Pitchfork_data_analysis_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pitchfork Music Reviews Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By: Chen Chen and Pouyan Chamanian\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import sqlite3 as sql\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from matplotlib import rcParams\n",
    "\n",
    "sb.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 20 ,15\n",
    "rcParams.update({'font.size': 20})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connecting to sqlite, reading data, assigning dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sql.connect('Downloads/database.sqlite')\n",
    "reviews = pd.read_sql_query(\"SELECT * FROM reviews\", conn)\n",
    "genres = pd.read_sql_query(\"SELECT * FROM genres\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewid</th>\n",
       "      <th>title</th>\n",
       "      <th>artist</th>\n",
       "      <th>url</th>\n",
       "      <th>score</th>\n",
       "      <th>best_new_music</th>\n",
       "      <th>author</th>\n",
       "      <th>author_type</th>\n",
       "      <th>pub_date</th>\n",
       "      <th>pub_weekday</th>\n",
       "      <th>pub_day</th>\n",
       "      <th>pub_month</th>\n",
       "      <th>pub_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22703</td>\n",
       "      <td>mezzanine</td>\n",
       "      <td>massive attack</td>\n",
       "      <td>http://pitchfork.com/reviews/albums/22703-mezz...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0</td>\n",
       "      <td>nate patrin</td>\n",
       "      <td>contributor</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22721</td>\n",
       "      <td>prelapsarian</td>\n",
       "      <td>krallice</td>\n",
       "      <td>http://pitchfork.com/reviews/albums/22721-prel...</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>contributor</td>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22659</td>\n",
       "      <td>all of them naturals</td>\n",
       "      <td>uranium club</td>\n",
       "      <td>http://pitchfork.com/reviews/albums/22659-all-...</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0</td>\n",
       "      <td>david glickman</td>\n",
       "      <td>contributor</td>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22661</td>\n",
       "      <td>first songs</td>\n",
       "      <td>kleenex, liliput</td>\n",
       "      <td>http://pitchfork.com/reviews/albums/22661-firs...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>jenn pelly</td>\n",
       "      <td>associate reviews editor</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22725</td>\n",
       "      <td>new start</td>\n",
       "      <td>taso</td>\n",
       "      <td>http://pitchfork.com/reviews/albums/22725-new-...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0</td>\n",
       "      <td>kevin lozano</td>\n",
       "      <td>tracks coordinator</td>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reviewid                 title            artist  \\\n",
       "0     22703             mezzanine    massive attack   \n",
       "1     22721          prelapsarian          krallice   \n",
       "2     22659  all of them naturals      uranium club   \n",
       "3     22661           first songs  kleenex, liliput   \n",
       "4     22725             new start              taso   \n",
       "\n",
       "                                                 url  score  best_new_music  \\\n",
       "0  http://pitchfork.com/reviews/albums/22703-mezz...    9.3               0   \n",
       "1  http://pitchfork.com/reviews/albums/22721-prel...    7.9               0   \n",
       "2  http://pitchfork.com/reviews/albums/22659-all-...    7.3               0   \n",
       "3  http://pitchfork.com/reviews/albums/22661-firs...    9.0               1   \n",
       "4  http://pitchfork.com/reviews/albums/22725-new-...    8.1               0   \n",
       "\n",
       "           author               author_type    pub_date  pub_weekday  pub_day  \\\n",
       "0     nate patrin               contributor  2017-01-08            6        8   \n",
       "1        zoe camp               contributor  2017-01-07            5        7   \n",
       "2  david glickman               contributor  2017-01-07            5        7   \n",
       "3      jenn pelly  associate reviews editor  2017-01-06            4        6   \n",
       "4    kevin lozano        tracks coordinator  2017-01-06            4        6   \n",
       "\n",
       "   pub_month  pub_year  \n",
       "0          1      2017  \n",
       "1          1      2017  \n",
       "2          1      2017  \n",
       "3          1      2017  \n",
       "4          1      2017  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewid</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22703</td>\n",
       "      <td>electronic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22721</td>\n",
       "      <td>metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22659</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22661</td>\n",
       "      <td>rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22725</td>\n",
