{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User-Based Collaborative Filtering Recommender System\n",
    "\n",
    "[Yahoo! Music User Ratings of Musical Artists, version 1.0 (423 MB)](http://webscope.sandbox.yahoo.com/catalog.php?datatype=r&did=1)\n",
    "    This dataset represents a snapshot of the Yahoo! Music community's preferences for various musical artists. The dataset contains over ten million ratings of musical artists given by Yahoo! Music users over the course of a one month period sometime prior to March 2004. Users are represented as meaningless anonymous numbers so that no identifying information is revealed. The dataset may be used by researchers to validate recommender systems or collaborative filtering algorithms. The dataset may serve as a testbed for matrix and graph algorithms including PCA and clustering algorithms. The size of this dataset is 423 MB.\n",
    "\n",
    "From the readme.txt:\n",
    "```\n",
    "This dataset consists of two files:\n",
    "1. ydata-ymusic-user-artist-ratings-v1_0.txt\n",
    "2. ydata-ymusic-artist-names-v1_0.txt\n",
    "\n",
    "The content of the two files are as follows:\n",
    "\n",
    "=====================================================================\n",
    "\n",
    "(1) \"ydata-ymusic-user-artist-ratings-v1_0.txt\" contains user ratings\n",
    "    of music artists. It contains 11,557,943 ratings of 98,211 artists\n",
    "    by 1,948,882 anonymous users. The format of each line of the file\n",
    "    is: anonymous_user_id (TAB) artist_id (TAB) rating. The ratings\n",
    "    are integers ranging from 0 to 100, except 255 (a special case\n",
    "    that means \"never play again\").\n",
    "\n",
    "Snippet:\n",
    "1       1000125 90\n",
    "1       1006373 100\n",
    "1       1006978 90\n",
    "1       1007035 100\n",
    "1       1007098 100\n",
    "\n",
    "====================================================================\n",
    "\n",
    "(2) \"ydata-ymusic-artist-names-v1_0.txt\" contains the artist_id and\n",
    "    name of each musical artist.\n",
    "\n",
    "Snippet:\n",
    "-100    Not Applicable\n",
    "-99     Unknown Artist\n",
    "1000001 Bobby \"O\"\n",
    "1000002 Jimmy \"Z\"\n",
    "1000003 '68 Comeback\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import timeit\n",
    "import psycopg2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import pandas.io.sql as psql\n",
    "import matplotlib.pylab as plt\n",
    "from collections import defaultdict\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "pd.options.display.max_columns=25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query the database\n",
    "The steps used to create and import data into the database are in the scripts/initialize_db.sh file of this repository. \n",
    "\n",
    "They are:\n",
    "* Create the ymusic_data database with the createdb command\n",
    "* Create 3 tables and define the schema\n",
    "* Remove the first two lines of the artist-names file\n",
    "* Import the data files into the respective psql tables.\n",
    "\n",
    "I connect with the database using `psycopg2` and query with `pandas.io.sql` (as `psql`) \n",
    "\n",
    "NOTE: The avg_rating in ym_artist and ym_user tables is deceptive because 255 means never play again, so it should probably be 0 for the range to make much sense. A very disliked artist could have an avg_rating greater than 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 1004623, 0),\n",
       " (2, 1018143, 0),\n",
       " (2, 1040071, 90),\n",
       " (2, 1053438, 90),\n",
       " (2, 1098087, 90),\n",
       " (2, 1098636, 90),\n",
       " (3, 1006316, 90)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = psycopg2.connect(\"dbname=ymusic_data user=btq\")\n",
    "# Open a cursor to perform database operations\n",
    "cur = conn.cursor()\n",
    "# Query the database and obtain data as Python objects\n",
    "cur.execute(\"SELECT * FROM ym_ratings WHERE uid <= 3 and uid > 1;\")\n",
    "cur.fetchall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! We can successfully query the db. lets get a sense of what the data look like. With the connection to the database established with psycopg2, I can use Pandas SQL functions to pull data into a dataframe.\n",
    "\n",
    "### Number of ratings for users\n",
    "I want to get a sense of what the numbers look like. Specifically, are there users with sufficient number of ratings from which to make recommendations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>uid</th>\n",
       "      <th>num_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uid  num_ratings\n",
       "0    1           40\n",
       "1    2            6\n",
       "2    3            1\n",
       "3    4           37\n",
       "4    5           72"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qu_string = \"SELECT uid, num_ratings FROM ym_user;\"\n",
    "all_user_df = psql.read_sql(qu_string, conn)\n",
    "all_user_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1948882 entries, 0 to 1948881\n",
      "Data columns (total 2 columns):\n",
      "uid            int64\n",
      "num_ratings    int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 44.6 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>num_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1948882.000000</td>\n",
       "      <td>1948882.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>974441.500000</td>\n",
       "      <td>59.305510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>562593.917997</td>\n",
       "      <td>164.149996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>487221.250000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>974441.500000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1461661.750000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1948882.000000</td>\n",
       "      <td>97993.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  uid     num_ratings\n",
       "count  1948882.000000  1948882.000000\n",
       "mean    974441.500000       59.305510\n",
       "std     562593.917997      164.149996\n",
       "min          1.000000        1.000000\n",
       "25%     487221.250000        6.000000\n",
       "50%     974441.500000       24.000000\n",
       "75%    1461661.750000       62.000000\n",
       "max    1948882.000000    97993.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print all_user_df.info()\n",
    "all_user_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     197982\n",
      "2      90045\n",
      "3      65106\n",
      "4      55777\n",
      "5      46411\n",
      "6      41710\n",
      "7      38205\n",
      "8      36514\n",
      "9      34200\n",
      "10     32161\n",
      "Name: num_ratings, dtype: int64\n",
      "Total number of ratings: 1948882\n"
     ]
    },
    {
     "data": {
      "image/png": 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+SZIWun66l8F4ODkbeEe9/J5pH2S7RSxdupTttnO85GwbGhrqdQnznm3cebZx59nGc1Mv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\nEvD/AfJBltfovlkVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41542d2410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print all_user_df['num_ratings'].value_counts()[0:10]\n",
    "print 'Total number of ratings:', all_user_df['num_ratings'].value_counts().sum()\n",
    "fig,ax = plt.subplots(figsize=(8,8))\n",
    "ax = all_user_df['num_ratings'].hist(color='DarkBlue',bins=100,range=(1,10000))\n",
    "ax.set_yscale('symlog')\n",
    "ax.set_xlim(0,10000)\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Number of Ratings for Users')\n",
    "ax.set_title('Histogram of num_ratings of All Users')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 10% of the users only rate one artist, and 30% have rated 10 or less. Therefore, 70% have rated more that 10 artists. These users will be better for user-based collaborative filtering, as they have enough preference data from which to make a good prediction from. This is why Netflix always presses you to rate several movies so they have enough data to make a recommendation on.\n",
    "\n",
    "### Number of ratings for artists\n",
    "I want to know the same thing for artists. How many times are each of them rated?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>aid</th>\n",
       "      <th>num_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1068731</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1075608</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1069922</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1034534</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1096781</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       aid  num_ratings\n",
       "0  1068731            1\n",
       "1  1075608            1\n",
       "2  1069922           10\n",
       "3  1034534            1\n",
       "4  1096781            5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qu_string = \"SELECT aid, num_ratings FROM ym_artist;\"\n",
    "all_artist_df = psql.read_sql(qu_string, conn)\n",
    "all_artist_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 97812 entries, 0 to 97811\n",
