analysis.s3.amazonaws.com/TR/FYXa9gvcj2bOXxVGrM1pB8eIcHiw5CYGmSUPl_U5SHnuPENwX_pKjpJvN7x7UIbEWw1MSP7lwF10kHYqk%3D/3/full.json?AWSAccessKeyId=AKIAJRDFEY23UEVW42BQ&Expires=1448414650&Signature=h3KjaV6PM0MUNWzTYPeFAKfrRQM%3D',\n",
       " 'audio_md5': '',\n",
       " 'danceability': 0.46879,\n",
       " 'duration': 202.45333,\n",
       " 'energy': 0.916008,\n",
       " 'instrumentalness': 0.0,\n",
       " 'key': 1,\n",
       " 'liveness': 0.305953,\n",
       " 'loudness': -4.555,\n",
       " 'mode': 0,\n",
       " 'speechiness': 0.109373,\n",
       " 'tempo': 77.602,\n",
       " 'time_signature': 4,\n",
       " 'valence': 0.469857}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['audio_summary'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what do each of these columns mean? \n",
    "\n",
    "**artist_discovery**: measure of how unexpectedly popular the artist is\n",
    "\n",
    "**artist_discovery_rank**: relative ranking of how unexpectedly popular the artist is\n",
    "\n",
    "**artist_familiarity**: measure of how familiar, or well known an artist is to the world\n",
    "\n",
    "**artist_familiarity_rank**: relative ranking of this familiarity compared to other artists\n",
    "\n",
    "**artist_hottness**: measure of how much an artist is trending based on social media, reviews, etc.\n",
    "\n",
    "**artist_hotness_rank**: relative rank of hotness compared to other artists\n",
    "\n",
    "**artist_id**: unique identifier for an artist\n",
    "\n",
    "**artist_location**: dictionary containing the latitude and longitude of the location that the artist operates in \n",
    "\n",
    "**artist_name**: the name of the artist (self-explanatory)\n",
    "\n",
    "**audio_summary**: dictionary containing details relating to the song audio such as key, time signature, tempo, and other abstract measures such as liveness, danceability, and energy\n",
    "\n",
    "**id**: unique identifier for a song\n",
    "\n",
    "**song_currency**: how recently popular the song is\n",
    "\n",
    "**song_hotness**: measure of how much a song is trending based on social media, reviews, etc.\n",
    "\n",
    "**song_type**: list of categories that the song falls under \n",
    "\n",
    "**title**: name of the song"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More filler text! Time to randomly generate some data (don't actually include this part in final result) and make some pretty graphs to examine our data :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the distribution of songs that fit into the various types. Do keep in mind that songs can fit more than one genre/type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAx8AAAITCAYAAABmEaFqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c298a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import collections\n",
    "\n",
    "# create a default dict of ints so we can keep track of how many songs are of each type\n",
    "d = collections.defaultdict(int)\n",
    "\n",
    "def track_song_types(typelist):\n",
    "    for song_type in typelist:\n",
    "        d[song_type] += 1\n",
    "\n",
    "test['song_type'].map(track_song_types)\n",
    "songtypedict = dict(d)\n",
    "\n",
    "# now that we've got a dictionary, let's plot it!\n",
    "plt.barh(range(len(songtypedict)), songtypedict.values(), align='center')\n",