       "      <td>electronic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   reviewid       genre\n",
       "0     22703  electronic\n",
       "1     22721       metal\n",
       "2     22659        rock\n",
       "3     22661        rock\n",
       "4     22725  electronic"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Artist Score Filter function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function which filters reviews dataframe based on score and artist name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>artist</th>\n",
       "      <th>score</th>\n",
       "      <th>best_new_music</th>\n",
       "      <th>author</th>\n",
       "      <th>author_type</th>\n",
       "      <th>pub_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>views</td>\n",
       "      <td>drake</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0</td>\n",
       "      <td>ryan dombal</td>\n",
       "      <td>senior editor</td>\n",
       "      <td>2016-05-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2282</th>\n",
       "      <td>if you're reading this it's too late</td>\n",
       "      <td>drake</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1</td>\n",
       "      <td>craig jenkins</td>\n",
       "      <td>contributor</td>\n",
       "      <td>2015-02-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>nothing was the same</td>\n",
       "      <td>drake</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1</td>\n",
       "      <td>jayson greene</td>\n",
       "      <td>senior editor</td>\n",
       "      <td>2013-09-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6049</th>\n",
       "      <td>take care</td>\n",
       "      <td>drake</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1</td>\n",
       "      <td>ryan dombal</td>\n",
       "      <td>senior editor</td>\n",
       "      <td>2011-11-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7719</th>\n",
       "      <td>thank me later</td>\n",
       "      <td>drake</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0</td>\n",
       "      <td>ryan dombal</td>\n",
       "      <td>senior editor</td>\n",
       "      <td>2010-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8853</th>\n",
       "      <td>so far gone</td>\n",
       "      <td>drake</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0</td>\n",
       "      <td>tom breihan</td>\n",
       "      <td>contributor</td>\n",
       "      <td>2009-06-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     title artist  score  best_new_music  \\\n",
       "853                                  views  drake    6.8               0   \n",
       "2282  if you're reading this it's too late  drake    8.3               1   \n",
       "3859                  nothing was the same  drake    8.6               1   \n",
       "6049                             take care  drake    8.6               1   \n",
       "7719                        thank me later  drake    8.4               0   \n",
       "8853                           so far gone  drake    7.4               0   \n",
       "\n",
       "             author    author_type    pub_date  \n",
       "853     ryan dombal  senior editor  2016-05-02  \n",
       "2282  craig jenkins    contributor  2015-02-17  \n",
       "3859  jayson greene  senior editor  2013-09-23  \n",
       "6049    ryan dombal  senior editor  2011-11-14  \n",
       "7719    ryan dombal  senior editor  2010-06-15  \n",
       "8853    tom breihan    contributor  2009-06-29  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def artistScoreFilter(score, artist):\n",
    "    reviews_filter = reviews.drop(['reviewid', 'url', 'pub_weekday','pub_month','pub_day','pub_year'], axis = 1)\n",
    "    results = reviews_filter.loc[(reviews_filter.score >= int(score))& (reviews_filter.artist == artist.lower())]\n",
    "    return results\n",
    "\n",
    "#Example output:\n",
    "artistScoreFilter(5, \"Drake\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Multiple Score function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function will show artists in descending order of mean score, filtered by number of albums.\n",
    "Can change number of top artists displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>reviewid</th>\n",
       "      <th>artist</th>\n",
       "      <th>avgscore</th>\n",
       "      <th>releases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21317</td>\n",
       "      <td>the velvet underground</td>\n",
       "      <td>9.457143</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18752</td>\n",
       "      <td>the beatles</td>\n",
       "      <td>8.890476</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21918</td>\n",
       "      <td>miles davis</td>\n",
       "      <td>8.730769</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20539</td>\n",
       "      <td>blur</td>\n",
       "      <td>8.585714</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21039</td>\n",
       "      <td>deerhunter</td>\n",
       "      <td>8.562500</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15961</td>\n",
       "      <td>tom waits</td>\n",
       "      <td>8.542857</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21197</td>\n",