      "Data columns (total 2 columns):\n",
      "aid            97812 non-null int64\n",
      "num_ratings    97812 non-null int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 2.2 MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aid</th>\n",
       "      <th>num_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>97812.000000</td>\n",
       "      <td>97812.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1050860.376498</td>\n",
       "      <td>1180.812487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29419.824983</td>\n",
       "      <td>13437.416486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000001.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1025077.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1051997.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1076519.250000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1101114.000000</td>\n",
       "      <td>783871.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  aid    num_ratings\n",
       "count    97812.000000   97812.000000\n",
       "mean   1050860.376498    1180.812487\n",
       "std      29419.824983   13437.416486\n",
       "min    1000001.000000       1.000000\n",
       "25%    1025077.750000       1.000000\n",
       "50%    1051997.500000       3.000000\n",
       "75%    1076519.250000      10.000000\n",
       "max    1101114.000000  783871.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_artist_df.info()\n",
    "all_artist_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     39813\n",
      "2      7777\n",
      "3      6435\n",
      "4      4917\n",
      "5      3777\n",
      "6      3143\n",
      "7      2473\n",
      "8      2050\n",
      "9      1691\n",
      "10     1527\n",
      "Name: num_ratings, dtype: int64\n",
      "Total number of ratings: 97812\n"
     ]
    },
    {
     "data": {
      "image/png": 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qLJ92KEmSbCGQpPE0D0TkHQSajQwEkjSO5oGIvINAs5GBQNKM1K37/kcbSlma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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f411dbe4590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print all_artist_df['num_ratings'].value_counts()[0:10]\n",
    "print 'Total number of ratings:', all_artist_df['num_ratings'].value_counts().sum()\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,8))\n",
    "ax = all_artist_df['num_ratings'].hist(color='DarkBlue',bins=100,range=(1,10000))\n",
    "ax.set_yscale('symlog')\n",
    "ax.set_xlim(0,10000)\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Number of Ratings for Artists')\n",
    "ax.set_title('Histogram of num_ratings of All Artists')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#clear these dfs\n",
    "del all_artist_df\n",
    "del all_user_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "41% of the artists are rated only one time, and 75% of artists are rated 10 times or less. That still leaves more that 20,000 artists with more than 10 ratings. \n",
    "\n",
    "### User-based Collaborative Filtering\n",
    "To determine how similar users are in their tastes, I will be comparing a user with every other user that has rated one of the items. For the first example, to make this more explainable, I will start with a user that has only rated 6 artists.\n",
    "\n",
    "#### 1.Query the db for all of this user's ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>uid</th>\n",
       "      <th>aid</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1004623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1018143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1040071</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1053438</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1098087</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1098636</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uid      aid  rating\n",
       "0    2  1004623       0\n",
       "1    2  1018143       0\n",
       "2    2  1040071      90\n",
       "3    2  1053438      90\n",
       "4    2  1098087      90\n",
       "5    2  1098636      90"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UID = 2\n",
    "qu_string = \"SELECT * FROM ym_ratings WHERE uid = %i ;\" % UID\n",
    "user_df = psql.read_sql(qu_string, conn)\n",
    "user_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.Query all the artists our UID has rated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>aid</th>\n",
       "      <th>name</th>\n",
       "      <th>num_ratings</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1040071</td>\n",
       "      <td>Ja Rule</td>\n",
       "      <td>337804</td>\n",
       "      <td>68.6612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1018143</td>\n",
       "      <td>Mint Condition</td>\n",
       "      <td>33387</td>\n",
       "      <td>50.2580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1004623</td>\n",
       "      <td>Case</td>\n",
       "      <td>29761</td>\n",
       "      <td>60.4288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1098087</td>\n",
       "      <td>Ashanti</td>\n",
       "      <td>2689</td>\n",
       "      <td>89.5288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1098636</td>\n",
       "      <td>Baby</td>\n",
       "      <td>35649</td>\n",
       "      <td>66.8373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1053438</td>\n",
       "      <td>Musiq</td>\n",
       "      <td>109104</td>\n",
       "      <td>63.0521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       aid            name  num_ratings  avg_rating\n",
       "0  1040071         Ja Rule       337804     68.6612\n",
       "1  1018143  Mint Condition        33387     50.2580\n",
       "2  1004623            Case        29761     60.4288\n",
       "3  1098087         Ashanti         2689     89.5288\n",
       "4  1098636            Baby        35649     66.8373\n",
       "5  1053438           Musiq       109104     63.0521"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_string = \"SELECT * FROM ym_artist WHERE aid = %s\" % \" OR aid = \".join([str(a) for a in user_df.aid.unique()])\n",
    "artists_df = psql.read_sql(qa_string,conn)\n",
    "artists_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives me an idea for the amount of data I will be pulling. It will be at least 337,804 ratings.\n",
    "\n",
    "#### 3.Query the rest of the data. This can return a very large data set. Be careful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for query:  62.6603560448\n",
      "Shape of data:  (548394, 3)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 548394 entries, 0 to 548393\n",
      "Data columns (total 3 columns):\n",
      "uid       548394 non-null int64\n",
      "aid       548394 non-null int64\n",
      "rating    548394 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 16.7 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>aid</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1004623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1018143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1040071</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1053438</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1098087</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uid      aid  rating\n",
       "0    2  1004623       0\n",
       "1    2  1018143       0\n",
       "2    2  1040071      90\n",
       "3    2  1053438      90\n",
       "4    2  1098087      90"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qstring = 'SELECT uid, aid, rating FROM ym_ratings WHERE aid IN (SELECT aid FROM ym_ratings WHERE uid = %i) ;' % UID\n",
    "tic=timeit.default_timer()\n",
    "data_df = psql.read_sql(qstring, conn)\n",
    "toc=timeit.default_timer()\n",
    "print 'Time for query: ', toc-tic\n",
    "print 'Shape of data: ', data_df.shape\n",
    "print data_df.info()\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It took about a minute to get the data, but it's here. Now I will re-assign 0's to 10 and 255 to 0. In the rating system, 255 mean never play again, so we'll make that absolute zero. Seeing as a 0 rating is not as extreme as rating it to never be played again, I recode the 0 ratings to 10. It would also make sense to simply equate a 255 rating as a 0, because there probably isn't much difference pragmatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#convert 0s to 10 (becuase we're pushing 255 to 0)\n",
    "data_df.loc[data_df['rating']==0,'rating']=10\n",
    "#convert 255 to 0\n",
    "data_df.loc[data_df['rating']==255,'rating']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>aid</th>\n",
       "      <th>Case</th>\n",
       "      <th>Mint Condition</th>\n",
       "      <th>Ja Rule</th>\n",
       "      <th>Musiq</th>\n",
       "      <th>Ashanti</th>\n",
       "      <th>Baby</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "aid  Case  Mint Condition  Ja Rule  Musiq  Ashanti  Baby\n",
       "uid                                                     \n",
       "2      10              10       90     90       90    90\n",
