01_Goodreads.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goodreads Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploratory data analysis of book reviews on [Goodreads](https://www.goodreads.com/)\n",
    "\n",
    "### Preliminary Questions\n",
    "* Which books have the highest ratings? \n",
    "* What does the distribution of ratings look like? \n",
    "* Which have been rated the most? \n",
    "* Do older books have higher ratings and more ratings than newer books?\n",
    "\n",
    "#### Other Questions to Consider (need new dataset)\n",
    "More F or M users? Do F or M authors receive higher ratings/more ratings? Do F users give F authors higher ratings (compared to F-M, M-F, M-M)? Same questions for genres. Same questions for years (do F or M rate older books higher/more often)? Age (older rate books differently)? Are older people leaving more reviews?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Read Data \n",
    "from CSV of 6000 books scraped from Goodreads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>rating</th>\n",
       "      <th>review_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>booktype</th>\n",
       "      <th>author_url</th>\n",
       "      <th>year</th>\n",
       "      <th>genre_urls</th>\n",
       "      <th>dir</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.40</td>\n",
       "      <td>136455</td>\n",
       "      <td>0439023483</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/153394.S...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>/genres/young-adult|/genres/science-fiction|/g...</td>\n",
       "      <td>dir01/2767052-the-hunger-games.html</td>\n",
       "      <td>2958974</td>\n",
       "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.41</td>\n",
       "      <td>16648</td>\n",
       "      <td>0439358078</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/1077326....</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>/genres/fantasy|/genres/young-adult|/genres/fi...</td>\n",
       "      <td>dir01/2.Harry_Potter_and_the_Order_of_the_Phoe...</td>\n",
       "      <td>1284478</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.56</td>\n",
       "      <td>85746</td>\n",
       "      <td>0316015849</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/941441.S...</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>/genres/young-adult|/genres/fantasy|/genres/ro...</td>\n",
       "      <td>dir01/41865.Twilight.html</td>\n",
       "      <td>2579564</td>\n",
       "      <td>Twilight (Twilight, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.23</td>\n",
       "      <td>47906</td>\n",
       "      <td>0061120081</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/1825.Har...</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>/genres/classics|/genres/fiction|/genres/histo...</td>\n",
       "      <td>dir01/2657.To_Kill_a_Mockingbird.html</td>\n",
       "      <td>2078123</td>\n",
       "      <td>To Kill a Mockingbird</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.23</td>\n",
       "      <td>34772</td>\n",
       "      <td>0679783261</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/1265.Jan...</td>\n",
       "      <td>1813.0</td>\n",
       "      <td>/genres/classics|/genres/fiction|/genres/roman...</td>\n",
       "      <td>dir01/1885.Pride_and_Prejudice.html</td>\n",
       "      <td>1388992</td>\n",
       "      <td>Pride and Prejudice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating review_count        isbn         booktype  \\\n",
       "0    4.40       136455  0439023483  good_reads:book   \n",
       "1    4.41        16648  0439358078  good_reads:book   \n",
       "2    3.56        85746  0316015849  good_reads:book   \n",
       "3    4.23        47906  0061120081  good_reads:book   \n",
       "4    4.23        34772  0679783261  good_reads:book   \n",
       "\n",
       "                                          author_url    year  \\\n",
       "0  https://www.goodreads.com/author/show/153394.S...  2008.0   \n",
       "1  https://www.goodreads.com/author/show/1077326....  2003.0   \n",
       "2  https://www.goodreads.com/author/show/941441.S...  2005.0   \n",
       "3  https://www.goodreads.com/author/show/1825.Har...  1960.0   \n",
       "4  https://www.goodreads.com/author/show/1265.Jan...  1813.0   \n",
       "\n",
       "                                          genre_urls  \\\n",
       "0  /genres/young-adult|/genres/science-fiction|/g...   \n",
       "1  /genres/fantasy|/genres/young-adult|/genres/fi...   \n",
       "2  /genres/young-adult|/genres/fantasy|/genres/ro...   \n",
       "3  /genres/classics|/genres/fiction|/genres/histo...   \n",
       "4  /genres/classics|/genres/fiction|/genres/roman...   \n",
       "\n",
       "                                                 dir rating_count  \\\n",
       "0                dir01/2767052-the-hunger-games.html      2958974   \n",
       "1  dir01/2.Harry_Potter_and_the_Order_of_the_Phoe...      1284478   \n",
       "2                          dir01/41865.Twilight.html      2579564   \n",
       "3              dir01/2657.To_Kill_a_Mockingbird.html      2078123   \n",