    "plt.yticks(range(len(songtypedict)), songtypedict.keys())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the top n artists? Let's define \"top\" here as the artists with the most number of songs in the top 1000 for  currency or hotness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA08AAAITCAYAAADfBSciAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHYpJREFUeJzt3XmMVfX5+PEHGdEpgiCI2q+0Ai6DMoxABQSh4kJpNdWo\nocRCxBi0bqTGCEZrRDGKWqgobqDg0hprozY1pdZaV2RrGXAhJFZTEBQ3VESGyozc3x+G+2MK0ocr\nwwzweiWTwLnnnnkufDKcN2fOnWaFQqEQAAAAbNUejT0AAADAzkA8AQAAJIgnAACABPEEAACQIJ4A\nAAASxBMAAEBCWWMPwPa3YMGCxh4BAACapF69epX8XPG0i/o2i4Ldz5IlSyIiomvXro08CTsba4dS\nWTuUytqhVEuWLImamppvdQzftgcAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAk\niCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAg\nngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4\nAgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJ\nAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcA\nAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAA\nABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAA\nSBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAg\nQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE\n8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLE\nEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBP\nAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwB\nAAAklDX2ADSMl19+ubFHYCeydOnSiIj4+OOPG3cQdjrWDqWydiiVtUOpli5dGkceeeS3OoZ42kWN\nnSKeKMXyxh6AnZa1Q6msHUpl7bBtPv9oaTx4q3hiC9odfFRjjwAAALsU9zwBAAAkiCcAAIAE8QQA\nAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAA\nQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAA\nCeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAk\niCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAg\nngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4\nAgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJ\nAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcA\nAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAA\nABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAA\nSBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAg\nQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAICE\nrcbTlVdeGRUVFVv9mDJlSqxYsSIqKirimWee2a7D3XzzzXH00UfHV199VW/7zJkzo6KiIoYPH77Z\nc8aNGxf9+/ffbjPMmzcvKioqYvHixdvtmFn//Oc/o6KiIt57770d/rkBAID6yrb24MUXXxxnn312\nREQUCoUYM2ZMdOrUKS666KLiPgcccEDU1tY2yHB9+/aNGTNmxJIlS6Jbt27F7bNnz4599903Xn31\n1aipqYnvfOc7xccWLFgQffv2bZB5AACA3ddWrzx17NgxunfvHt27d4+qqqooLy+Ptm3bFrd17949\nDjjggAYbrlevXtG8efN49dVX622fM2dOjBgxIurq6mL+/PnF7WvWrIm33347jj322AabCQAA2D1t\n13ueVqxYEaNGjYqjjz46BgwYEPfcc0+9x2tqamL8+PHRv3//qKqqihEjRsSSJUu+8Xj77LNPHHnk\nkVFdXV3ctmzZsnj33Xdj8ODB0bVr13jllVeKj1VXV8eGDRvqxdNDDz0UgwcPjsrKyjj11FNj5syZ\n9T5HXV1dTJ48OY4//vjo3r17nHnmmTFnzpxvnOmTTz6JIUOGxBlnnBFffPFF6nU98cQT0bdv35gz\nZ06cdtppUVlZGaeccko899xz9Y794osvxumnnx5VVVUxbNiwWL58+TfOAQAA7FjbNZ5+85vfRFVV\nVdx7770xaNCguO222+L555+PiK+/7e/CCy+MmTNnxi9/+cuYPHly7LXXXjFixIitRkKfPn1i0aJF\nxd/Pnj079t9//zj88MOjb9++MXv27OJj1dXV0bFjx/i///u/iIiYMmVK3HLLLXHqqafGvffeG/36\n9YvLL788nn766eJzrrnmmnjggQdi5MiRcdddd0Xnzp1j1KhRsXDhws1mWbt2bZx//vlRVlYW999/\nf+yzzz7p17V27dq4+uqrY/jw4XHvvfdG27Zt47LLLovVq1dHRMSiRYviwgsvjC5dusSdd94Z/fv3\nj2uvvTaaNWtW4t8GAACwPW31nqdtdeaZZ8Yll1wSERE/+MEP4q9//WvMnz8/Bg0aFLNmzYp58+bF\njBkzileGBgwYEKecckrcfffdceONN27xmL1794777rsvVq1aFe3atYvZs2cXn9+vX7+YPn16fPjh\nh9GhQ4eorq4uPvb555/H1KlTY9SoUTF69Oji/mvXro2JEyfGkCFD4u23344nn3wybrjhhjjrrLMi\nIuK4446Ljz76KG677bZ48MEHi3PU1tbG6NGj4/PPP4/f/e530bZt24iI9Ouqra2NMWPGxJAhQyIi\nol27dnHaaafF/Pnz4+STT4777rsvOnfuHBMnTizOsXr16vjtb3+7nf52AACAb2O7Xnnq0aNH8dfN\nmzePAw44ID7//POI+Ppd68rLy+OYY46