       "10    NaN             NaN       50    NaN      NaN   NaN\n",
       "20    NaN             NaN       80     70      NaN   NaN\n",
       "21    NaN             NaN       90    NaN      NaN   NaN\n",
       "28    NaN             NaN      100    100      NaN   100"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a lookup-like dict for artist names\n",
    "aid_lookup = pd.Series(artists_df['name'].values,index=artists_df['aid']).to_dict()\n",
    "\n",
    "#Pivot data frame to be indexed by uid and the columns with be the artists\n",
    "pivoted_df = pd.pivot_table(data_df,values='rating',columns=['aid'],index=['uid'],fill_value=None)\n",
    "pivoted_df.rename(columns=aid_lookup,inplace=True)\n",
    "pivoted_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>aid</th>\n",
       "      <th>Case</th>\n",
       "      <th>Mint Condition</th>\n",
       "      <th>Ja Rule</th>\n",
       "      <th>Musiq</th>\n",
       "      <th>Ashanti</th>\n",
       "      <th>Baby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29761.000000</td>\n",
       "      <td>33387.000000</td>\n",
       "      <td>337804.000000</td>\n",
       "      <td>109104.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>35649.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>62.210141</td>\n",
       "      <td>52.971636</td>\n",
       "      <td>69.781953</td>\n",
       "      <td>64.744427</td>\n",
       "      <td>89.614355</td>\n",
       "      <td>68.373980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.591502</td>\n",
       "      <td>37.234335</td>\n",
       "      <td>34.004977</td>\n",
       "      <td>35.885274</td>\n",
       "      <td>11.418987</td>\n",
       "      <td>36.540526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "aid            Case  Mint Condition        Ja Rule          Musiq  \\\n",
       "count  29761.000000    33387.000000  337804.000000  109104.000000   \n",
       "mean      62.210141       52.971636      69.781953      64.744427   \n",
       "std       36.591502       37.234335      34.004977      35.885274   \n",
       "min        0.000000        0.000000       0.000000       0.000000   \n",
       "25%       10.000000       10.000000      50.000000      30.000000   \n",
       "50%       79.000000       50.000000      90.000000      83.000000   \n",
       "75%      100.000000       90.000000      90.000000      98.000000   \n",
       "max      100.000000      100.000000     100.000000     100.000000   \n",
       "\n",
       "aid        Ashanti          Baby  \n",
       "count  2689.000000  35649.000000  \n",
       "mean     89.614355     68.373980  \n",
       "std      11.418987     36.540526  \n",
       "min       1.000000      0.000000  \n",
       "25%      90.000000     30.000000  \n",
       "50%      90.000000     90.000000  \n",
       "75%      90.000000    100.000000  \n",
       "max     100.000000    100.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bf2VO0sziJ//hu8fEyvC1qyWu10tTmemt+DRFdWMxnhp73VjL/ZOY9/f6aAtP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A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\nx54DhpnxUiw+kkJQkhIfq0B42k+4UbXfz6d6RZolmi9esteZLtxZ/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H/x98U16vUFMNP5\nNnfS8deey6Jn32djJxt7r28s5ccUx2bROsMh73dCPM/n7np5ZnzOj623H6Q+1rVmXk+TQTuCdC3K\nfIHTfkK7UmalMqvuGLhuXhS5XnN557G9p+WahyNtLdH3H1tF4jtrQe7jZGvZVtm2cRBZf0xrzf4g\nwXWgVZKchpqzbgglhxdXzlP6ZUqv2hhGccy9PTv3vbTd4oWbN+GSuOCVI9I7OzsCWNnd3e1c9Tc/\naXvSoJJCECcJ3zu0x31pXSH9xemRLKUzD+eY/z5zlIvOkkBYmicTc33pPB/yauVq5SLawOnIRjuu\nL50vrCs6Y1upEpMUgtNxiDIaB6tkl0U+11x7D8rMOvdSgOfa87x/EjKJFWs1h0EIH5yOaddcApnw\nw+MJFdfhV26UKXtePsAtzCROq+odAtfFlZaX+GiokELnRUeRUrx/kCoulmcjrFlR5HZjynIxX9Q2\nCUP+bN9+vtkOL3SkAYaJ/W3TLJY+zSzRms5o+vfLJuVGZap4VZxoFmUWjrr2+2uVqYrbRcUXJync\naKtMrlwppUEi2W446cSs+P5JQtXTvLQ5xUfb5+bNUPHtDxLe7SbUS5KX0mrkzihGY1gpe8go4mSo\naVWsTPj7B4mNq61IHDErppH9P1aKvz6y/f+FzeRKxPzP7PNvVd+fWUCL77XYr+Mk4Tvp2LuRppeL\nVlxUhbJ0jCKxVIrZXPGwF+bp4+tp9b02DoLZBWx+UXvcjzkYRqxVHa41pnPdorE6CkO+e2j76e21\niCCdb4vt2+tHHA4sn0dfSUBwPLaKrmehbRPCcDxSvDdKWC4rXmp5vN1N0FqzXZdoCdrY65cV9EPF\n6UTR0QZfaD7sKqo+NEug0jX3fjfhcGALl5a7Q5qeoDNSLFUE+48mdIaKRjldvHWC1g4bdRfHSfjT\nNA293tYsL3jul9nPmgOd2dNkMJ7m2Ks8L6seaz9HaR1R8Xfz15sfL0+6TvH3kVIcD21Od9G15s/V\nngvSLarhAWhXZiXQ54OJxd+2KhVK/rRtRYe8WBw/iWO+na7xz28k+Rqe/T87PtGKk7HmdAzLgXWw\n59flGcfeccAYlBHIdOntR7ak/2hoOeg3fYmJJR+eRnSCEV/ZqFJJg0afdEPOQs1azSXwfY7ORrzX\n1WzUHO6sypy1I4OzCDTbjfQ+tebNY53e02fAI72zs9MC/jvg7wGnwMbOzs5/A/wvu7u7f/7EK/yE\nbBFeatGuMlKKQYGPcT4S/Wls3sHeqvvnKmmLBYgX4aKKFDNZpPHNfRupvLMWpJLP5Bin7HqJ1rm6\n2G/crNGuOhgD7x3HYKBdlxwOFT86mrASOOg0dfn2oY2EGK157zhirWZpax6G4CBpBYJ7J2M0hnbF\nIVGGoCwwRuRYKwCDYq8b4TuSX7nuzBQzzEurR0rl0dtIPW39OYTARE8/X2TapLzOzLJuZM923kpX\n8Akz6NAUbz/F1c9DirQxdCdTDPh824rHJlpzOrbYyWxi6oxiHCFoV6cT32bdo+pKlJ49V3a+TLlN\nG8PJWBF4ksDLonFwOrH8mEaDJyXHY6umuFKWHKSsHS+ueBQTeOci+U9+TM/sC2YPuuFMX140b0kh\n0EC63z7XD4pz4HrN5XioOEtrI4pmUrBCBpewWRJFP1Q8LFv54azeQGk4GSe0qw6xNuz1Yg76lvPc\nS1loFomMRFrTz+YIfb7HJlrTGSaMYk2jJOmGiigxljbUGHoThUo3nM2y5Hik6E80hyPFOLSqiUfD\nhEFs2Ky79CeGD49DXAnrNcn90xgfQ9WB1YpDL1b0QsVKxcFNM4RHkwStDaHSHEUwUYbViqLkSr60\n4vOj44i9nmG9BrrmoIEkffaflk/6mX325kgbiNrrx3hSXZmCLrOr8jXv9Sc87E1wpeRhz6XkeFfG\nu1+E+c7Ws/k2Fds638YiBV523kxXIYdwCcFSOuwvgmkqo3n/OGIYKa4vezlrR/G6xbojsLzv3VAh\nhaRZsZvoF9o+YBBmupZ+cBzRGcYsBXLGaX//OGQUWzE7jKBZcqh4AoRhfxAhhS1YtMrLJhezA1BK\ncRpOPz/JrhKR/n3gm8DvAt8u/O2/BX7lCr//G7GLOo8nJZnYTjeEUEUzFE+LHKLs30/qeBmvo9Ww\nn9INFVP/F7GJZOnO+UVhENuXlihFosGVIo0qZ0wYlo2j2BqtBcpI2oGDATaqJaJ4woOzCFcavLTt\nTiljpZj++sVWiVbFIvikVLzcDqh6EiEFv7jl8XzL4zS0C+KDboQU0Cw5abumKa2HPUt8Pk/r50rJ\nenXKJZtZ5iguerZF84GGN/28CLKRZSZWq1Oy/CcVkEySq0GapteZ4qIXtdkO8vP3Od8OgMOhpQRc\nLttU0nZDWkl3MysiYfufiyzcU5FO72iocqaR9ZrHWtUF7E57tSr54EQg0+cihKEeuLjSoeS6vNK2\n/XWtWsqjHUUqou2Gj+c43EhnSO9ZNPpnyjL2mKIV++pm3bOqXguGSXEzB7ZvNssuzbKTq8xmxyht\nF0QhdA7detCNiJTmBWMlhTsj63Rm85JAcK3h0Z/YY/sjTb0C3Yk5JzKijcEFSvam6IWwWp1mbyKl\neJimi5dKLs0qHI8chmiaVUFnoDibKOoly0EvBHypbceRNnC75RErTaIl1cChHxrGsWKpJAiVlfRe\nKmlUklCWglbgMIhTTLSAVt1hHEqWApeqI5gYxd4oIgFuNkooT3JtqcRpqBmFmqWyQBuBjy1QhC94\nIdPPkEkhqHiWpeVJXMyXrT+LONfnYRhHw4RxDDVPz6gcX2RPA3m5rK1Ff2URjApItROmRflCCDzX\nNtBZcP3MLzgaxQhhWArkjGOeXbdYd6SNBTjLwpwAINKMUPZME60xAlpVl1dWy/l5T8YKTwoaJQct\nbKb++lIJz4Gseksg2Ki77A9iKNDvZfdUTp/5VWq4rjJGX9jd3f27ADs7OwZgd3f3L3Z2dupX+O1P\nzOYJw+ejgJlJKfNiuqJC16Ld4vyLXdSJchOGo6HlBV6veecm92J0dL6dn3QtY8VyIPnKZjVPrSqt\nGUXWkf7gOMIIyStrAfuDmM4oYTlw+MH+iNOJol1yWKraNERnFCOM4SxUeZSzF2rKjqAZCP7yka3U\n/bdeXaKSppuWypIHZwl/uRfSKAvWqg6HfU3Zc7ibRs4zmVCD4ZXVgHeOLIQkS2+tVG1q5lE/5P3j\nkKXAOccH7UqZi9bMO5iLpLvnzXNdMs0b13HOOaUPehPudey17xa4ni/qD9m1s4LMy44rmiNt1fJF\nfNeTJOFP7tuI/7/5SmkGi39+0tQ86sd0Q4ebyxYbWuR9zVLix/2YHx5NiDU0qx7Pr1TQxqafhSCX\neZ2/t0e9iMNRhCuh5lsu2x8eTjidKJ5vOakyZkw/UsTCyiJv1f2ZqHtm4ycz/zyzL5htN3y7aC2q\nMsYu9I96EaNI59HQSfr/RXMkgJSzWNG9vo1cHQ1j9no22yJEiEHRmSiMtsXMRXlyKUTuiEshuLNm\nhU0+6Yds4bNad2ccmKwto2iqCKv1dMw96ofc64QMIkXFl9xelhz3DY4DN5c8tHZoV22JX7vqslZ1\n+PYn9k5/42aNMI75f0+stsC/+qUKq2WHb90fcTJWvLJW4uV2wL3jiLIjebcHZdfw0rqLlBpl0lJr\nIzibJAhtCH2JI2whoiehWnXxPckPj0I6A0W77HA60miTcG1J5BLhzxzpz4dZ6OeUDepS/+ACK7Jp\nFDOn8073csklcMFz0sykf/EaWczOZ2vgogDgk4JL2fHz6sHzgcfievaoFxEqmw1V5uKgmO843GkH\nfPPDPm8dhFxrBLk/lP1mfimWRtAoSe6sBbmPtCiA9eKKXTszqEgmSNSuuqyn1Hb7g9hmywUcDTXt\nsstmuhkoUhLn7fU8VmtO/vlJdpUxOtrZ2bmzu7v7TvaHnZ2d23wOMk7z+J6FnUdrurbvsOwLliqp\nA7agWCyzRFtH5iLp7/yaiDzlOR/JLkZH580YK2c5ihVrtZAbS7bgTAhBxU2jk5BzBMOsApkUcBYp\nlICtusFW3FvMHog0Wi7wXYdm1WWp5FByBIcjTTlOUsVDF2USpIDeROMhOZ0kOCmn80z0mOk9aaM5\nGiVsNnxuLtuOazAslSXtsnfufrUxnE2ezmktmlKKFJGSpljkQufUGDgYxinG2L80m+BKyc3lUv65\n2NZFx0+f/+VtLc19n2jNo140Q5MHsFHzqfrRzLEL+5jIInrW+QZyRcXOUKWY/MUSwALBrZbHVi3I\n+6RI/66NIXAgnrshmaahi1mFhr94AntmX1yTQuCIxZGjolASQN25mH0n6yMWOmEDAJs1neLzLURj\nrerhCGhVnJQ5AzYqDpGy0eW1qjuD/5zfhEfG4iKHseZu1V+IFRWCHEPZTmsObPrZQkqWAsdGnIWk\nH0eMQkXTd+09CRv50sbey3J5KmQ0MYaxXXvpDBOkcLhe9/CkASS+64KIkY6VBjcm5coXNroukKzV\nHHpjxeFQ4XoGxwjiVJHW92AlcPigExElmm4IZd9hteoAmnToPWPx+JyY3ehdTejmovXnqs739pLP\nrWUfbWw28Em/yfLMj3rxpVR/T9PWzObXxczBzqLXnpS8vnWxIEtmrhVSwGEaSJtfG4vX7EVTYbnM\nik6+Nobths+NpWDmuPnMeJJqX2gDZyPDKNRQnr6LizLjkycjOqb3doVj/lPgz3d2dr4JbO7s7OwC\nvwn806tf5rO3Il4nU7MrphSzh+J7HreX7G2W/Gk6YB5PC1O4waN+SGeoaFcu6YhG0CpPsT7zu7cs\nwgjkyobZ37YbPkJqToezS5TvOKzXfEDzYruUF/hda8i8Y9xYsunKtw4m+QSbuUj1FPjrO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pRbSro++/n+Fl563X+Ta3QtPPjVKpsN+t7KWZ28tplvUFcsDXQx4FtWOa96e0phfH/PaX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\nB7eMlXxutoYnxRB8rvvMJuenw3FPsdoMWK0H7Lb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VvXabf5zc/1dehUHsjdGIK625fdzn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\nWXGl4JnFMpea3plHXVKIEczq7PerNY+VmvHc7LYNa5Nl9krLSYFSEDu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sNQtj+LPffZSw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lglhaAXhiilOEraNy8lMvl3+2SA1hrXkRy0AkpScr9rvAJLlQqBNHCNoLi51yPQiuV6ifmGy1Er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\nooGirzxWKh6d82EeFvV3bkI7fmrkB7VEzlJgPsvyfpj3f5rEu1ntvc53s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VTMY6V43LZr\n/G4zmPCUAxOGp+I6v1b18jo8vrBJjY7TQxvDXx1e0OkrVqshr2+OQSJipXjnMKIdpdxbKXO7FfLx\nmTPq1fJnj6FTJ3OJ3jnKcLwLFO3z5LqK9H+HVZj/j729veP79++vA/85MNrb2/ufrllGUe4B/3hv\nb+//vX///j8H/iz73lmnPzM5GI4pqv/2mma7Oj9OstPv8zsf2Y7erHso43E2UBwNYjAW+uZWs0Rv\nOORPHw8QwPPLPq1qNY/PFGKcfCYQnEUR/3dGXf0fvwHrU7SiRWVQeUlO4QmwVq1MKLIazTefDvDT\nIb+XUUf/4hp844nP4/MUlSmmt1sBZ5GFXbtdMfyjb1vr51d3FO1BylLF48FZzErF5/PrJU76KcN4\nxL98astsBCO8MMQheOx3Y0sN2lVcjBQvrvn88UNbzzLQqJZR2sLbeELk8DZPuwnHg4h3OxZs5pWh\npQh3bW4PEtZqMp/QJ4MUISYTOfajiL88tvVq1nszFxVtDPu9iN9+t4sG/u0XNS+vNyYskN86sOP6\n5laVo8GA338YkWrox1AKPIyG85GiVfF4a6uGLyWn/T7/KBu7/+QNGM6gaXUW809OunztSBfq5LNW\n81iv+vzZI6uMbjd8jvop7cGQPzyISTWc6gu+cneJVCv+/FEfT8J6LaAeSt4/GvCHj209lyo+t1vV\nDE885WiQ0BnoPCH06WDAH3xix2Rj2UeoAGV0noC2VvVoDzQrFZ9EjanPi+1ar3n8yUO70H35bp0P\njnv8qyM7d57binJK3GI8+pfv1nPcZ43mqGfHcK1q48C/bc+wbDZTHpz32agF+YHKjblBcdgZ8scZ\n1auMYW0p5HSgSY3msJewUffRWtAeDvmj/RgNLK8NcurczbpPe5AisOEZvhQoL8nfvV9/PmW1Ws4Y\nEi21uzF20VYozjoRv+8oaTHoIOCD9pCLkeJOM7BoHsBZdcTv7duEwIukw8/fXaY9SGz4hZGA4eF5\nwijVvNCEP8wMUF9oxsR4HCnDRaxZq3lEI01sBPeWS5ydjXI6+F9WMX/+ABCCSiBJtaAzSkk1rAeC\nrx3ZMuv+iEeRpfbdrnt8LdJ0hiqHjXqxKfmDp/batXXDMjdyI2P5LC2SP24yveYvUmav2w+90Yh/\n9j27nvzdN/2F2M3P8vxZ8kmBInyl4XN3AR15L47Z+45daO+/tnxJmYYs5ncw4H//ljXgONryeSGu\nwzTld75/Tj/R/Mqd2gSBynR/PR4M+NN9a1j71zGsL18+sGtjyb3+IttPbm9FOUU5kIcrutyfTjTk\nd/ct1Ohp2qVRCTKIvYROpDDGwsSeRkP+5OMIDfz8Buz3vJyg78lFwmmU4kIBDwYD/mk2fv/hG3B0\nYZ+3VvUvhfNOU7Tfmtv7Vq6rSP+ne3t7r7o/9vb2joF/cP/+/XeAT6NIHxQ+e8Bu9vku8OA6BWSm\n9pmyvW0VGk9KHn3/+/n3sYY4bHFnpcIwsJ20vVbLqLRBnZwQStvRzeVlgtQn9qJs8xWsLFXZXm8Q\nnJ8jxTlaQ+I32dhcZRhEKKMRgBSSLy6V8KTkk3Ybgy2z3mqxs7k5UVelNcPAKlpHnacTv62uruX1\n297WxGnKn390QnQ6pqVuhD5t5TPEY6XqY/BIZEDqJby8W6Ukh7BvJ6/0PGrNBsu1EkejPo16QOyX\nqTWGyF46pggvl3hhcx3Pl2wuV3g4OAM09boh9VLwhjlNca3hs9JaRWtN7A+RCFaWKmyt1ojDiNPh\nE+Cyu0Ib6AwVZ5HCGI/dZoAQs+Om3DdqpCasqlKM0TUedy22tTE2nneRhUFATn+6SMScz1eJGilM\nME54K8ZmH/YSOoPkUnnrNZ9qIOknmueXfdaqAX/xSY9UW5pt1wyZ9ZHAsFT2WKna0ICr62qeqQ32\njsUisFZlKcTcUGQ3T2IN50PFWiFRcLcZZgdPq6Q6OnELUCFYrXko7WVzxNCOUvqj+egTekYbp9sg\nhaW4NVn49JOLhONBykT6ccnDaMNhL7Hx/wIaZQlGYGlmCs/UmkedmCjRNEqCasmjFso8JyPvK2Gp\na8NU0081/ZFla1wp+dxe8ulkYU5gcwpircEYKoGg5AuiWDPSsB4UPAAlj2pqiBKLOHI+1AQelB3m\n3acgr7iRG7mR2SKEyNeoZ80ZeeZnzfn8acQpvv1Y4wmbC+Jk0eFJCKucTZc1Kw7ZlSJLi3Os5u0p\nxb38aXdqfxTQiTRH/YSVip/v8a4wKS0HwGrNm8gd86XghZUy2w3Lv/C0nzItDmp1Ua7QdeS6MdIf\nAL+5t7f3/xW++zXgf93b27v3rA+9f//+NvAPgcfZv2WgCqwB/83e3t7ZFUWY68YapVrzzYx2e325\nimQSV3A6xvIvHll3ypduNznspTNDO7QxvHfUpZtothvlPMmnWF7x9PZu5qJ5ZY6Lphhj+3ZW1yJ1\ntfvNKoxDwICXjVsKRz3NStXPSR6O+ilKa44HCZ3IsF6zp6qdLM61GHN12EtJtGar7vPeSZ90ZNhc\ntkgTrr0uJrWIofm9wwsMNgTGhZ8U+6D43XezcJnXtlp5jLRzu5xGKS+slGfGTIG1fr7zSQeNptWs\n8cLy5Thp1z/nA2sBaFQqE2gIbmyL4UBP+zYefKvg4poO7QBo96yFdnUqBKJ4jzaGQRzz3kGX1Gi2\nl+rsNseID8Vwh4edEcdRTBAnUPJYroZIJMZYhXK95lP2/QzRJKYXJ6xUPF7fbF7qZ1emG8+3s3n2\nesEV5a5/cmHDGVwoznRoh2v7dGhHkUq9GGPr4oItQ+A4DrzYR+eDAX/12I79va0GAo/dAvKIq58b\nn/3M7XirMCbFMh30lPbsgvhcYUyAfJ46j4Y2hm9lc8/FaLtNRRvrXrRekwQPASK11odqlU/OY9r9\nhNWaZLdZzvsmTlPePejgAS9ttqiEIX/w8QW9WLFS8WkEHqsNwWlfkyhDqCyNbeJZitq1mrRMl8Kw\nVvOJUzKMZ033wtLBLy+XMBo6kWG5KlirhpwMbNtLPhxk1ME7aw1a5XI+t/e7ManS7DQCQt8nUSqn\nuX15d4VqENzE2f6YyI9LjPRNaIeV6/bDVVTci57/rOP3KNt77iywRjuZF9oBkxbkmpcghGD5ijAv\nbQwPOpZj4Lmlysy8Hxivz0W9ZR77ZKwU38woyn9uiqJ81p76XqZj3FurZYhrMg9tLYZfvp2t8a8X\n1vh5ceUPO9ZoeXeplv8+a34Uw3w/v9nk1u4ufAYx0v818H/ev3+/BJwDS0AK/BfXvH9C9vb2ngJ/\n59Pc+6yijeHC2I56rRZMJB2532GskOwuWWWzCOQ96+Wql0OqJTPB+gezCUZeyRSbeS9pMQShrWfH\n9o5ZBa0l69UN+yIf9lK2GpNwPLealuWsHSnKvqI9MMRKs1U2eZKTq+dmHb6TKbRr1RKmLNis+QWK\n8QBjxrHXTqEYGo/zoaLejbnVLE3AmRXLl0Lwhc3mpfb7UrJa81mpeew2wkttdp9TrXnnXOEB/+7O\nYjKWbmpf4Eb2t8EmIRTb4shTziIyRXrxQrtaWMRmhZS4hWW16nE0FAg83qpPxnkXP99uhZaJqRqw\nVQ9y0Hdb/jghQiDYqAWEUlJEkpiVG+D66/WpWK7iIcyRxdxpZVbZggINs+evFIITNbtv3GLpaFWn\nD5BgSUY+tmsRb+zCaq18qe7u79Dz8tARbSYTUcfz2oWEXD4Iuz4r9tWTixhh/JzIyM2to74lbNlt\nBuw2Q0uz3hvyJw8TqmcpW5+zBzsHTQj23XznaEiqU94/EyQGXt6WhJ7HV59v2oNAN8EYzVnfMhjW\nA0nsl1iveZxmFOPbjTJg3ZMAD8/tuLy4EkBYoVWBjWrJ0tSaBLDWlJO+3ZSXKpI/y/x5v1ZKiVIb\nJ2+rKXj3OGa/q/jy3QaelHx4Yev/8i43ciOX5KdRgXbyLOEU1+kHbQznI3tdvXQ1HvJV4SRXPffW\nM+Q0zFKgnUxae+13LXN1/QO5GLWiyGew1LJ1XdRmX0o2l8eQfvPKdut0bMZKs1v7Z+VSrVUrE2W5\nHBjHZO3JsU5yuzVppJnXNm0MkZ7Mp1sk11Kk9/b2/tn9+/dvAy8CK0AHeH9vb++yrfzHTFKtOcjA\nVLW5TP4xHRtVhFJZJAKBt2AgriPTL5M2hu5wkpDFJkLpXEFdrVpM7LePBpxHmhdXQ7brts7FtoSe\nxxubVT4+jfj6fsQg1jytjTiNDOu18bBLIXJXulPsjjKMYTehT6cwj4EsBvx6bSyC2BefW/x7Hiax\n0pp+bJWORd4Tax0dY3w6pdlRhS6C2rmOXLXwaWPoxWPPwiLmRk/ITKFLM4XOLoKPuxF//HDIctnj\ntc0KvgyQIr703OkEwulE1OLBwS08axm2clExdBjo83DV4ep+WzT/FdDJEPr0FdfCOIFRCpHHdm83\ngolkEGt5t56H12fEHp5GKUKMD7hubk/W2cbD73dHCOzhcKXskyrNuYJEKULPwvAN05THFzZWTxnN\n+dDY0A0Dx4OUWhjmh/POMOI0UiyVPOqhhcMzRuJJWKvJ3NvhSUFqDKn2WKv4NiRFCFKteHimOY8g\n1pruSLNez5J0LiyOYLNUIso2iCSF04FiueLjC8FOI+BRx1rbtTEMRyM+6dk+HcVxjhBzIzdyIz86\nuU5M9mcdvz7P+LLIuLeIV2FavGueWZ5lH3ahek8ukpwpuJhL5YyHxXoWvfvtyO6vG3U/M7KME82d\nfjOvb10IYLH9i2ShIn3//v2/v7e397/cv3//t+b8zt7e3j+48ik/QvGlZHOKEGSRLFKU3O+zJtk8\n6/U869qsFyX0PF7dKOV1dUmL7YF1G+82Aw56CanWOf6yI4WRQkwov66M0PfZrAesVDyEkKxVx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Xylyd70f2SgZ2kMrnr+80ydsW//Vzup7JvgetiGl0e/zx+y0k8BsvEMktWRinkf6n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D7/Sp2CKdlveenUce3JwBGCr9z2FANPruW6FBPhOQAOFSyKySJOtMtSowqp7NB094UgHXeh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HpBDwpY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\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N1ggyzsVBmwb4NZph9siz5Bn2/EXvfEVnOGRjfT3SMcl/TXbn3NU0Xi+Phfiotu0MhyOH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\nl7PA1UKIHxNCpKSUvyGl/NfAfR/z/v9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f411a061550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lib import btq_pandas_plotting as btq\n",
    "btq.scatter_matrix_and1(pivoted_df, alpha=0.2, figsize=(12,12), rowname=UID);\n",
    "plt.savefig('img/Scatter_matrix_and1.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UID 2 is the black dot in the scatter matrix above, all other ratings are in blue, with histograms in red along the diagonal. It is apparent from the figure above that UID 2 is with the majority except for ratings for Case and Mint Condition. Next, similarity scores will be computed using euclidean distance and Pearson correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aid\n",
       "Case              10\n",
       "Mint Condition    10\n",
       "Ja Rule           90\n",
       "Musiq             90\n",
       "Ashanti           90\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract the UID row from the pivoted data\n",
    "uid_row_df = pivoted_df.loc[UID]\n",
    "pivoted_df = pivoted_df[pivoted_df.index != UID]\n",
    "uid_row_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity metrics\n",
    "#### Euclidean distance\n",
    "Briefly, the straight line distance between two points. The formula is:\n",
    "$d = \\sqrt{\\displaystyle\\sum_{i=1}^{artists}\\left(x_i - y_i\\right)^2}$\n",
    "\n",
    "To convert this to a similarity score, $d = \\frac{1}{d+1}$ Two users who rate every artist the same would have a value of 1. Computed in one line with pandas:\n",
    "\n",
    "` pivoted_df.dropna().rsub(uid_row_df).pow(2).sum(axis=1).pow(.5).add(1).rdiv(1) `\n",
    "\n",
    "#### Pearson correlation coefficient\n",
    "$r = \\frac{n\\sum x_iy_i-\\sum x_i\\sum y_i}{\\sqrt{n\\sum x_i^2-(\\sum x_i)^2}~\\sqrt{n\\sum y_i^2-(\\sum y_i)^2}}, where \\sum = \\sum_{i=1}^{artists}$\n",
    "\n",
    "The correlation coefficient is scale independent, so it is often a cleaner measure of similarity. Computed in one line in pandas:\n",
    "\n",
    "` pivoted_df.dropna().corrwith(uid_row_df,axis=1) `\n",
    "\n",
    "#### Making recommendations\n",
    "\n",
    "$R(aid) = \\frac{\\displaystyle\\sum_{u=1}^{users}(rating_u(aid) * similarity_u)}{\\displaystyle\\sum_{u=1}^{users}similarity_u}$\n",
    "\n",
    "To make a recommendation, the user's rating for an artist is multiplied by the similarity metric of that user. That is done for all recommending users, and then summed. that value is then normalized by the sum of similarity metrics. If a user does not have a rating for that aid, then it is not included in the numerator or denominator. \n",
    "\n",
    "Below, I will test to see how well predictions match the actual rating for each of the artists (using leave one out). I use both metrics so we have a comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Case\n",
      "(29760, 5)\n",
      "Pearson corr. prediction=  58.1745159472\n",
      "Euclidean dist. prediction=  50.4224953442\n",
      "Actual=  10.0\n",
      "\n",
      "Mint Condition\n",
      "(33386, 5)\n",
      "Pearson corr. prediction=  82.2323557971\n",
      "Euclidean dist. prediction=  35.2009482538\n",
      "Actual=  10.0\n",
      "\n",
      "Ja Rule\n",
      "(337803, 5)\n",
      "Pearson corr. prediction=  78.7198717274\n",
      "Euclidean dist. prediction=  69.7630477326\n",
      "Actual=  90.0\n",
      "\n",
      "Musiq\n",
      "(109103, 5)\n",
      "Pearson corr. prediction=  65.4158308251\n",
      "Euclidean dist. prediction=  59.2105571198\n",
      "Actual=  90.0\n",
      "\n",
      "Ashanti\n",
      "(2688, 5)\n",
      "Pearson corr. prediction=  92.0592327549\n",
      "Euclidean dist. prediction=  94.1420253827\n",
      "Actual=  90.0\n",
      "\n",
      "Baby\n",