       "4                dir01/1885.Pride_and_Prejudice.html      1388992   \n",
       "\n",
       "                                                name  \n",
       "0            The Hunger Games (The Hunger Games, #1)  \n",
       "1  Harry Potter and the Order of the Phoenix (Har...  \n",
       "2                            Twilight (Twilight, #1)  \n",
       "3                              To Kill a Mockingbird  \n",
       "4                                Pride and Prejudice  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"./data/goodreads.csv\", header=None,\n",
    "               names=[\"rating\", 'review_count', 'isbn', 'booktype','author_url', 'year', \n",
    "                      'genre_urls', 'dir','rating_count', 'name'],\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df contains 6000 rows and 10 columns. Each row shows aggregate information for 1 book, including the average rating, total number of reviews, genre(s), and year published."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Cleaning and Prep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. Remove rows with null values \n",
    "After further exploration of **booktype**, I found that there were some rows containing None and null values. These should be removed from my analysis."
   ]
  },
  {
   "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>rating</th>\n",
       "      <th>review_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>booktype</th>\n",
       "      <th>author_url</th>\n",
       "      <th>year</th>\n",
       "      <th>genre_urls</th>\n",
       "      <th>dir</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3643</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dir37/9658936-harry-potter.html</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5282</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dir53/113138.The_Winner.html</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rating review_count  isbn booktype author_url  year genre_urls  \\\n",
       "3643     NaN         None  None     None       None   NaN        NaN   \n",
       "5282     NaN         None  None     None       None   NaN        NaN   \n",
       "\n",
       "                                  dir rating_count  name  \n",
       "3643  dir37/9658936-harry-potter.html         None  None  \n",
       "5282     dir53/113138.The_Winner.html         None  None  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"booktype\"] != \"good_reads:book\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# remove null rows\n",
    "df = df.loc[df[\"isbn\"] != \"None\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5998, 10)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data frame now contains 5998 rows after removing the 2 null rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. Remove rows with null years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5993, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.year.notnull()]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the null years removes an additional 5 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c. Convert data types\n",
    "We can now convert year to an int and will also convert counts to int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"year\"]=df[\"year\"].astype(int)\n",
    "df[\"review_count\"] = df[\"review_count\"].astype(int)\n",
    "df[\"rating_count\"] = df[\"rating_count\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d. Remove books with 0 reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.loc[df[\"review_count\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5985, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That removed another 8 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Querying and Initial Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. Explore rating distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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llVizZk0cOnQoIkbn3BhuPEbb3IiImD59elx//fXR0NAQ06dPj8suu6zu86MmwXz33Xdj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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d08ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.rating)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.041191311612362"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rating.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hcDhpzzVaBh5Dn2XdtW0sSvQ95mslXjLnUVZWNui+hEJdVlamn/zkJ/rWt76lf//737p5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\ngLR+Ys0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110f7d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"whitegrid\"):\n",
    "    df.rating.hist(bins=20, alpha=0.8);\n",
    "    sns.despine(left=True, bottom=True, right=True)\n",
    "    plt.axvline(df.rating.mean(), color='r')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the ratings are just above 4 — the mean is 4.04. <br>What is the percentage of ratings above/below 4. How many are less than 3? How many are a perfect 5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56274018379281532"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df.rating > 4) / df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df.rating < 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df.rating == 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. Books with the highest/lowest ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lowest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating                                                          2\n",