Jurq6qKuri0KhEP3794+5c+d+4zF79eoVZWVlsXDhwtiw\nYUPMnz8/+vXrFxFfB1qLFi1izpw5UVtbG6+//nrxzSIWLVoU69evjx/+8IfFz1dXVxcDBgyI5cuX\nx4oVK4r3Sw0cOLDePgMHDowFCxZEXV1dcY7rrrsuXnnllbj00ktj//33L27fltd19NFHF3+98V6x\nmpqaiPj6qtmAAQPq7T948ODknzwAANDQtuuVp/Ly8nq/b9asWWzYsCEiIj777LNYt25dvXfN22jP\nPff8xmO2bNkyunXrFgsXLowOHTrE6tWri/G09957R48ePeIf//hHHHLIIfHll18W4+mzzz6LiIhh\nw4ZtdsxmzZrFRx99VNxn4MCBW9zn008/Lf7+gw8+iG7dusWUKVPiRz/6UbRo0WKbX9fee+9d/PUe\ne3zdrYVCISK+vlK28WrWRu3bt//GPxcAAGDH2q7xtDWtWrWKdu3axdSpU+tt3xgPW9OnT59YuHBh\ntGnTJrp06RIdOnQoPtavX7/405/+FF26dInDDjss9ttvv+Lni4i4884748ADD9zsc3bq1CkWL14c\nzZo1i9///vfRvHnzzWZq06ZNcduECRPiu9/9bpx++ulx7733xqWXXvqtX9em2rRpEx9//HG9bRvj\nDgAAaHzb9dv2/tumb3bQq1ev+OSTT6K8vDyOOuqo4sef//zneOqpp7Z6nN69e8eSJUtiwYIFxatO\nGx177LGxbNmymDdvXr3HqqqqoqysLFatWlXv87311ltx9913F2cqFAqxZs2aevvMmzcvHnrooSgr\n+/9t2a5duzj00ENj+PDhMW3atFi6dOm3fl2b6tOnTzz//PP1fiDwiy++mH4+AADQsLYpnrb1asqm\n+59wwglRWVkZ559/fvzxj3+MuXPnxvXXXx8zZsyILl26bPU4PXv2jHXr1sWsWbM2i6fKysooLy+P\nWbNm1XuL8v322y9GjBgREyZMiGnTpsXcuXPjgQceiGuvvTb23nvvaNmyZXTt2jUGDx4cV1xxRTzy\nyCMxd+7cuP3222PSpElx0EEHbfGd7i655JJo3bp1jBs37lu/rk1deOGF8eGHH8bFF18cL774Ykyd\nOjUeeeSR9PMBAICGtU3xtK1vm73p/nvssUfcf//90a9fv7j11lvjggsuiAULFsSECRNi6NChWz1O\neXl5dO/ePSK+vgr1359j431OxxxzTL3HxowZExdddFH84Q9/iFGjRsXDDz8c55xzTkyYMKG4z69/\n/es444wziu/MN3PmzLj88svjsssu2+Lr2GeffWLMmDExb968eOqpp9Kv63/92R166KExffr0+PTT\nT2P06NHxl7/8JcaNG+etygEAoIloVtjWy0k0eQsWLIhxj6xo7DEAAKDJWLVicdwx5kfRq1evko/R\noPc8AQAA7CrEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAA\nQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAA\nCeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAk\niCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAg\nngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4\nAgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJ\nAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcA\nAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAA\nABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAA\nSBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAg\nQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE\n8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACWWNPQANY9WKxY09AgAANBmff7T0Wx9DPO2ibr5k\nQGOPwE5k6dKlERFxyCGHNOoc7HysHUpl7VAqa4dSLV3a8VsfQzztogYMEE/ktW/fPiIiunbt2siT\nsLOxdiiVtUOprB1K1b59+6ipqflWx3DPEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE\n8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLE\nEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBP\nAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwB\nAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQA\nAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAA\nQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAA\nCeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAk\niCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAg\nngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4\nAgAASBBPAAAACeIJAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJ\nAAAgQTwBAAAkiCcAAIAE8QQAAJAgngAAABLEEwAAQIJ4AgAASBBPAAAACeIJAAAgQTwBAAAkiCcA\nAIAE8QQAAJAgngAAABLEEwAAQEKzQqFQaOwh2L4WLFjQ2CMAAECT1KtXr5KfK54AAAASfNseAABA\ngngCAABIEE8AAAAJ4gkAACBBPAEAACSIJwAAgATxBAAAkCCeAAAAEsQTAABAgnjayTz22GMxePDg\nqKqqimHDhsWiRYu2uv+bb74Z55xzTvTo0SMGDRoU06ZN20GT0tRs69qprq6OESNGxDHHHBMDBgyI\nsWPHxqpVq3bQtDQl27p2NjVlypSoqKhowOloyrZ17XzyyScxZsyY6NOnTxxzzDFx4YUXxvLly3fQ\ntDQV27puXnvttRg+fHj06tUrTjrppJgyZUrU1dXtoGlpiv7+979Hz549/+d+pZwni6edyJNPPhnj\nxo2L0047Le64445o1apVnHfeebFixYot7r9q1ao499xzo3nz5jF58uQYOnRo3HbbbTF9+vQdPDmN\nbVvXzttvvx0jR46MVq1axaRJk2Ls2LFRXV0d5513nn+QdjPbunY29eabb8Y999wTzZo12wGT0tRs\n69qpra2Nc889N95444244YYb4qabborly5fHqFGjora2dgdPT2PZ1nXz3nvvxciRI6O8vDzuuOOO\nGDlyZNx3330xceLEHTw5TUV1dXVcccUV/3O/ks+TC+wUNmzYUBg0aFBh3LhxxW21tbWFE088sTB+\n/PgtPmfy5MmFvn37Fv7zn/8Ut912222F3r17F2praxt8ZpqGUtbOuHHjCieddFKhrq6uuO21114r\nHHHEEYUXXnihwWemaShl7WxUV1dXOPPMMwsDBw4sVFRUNPSoNDGlrJ3HHnusUFVVVVi5cmVx25Il\nSwoDBgwoLF68uMFnpvGVsm7uv//+Qvfu3Qvr1q0rbps0aVKhZ8+eDT4vTcuXX35ZmDp1aqFbt26F\n3r17F3r06LHV/Us9T3blaSexbNmyeO+99+KEE04obisrK4vjjz8+Xn755S0+Z/bs2XHsscfGXnvt\nVdx24oknxurVq+ONN95o8JlpGkpZO4cddljxf2M26tSpU0REvPvuuw07ME1GKWtnowceeCDWrVsX\nw4cPj0Kh0NCj0sSUsnaeffbZGDhwYBx44IHFbRUVFfHSSy/FkUce2eAz0/hKWTdr1qyJsrKyeuc6\n++67b9TU1MT69esbfGaajpdeeimmTZsWY8eOTf3bU+p5snjaSSxdujQiIr7//e/X237wwQfH8uXL\nt7hAli1bFt/73vfqbevYsWO947HrK2XtnH322XH22WfX2/bcc89FRETnzp0bZlCanFLWTsTXX3um\nTJkS48ePjz333LOhx6QJKmXtvPnmm9GpU6eYMmVK9O/fPyorK+OCCy6IlStX7oiRaQJKWTdDhgyJ\n2tramDhxYqxevTpee+21ePDBB+Pkk0+OFi1a7IixaSIqKyvjueeei+HDh6f2L/U8WTztJL744ouI\niGjZsmW97S1btowNGzZETU3NFp+zpf03PR67vlLWzn9buXJl3HLLLVFZWRl9+/ZtkDlpekpZO4VC\nIX71q1/F6aefnrpZl11TKWtn1apV8fjjj8esWbPixhtvjFtuuSXeeuutOP/88+Orr77aIXPTuEpZ\nN0cccUSMHz8+ZsyYEX369ImhQ4dG+/bt48Ybb9whM9N0HHDAAbHPPvuk9y/1PLmstPHY0Tb+b8s3\n3Xi9xx6bd3ChUPjG/d3AvfsoZe1sauXKlTFy5MiIiJg0adJ2nY2mrZS18+ijj8by5cvjnnvuadDZ\naNpKWTt1dXVRV1cX9913X/EEqGPHjnHWWWfFM888Ez/+8Y8bbmCahFLWzfPPPx9XX311nHXWWfGT\nn/wkPvjgg7j99tvjggsuiBkzZrj6xDcq9TzZlaedRKtWrSIiYu3atfW2r127Npo3bx7l5eVbfM6W\n9t/0eOz6Slk7G7355psxbNiwWLt2bUyfPr14OZvdw7aunZUrV8att94aV111Vey1115RV1dXPBn6\n6quv3Pu0Gynl607Lli2jqqqq3v8cd+vWLVq3bh3/+te/GnZgmoRS1s3EiRPjuOOOi+uuuy769OkT\nP/3pT2Pq1KmxYMGCeOqpp3bI3OycSj1PFk87iY3f//vfP+9i+fLlxRv5t/Scd955Z7P9I+Ibn8Ou\np5S1ExHx6quvxs9//vMoKyuLRx55JA4//PAGnZOmZ1vXzpw5c6KmpiZGjx4d3bp1i27dusXNN98c\nERFHHXVU3HnnnQ0/NE1CKV93vve9723xBv+6ujrfLbGbKGXdLFu2LKqqqupt69y5c7Rp0ybefvvt\nhhmUXUKp58niaSdxyCGHxEEHHRR/+9vfittqa2vjhRde+MZ7UI499tiYM2dOrFu3rrjt2WefjbZt\n20bXrl0bfGaahlLWzsafrdKhQ4d49NFHN7uhkt3Dtq6dE044IR5//PF6H+eee25ERDz++OMxdOjQ\nHTY7jauUrzvHHXdcVFdXx4cffljcNn/+/KipqYkePXo0+Mw0vlLWzcEHHxzV1dX1ti1btiw+++yz\nOPjggxt0XnZupZ4nNx83bty4HTAf31KzZs2iRYsWcdddd0VtbW2sX78+brrppli6dGlMmDAhWrdu\nHe+88078+9//Lr7Na5cuXeLhhx+OOXPmRNu2bePpp5+Oe+65Jy699NLo1atXI78idpRS1s6VV14Z\nb731Vlx11VUREfH+++8XP5o3b77ZDZbsmrZ17ey9997RoUOHeh9vvfVWzJo1K66//nrrZjdSyted\nI444Ip544ol49tlnY//994/FixfHtddeGxUVFXHZZZc18itiRyhl3bRu3Truv//+eP/996O8vDwW\nLlwY11xzTbRq1Squu+467/i5m5o/f34sXLgwfvGLXxS3bbfz5FJ/EBWNY/r06YXjjz++UFVVVRg2\nbFhh0aJFxcfGjh272Q+jfP311wvDhg0rVFZWFgYNGlSYNm3ajh6ZJiK7dtavX1846qijChUVFYUj\njjhis4/p06c31kugkWzr151NzZgxww/J3Y1t69p55513ChdddFGhR48ehd69exeuvPLKwpo1a3b0\n2DSybV03L7zwQuFnP/tZoWfPnoXjjz++cPXVVxdWrVq1o8emCbnjjjs2+yG52+s8uVmh4A5eAACA\n/8U9TwAAAAniCQAAIEE8AQAAJIgnAACABPEEAACQIJ4AAAASxBMAAECCeAIAAEgQTwAAAAn/DyaW\n1DVkug2wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc2a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = test.sort_values('song_currency', ascending=False)[:1000]\n",
    "prolific=test.artist_name.value_counts()\n",
    "#prolific[prolific > 15].plot(kind=\"barh\");\n",
    "prolific.plot(kind=\"barh\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA08AAAITCAYAAADfBSciAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1DVkug2wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bcc0e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = test.sort_values('song_hotttnesss', ascending=False)[:1000]\n",
    "prolific=test.artist_name.value_counts()\n",