      "(35648, 5)\n",
      "Pearson corr. prediction=  74.7564363147\n",
      "Euclidean dist. prediction=  54.7214424553\n",
      "Actual=  90.0\n"
     ]
    }
   ],
   "source": [
    "num = 5 #number of the top similar users to use for predictions\n",
    "column_list = pivoted_df.columns\n",
    "\n",
    "for col in pivoted_df.columns:\n",
    "    print '\\n',col\n",
    "    keep_cols = column_list.drop(col)\n",
    "    #create a test set that contains the column to test\n",
    "    test_col_df = pivoted_df[col].dropna()\n",
    "    #create a train dataframe that does not contain the test column\n",
    "    train_df = pivoted_df.loc[test_col_df.index,keep_cols]\n",
    "    print train_df.shape\n",
    "    uid_test_col = uid_row_df[col]\n",
    "    #euclid. dist from uid. then normalized through inversion to yield a similarity value\n",
    "    dist_s = train_df.dropna().rsub(uid_row_df).pow(2).sum(axis=1).pow(.5).add(1).rdiv(1).sort_values(ascending=False)[0:num]\n",
    "    #name the series so we have a column label later\n",
    "    dist_s.name = 'dist_sim'\n",
    "    #print dist_s\n",
    "    #pearson correlation\n",
    "    corr_s = train_df.dropna().corrwith(uid_row_df,axis=1).sort_values(ascending=False)[0:num]\n",
    "    #name the series so we have a column label later\n",
    "    corr_s.name = 'corr_sim'\n",
    "    #print corr_s\n",
    "    \n",
    "    # concatenate correlations with the rating values for this column\n",
    "    corr_df = pd.concat([test_col_df.loc[corr_s.index], corr_s],axis=1)\n",
    "    # multiply each rating by similarity metric\n",
    "    corr_df = corr_df.assign(csx = corr_df[col]*corr_df['corr_sim'])\n",
    "    # normalize sum of products by the sum of similarity to normalize\n",
    "    corr_pred = corr_df['csx'].sum() / corr_df['corr_sim'].sum()\n",
    "    print 'Pearson corr. prediction= ', corr_pred\n",
    "    \n",
    "    # concatenate distances with the rating values for this column\n",
    "    dist_df = pd.concat([test_col_df.loc[dist_s.index], dist_s],axis=1)\n",
    "    # multiply each rating by similarity metric\n",
    "    dist_df = dist_df.assign(csx = dist_df[col]*dist_df['dist_sim'])\n",
    "    # normalize sum of products by the sum of similarity to normalize\n",
    "    dist_pred = dist_df['csx'].sum() / dist_df['dist_sim'].sum()\n",
    "    print 'Euclidean dist. prediction= ', dist_pred\n",
    "    \n",
    "    print 'Actual= ', uid_test_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Need more samples\n",
    "\n",
    "Some of the predictions are close, others are off by as much as 72! Sometimes the distance metric provides the more accurate prediction, other times the correlation metric does. As the number of ratings increases, we should get better preditions. I will drop all users that have not rated all 6 artists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(81, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>aid</th>\n",
       "      <th>Case</th>\n",
       "      <th>Mint Condition</th>\n",
       "      <th>Ja Rule</th>\n",
       "      <th>Musiq</th>\n",
       "      <th>Ashanti</th>\n",
       "      <th>Baby</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45546</th>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89601</th>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99795</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102565</th>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128685</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "aid     Case  Mint Condition  Ja Rule  Musiq  Ashanti  Baby\n",
       "uid                                                        \n",
       "45546     80              10       80     10      100    10\n",
       "89601     90              90       90    100       90    70\n",
       "99795     10              10       10     20       70    20\n",
       "102565   100              10       90    100       90   100\n",
       "128685   100             100       90    100      100    90"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings_complete_df = pivoted_df.dropna()\n",
    "print all_ratings_complete_df.shape\n",
    "all_ratings_complete_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Down to 81 users who have rated all 6 artists. The top 5 for each metric will be my recommenders for UID 2. There may be some overlap in the two top 5s, so I am not guaranteed to have 10 recommenders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uid\n",
      "1652817    0.062500\n",
      "1709192    0.054584\n",
      "1728147    0.018882\n",
      "1937099    0.016861\n",
      "427575     0.014688\n",
      "Name: dist_sim, dtype: float64\n",
      "uid\n",
      "1652817    0.990256\n",
      "1709192    0.985184\n",
      "1937099    0.956183\n",
      "1655700    0.918559\n",
      "654010     0.918025\n",
      "Name: corr_sim, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#num is the top num of similarity values to use. 5 is the top 5 (for both corr. and dist.)\n",
    "num=5\n",
    "#euclid. dist from uid. then normalized through inversion to yield a similarity value\n",
    "all_dist_s = all_ratings_complete_df.dropna().rsub(uid_row_df).pow(2).sum(axis=1).pow(.5).add(1).rdiv(1) #.sort_values(ascending=False)[0:num]\n",
    "all_dist_s.name = 'dist_sim'\n",
    "dist_s = all_dist_s.sort_values(ascending=False)[0:num]\n",
    "print dist_s \n",
    "#pearson correlation\n",
    "all_corr_s = all_ratings_complete_df.dropna().corrwith(uid_row_df,axis=1) #.sort_values(ascending=False)[0:num]\n",
    "all_corr_s.name = 'corr_sim'\n",
    "corr_s = all_corr_s.sort_values(ascending=False)[0:num]\n",
    "print corr_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1652817 1709192 1728147 1937099  427575 1655700  654010]\n"
     ]
    }
   ],
   "source": [
    "#Unique list of most similar uids\n",
    "most_sim = dist_s.append(corr_s).index.unique()\n",
    "print most_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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aagKH2mZVVbl48SI3b95kbm6OwcFBent70Tdpu1T9dlpV+DAWhTxMZug0emES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\nkKAePXqk+vTpo+Li4oweK7d1QU4kT33z1PUA4PLly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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41192eaed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(9,9))\n",
    "ax.get_xaxis().set_visible(False)\n",
    "#uid_row_df.plot(ax=ax)\n",
    "#ax.hold(True)\n",
    "all_ratings_complete_df.loc[most_sim].T.plot(ax=ax,alpha=0.6,table=all_ratings_complete_df.loc[most_sim])\n",
    "ax.hold(True)\n",
    "uid_row_df.plot(ax=ax,color='k',linestyle=':',label=UID,alpha=0.4)\n",
    "ax.set_ylim(0,105)\n",
    "ax.set_ylabel('Rating')\n",
    "ax.set_xlabel('Artist')\n",
    "ax.set_title('Ratings of UID=%i and most similar other users' % UID)\n",
    "plt.legend(loc='best')\n",
    "plt.savefig('img/SimilarUsersRatingsUID%i.png'%UID,dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot nicely shows the ratings of the most similar users and UID 2. Notice how Case and Mint Condition, the 2 lowest rated artists by UID 2 has a lot of variance even with the most similar users. Ja Rule and Ashanti, on the other hand, are rated favorably by all users in the plot.\n",
    "\n",
    "#### Pull all the ratings for our most similar users\n",
    "Next, clear all the other dataframes and pull the data for the most similar users to make recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for query:  0.011449098587\n",
      "Shape of data:  (3417, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>aid</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>427575</td>\n",
       "      <td>1000026</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>427575</td>\n",
       "      <td>1000084</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>427575</td>\n",
       "      <td>1000125</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>427575</td>\n",
       "      <td>1000152</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>427575</td>\n",
       "      <td>1000193</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      uid      aid  rating\n",
       "0  427575  1000026      50\n",
       "1  427575  1000084       0\n",
       "2  427575  1000125      50\n",
       "3  427575  1000152      10\n",
       "4  427575  1000193       0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clear other dfs\n",
    "del artists_df, data_df, pivoted_df, all_ratings_complete_df\n",
    "#Query database for all the ratings from the most similar users\n",
    "qa_string = \"SELECT * FROM ym_ratings WHERE uid = %s\" % \" OR uid = \".join([str(a) for a in most_sim])\n",
    "tic=timeit.default_timer()\n",
    "rec_df = psql.read_sql(qa_string, conn)\n",
    "toc=timeit.default_timer()\n",
    "print 'Time for query: ', toc-tic\n",
    "print 'Shape of data: ', rec_df.shape\n",
    "rec_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Shape of data:  (7, 1611)\n"
     ]
    },
    {
     "data": {
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       "      <th>aid</th>\n",