       "      <td>beach house</td>\n",
       "      <td>8.533333</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16815</td>\n",
       "      <td>mission of burma</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21542</td>\n",
       "      <td>kanye west</td>\n",
       "      <td>8.425000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22248</td>\n",
       "      <td>scott walker</td>\n",
       "      <td>8.357143</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reviewid                  artist  avgscore  releases\n",
       "0     21317  the velvet underground  9.457143         7\n",
       "1     18752             the beatles  8.890476        21\n",
       "2     21918             miles davis  8.730769        13\n",
       "3     20539                    blur  8.585714         7\n",
       "4     21039              deerhunter  8.562500         8\n",
       "5     15961               tom waits  8.542857         7\n",
       "6     21197             beach house  8.533333         6\n",
       "7     16815        mission of burma  8.500000         6\n",
       "8     21542              kanye west  8.425000         8\n",
       "9     22248            scott walker  8.357143         7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def multipleScore(releases, displayAmt):\n",
    "    query = \"SELECT reviewid, artist, AVG(score) as avgscore, COUNT(artist) as releases FROM reviews GROUP BY artist HAVING COUNT(artist) > {} ORDER BY AVG(score) DESC LIMIT {}\".format(releases, displayAmt)\n",
    "    multartists = pd.read_sql_query(query, conn)\n",
    "    return multartists\n",
    "\n",
    "#Example output:\n",
    "multipleScore(5, 10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Top Artists by Genre function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function which displays top aritsts based on mean score, filtered by genre and number of albums.\n",
    "Can changed number of top artists displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist</th>\n",
       "      <th>avgscore</th>\n",
       "      <th>releases</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>miles davis</td>\n",
       "      <td>8.730769</td>\n",
       "      <td>13</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>john zorn</td>\n",
       "      <td>7.914286</td>\n",
       "      <td>7</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>matthew shipp</td>\n",
       "      <td>7.912500</td>\n",
       "      <td>8</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amon tobin</td>\n",
       "      <td>7.742857</td>\n",
       "      <td>7</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shining</td>\n",
       "      <td>7.620000</td>\n",
       "      <td>5</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>spring heel jack</td>\n",
       "      <td>7.340000</td>\n",
       "      <td>5</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>zs</td>\n",
       "      <td>7.314286</td>\n",
       "      <td>7</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>daedelus</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>9</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>thievery corporation</td>\n",
       "      <td>5.760000</td>\n",
       "      <td>5</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>unkle</td>\n",
       "      <td>5.140000</td>\n",
       "      <td>5</td>\n",
       "      <td>jazz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 artist  avgscore  releases genre\n",
       "0           miles davis  8.730769        13  jazz\n",
       "1             john zorn  7.914286         7  jazz\n",
       "2         matthew shipp  7.912500         8  jazz\n",
       "3            amon tobin  7.742857         7  jazz\n",
       "4               shining  7.620000         5  jazz\n",
       "5      spring heel jack  7.340000         5  jazz\n",
       "6                    zs  7.314286         7  jazz\n",
       "7              daedelus  6.600000         9  jazz\n",
       "8  thievery corporation  5.760000         5  jazz\n",
       "9                 unkle  5.140000         5  jazz"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def topArtistsinGenre(genre, releases, displayAmt):\n",
    "    query = \"SELECT artist, AVG(score) as avgscore, COUNT(artist) as releases, genre FROM reviews LEFT JOIN genres USING(reviewid) WHERE genre == \\'{}\\' GROUP BY artist HAVING COUNT(artist) > {} ORDER BY AVG(score) DESC LIMIT {}\".format(genre,releases, displayAmt)\n",
    "    withGenre = pd.read_sql_query(query, conn)\n",
    "    return withGenre\n",
    "\n",
    "#Example output:\n",
    "topArtistsinGenre('jazz', 4, 10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating function to visualize artist album scores over time, takes artist name as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def ArtistScoreTimeVis(name):\n",
    "    artist = reviews.loc[reviews['artist']==name.lower()]\n",
    "    x = artist['pub_date'].iloc[::-1]\n",
    "    y = artist['score'].iloc[::-1]\n",
    " \n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes([.1,.1,1,1])\n",
    "    ax.set_ylim([0,11])\n",
    "    ax.set_ylabel('Album Score')\n",