    "prolific.plot(kind=\"barh\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what if we define it differently? Let's see our top artists based on artist discovery, familiarity, and hotness. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   artist_discovery_rank  artist_name\n",
      "0                    966  The Weekend\n",
      "   artist_familiarity_rank  artist_name\n",
      "0                    21048  The Weekend\n",
      "   artist_hotttnesss_rank  artist_name\n",
      "0                    1519  The Weekend\n"
     ]
    }
   ],
   "source": [
    "# get just our artist information\n",
    "artists = test[['artist_discovery_rank','artist_familiarity_rank', 'artist_hotttnesss_rank', 'artist_name']]\n",
    "\n",
    "# drop duplicates because we have a dataframe full of songs from multiple artists\n",
    "artists = artists.drop_duplicates()\n",
    "\n",
    "artists = artists.sort_values('artist_discovery_rank', ascending=False)[:30]\n",
    "print artists[['artist_discovery_rank', 'artist_name']]\n",
    "\n",
    "artists = artists.sort_values('artist_familiarity_rank', ascending=False)[:30]\n",
    "print artists[['artist_familiarity_rank', 'artist_name']]\n",
    "\n",
    "artists = artists.sort_values('artist_hotttnesss_rank', ascending=False)[:30]\n",
    "print artists[['artist_hotttnesss_rank', 'artist_name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd also like to be able to predict popularity, hotness, or any of the other rankings based off of song features. Let's check out the distribution of how fast our songs are (tempo) or how long they are (duration). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>artist_discovery</th>\n",
       "      <th>artist_discovery_rank</th>\n",
       "      <th>artist_familiarity</th>\n",
       "      <th>artist_familiarity_rank</th>\n",
       "      <th>artist_hotttnesss</th>\n",
       "      <th>artist_hotttnesss_rank</th>\n",
       "      <th>artist_id</th>\n",
       "      <th>artist_location</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>audio_summary</th>\n",
       "      <th>id</th>\n",
       "      <th>song_currency</th>\n",
       "      <th>song_hotttnesss</th>\n",
       "      <th>song_type</th>\n",
       "      <th>title</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.618004</td>\n",
       "      <td>966</td>\n",
       "      <td>0.447882</td>\n",
       "      <td>21048</td>\n",
       "      <td>0.689561</td>\n",
       "      <td>1519</td>\n",
       "      <td>AR0T77X1187FB46E52</td>\n",
       "      <td>{u'latitude': 42.9869, u'location': u'London, ...</td>\n",
       "      <td>The Weekend</td>\n",
       "      <td>{u'time_signature': 4, u'analysis_url': u'http...</td>\n",
       "      <td>SOBWZNS12A8C13A053</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[studio, electric, vocal]</td>\n",
       "      <td>Bring It On</td>\n",
       "      <td>77.602</td>\n",
       "      <td>202.45333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   artist_discovery  artist_discovery_rank  artist_familiarity  artist_familiarity_rank  artist_hotttnesss  artist_hotttnesss_rank           artist_id                                    artist_location  artist_name                                      audio_summary                  id  song_currency  song_hotttnesss                  song_type        title   tempo   duration\n",