       "      <th>112</th>\n",
       "      <th>The 2 Live Crew</th>\n",
       "      <th>2Pac</th>\n",
       "      <th>3rd Bass</th>\n",
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       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
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       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>654010</th>\n",
       "      <td>100</td>\n",
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       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1652817</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655700</th>\n",
       "      <td>92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1709192</th>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728147</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937099</th>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 1611 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "aid      112  The 2 Live Crew  2Pac  3rd Bass  3T  69 Boyz  702  8Off  \\\n",
       "uid                                                                     \n",
       "427575    50                0    50        10 NaN        0   80     0   \n",
       "654010   100              NaN    90       NaN NaN      NaN  NaN   NaN   \n",
       "1652817  100                0   NaN         0   0        0  NaN   NaN   \n",
       "1655700   92              NaN    90       NaN NaN      NaN  NaN   NaN   \n",
       "1709192  100              NaN   NaN       NaN NaN      NaN  NaN   NaN   \n",
       "1728147   70                1   NaN       NaN NaN       10   50   NaN   \n",
       "1937099  100              NaN    90       NaN NaN      NaN   90   NaN   \n",
       "\n",
       "aid      95 South  A Tribe Called Quest  Aaliyah  Paula Abdul ...   Baby Bash  \\\n",
       "uid                                                           ...               \n",
       "427575          0                    81      100           66 ...         NaN   \n",
       "654010        NaN                    50       90          NaN ...         NaN   \n",
       "1652817       NaN                   NaN      100            0 ...         NaN   \n",
       "1655700       NaN                   NaN       90          NaN ...         NaN   \n",
       "1709192       NaN                   NaN      100          NaN ...         NaN   \n",
       "1728147       NaN                    30       70          NaN ...         NaN   \n",
       "1937099       NaN                   NaN      100          NaN ...          70   \n",
       "\n",
       "aid      Kevin Lyttle  Cerati/Melero  Now City  Jhene  George Pajon, Jr.  \\\n",
       "uid                                                                        \n",
       "427575            NaN            NaN       NaN    NaN                NaN   \n",
       "654010             90            NaN       NaN    NaN                NaN   \n",
       "1652817           NaN              0         0      0                  0   \n",
       "1655700           NaN            NaN       NaN    NaN                NaN   \n",
       "1709192           NaN            NaN       NaN    NaN                NaN   \n",
       "1728147           NaN            NaN       NaN    NaN                NaN   \n",
       "1937099           NaN            NaN       NaN    NaN                NaN   \n",
       "\n",
       "aid      G-Unit  Jae Millz  Van Hunt  Joss Stone  I-20  Jin  \n",
       "uid                                                          \n",
       "427575       50        NaN         0           0    70  NaN  \n",
       "654010       90        NaN       NaN         NaN    90  NaN  \n",
       "1652817     100        100       NaN         NaN   NaN  NaN  \n",
       "1655700      78        NaN       NaN         NaN   NaN  NaN  \n",
       "1709192     NaN        NaN       NaN         NaN   NaN  NaN  \n",
       "1728147      90         50         0          30    50   70  \n",
       "1937099      50        NaN       NaN         NaN   NaN  NaN  \n",
       "\n",
       "[7 rows x 1611 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a lookup-like dict for artist names\n",
    "qu_string = \"SELECT aid, name FROM ym_artist;\"\n",
    "all_artist_df = psql.read_sql(qu_string, conn)\n",
    "aid_lookup = pd.Series(all_artist_df['name'].values,index=all_artist_df['aid']).to_dict()\n",
    "\n",
    "#Pivot data so rows are uids and columns are artists\n",
    "rec_pivot_df = pd.pivot_table(rec_df,values='rating',columns=['aid'],index=['uid'],fill_value=None)\n",
    "rec_pivot_df.rename(columns=aid_lookup,inplace=True)\n",
    "print 'Shape of data: ', rec_pivot_df.shape\n",
    "rec_pivot_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of columns with only one rating. That will skew our measures. For example, if only one user rates an artist 100, then the computed recommendation would be 100. Therefore, I will drop all artist columns that have fewer that 4 ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>90</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 241 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "aid      112  2Pac  Aaliyah  Yolanda Adams  Aerosmith  Babyface  \\\n",
       "uid                                                               \n",
       "427575    50    50      100              0          0         0   \n",
       "654010   100    90       90            NaN         90       NaN   \n",
       "1652817  100   NaN      100              0        NaN         0   \n",
       "1655700   92    90       90             90        NaN        80   \n",
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       "1728147   70   NaN       70             10         10       NaN   \n",
       "1937099  100    90      100            NaN        NaN        70   \n",
       "\n",
       "aid      Backstreet Boys  Erykah Badu  Anita Baker  Bell Biv DeVoe  \\\n",
       "uid                                                                  \n",
       "427575                 0           67            0              20   \n",
       "654010               100           81          NaN             NaN   \n",
       "1652817              NaN            0            0               0   \n",
       "1655700              NaN          100           80             NaN   \n",
       "1709192                0          NaN          NaN             NaN   \n",
       "1728147               10           10          NaN              10   \n",
       "1937099               90           90           50              90   \n",
       "\n",
       "aid      Eric Benet  Blackstreet   ...    Amerie  Kelly Rowland  \\\n",
       "uid                                ...                            \n",
       "427575            0           10   ...        60             15   \n",
       "654010          NaN          100   ...       NaN            100   \n",
       "1652817           0            0   ...         0            100   \n",
       "1655700          84           80   ...        89            100   \n",
       "1709192           0          100   ...        80            NaN   \n",
       "1728147           5           30   ...        80             50   \n",
       "1937099          70           90   ...        90             90   \n",
       "\n",
       "aid      Marques Houston  Wayne Wonder  Pharrell  David Banner  Chingy   JS  \\\n",
       "uid                                                                           \n",
       "427575                90            40       NaN            70      70   50   \n",
       "654010               100            90       100           NaN     100  NaN   \n",
       "1652817              NaN           100       100           100      90    0   \n",
       "1655700               80           NaN       NaN            75      80  NaN   \n",
       "1709192              100           NaN       NaN            90     100  100   \n",
       "1728147              NaN            70        70            60      90  NaN   \n",
       "1937099               80           100        90           NaN      80   70   \n",
       "\n",
       "aid      Murphy Lee  Lil Scrappy  Bad Boy's Da Band  G-Unit  \n",
       "uid                                                          \n",
       "427575          NaN          NaN                100      50  \n",
       "654010           90           90                NaN      90  \n",
       "1652817         100            0                100     100  \n",
       "1655700         NaN          NaN                NaN      78  \n",
       "1709192         100          NaN                NaN     NaN  \n",
       "1728147          80           50                 70      90  \n",
       "1937099          90           30                 90      50  \n",
       "\n",
       "[7 rows x 241 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop columns containing half the rowsize or greater number of NaNs \n",
    "rec_pivot_df.dropna(thresh=rec_pivot_df.shape[0]/2+1,axis=1,inplace=True)\n",
    "#Remove the columns UID has already rated\n",
    "rec_pivot_df.drop(uid_row_df.index,axis=1,inplace=True)\n",