    "    ax.set_title(name + \" album review scores over time\")\n",
    "    \n",
    "    ax.plot(x,y,marker = '+', mew = 15)\n",
    "    plt.show()\n",
    "\n",
    "#Example output:\n",
    "ArtistScoreTimeVis('drake')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Visualization for Top Artist Averages and Genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function will take minimum number of artist releases and display amount, and will return visualization of genre distribution for top artists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    reviewid                  artist  avgscore  releases         genre\n",
      "0      21317  the velvet underground  9.457143         7          rock\n",
      "1      21844  prince, the revolution  9.300000         6       pop/r&b\n",
      "2      12426              max tundra  9.000000         6    electronic\n",
      "3      20747                pavement  9.000000         5          rock\n",
      "4      18752             the beatles  8.890476        21          rock\n",
      "5      20148          sleater-kinney  8.860000         5          rock\n",
      "6      22255          caetano veloso  8.840000         5        global\n",
      "7      21097           joanna newsom  8.760000         5  folk/country\n",
      "8      21918             miles davis  8.730769        13          jazz\n",
      "9      14755  dexys midnight runners  8.633333         6    electronic\n",
      "10     20539                    blur  8.585714         7          rock\n",
      "11     21039              deerhunter  8.562500         8          rock\n",
      "12     15961               tom waits  8.542857         7          rock\n",
      "13     18667             arcade fire  8.540000         5          rock\n",
      "14     21197             beach house  8.533333         6          rock\n",
      "15     19923       captain beefheart  8.500000         6  experimental\n",
      "16     13742               kraftwerk  8.500000         6    electronic\n",
      "17     16815        mission of burma  8.500000         6         other\n",
      "18     20985               fka twigs  8.466667         6    electronic\n",
      "19     15883                   luomo  8.440000         5    electronic\n",
      "20     20991              panda bear  8.440000         5    electronic\n",
      "21     21542              kanye west  8.425000         8           rap\n",
      "22     15959              john fahey  8.400000         5  folk/country\n",
      "23     13585                 luciano  8.400000         6    electronic\n",
      "24     14083             galaxie 500  8.366667         6  experimental\n",
      "25     19798          perfume genius  8.366667         6  experimental\n",
      "26      2862                  the ex  8.366667         9  experimental\n",
      "27     16503           spiritualized  8.360000        10  experimental\n",
      "28     22248            scott walker  8.357143         7          rock\n",
      "29     19441      circulatory system  8.350000         8  experimental\n",
      "30     22014             grace jones  8.333333         6    electronic\n",
      "31     16108       throbbing gristle  8.333333         9    electronic\n",
      "32     22642           leonard cohen  8.325000         8          rock\n",
      "33     18438                califone  8.318182        11          rock\n",
      "34     22402            led zeppelin  8.316667         6          rock\n",
      "35     19824                drexciya  8.300000         5    electronic\n",
      "36     19772               fela kuti  8.300000         5        global\n",
      "37     19291                 fennesz  8.300000         9  experimental\n",
      "38     19945           blut aus nord  8.300000         6         metal\n",
      "39     22420                bon iver  8.280000         5          rock\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def AvgArtistScoreVis(releases, top_number):\n",
    "    query = \"SELECT reviewid, artist, AVG(score) as avgscore, COUNT(artist) as releases, genre FROM reviews LEFT JOIN genres USING(reviewid) GROUP BY artist HAVING COUNT(artist) > {} ORDER BY AVG(score) DESC LIMIT %s\".format(releases) % top_number\n",
    "    withGenre = pd.read_sql_query(query, conn)\n",
    "    withGenre_withOther = withGenre.fillna({\"genre\": \"other\"})\n",
    "    print withGenre_withOther\n",
    "    genre_count = Counter(withGenre_withOther['genre'])\n",
    "    z = genre_count.values()\n",
    "    artist_names = genre_count.keys()\n",
    "    plt.bar(artist_names, z)\n",
    "    plt.ylabel(\"Number of Reviews\")\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "#Example output:\n",
    "AvgArtistScoreVis(4, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking at Reviewer Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Average Reviewer Score function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function which takes minimum reviews written by reviewer and display amount, and returns list of top avg scores by reviewers that meet minimum review number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewid</th>\n",
       "      <th>author</th>\n",