       "review_count                                                  368\n",
       "isbn                                                   0983650322\n",
       "booktype                                          good_reads:book\n",
       "author_url      https://www.goodreads.com/author/show/9414.Vic...\n",
       "year                                                         2011\n",
       "genre_urls      /genres/young-adult|/genres/science-fiction|/g...\n",
       "dir                            dir38/12393909-revealing-eden.html\n",
       "rating_count                                                  688\n",
       "name                         Revealing Eden (Save the Pearls, #1)\n",
       "Name: 3738, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['rating'].idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating                                                          5\n",
       "review_count                                                   28\n",
       "isbn                                                          NaN\n",
       "booktype                                          good_reads:book\n",
       "author_url      https://www.goodreads.com/author/show/6467808....\n",
       "year                                                         2014\n",
       "genre_urls                       /genres/poetry|/genres/childrens\n",
       "dir                dir18/22204746-an-elephant-is-on-my-house.html\n",
       "rating_count                                                   64\n",
       "name                                   An Elephant Is On My House\n",
       "Name: 1718, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['rating'].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 4 books in the sample have average ratings under 3. The lowest rated book is *Revealing Eden*. Also in the bottom 4, no surprise, is Hitler's *Mein Kampf*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>rating</th>\n",
       "      <th>review_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>booktype</th>\n",
       "      <th>author_url</th>\n",
       "      <th>year</th>\n",
       "      <th>genre_urls</th>\n",
       "      <th>dir</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2609</th>\n",
       "      <td>2.90</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/7707820....</td>\n",
       "      <td>2013</td>\n",
       "      <td>/genres/romance|/genres/realistic-fiction|/gen...</td>\n",
       "      <td>dir27/19546932-how-to-be-a-perfect-girl.html</td>\n",
       "      <td>31</td>\n",
       "      <td>How To Be A Perfect Girl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3738</th>\n",
       "      <td>2.00</td>\n",
       "      <td>368</td>\n",
       "      <td>0983650322</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/9414.Vic...</td>\n",
       "      <td>2011</td>\n",
       "      <td>/genres/young-adult|/genres/science-fiction|/g...</td>\n",
       "      <td>dir38/12393909-revealing-eden.html</td>\n",
       "      <td>688</td>\n",
       "      <td>Revealing Eden (Save the Pearls, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5844</th>\n",
       "      <td>2.97</td>\n",
       "      <td>1399</td>\n",
       "      <td>0395083621</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/30691.Ad...</td>\n",
       "      <td>1925</td>\n",
       "      <td>/genres/history|/genres/non-fiction|/genres/bi...</td>\n",
       "      <td>dir59/54270.Mein_Kampf.html</td>\n",
       "      <td>12417</td>\n",
       "      <td>Mein Kampf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5979</th>\n",
       "      <td>2.77</td>\n",
       "      <td>800</td>\n",
       "      <td>0060988649</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/7025.Gre...</td>\n",
       "      <td>2001</td>\n",
       "      <td>/genres/fantasy|/genres/fiction|/genres/myster...</td>\n",
       "      <td>dir60/24929.Lost.html</td>\n",
       "      <td>11128</td>\n",
       "      <td>Lost</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rating  review_count        isbn         booktype  \\\n",
       "2609    2.90             8         NaN  good_reads:book   \n",
       "3738    2.00           368  0983650322  good_reads:book   \n",
       "5844    2.97          1399  0395083621  good_reads:book   \n",
       "5979    2.77           800  0060988649  good_reads:book   \n",
       "\n",
       "                                             author_url  year  \\\n",
       "2609  https://www.goodreads.com/author/show/7707820....  2013   \n",
       "3738  https://www.goodreads.com/author/show/9414.Vic...  2011   \n",
       "5844  https://www.goodreads.com/author/show/30691.Ad...  1925   \n",
       "5979  https://www.goodreads.com/author/show/7025.Gre...  2001   \n",
       "\n",
       "                                             genre_urls  \\\n",
       "2609  /genres/romance|/genres/realistic-fiction|/gen...   \n",