    "rec_pivot_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By dropping the excessive NaN columns, the dataframe has gone from 1611 to 241 columns.\n",
    "\n",
    "#### Recommend New Artists\n",
    "Finally, I can make recommendations for UID 2 by using the 241 artist ratings from the 7 most similar users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations based on correlation\n",
      "aid\n",
      "Beyonce         93.345991\n",
      "Aaliyah         93.110502\n",
      "Lil' Flip       92.863811\n",
      "Murphy Lee      92.411505\n",
      "Tupac Shakur    91.990787\n",
      "Name: corr_pred, dtype: float64\n",
      "\n",
      "Recommendations based on distance\n",
      "aid\n",
      "Lil' Flip     96.240053\n",
      "Murphy Lee    96.024107\n",
      "Aaliyah       95.895756\n",
      "Next          95.065186\n",
      "Beyonce       94.979604\n",
      "Name: dist_pred, dtype: float64\n"
     ]
    },
    {
     "data": {
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SJEkVDNSSJElSBQO1JEmSVMFALUmSJFUwUEuSJEkVDNSSJElSBQO1JEmSVMFA\nLUmSJFUwUEuSJEkVDNSSJElSBQO1JEmSVMFALUmSJFUwUEuSJEkVDNSSJElSBQO1JEmSVMFALUmS\nJFUwUEuSJEkVDNSSJElSBQO1JEmSVMFALUmSJFUwUEuSJEkVDNSSJElSBQO1JEmSVMFALUmSJFUw\nUEuSJEkVDNSSJElSBQO1JEmSVMFALUmSJFUwUEuSJEkVDNSSJElSBQO1JEmSVMFALUmSJFUwUEuS\nJEkVDNSSJElSBQO1JEmSVMFALUmSJFUwUEuSJEkVDNSSJElSBQO1JEmSVGH1QRUcY1wbOAVYHxgB\n3gf8Bvg8JejfALw0pXTXoOooSZIkTWeQPdSvAC5NKT0VeCFwDCVUfyKltBNwBbDf4KonSZIkTW+Q\ngfpGYMPm9w2Am4CdgG82084Adh1AvSRJkqSeDSxQp5S+Ajw4xng58F3gP4C1W4Z43AhsMqj6SZIk\nSb0Y5BjqFwPXpZSeHWPcFjih7SWLel3X6Ohox3kjI7fPrILAyMiajI6uN8NlB1PuIMsexnJry55O\nt/dmvw2qbLdZ0+naZt8yMuP1joyMzPhvUVPuIMsexnIHXfZ0FmI7stC2uR/lDixQA/8CnAuQUvpV\njHFT4LYY45oppTuBTYGxXlY0Ntb5ZePjS4C1ZlTB8fE7GRu7eYbLDqbcQZY9jOXWlt3N6Oho1/dm\nPw2qbLd51ZddiLq32eMzXu/4+PiM/xY15Q6y7GEsd9BldzOM7ciwlj2s5XZqtwc5hvp3wBMBYowP\nAf4OfAvYu5m/F3DOYKomSZIk9WaQPdTHASfGGC8AFgMHABk4JcZ4AHANcPLgqidJkiRNb2CBOqV0\nG/CvU8zaba7rIkmSJM2U35QoSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM\n1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIk\nSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIF\nA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7Uk\nSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJU\nwUAtSZLoF4tbAAAgAElEQVQkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIk\nSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIF\nA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7Uk\nSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJU\nwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUYfVB\nFh5jfDHwZuAu4D3Ar4DPU4L+DcBLU0p3Da6GkiRJUncD66GOMW5ACdE7AM8B9gDeBxybUtoJuALY\nb1D1kyRJknoxyB7qXYFvpZSWAkuBA2OMVwIHNvPPAP4DOG5A9ZMkSZKmNchA/VBg7RjjN4D1gMOA\nJS1DPG4ENhlQ3SRJkqSeDDJQLwI2APakhOvvNtNa5/dkdHS047yRkdtnVjtgZGRNRkfXm+Gygyl3\nkGUPY7m1ZU+n23uz3wZVttus6XRts28ZmfF6R0ZGZvy3qCl3kGUPY7mDLns6C7EdWWjb3I9yBxmo\n/wj8IKW0DLgyxngrcFeMcc2U0p3ApsBYLysaG+v8svHxJcBaM6rg+PidjI3dPMNlB1PuIMsexnJr\ny+5mdHS063uznwZVttu86ssuRN3b7PEZr3d8fHzGf4uacgdZ9jCWO+iyuxnGdmRYyx7Wcju124MM\n1OcBJ8UYP0TpqV4HOAfYG/gisFfzXJIkSZq3pg3UMcZlwESH2X8Ffgi8MaV05aoUnFIaizF+FfhR\ns/6DgJ8Cn48xHgBcA5y8KuuUJEmS5lovPdSvBJ4BfBq4HhgF9ge+D/wY2Ac4AdhlVQtPKR0PHN82\nebdVXY8kSZI0KL0E6jellB7T8vzKGONFwI9SSicB74kxXtqf6kmSJEnzWy9f7PLAGOM/tU3bnOaW\ndjHG7eg8JESSJEm6V+ulh/rtwE9jjBn4C3BfYFvgHc38rwOv60/1JEmSpPlt2kCdUvqv5stXtgfW\nB/4G/Cyl9IfmJZullO7uYx0lSZKkeauXIR8AWwH3o3zZyv2Ap8YY9wUwTEuSJGkh6+W2ef9NuYPH\nZUBreJ4AvtSnekmSJElDoZcx1E8BHpZSWtrvykiSJEnDppchH1cB/+h3RSRJkqRh1EsP9WnA2c23\nGt7SOiOl5JAPSZIkLWi9BOrnNY+xbbpjqCVJkrTg9XLbvFX+SnFJkiRpoegYqGOMh6WUDo0xfrbT\na1JKB/SnWpIkSdJw6NZDfWPzeH2H+X7duCRJkha8joE6pfTJ5tdbUkofa58fY/xw32olSZIkDYlu\nQz62AR4NHBJj/CPlWxInrQccCBzS3+pJkiRJ81u3IR9LgCcD6wPtY6XvAt7Sr0pJkiRJw6LbkI+L\ngYtjjP8vpXRc+/wY4xP7WjNJkiRpCPRy27zjYow7AA9j+TcrrgMcBmzcx7pJkiRJ8960gTrGeBTw\nCuAS4HHA/wGPAN7T15pJkiRJQ2C16V/CC4AtUko7A79PKT0ZeAmwST8rJkmSJA2DXgL1XSmlW1pf\nn1L6FrBH32olSZIkDYlph3wA/xdjPIcSoHOM8QjgF5Rb50mSJEkLWi891K8AvpVSugP4d+DxwDuB\ng/tYL0mSJGko9HKXj9uBjzS//w54Rr8rJUmSJA2Lbt+UuBbwDuCfgYuAD6WUljXzngJ8OKW0/ZzU\nUpIkSZqnuvVQfxzYEDgD2BdYHGNMwFHADsAH+189SZIkaX7rNoZ6J+BfU0qfBfYEDgF+SLkg8WEp\npaPnoH6SJEnSvNath/rulNJdACmlm2OMfwG2Syn9eW6qJkmSJM1/3XqoJ9qe32mYliRJklbUrYd6\n9RjjJsCiDs9JKY31s3KSJEnSfNctUG8B/J6WAA1c3/L7BLC4H5WSJEmShkXHQJ1S6uVLXyRJkqQF\nzdAsSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUoeNFiTHGq1j5XtQrSCk9bNZrJEmSJA2RbrfNe0nz\n+FTgMcAXgJuB+wP7Ahf2t2qSJEnS/NfttnkXAcQYjwGekFK6p7c6xvhV4CfAh/teQ0mSJGke62UM\n9f2BDdumrQdsNPvVkSRJkoZLtyEfkz4L5BjjBcAtwP2AJwOf6WO9JEmSpKEwbQ91Sun9wFOA84DL\ngW8Bu6WUDu1z3SRJkqR5r9fb5t0A3AbcmlL6DPCn/lVJkiRJGh7TBuoY4zMpPdN7AW9pJr8vxviO\nflZMkiRJGga99FB/DNg+pbQnpZca4A0sv62eJEmStGD1EqgXpZSuaH6fAEgpLQUW9a1WkiRJ0pDo\n5S4fOcb4XkpPNTHGtYDXUYaBSJIkSQtaLz3Ur6XcJu/PwFaUW+c9GTigj/WSJEmShkIvgXr1lNKu\nwLrApsBazXjqh/azYpIkSdIw6GXIx9nAI1NKt9FclBhjXBs4i5W/QVGSJElaUDoG6hjjq4CjgPvF\nGMfbZq8GXNTPikmSJEnDoOOQj5TS54CNgO8Cj2j72QzYeQ7qJ0mSJM1rXcdQp5TuBnYH9gauSyld\nA9wOvAxYo//VkyRJkua3Xi5KPB54EsuHh9wObAuc0K9KSZIkScOil4sSt08pbTX5JKV0a4zxJcCl\n/auWJEmSNBx6+qbEGOMD2qY9mN7CuCRJknSv1ksoPgL4ZYzxIuCvwMbADvjFLpIkSdL0PdQppVOA\n7Sj3o74c+CbwqJTS1/pcN0mSJGne6xioY4z/3DzuQPmGxEuA7zWPD2mmS5IkSQtatyEfHwZ2Bb7Y\nYf4E8LBZr5EkSZI0RDoG6pTSrs3j5nNXHUmSJGm4dPvq8ROnWziltN/sVkeSJEkaLt0uSryi+bmT\n8jXjfweuA+4Angrc3O/KSZIkSfNdtyEfRwDEGM8HHp9S+svkvBjjRsBX+189SZIkaX7r5YtdNmfl\n3ui/AA+Z/epIkiRJw6WXL3a5GDg/xngacAtwP+B5wM/7WTFJkiRpGPQSqF8O7Ac8BdiA0lt9JnB8\nH+slSZIkDYVpA3VK6Q7gUzHGbwIbp5R+0f9qSZIkScNh2kAdY9wcSMAWwO3AaIzxFCCllM7sc/0k\nSZKkea2XixK/CHw4pbQ+ZQw1wHuBw/tVKUmSJGlY9BKoN04pfbn5fQIgpXQlMNK3WkmSJElDopdA\nfXOM8amtE2KM21G+6EWSJEla0Hq5y8fBwDdjjNcBm8UYLwY2Afbua80kSZKkIdDLXT4uijE+hHLb\nvPWAMeDHzd0/JEmSpAWta6COMS4CLkgp7QScPTdVkiRJkoZH1zHUKaUJ4CcxxpfEGNeZozpJkiRJ\nQ6OXMdQvAt4AnBxjvLuZtgiYSCl5pw9JkiQtaL0E6h36XgtJkiRpSE03hnpDYFvgTuCilNLSOamV\nJEmSNCQ6jqFu7j19GfBu4D+By2OMj5qrikmSJEnDoFsP9ZHAc1NKPwCIMe4OfAh45mxWIMZ4H+DX\nwPuA84HPU4L+DcBLU0p3zWZ5kiRJ0mzqdpeP9SbDNEBK6WzgoX2ow7uBPze/vw84trlN3xXAfn0o\nT5IkSZo13QL13VNMWzabhccYAxCAsyh3DtkJOKOZfQaw62yWJ0mSJM22bkM+Vo8xbkIJulNOSymN\nVZb/YeAg4JXN87VbhnjcSPmKc0mSJGne6haotwB+z4qBGuD65nECWDzTgmOMLwW+l1K6NsY41Uva\ny+1odHS047yRkdtXvXL3LLsmo6PrzXDZwZQ7yLKHsdzasqfT7b3Zb4Mq223WdLq22bfM/OsNRkZG\nZvy3qCl3kGUPY7mDLns6C7EdWWjb3I9yOwbqlFLXb1GcBc8GNo8x7gVsCowDf48xrplSurOZ1lMP\n+NhY55eNjy8B1ppRBcfH72Rs7OYZLjuYcgdZ9jCWW1t2N6Ojo13fm/00qLLd5lVfdiHq3maPz3i9\n4+PjM/5b1JQ7yLKHsdxBl93NMLYjw1r2sJbbqd3u5Ytd+iKltM/k7zHG9wBXU75EZm/gi8BewDkD\nqZwkSZLUo373QvdqcnjHocDLY4zfA9YHTh5clSRJkqTpDayHulVK6bCWp7sNrCKSJEnSKpovPdSS\nJEnSUDJQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIk\nVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQ\nS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5Ik\nSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM\n1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIk\nSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIF\nA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7Uk\nSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJU\nwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAtSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUwUAt\nSZIkVTBQS5IkSRUM1JIkSVIFA7UkSZJUYfVBFh5j/BDwZGAx8EHgJ8DnKUH/BuClKaW7BldDSZIk\nqbuB9VDHGHcGtk4p7QDsDnwMeB/wiZTSTsAVwH6Dqp8kSZLUi0EO+fg+8MLm978CawM7Ad9spp0B\n7DqAekmSJEk9G9iQj5TSMmBp83R/4CzgGS1DPG4ENhlE3SRJkqReDXQMNUCM8fmUoR27Ab9rmbVo\nMDWSJEmSejfoixKfAbyd0jN9a4zx1hjjmimlO4FNgbFe1jM6Otpx3sjI7TOu38jImoyOrjfDZQdT\n7iDLHsZya8ueTrf3Zr8Nqmy3WdPp2mbfMjLj9Y6MjMz4b1FT7iDLHsZyB132dBZiO7LQtrkf5Q4s\nUMcY1wU+BDwtpXRLM/nbwF7Al5rHc3pZ19hY59w9Pr4EWGtGdRwfv5OxsZtnuOxgyh1k2cNYbm3Z\n3YyOjnZ9b/bToMp2m1d92YWoe5s9PuP1jo+Pz/hvUVPuIMsexnIHXXY3w9iODGvZw1pup3Z7kD3U\n/wpsCKQY4yJgAng5cEKM8UDgGuDkAdZPkiRJmtYgL0o8Hjh+ilm7zXVdJEmSpJnymxIlSZKkCgZq\nSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIk\nqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKB\nWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIk\nSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapg\noJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYk\nSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIq\nGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKgl\nSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKkCgZqSZIkqYKBWpIkSapgoJYkSZIqGKglSZKk\nCqsPugKSJElz4fql1zN221jH+SO3jDA+Pj7lvNG1R9l0yab9qpqGnIFakiQtCGO3jbHH6XvMaNnT\n9zjdQK2OHPIhSZIkVTBQS5IkSRUM1JIkSVIFx1BLFa6/fk3GxhZPOW9k5HbGx5d0XHZ09G423fTO\nWS93kGXP122uKXe6svu5zZKk4WCgliqMjS1mjz3W6/KKtTrOOf30v7LpDK9vmb7cQZY9/7a5ptze\nyu7PNkuShoOBWpIkqc+63bKv2+36wFv2DQMDtSRJUp8N6pZ9g7r3dk25/Sy7X+UaqCVJku6lBhXk\na8odZNkzLde7fEiSJEkVDNSSJElShXk55CPGeDTwRGAZ8KaU0k8HXCVJkiRpSvOuhzrGuCOwRUpp\nB+BVwMcHXCVJkiSpo3kXqIGnAacDpJQuBdaLMa4z2CpJkiRJU5uPgfqBwE0tz//UTJMkSZLmnUUT\nExODrsMKYozHAWemlM5onl8IvDKl9LsOi8yvDZCkVbNo0BWYY7bZkobdSu32fLwocYwVe6RHgRu6\nvH6hHYwkaZjZZku615mPQz7OA/YGiDE+Frg+pXTbYKskSZIkTW3eDfkAiDF+ANgJuBs4KKX0qwFX\nSZIkSZrSvAzUkiRJ0rCYj0M+JEmSpKFhoJYkSZIqGKglSZKkCvPxtnmaZTHGbYFAuf/rb1JKvx1w\nlaRZEWNcM6V0Z4xxfeAhKaX/N+g6afbZhkmzy7Zz9nlR4hyKMa4L3I+W+7CmlK7tc5mfBJ4A/Lgp\nd3vgf1NKB/ez3Jbyn0T5Zz01xrhJSqnbPcVnq8xPpJRe3+9y5pMY427ABs1+PgH4J+ColNLX+1zu\n2sDTWPl9fUo/y23KPhb4KXA2cD7wQ2BZSunAPpe7f0rphH6WoeUG2YYNov1qyp3zY0VL2a9KKX2u\nbdq/p5SOnovyF4pBtdlN2YNqOwd5vOh7u70ge6gHFPKOA55F+ZKayTfSBLBdn4t+QkrpnjJijKsB\nP+hzmZNlHQVsBmwBnAocGGPcIKX0xj4XvSzGeABwMTA+OTGl9Jt+FRhjPIku3wCXUtqvX2U3DgOe\nEWPck3K7yR0p93Tvd+N8LnAtcH3LtLn6lP7olNIbYoz/BpyQUvpojPFbc1Du02KMF6WULp2DsjSg\nNmxQ7degjhUxxqcDu5Vf45Yts9YAItD3QD2Xx+YF3GbD4NrOQR4v+t5uL7hAPcCQ9zhgs5TSXJ8S\nyDHG0ZTSWPN8Y+CSOSr78SmlXWKM3wVIKb23+Sr5ftu2+XlRy7QJ4Kl9LPOrzePzKI3jBZRrFHYB\n7upjuZPuTCn9Lca4B3BcSukfMca5+P++O6W07xyUM5U1Y4ybAi8B9my2d705KPcJwK9jjH+nfGBb\nBEyklO4/B2UvRINqwwbVfg3qWPEjSlu1Oyvu32XA56ZcYhYN4Ni8UNtsGFzbOcjjRd/b7QUXqBlc\nI/lzYCPgpjkoq1UArowxXgYsBh5OOUD9hPJm6mevxxoxxjVoPoHGGDcC7tPH8gBIKe3S7zKmKPMs\ngBjjm1JKT2+ZdWqM8cw5qMIfmh6G+6aUfhBjfDHQt28YjTEuaX79Toxxd+Ai4B+T81NKS/tVdotP\nAv8DfCml9PsY4/tZfpDsm5TSI/pdhlYwqDZsIO0XAzpWpJRuBS6IMR6eUvry5PTmjMDB9P9DzJwe\nmxdam91mTtvO+XC8mIt2eyEG6jltJCcbfcppsytjjJdT3kiTn476PeTjhX1efzcfofR6bBZjPJsy\nRuxN/S40xngTy08jrQHcF7hqjoLQhjHG59CMSQMeDzxoDsp9GbA1MHk66zfAq/pY3iWUfbwIeGXb\nvAngYX0sG7hn3N0pLb06756LXr0Y44OA9wDrp5ReGGPcB/hhSumafpe9QA2qDZvT9mseHCsm7Rpj\nfBnweuABwMeAc+ag3EF9gFkobfY9BtB2Dvx4MRft9kIM1EcztyFv7y7zHtivQmOMB6aUjqM0iiv9\no6SU3tKvslvK+HqM8TxKozEO5JTS7XNQ7satz2OMj6Kc2poLLwPeDRxJaTwuZeUGpB9OA/Zp2b8b\nAZ8HtulHYSmlzQFijA9OKV3XOi/G+Mh+lNkuxrgzcAywJrAV8P4Y4/dTSuf2uejPNeW+rXl+I/Bf\nlFPFmiWDbsMG0H4N5FjRLqX06hjjEygB81bgaXN0QeRcH5snLYg2u9Vct53z4XjBHLTbCy5Qp5RO\nizGey/JG8rJ+nm6Y/PTTfBJ8BrBhM2sEeDvl9GU/XN08/rpP659WyyfCDVJKe8cY94kxznlPXkrp\nlzHGHeaorF83vTujKaWr5qLMxieBc2KMrwcOonzif16/Cmt6j+4PnBRjfAXLL55anXLqcMsOi86m\n91HGxU+eqjwG+Ablwpd+WpxSOjvG+BaAlNL5McZD+1zmQnR18ziQNmyu268BHitWEGN8AfBm4B3A\nJsDJMca3pZR+3M9y5/rY3FLugmiz28xp2zlPjhd9b7cXXKCOMUbgRSmlPZvn58UYP5tS6vfYywT8\njfJp6JuUN/NhfSxvcYzxWcz9mO1WA+nJizF+hRV7tEaZo7FpzWmkdzVPt4kxfhz4ab9vC9Q0FJdR\nrhC/MKX0tH6WR+k92o/SEH6qZfoy4At9LnvSXSmlP8cYJwBSSjfGGJfNRbkxxqdS/sceAOwJ9P3M\nywI06DZsUGci5vpY0e5ZwLNTSn8BiDF+Cfg48Nx+FjqoY/MCarNbzXXbOS+OF/1utxdcoKZcXPHM\nlufPo9yH8f+3d+dhlhVlnse/VcXmCqWIG2pLCwgiAqMlCMo2CKjQSBc/VKQpG1zAdmxckBZwRFAB\nZUAFGanRFmV9AZdRbEREUEAUQWS1agRBoLTZcWkWqcr5I86tOnVJEsmbEXHznt/neXjynnszK4Jc\n3jdunIg3cg+oV4uIXSVd2JSrWQ2YD+T6o51o3eEYaUNCbrVm8o5rPR4jJadfFWgX0kzDJix7p38A\nafd4lp9za91lzwrAns0tW3Ktu4yInwA/kXRKRJyvdDjA4oj4Y472HsNvJX0CWF3S7sAupHWIue0N\nHEa6RXsuqT7yvALtdk3tGFYrfpXOFcuJiH0krSlp/Yi4GFgUEVkH041aubkTMbtP0dg5JPkie9zu\n4oB6Fsu/K5lJq8B4Rk+S9GLgEaUan7eSygPlclpEnNe7kLRxRPwyY3vjqTWTtwewb0QshqVrtC4B\nSiz7WBwRD/fe+QMPZW5vonWXJcyQtAB4EFipmeV4V0RcUqDtdwFvAy4GNiXdsjyzQLsvjIjlNg81\nt8m/UaDtLqkdw2rFr9K5YjmS9ifFlacCrwCOlLQoIo7K3HSt3Ny1mA31YmfNfJE9bs+cqn9oGvkC\nqRbht5VK41xFup2V28Gkd8GHkU4n+h3plziXA/uuj87Y1mPZm/RH23tHuBFlZvKuAM6RtIakg4BT\ngQ8WaBfgYklfB9aU9BFSwMpWMD8ibmnWXv4dcFDr+hjgRbnabTkU2CoiXhER65FmmI4o0C7A80nr\nLPcjlRrblDIDjx9K+kYzy9LTqZM5C6kdw2rFr9K5ot8uEbE5cE9zvT/pzURutXJz12I21IudNfNF\n9rjduRnqiPi6pG+S1vQ8Qtq5XaJm7nrAGRFxJ2U2l/S/sy/xTn85kU656n9HuBuZ3wlHxJckXU26\npfNjYE5EPPw4XzZVbR8saQvgGtLGmg9FxE8LNP1pYM/W9b6kd96bZ2734WidZhYRt0oqcSgCpLV3\n75e0KWlX/iGkBLx95nYvJ62vvUDS/hFxIRX+vjqgagyrFb8onyv6zWo+9mZsV6HAWKFWbu5gzIZ6\nsbNmvsgetzs3oJa0PfBuWmfJSyIicp6iR9PedyXdDZwGfCMicm6U6y8zVfrUrcdyKJkS0jibEW8H\ntgNObn7GytFuXx9WIJ32tSQijpW0gaQVIyJ30JgVETe2rktt5LpJ0vGkNYczSBuobpzwK6bOIxFx\nldIJa8dGxCWSZj3uVw1uLCK+J+lK4CuSdqCDsbSAYYxh2eJXS+lc0e9USRcAL5F0Amlz5LG5G62V\nmzsYs6Fe7KyZL7LH7S4mgWNJtS1vK9loRHwS+KSkF5Jun10i6Qbgf0fERRmaXFPSfo91HRFfHOdr\nSsg5y3TcBK+VquM6n1QNYCvgs83Hg1j+GPQczpZ0GWlWfhZpvfjXM7cJaS3eW0mzKmOkOwJnTPgV\nU2eFZknPPwCHNJt6nlag3YUAEfEH4A2SPkg61tam1jDGsOyz5BVyBQBKpeMA/kzakLcGacZ2EVDi\nLm6V3Ez3YjbUi50180X2uN3FAfVNkf/gh3FJegHpl2lXUo3V7wLvlPTmiJjqAvanAM+a4LqWbLNM\nvWSjx67jWuIP9wUR8Q4tOz73uOY2cVYRcZSkbwAbk26XfiZXvdw+M0jJYAbLfralZhLfTtrgs0tE\nPChpLeA9uRuNiPdIWhl4bkTcHBFHU2ePwqgbxhhW5He7cK7oab9ZGAP+QDq1cQ/SyYG5q4zUys1d\ni9lQKXZSMV+UiNtdHFAvkBSkjQfts+SzznZI+gkpOJ1CqvF5V/PSKU0ZnSkVESXrli5Hyx/93TaD\ndDsvt5p1XFdqylz1js9dj3QaVRZqTpNrbt21v+ebNbdLc5+I+RXgXtItvJWALUnf93dmbhdSwr8X\neFezW/wXFCiP2JSZOqS57NWtvTwiSs0udUKtGFY7fpXOFT0RcVJfP3YnbUj8FmnmNrcquZnuxWyo\nFDupmC9KxO0uDqjvb/6b/XifOMX2iYgFj/Haa4v2JLPoO/q7gpp1XD9Kqp26dnObFlK1gFxubj6O\nd5pciXf+a0ZEe2PN6c36yxJqBed/Yfy6tR5Qj4AhiF9Vc4WkrYFPkqolbR8Rd+Rus1ErN3ctZkO9\n2LaxY5MAABjMSURBVFkzX2SP250bUEfEoUrn2G8MLCadiHRprvYkfTPSyU8Xt+pc9m6tjUXEGhHx\nYK72O6pmHddVI2ITSWuQdjTfl7Ox1i3Ss4BtaW3oKWQlSc+LiEWw9LjmFQu1XSs4l65bax1QO1dI\n2oBUwuzPwJ59G+ayK52bW7oWs6Fe7KyZL7LH7c4NqCUdA6wFXAQ8mbQg/4qIOHjir5ycJkAWn/WQ\ntBfp3e43I+JPJdseAv11XJ/O8sed5rSfpEsKzur0fB+4hbSBqKfEbMdBpPqeS0h17ZeQNp6UUCs4\n99et3ZmMdWu7qmsxrFauaLmKdFreFcBB0tKiSDNIA/p/ztl46dzc0rWYDfViZ818kT1ud25ADfy3\niHhd6/oISTl3TveXcltOiVJutUjaICLGu62VVUT8sHVZuo7r04FbJd1I2iHf61Pu42QXR8Qemdt4\nlKaW53pKxfKXRMT9BZuvEpwr1q21gkrHryHIFTVqXrcVzc0tnYrZjVqx80Iq5YsScbuLA+oVJT0p\nIh4AkPQUlhWyz6FWKbenkoLznq2ZhqUKlZw6RtIzSJsET4uI3xVoE0mHAO/rfz4i1sjY5kqkDQ97\ns+xW0tNI67Q+lrHdJzcPz5e0I+mI9faGnqzlriTtS1p3tyrpWNleu2vlbLdp40KWBeex3Ldqe1Sv\nbm3X1I5hpeNX1bKfBStMPJaiubmrMbtp40LqxM5q+aJE3O7igPoY4GpJC0nvzF4CfDhXYxVLua0+\nwWulytRs12wI3ImUnJ5KOgnq1My3cHcDXhxlD0P4TPPxloh4CJYG7LuBvchXZeQ60s9zBtB/S3aM\ndAs1p/eSbp39Z+Z2lpL0HOBw0t/ulcAhEfEXSS8HvhgRuTdu1apb2zVVY1jp+DUkZT9rKpqb6WDM\nHoLYWTxftGSP210cUF8PbASsQ/rlXVjiHSHlS7l9NSJukbR+xjYeV0TcJ2kBaa3YVsArgX0k/a+I\nOC1Ts9fTesdfyGsiYrki8c0GiA+Sitdn+VlHxItz/LtPwM+B/yr85uUrwOmkoDgX+FJT6mxr8ibg\nnip1azuoegyrFL9qlv2sqXRu7mLMrh07a+SLnuxxu4sD6mOA9m28EoNpKF/K7f3AB4DjWfZuuGeM\nFKSzknQY6WCCG0ilaQ6IiEckPYn0hzWlCam1BnE1Uk3TK1n+VlrONYiLx3syIpY0sx5ZSHo66Wf8\n7t7vsqRNSD//d0bEwxN9/RS4GrhF0n+Svte9DUw5Z8afEhG9v5vDJd0CHElaEzfuz2GKFa1b22FV\nY1jp+NVSs+xnTaVzcxdjdu3YWSNf9GSP250bUFdchjBeKbdsm0Ai4gPNx637X5O0ea52+9wObN6/\nPisiHpCUo97lRGsQc7tL0hYRcXH7SUlvJBXRz+V4UkH+B3pPRMSVkq4inQL1qLXkU+w9wMuA32du\np60/8N9UYk+ApO1Ih06069b+mhSg98ndftcMQQwrHb96iuaKYVEhN3cxZleJnS3F80XJuN25ATUs\nvY23kLK38fpLua1G2nRTw3ygxG3U3wJfkdSrs9l7N7pNRFw21Y1FxEWSNo6IX8LSuqpvJgWNU6a6\nvT7/CpytdDDAVaTNNK8mbYLYPmO7a8fy9UQBiIhjJP04Y7s9PwXuKnwLb1YzS9ibsZzZvs44s7Uv\n8FXgJuAc0mzLjyOixnrArisRw4rGr5ZhyhVFFc7NXYzZtWJnT418USxud25A3Xcb72sUuo0XET+U\n9FzgLcBdpDI1x+Ro629Qqoj8saTbWLeXaEzSEcB6wD80my8uBD4PbNkMtD+Uq+2I+I2kjYHtgJeS\n3v0eB/wgInJuoJo5wWslkvDfk27h3cjyt/Bylpx6IWljT/v3uHedbVNPROwKIOmlwOtIm2sOk/R7\n4IKIOCxHuzauEjGsaPzqGbJcUUzp3NzRmF0ldrYUzxcl43bnBtQUvo3XlF2aC7wNeB5pk8mqEbH2\nVLf1BJQqHn9TRJxXqC2AbVubTPYAvhcRnwAoMVsbEUtIxfq//3ifO4VukrR7RCxXAaApT3RNgfYf\nNdOSW+2NmBHxa0m/BRaSkv+bSH/fHlCXUyKGFY1fQ5orSiq+xKZrMbt27KRCvugpEbe7OKA+BzhK\n0uyI2E3SW4CfRsQtmW7j/R44G3hfRFwDIGnbDO0sR9LljJ90ZgAvytz2fs3D30sK0vql9ubAXGu2\n/tx6vB3w5dZ16aofpfwP4OuS9mfZbcs5pHWXb8nVaLOxZo+IOKG5nkcKljeR1qqNHEk7kGY4NiN9\nn39OqiE7PyLurNm3UVQrhlWMX1VyxRApnZtrqRKza6qZL0rG7S4OqOcDnwMObK7vIK2vedTGlyky\nj1Tn8ExJ5wDfytROv7mF2hlP7+jcW5uPswu1u6T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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41189f2050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#concatenate the dist and corr similarity metrics for the respective uids (use the index form rec_pivot_df)\n",
    "corr_sim = all_corr_s.loc[rec_pivot_df.index]\n",
    "dist_sim = all_dist_s.loc[rec_pivot_df.index]\n",
    "\n",
    "fig,(ax1,ax2) = plt.subplots(1,2,figsize=(12,8),sharey=True)\n",
    "\n",
    "#Calculate correlation-based user recommendation\n",
    "corr_pred = rec_pivot_df.apply(lambda x: x*corr_sim,axis=0).sum() / rec_pivot_df.apply(lambda x: x.notnull().mul(corr_sim)).sum()\n",
    "corr_pred.name = 'corr_pred'\n",
    "print 'Recommendations based on correlation'\n",
    "print corr_pred.sort_values(ascending=False)[0:5]\n",
    "\n",
    "#Create a df of top and bottom 5 recommended artists and plot them\n",
    "corr_pred_plot = corr_pred.sort_values(ascending=False)[0:5].append(corr_pred.sort_values(ascending=False)[-6:-1])\n",
    "corr_pred_plot.plot(kind='bar',ax=ax1)\n",
    "ax1.set_title('Correlation Similarity')\n",
    "ax1.set_xlabel('Artist')\n",
    "ax1.set_ylabel('Predicted Rating')\n",
    "\n",
    "#Calculate distance-based user recommendation\n",
    "dist_pred = rec_pivot_df.apply(lambda x: x*dist_sim,axis=0).sum() / rec_pivot_df.apply(lambda x: x.notnull().mul(dist_sim)).sum()\n",
    "dist_pred.name = 'dist_pred'\n",
    "print '\\nRecommendations based on distance'\n",
    "print dist_pred.sort_values(ascending=False)[0:5]\n",
    "\n",
    "#Create a df of top and bottom 5 recommended artists and plot them\n",
    "dist_pred_plot = dist_pred.sort_values(ascending=False)[0:5].append(dist_pred.sort_values(ascending=False)[-6:-1])\n",
    "dist_pred_plot.plot(kind='bar',color='g',ax=ax2)\n",
    "ax2.set_title('Distance Similarity')\n",
    "ax2.set_xlabel('Artist')\n",
    "ax2.set_ylabel('Predicted Rating')\n",
    "fig.suptitle('Top 5 and Bottom 5 Recommendations from',fontsize=14,fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weezer is the anti-Beyonce. Oh, and no one likes Creed.\n",
    "The correlation and distance based similarity metrics produced very similar top and bottom 5s, with 9 of 10 artists present in both. Weezer is the bottom of both recommendations, indicating that other users who rated like UID 2 would probably not like Weezer. Aaliyah, Beyonce, Lil' Flip, and Murphy Lee are the highest recommendations for UID 2.\n",
    "\n",
    "This concludes this notebook's exploration of user-based collaborative filtering for recommendations. Coming soon, I will demonstrate item-based collaborative filtering with the same dataset."
   ]
  }
 ],
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