       "      <th>avgscore</th>\n",
       "      <th>articles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22706</td>\n",
       "      <td>seth colter walls</td>\n",
       "      <td>7.724138</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15413</td>\n",
       "      <td>scott plagenhoef</td>\n",
       "      <td>7.669106</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22674</td>\n",
       "      <td>mark richardson</td>\n",
       "      <td>7.578571</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22666</td>\n",
       "      <td>philip sherburne</td>\n",
       "      <td>7.565550</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15221</td>\n",
       "      <td>dominique leone</td>\n",
       "      <td>7.495604</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21676</td>\n",
       "      <td>brandon stosuy</td>\n",
       "      <td>7.493750</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22715</td>\n",
       "      <td>marc masters</td>\n",
       "      <td>7.468910</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22694</td>\n",
       "      <td>andy beta</td>\n",
       "      <td>7.468800</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22326</td>\n",
       "      <td>grayson haver currin</td>\n",
       "      <td>7.391176</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22157</td>\n",
       "      <td>david drake</td>\n",
       "      <td>7.389394</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reviewid                author  avgscore  articles\n",
       "0     22706     seth colter walls  7.724138        58\n",
       "1     15413      scott plagenhoef  7.669106       123\n",
       "2     22674       mark richardson  7.578571       476\n",
       "3     22666      philip sherburne  7.565550       209\n",
       "4     15221       dominique leone  7.495604       273\n",
       "5     21676        brandon stosuy  7.493750       288\n",
       "6     22715          marc masters  7.468910       312\n",
       "7     22694             andy beta  7.468800       250\n",
       "8     22326  grayson haver currin  7.391176       136\n",
       "9     22157           david drake  7.389394        66"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def avgReviewerScore(min_reviews, displayAmt):\n",
    "    query = \"SELECT reviewid, author, AVG(score) as avgscore, COUNT(author) as articles FROM reviews GROUP BY author HAVING COUNT(author)>= {} ORDER BY AVG(score) DESC LIMIT {}\".format(min_reviews, displayAmt) \n",
    "    avgreviewer = pd.read_sql_query(query, conn)\n",
    "    return avgreviewer\n",
    "\n",
    "#Example output:\n",
    "avgReviewerScore(50, 10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Dataframe showing authors and their mean scores and number of articles, grouped by genre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviewid</th>\n",
       "      <th>author</th>\n",
       "      <th>avgscore</th>\n",
       "      <th>genre</th>\n",
       "      <th>articles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21203</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>7.242857</td>\n",
       "      <td>None</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21950</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>7.261538</td>\n",
       "      <td>electronic</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21753</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>7.320690</td>\n",
       "      <td>experimental</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21622</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>folk/country</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19984</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>8.550000</td>\n",
       "      <td>global</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20652</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>jazz</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16908</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>metal</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14536</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11134</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>rap</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20949</td>\n",
       "      <td>aaron leitko</td>\n",
       "      <td>7.035185</td>\n",
       "      <td>rock</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19947</td>\n",
       "      <td>abby garnett</td>\n",
       "      <td>7.150000</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21050</td>\n",
       "      <td>abby garnett</td>\n",
       "      <td>6.933333</td>\n",
       "      <td>electronic</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19321</td>\n",
       "      <td>abby garnett</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22138</td>\n",
       "      <td>abigail covington</td>\n",
       "      <td>7.050000</td>\n",
       "      <td>rap</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20968</td>\n",
       "      <td>abigail garnett</td>\n",
       "      <td>7.175000</td>\n",
       "      <td>electronic</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6794</td>\n",