       "0          0.618004                    966            0.447882                    21048           0.689561                    1519  AR0T77X1187FB46E52  {u'latitude': 42.9869, u'location': u'London, ...  The Weekend  {u'time_signature': 4, u'analysis_url': u'http...  SOBWZNS12A8C13A053              0                0  [studio, electric, vocal]  Bring It On  77.602  202.45333"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_tempo(audio_deets):\n",
    "    return audio_deets['tempo']\n",
    "\n",
    "def get_duration(audio_deets):\n",
    "    return audio_deets['duration']\n",
    "\n",
    "test['tempo'] = test['audio_summary'].map(get_tempo)\n",
    "test['duration'] = test['audio_summary'].map(get_duration)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  2.]),\n",
       " array([ 35.5   ,  39.9807,  44.4614,  48.9421,  53.4228,  57.9035,\n",
       "         62.3842,  66.8649,  71.3456,  75.8263,  80.307 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwAAAAIbCAYAAABc5/liAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2ee810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# temporary because we only have one data point\n",
    "test_tempo = [77.602, 35.5, 80.307, 40.899]\n",
    "#plt.hist(test['tempo'])\n",
    "plt.hist(test_tempo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  0.,  0.,  1.,  0.,  3.,  0.,  0.,  0.,  1.]),\n",
       " array([ 180.30285 ,  186.311072,  192.319294,  198.327516,  204.335738,\n",
       "         210.34396 ,  216.352182,  222.360404,  228.368626,  234.376848,\n",
       "         240.38507 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwUAAAIbCAYAAAC6zjImAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c45f5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# temporary because we only have one data point\n",
    "test_duration = [202.45333, 210.38572, 211.38927, 180.30285, 240.38507, 215.85375]\n",
    "#plt.hist(test['duration'])\n",
    "plt.hist(test_duration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Main Stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do some visualizations of our post-analysis models!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's examine our predictive features with a scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.pairplot(all2014, vars={\"ranking\", \"rankmean\", \"rankstd\", \"weekson\"}, hue = \"winner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here let's declare a function to check how well our model functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compare_results(stars_actual, stars_predicted, ylow=1, yhigh=6, model=\"\", predicteds=False, onsame=False, axis=False):\n",
    "    \"\"\"\n",
    "    plot predicted results against actual results. Takes 2 arguments: a\n",
    "    numpy array of actual ratings and a numpy array of predicted ratings\n",
    "    scatterplots the predictions, a unit slope line, line segments joining the mean,\n",
    "    and a filled in area of the standard deviations.\"\n",
    "    \"\"\"\n",
    "    if onsame:\n",
    "        ax=onsame\n",
    "    elif axis:\n",
    "        ax=axis\n",
    "    else:\n",
    "        fig=plt.figure()\n",
    "        ax=plt.gca()\n",
    "    df=pd.DataFrame(dict(actual=stars_actual, predicted=stars_predicted))\n",
    "    xp=[]\n",
    "    yp=[]\n",
    "    for k,v in df.groupby('actual'):\n",
    "        xp.append(k)\n",
    "        yp.append(v.predicted.mean())        \n",
    "    \n",
    "    cl, = ax.plot(xp,yp, 's-', label=\"means for %s\" % model)\n",
    "    c=cl.get_color()\n",
    "\n",