       "3738  /genres/young-adult|/genres/science-fiction|/g...   \n",
       "5844  /genres/history|/genres/non-fiction|/genres/bi...   \n",
       "5979  /genres/fantasy|/genres/fiction|/genres/myster...   \n",
       "\n",
       "                                               dir  rating_count  \\\n",
       "2609  dir27/19546932-how-to-be-a-perfect-girl.html            31   \n",
       "3738            dir38/12393909-revealing-eden.html           688   \n",
       "5844                   dir59/54270.Mein_Kampf.html         12417   \n",
       "5979                         dir60/24929.Lost.html         11128   \n",
       "\n",
       "                                      name  \n",
       "2609              How To Be A Perfect Girl  \n",
       "3738  Revealing Eden (Save the Pearls, #1)  \n",
       "5844                            Mein Kampf  \n",
       "5979                                  Lost  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"rating < 3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 books have a perfect rating of 5. These have very low review counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>rating</th>\n",
       "      <th>review_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>booktype</th>\n",
       "      <th>author_url</th>\n",
       "      <th>year</th>\n",
       "      <th>genre_urls</th>\n",
       "      <th>dir</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>5.0</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/6467808....</td>\n",
       "      <td>2014</td>\n",
       "      <td>/genres/poetry|/genres/childrens</td>\n",
       "      <td>dir18/22204746-an-elephant-is-on-my-house.html</td>\n",
       "      <td>64</td>\n",
       "      <td>An Elephant Is On My House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1300589469</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/6906561....</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dir22/17287259-a-book-about-absolutely-nothing...</td>\n",
       "      <td>63</td>\n",
       "      <td>A Book About Absolutely Nothing.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5564</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/7738947....</td>\n",
       "      <td>2014</td>\n",
       "      <td>/genres/romance|/genres/new-adult</td>\n",
       "      <td>dir56/21902777-untainted.html</td>\n",
       "      <td>14</td>\n",
       "      <td>Untainted (Photographer Trilogy, #3)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rating  review_count        isbn         booktype  \\\n",
       "1718     5.0            28         NaN  good_reads:book   \n",
       "2145     5.0             3  1300589469  good_reads:book   \n",
       "5564     5.0             9         NaN  good_reads:book   \n",
       "\n",
       "                                             author_url  year  \\\n",
       "1718  https://www.goodreads.com/author/show/6467808....  2014   \n",
       "2145  https://www.goodreads.com/author/show/6906561....  2012   \n",
       "5564  https://www.goodreads.com/author/show/7738947....  2014   \n",
       "\n",
       "                             genre_urls  \\\n",
       "1718   /genres/poetry|/genres/childrens   \n",
       "2145                                NaN   \n",
       "5564  /genres/romance|/genres/new-adult   \n",
       "\n",
       "                                                    dir  rating_count  \\\n",
       "1718     dir18/22204746-an-elephant-is-on-my-house.html            64   \n",
       "2145  dir22/17287259-a-book-about-absolutely-nothing...            63   \n",
       "5564                      dir56/21902777-untainted.html            14   \n",
       "\n",
       "                                      name  \n",
       "1718            An Elephant Is On My House  \n",
       "2145      A Book About Absolutely Nothing.  \n",
       "5564  Untainted (Photographer Trilogy, #3)  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"rating == 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is only 1 book with a rating > 4.75 and more than 1000 reviews - *Words of Radiance*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>rating</th>\n",
       "      <th>review_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>booktype</th>\n",
       "      <th>author_url</th>\n",
       "      <th>year</th>\n",
       "      <th>genre_urls</th>\n",
       "      <th>dir</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>4.76</td>\n",
       "      <td>3361</td>\n",
       "      <td>0765326361</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/38550.Br...</td>\n",
       "      <td>2014</td>\n",
       "      <td>/genres/fantasy|/genres/fantasy|/genres/epic-f...</td>\n",
       "      <td>dir16/17332218-words-of-radiance.html</td>\n",
       "      <td>32304</td>\n",
       "      <td>Words of Radiance (The Stormlight Archive, #2)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rating  review_count        isbn         booktype  \\\n",
       "1595    4.76          3361  0765326361  good_reads:book   \n",
       "\n",
       "                                             author_url  year  \\\n",