       "      <td>adam dlugacz</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>metal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6794</td>\n",
       "      <td>adam dlugacz</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>rock</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12861</td>\n",
       "      <td>adam moerder</td>\n",
       "      <td>6.481818</td>\n",
       "      <td>None</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13207</td>\n",
       "      <td>adam moerder</td>\n",
       "      <td>5.878788</td>\n",
       "      <td>electronic</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12659</td>\n",
       "      <td>adam moerder</td>\n",
       "      <td>6.729630</td>\n",
       "      <td>experimental</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10942</td>\n",
       "      <td>adam moerder</td>\n",
       "      <td>6.437500</td>\n",
       "      <td>folk/country</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5427</td>\n",
       "      <td>adam moerder</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>jazz</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11038</td>\n",
       "      <td>adam moerder</td>\n",
       "      <td>5.866667</td>\n",
       "      <td>metal</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12403</td>\n",
       "      <td>adam moerder</td>\n",
       "      <td>5.878571</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>13207</td>\n",
       "      <td>adam moerder</td>\n",
       "      <td>6.149102</td>\n",
       "      <td>rock</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4109</td>\n",
       "      <td>adam ohler</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>rock</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12790</td>\n",
       "      <td>adrienne day</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7209</td>\n",
       "      <td>al shipley</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5424</td>\n",
       "      <td>al shipley</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>electronic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8582</td>\n",
       "      <td>al shipley</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>experimental</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1842</th>\n",
       "      <td>4032</td>\n",
       "      <td>zach baron</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>jazz</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>11986</td>\n",
       "      <td>zach baron</td>\n",
       "      <td>7.065217</td>\n",
       "      <td>metal</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844</th>\n",
       "      <td>10522</td>\n",
       "      <td>zach baron</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>10522</td>\n",
       "      <td>zach baron</td>\n",
       "      <td>5.633333</td>\n",
       "      <td>rap</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>12391</td>\n",
       "      <td>zach baron</td>\n",
       "      <td>6.774194</td>\n",
       "      <td>rock</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>725</td>\n",
       "      <td>zach hammerman</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>2259</td>\n",
       "      <td>zach hooker</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>8004</td>\n",
       "      <td>zach hooker</td>\n",
       "      <td>6.760000</td>\n",
       "      <td>electronic</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>8597</td>\n",
       "      <td>zach hooker</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>experimental</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851</th>\n",
       "      <td>8004</td>\n",
       "      <td>zach hooker</td>\n",
       "      <td>7.675000</td>\n",
       "      <td>jazz</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>8004</td>\n",
       "      <td>zach hooker</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>5452</td>\n",
       "      <td>zach hooker</td>\n",
       "      <td>7.371429</td>\n",
       "      <td>rock</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>18461</td>\n",
       "      <td>zach kelly</td>\n",
       "      <td>6.835294</td>\n",
       "      <td>None</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>18920</td>\n",
       "      <td>zach kelly</td>\n",
       "      <td>6.658491</td>\n",
       "      <td>electronic</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>18296</td>\n",
       "      <td>zach kelly</td>\n",
       "      <td>6.488889</td>\n",
       "      <td>experimental</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>14462</td>\n",
       "      <td>zach kelly</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>global</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1858</th>\n",
       "      <td>18592</td>\n",
       "      <td>zach kelly</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>metal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1859</th>\n",
       "      <td>18486</td>\n",
       "      <td>zach kelly</td>\n",
       "      <td>6.615000</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>18881</td>\n",
       "      <td>zach kelly</td>\n",
       "      <td>6.307143</td>\n",
       "      <td>rap</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>21063</td>\n",
