lastfm analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. By Users, data consists of:\n",
    "  1. Artist, Album, Track, Timestamp\n",
    "\n",
    "Simplest, just Users and Artists\n",
    "\n",
    "## Notes\n",
    "\n",
    "1. Some artists very popular, probably **don't** to display the really popular ones\n",
    "2. Filter by (continuous) date, and time of day and year (spherical)\n",
    "\n",
    "## Plots\n",
    "\n",
    "1. By user, pairwise links between users given dirichlet over tracks played per artist\n",
    "2. By artist, as above but over users\n",
    "3. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "File b'/Users/smason/Downloads/olorton.csv' does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-96-798f701f5367>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m imp = pd.read_csv(\"/Users/smason/Downloads/olorton.csv\", header=False,\n\u001b[0;32m----> 2\u001b[0;31m                   names=[\"artist\",\"album\",\"track\",\"timestamp\"])\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mimp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"unit\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mimp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"artist\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"unit\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\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\"unit\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/smason/.virtualenvs/py3/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)\u001b[0m\n\u001b[1;32m    472\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/smason/.virtualenvs/py3/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/smason/.virtualenvs/py3/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    564\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_options_with_defaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/smason/.virtualenvs/py3/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m    703\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    706\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/smason/.virtualenvs/py3/lib/python3.4/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1072\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1073\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1074\u001b[0m         \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:3173)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:5912)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: File b'/Users/smason/Downloads/olorton.csv' does not exist"
     ]
    }
   ],
   "source": [
    "imp = pd.read_csv(\"/Users/smason/Downloads/olorton.csv\", header=False,\n",
    "                  names=[\"artist\",\"album\",\"track\",\"timestamp\"])\n",
    "imp[\"unit\"] = 1\n",
    "imp.groupby(\"artist\")[[\"unit\"]].count().reset_index().groupby(\"unit\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imp = []\n",
    "for f in os.listdir(\"data\"):\n",
    "    path = \"data/%s\" % f\n",
    "    tmp = pd.read_csv(path, header=False,\n",
    "                  names=[\"artist\",\"album\",\"track\",\"timestamp\"])\n",
    "    tmp.timestamp = pd.to_datetime(tmp.timestamp)\n",
    "    tmp[\"user\"] = re.sub(r'\\.csv','',f)\n",
    "    imp.append(tmp)\n",
    "imp = pd.concat(imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "user_artists = imp.groupby([\"user\",\"artist\"])[[\"timestamp\"]].count().unstack(0)\n",
    "user_artists[user_artists.isnull()] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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48HI++GATVquNn/70Z/z1rw9y9Gg11113E1dddTUFBdtZseLZkx4PYBgGDz/8ID09Lr7x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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114806d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "for f in np.unique(imp.user):\n",
    "    im = imp[imp.user == f].groupby(\"artist\")[[\"timestamp\"]].count().reset_index().groupby(\"timestamp\").count()\n",
    "    plt.loglog(im.index, np.cumsum(im.artist[::-1])[::-1], label=f)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imp[\"unit\"] = 1\n",
    "ip = pd.pivot_table(imp, values=[\"unit\"],index=[\"artist\"],columns=\"user\", aggfunc=\"count\").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>corpsetastic</th>\n",
       "      <th>obefm</th>\n",
       "      <th>olorton</th>\n",
       "      <th>tumultodevozes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">unit</th>\n",
       "      <th>corpsetastic</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056223</td>\n",
       "      <td>0.212750</td>\n",
       "      <td>0.146800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obefm</th>\n",
       "      <td>0.056223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068947</td>\n",
       "      <td>0.094989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>olorton</th>\n",
       "      <td>0.212750</td>\n",
       "      <td>0.068947</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tumultodevozes</th>\n",
       "      <td>0.146800</td>\n",
       "      <td>0.094989</td>\n",
       "      <td>0.057684</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            unit                                   \n",
       "user                corpsetastic     obefm   olorton tumultodevozes\n",
       "     user                                                          \n",
       "unit corpsetastic       1.000000  0.056223  0.212750       0.146800\n",
       "     obefm              0.056223  1.000000  0.068947       0.094989\n",
       "     olorton            0.212750  0.068947  1.000000       0.057684\n",
       "     tumultodevozes     0.146800  0.094989  0.057684       1.000000"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(ip+0.5).corr()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}