       "1595  https://www.goodreads.com/author/show/38550.Br...  2014   \n",
       "\n",
       "                                             genre_urls  \\\n",
       "1595  /genres/fantasy|/genres/fantasy|/genres/epic-f...   \n",
       "\n",
       "                                        dir  rating_count  \\\n",
       "1595  dir16/17332218-words-of-radiance.html         32304   \n",
       "\n",
       "                                                name  \n",
       "1595  Words of Radiance (The Stormlight Archive, #2)  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.rating > 4.75) & (df.review_count > 1000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c. Oldest book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating                                                        3.6\n",
       "review_count                                                 1644\n",
       "isbn                                                   0141026286\n",
       "booktype                                          good_reads:book\n",
       "author_url      https://www.goodreads.com/author/show/5158478....\n",
       "year                                                        -1500\n",
       "genre_urls      /genres/religion|/genres/literature|/genres/an...\n",
       "dir                        dir14/19351.The_Epic_of_Gilgamesh.html\n",
       "rating_count                                                42026\n",
       "name                                        The Epic of Gilgamesh\n",
       "Name: 1398, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['year'].idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The book *The Epic of Gilgamesh* is the oldest book in the sample, from 1644."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### d. Most reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b1b0748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.review_count)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2377.5049289891394"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.review_count.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most books have around 2000 reviews. Only a few outliers have more than 100,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating                                                        4.4\n",
       "review_count                                               136455\n",
       "isbn                                                   0439023483\n",
       "booktype                                          good_reads:book\n",
       "author_url      https://www.goodreads.com/author/show/153394.S...\n",
       "year                                                         2008\n",
       "genre_urls      /genres/young-adult|/genres/science-fiction|/g...\n",
       "dir                           dir01/2767052-the-hunger-games.html\n",
       "rating_count                                              2958974\n",
       "name                      The Hunger Games (The Hunger Games, #1)\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['review_count'].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The Hunger Games (#1)* is the most reviewed book in the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>rating</th>\n",
       "      <th>review_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>booktype</th>\n",
       "      <th>author_url</th>\n",
       "      <th>year</th>\n",
       "      <th>genre_urls</th>\n",
       "      <th>dir</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.40</td>\n",
       "      <td>136455</td>\n",
       "      <td>0439023483</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/153394.S...</td>\n",
       "      <td>2008</td>\n",
       "      <td>/genres/young-adult|/genres/science-fiction|/g...</td>\n",
       "      <td>dir01/2767052-the-hunger-games.html</td>\n",
       "      <td>2958974</td>\n",
       "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.56</td>\n",
       "      <td>85746</td>\n",
       "      <td>0316015849</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/941441.S...</td>\n",
       "      <td>2005</td>\n",
       "      <td>/genres/young-adult|/genres/fantasy|/genres/ro...</td>\n",
       "      <td>dir01/41865.Twilight.html</td>\n",
       "      <td>2579564</td>\n",
       "      <td>Twilight (Twilight, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4.43</td>\n",
       "      <td>112279</td>\n",
       "      <td>0525478817</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/1406384....</td>\n",
       "      <td>2012</td>\n",
       "      <td>/genres/young-adult|/genres/book-club|/genres/...</td>\n",
       "      <td>dir01/11870085-the-fault-in-our-stars.html</td>\n",
       "      <td>1150626</td>\n",
       "      <td>The Fault in Our Stars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4.34</td>\n",
       "      <td>82098</td>\n",
       "      <td>0062024035</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/4039811....</td>\n",
       "      <td>2011</td>\n",
       "      <td>/genres/young-adult|/genres/science-fiction|/g...</td>\n",
       "      <td>dir01/13335037-divergent.html</td>\n",
       "      <td>1127983</td>\n",
       "      <td>Divergent (Divergent, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>3.94</td>\n",