       "      <td>zach kelly</td>\n",
       "      <td>6.487179</td>\n",
       "      <td>rock</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>5603</td>\n",
       "      <td>zach vowell</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>21413</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>7.016667</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>22027</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>6.225000</td>\n",
       "      <td>electronic</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>21879</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>7.357143</td>\n",
       "      <td>experimental</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>21250</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>folk/country</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>22015</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>jazz</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>22721</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>7.217391</td>\n",
       "      <td>metal</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>22471</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>pop/r&amp;b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>21879</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>7.466667</td>\n",
       "      <td>rap</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>22602</td>\n",
       "      <td>zoe camp</td>\n",
       "      <td>6.497222</td>\n",
       "      <td>rock</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1872 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      reviewid             author  avgscore         genre  articles\n",
       "0        21203       aaron leitko  7.242857          None        14\n",
       "1        21950       aaron leitko  7.261538    electronic        13\n",
       "2        21753       aaron leitko  7.320690  experimental        29\n",
       "3        21622       aaron leitko  7.100000  folk/country         3\n",
       "4        19984       aaron leitko  8.550000        global         2\n",
       "5        20652       aaron leitko  7.600000          jazz         5\n",
       "6        16908       aaron leitko  6.200000         metal         6\n",
       "7        14536       aaron leitko  7.300000       pop/r&b         1\n",
       "8        11134       aaron leitko  8.100000           rap         1\n",
       "9        20949       aaron leitko  7.035185          rock        54\n",
       "10       19947       abby garnett  7.150000          None         2\n",
       "11       21050       abby garnett  6.933333    electronic         3\n",
       "12       19321       abby garnett  7.000000       pop/r&b         1\n",
       "13       22138  abigail covington  7.050000           rap         2\n",
       "14       20968    abigail garnett  7.175000    electronic         4\n",
       "15        6794       adam dlugacz  6.300000         metal         1\n",
       "16        6794       adam dlugacz  6.300000          rock         1\n",
       "17       12861       adam moerder  6.481818          None        11\n",
       "18       13207       adam moerder  5.878788    electronic        66\n",
       "19       12659       adam moerder  6.729630  experimental        27\n",
       "20       10942       adam moerder  6.437500  folk/country         8\n",
       "21        5427       adam moerder  2.700000          jazz         1\n",
       "22       11038       adam moerder  5.866667         metal         6\n",
       "23       12403       adam moerder  5.878571       pop/r&b        14\n",
       "24       13207       adam moerder  6.149102          rock       167\n",
       "25        4109         adam ohler  4.200000          rock         1\n",
       "26       12790       adrienne day  6.200000          None         1\n",
       "27        7209         al shipley  6.300000          None         2\n",
       "28        5424         al shipley  5.100000    electronic         1\n",
       "29        8582         al shipley  5.800000  experimental         1\n",
       "...        ...                ...       ...           ...       ...\n",
       "1842      4032         zach baron  6.700000          jazz         1\n",
       "1843     11986         zach baron  7.065217         metal        23\n",
       "1844     10522         zach baron  7.100000       pop/r&b         3\n",
       "1845     10522         zach baron  5.633333           rap         3\n",
       "1846     12391         zach baron  6.774194          rock        62\n",
       "1847       725     zach hammerman  7.500000       pop/r&b         1\n",
       "1848      2259        zach hooker  7.000000          None         1\n",
       "1849      8004        zach hooker  6.760000    electronic        10\n",
       "1850      8597        zach hooker  5.333333  experimental         3\n",
       "1851      8004        zach hooker  7.675000          jazz         4\n",
       "1852      8004        zach hooker  7.400000       pop/r&b         2\n",
       "1853      5452        zach hooker  7.371429          rock        21\n",
       "1854     18461         zach kelly  6.835294          None        17\n",