    "    sig=df.groupby('actual').predicted.std().values\n",
    "    ax.fill_between(xp, yp - sig, yp + sig, \n",
    "                 color=c, alpha=0.2)\n",
    "    if predicteds:\n",
    "        ax.plot(df.actual, df.predicted, '.', color=c, alpha=0.1, label=\"predicted for %s\" % model)\n",
    "\n",
    "    if not onsame:\n",
    "        ax.plot([1,5],[1,5], 'k', label=\"slope 1\")\n",
    "        ax.set_xlabel(\"actual\")\n",
    "        ax.set_ylabel(\"predicted\")\n",
    "        ax.set_ylim([ylow,yhigh])\n",
    "        ax.set_xlim([0.9, 5.1])\n",
    "    ax.legend(frameon=False, loc=\"upper left\")\n",
    "    rmse=get_rmse(stars_actual, stars_predicted)\n",
    "    print \"RMSE for %s\" % model, rmse\n",
    "    return ax,rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print \"hello from the bayesian asians\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Yo I wrote some utility functions\n",
    "from project import File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This is how to use a file class.\n",
    "storage = File()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Store things like in a dictionary\n",
    "# storage[filename] = variable to store on disk\n",
    "# Or, storage[(filename, message to display)] = variable\n",
    "storage[['tempfile', 'we stored something!']] = [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Files:\n",
      "   (0) tempfile\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prints all the files you've stored\n",
    "storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we stored something!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab using the index in front\n",
    "storage[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we stored something!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or using the actual name you put it in with\n",
    "storage['tempfile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sucess\n"
     ]
    }
   ],
   "source": [
    "# Delete with the del command\n",
    "del storage[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "Doesn't Exist!",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-300ceb06c1b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstorage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tempfile'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/Allen/Documents/Data Science Project/Bayesian-Asians-Project/project/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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---> 35\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\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[0m\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__delitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/Users/Allen/Documents/Data Science Project/Bayesian-Asians-Project/project/store.pyc\u001b[0m in \u001b[0;36mfget\u001b[0;34m(varname, verbose)\u001b[0m\n\u001b[1;32m     80\u001b[0m                         \u001b[0;32mreturn\u001b[0m \u001b[0mfread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBASEDIR\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mvarname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m                         \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Doesn't Exist!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m: Doesn't Exist!"
     ]
    }
   ],
   "source": [
    "# Things no longer exist!\n",
    "storage['tempfile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}