       "1855     18920         zach kelly  6.658491    electronic        53\n",
       "1856     18296         zach kelly  6.488889  experimental         9\n",
       "1857     14462         zach kelly  7.800000        global         1\n",
       "1858     18592         zach kelly  5.666667         metal         3\n",
       "1859     18486         zach kelly  6.615000       pop/r&b        20\n",
       "1860     18881         zach kelly  6.307143           rap        14\n",
       "1861     21063         zach kelly  6.487179          rock        78\n",
       "1862      5603        zach vowell  7.100000       pop/r&b         1\n",
       "1863     21413           zoe camp  7.016667          None         6\n",
       "1864     22027           zoe camp  6.225000    electronic         4\n",
       "1865     21879           zoe camp  7.357143  experimental         7\n",
       "1866     21250           zoe camp  6.800000  folk/country         1\n",
       "1867     22015           zoe camp  7.600000          jazz         1\n",
       "1868     22721           zoe camp  7.217391         metal        23\n",
       "1869     22471           zoe camp  5.800000       pop/r&b         1\n",
       "1870     21879           zoe camp  7.466667           rap         3\n",
       "1871     22602           zoe camp  6.497222          rock        36\n",
       "\n",
       "[1872 rows x 5 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def reviewerGenres():\n",
    "    query = \"SELECT reviewid, author, AVG(score) as avgscore, genre,COUNT(author) as articles FROM (SELECT reviewid, author, score, genre FROM reviews LEFT JOIN genres USING (reviewid) ORDER BY author) GROUP BY author, genre\"\n",
    "    revGenres = pd.read_sql_query( query, conn)\n",
    "    return revGenres\n",
    "\n",
    "reviewerGenres()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Genre Distribution for Top Authors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function takes the display amount and returns a visulization of genre distribution for each top author."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def TopAuthorsGenreDist(num_authors):\n",
    "    query = \"SELECT reviewid, author, AVG(score) as avgscore, genre,COUNT(author) as articles FROM (SELECT reviewid, author, score, genre FROM reviews LEFT JOIN genres USING (reviewid) ORDER BY author) GROUP BY author, genre\"\n",
    "    revGenres = pd.read_sql_query(query, conn)\n",
    "    query1 = \"SELECT reviewid, author, title, artist, score, COUNT(author) as articles, pub_date, genre FROM reviews LEFT JOIN genres USING(reviewid) GROUP BY author ORDER BY COUNT(author) DESC LIMIT %s\" % num_authors\n",
    "    authors_byArticles = pd.read_sql_query(query1,conn)\n",
    "\n",
    "    top_authors = authors_byArticles['author'].tolist()\n",
    "\n",
    "    revGenres = revGenres.sort_values(by = ['articles'], ascending = [False])\n",
    "\n",
    "    authors_genre_pivot = revGenres.pivot(index = 'genre',columns = 'author', values = 'articles')\n",
    "    for author in authors_genre_pivot:\n",
    "        if author not in top_authors:\n",
    "            authors_genre_pivot = authors_genre_pivot.drop([author], axis=1)\n",
    "\n",
    "\n",
    "    x = list(authors_genre_pivot.columns)\n",
    "    y = list(authors_genre_pivot.loc['rock'])\n",
    "    z = list(authors_genre_pivot.loc['experimental'])\n",
    "    k = list(authors_genre_pivot.loc['rap'])\n",
    "    l = list(authors_genre_pivot.loc['electronic'])\n",
    "    m = list(authors_genre_pivot.loc['jazz'])\n",
    "    n = list(authors_genre_pivot.loc['metal'])\n",
    "    o = list(authors_genre_pivot.loc['global'])\n",
    "    p = list(authors_genre_pivot.loc['folk/country'])\n",
    "    q = list(authors_genre_pivot.loc['pop/r&b'])\n",
    "\n",
    "\n",
    "    N = len(x)\n",
    "    ind = np.arange(N)\n",
    "    width = 0.1\n",
    "\n",
    "    ax = plt.subplot(111)\n",
    "    bar1 = ax.bar(ind - width*4, y, width, color='b')\n",
    "    bar2 = ax.bar(ind - width*3, z, width, color='g', align='center')\n",
    "    bar3 = ax.bar(ind - width*2, k, width, color='r', align='center')\n",
    "    bar4 = ax.bar(ind - width, l, width, color='c', align='center')\n",
    "    bar5 = ax.bar(ind, m, width, color='m', align='center')\n",
    "    bar6 = ax.bar(ind + width, n, width, color='y', align='center')\n",
    "    bar7 = ax.bar(ind + width*2, o, width, color='k', align='center')\n",
    "    bar8 = ax.bar(ind + width*3, p, width, color='orange', align='center')\n",
    "    bar9 = ax.bar(ind + width*4, q, width, color='gray', align='center')\n",
    "\n",
    "\n",
    "    ax.set_ylabel('Number of Reviews')\n",
    "    ax.set_xlabel('Authors')\n",
    "    ax.set_title('Number of Reviews by Genre')\n",
    "    ax.set_xticks(ind + width / 3)\n",
    "    ax.set_xticklabels((x))\n",
    "\n",
    "    ax.legend((bar1[0], bar2[0], bar3[0], bar4[0], bar5[0], bar6[0], bar7[0], bar8[0], bar9[0]), ('rock', 'experimental', 'rap', 'electronic', 'jazz','metal','global','folk/country','pop/r&b'))\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "#Example output:\n",
    "TopAuthorsGenreDist(4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}