       "      <td>87076</td>\n",
       "      <td>0307588378</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/2383.Gil...</td>\n",
       "      <td>2012</td>\n",
       "      <td>/genres/fiction|/genres/mystery|/genres/book-c...</td>\n",
       "      <td>dir06/19288043-gone-girl.html</td>\n",
       "      <td>682110</td>\n",
       "      <td>Gone Girl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1841</th>\n",
       "      <td>4.03</td>\n",
       "      <td>83643</td>\n",
       "      <td>0439023513</td>\n",
       "      <td>good_reads:book</td>\n",
       "      <td>https://www.goodreads.com/author/show/153394.S...</td>\n",
       "      <td>2010</td>\n",
       "      <td>/genres/young-adult|/genres/science-fiction|/g...</td>\n",
       "      <td>dir19/7260188-mockingjay.html</td>\n",
       "      <td>1316704</td>\n",
       "      <td>Mockingjay (The Hunger Games, #3)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rating  review_count        isbn         booktype  \\\n",
       "0       4.40        136455  0439023483  good_reads:book   \n",
       "2       3.56         85746  0316015849  good_reads:book   \n",
       "26      4.43        112279  0525478817  good_reads:book   \n",
       "43      4.34         82098  0062024035  good_reads:book   \n",
       "571     3.94         87076  0307588378  good_reads:book   \n",
       "1841    4.03         83643  0439023513  good_reads:book   \n",
       "\n",
       "                                             author_url  year  \\\n",
       "0     https://www.goodreads.com/author/show/153394.S...  2008   \n",
       "2     https://www.goodreads.com/author/show/941441.S...  2005   \n",
       "26    https://www.goodreads.com/author/show/1406384....  2012   \n",
       "43    https://www.goodreads.com/author/show/4039811....  2011   \n",
       "571   https://www.goodreads.com/author/show/2383.Gil...  2012   \n",
       "1841  https://www.goodreads.com/author/show/153394.S...  2010   \n",
       "\n",
       "                                             genre_urls  \\\n",
       "0     /genres/young-adult|/genres/science-fiction|/g...   \n",
       "2     /genres/young-adult|/genres/fantasy|/genres/ro...   \n",
       "26    /genres/young-adult|/genres/book-club|/genres/...   \n",
       "43    /genres/young-adult|/genres/science-fiction|/g...   \n",
       "571   /genres/fiction|/genres/mystery|/genres/book-c...   \n",
       "1841  /genres/young-adult|/genres/science-fiction|/g...   \n",
       "\n",
       "                                             dir  rating_count  \\\n",
       "0            dir01/2767052-the-hunger-games.html       2958974   \n",
       "2                      dir01/41865.Twilight.html       2579564   \n",
       "26    dir01/11870085-the-fault-in-our-stars.html       1150626   \n",
       "43                 dir01/13335037-divergent.html       1127983   \n",
       "571                dir06/19288043-gone-girl.html        682110   \n",
       "1841               dir19/7260188-mockingjay.html       1316704   \n",
       "\n",
       "                                         name  \n",
       "0     The Hunger Games (The Hunger Games, #1)  \n",
       "2                     Twilight (Twilight, #1)  \n",
       "26                     The Fault in Our Stars  \n",
       "43                  Divergent (Divergent, #1)  \n",
       "571                                 Gone Girl  \n",
       "1841        Mockingjay (The Hunger Games, #3)  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.review_count > 80000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 6 books that have more than 80,000 reviews. *The Fault in Our Stars* has the 2nd most reviews and released later than the most reviewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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JlmXJ19dX6enpatmypSSpXbt22rZtm3x8fNS0aVPZ7XbZ7XaFhIQoIyNDDodD//u//+ta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\n/wMTzBmW4cA3HwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11107bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meanrat=df.rating.mean()\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    df.rating.hist(bins=10, alpha=0.8);\n",
    "    sns.despine(left=True, bottom=True, right=True)\n",
    "    plt.axvline(meanrat, 0, 0.75, color='r', label='Mean')\n",
    "    plt.xlabel(\"average rating of book\")\n",
    "    plt.ylabel(\"counts\")\n",
    "    plt.title(\"Ratings Histogram\")\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BM7OMuQTMzDLmEjAzy1jpEpC0QNI3JD2RHl8r6YCkF9P3a+qW3SZpXNIJSRvK7tvMzMpp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\nnaOs6no8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116148438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.review_count.hist(bins=np.arange(0, 40000, 400))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rescale x-axis to be logarithmic to more easily see structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c760b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.review_count.hist(bins=100, alpha=0.4)\n",
    "plt.xscale(\"log\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lZaqurpbD4Yg/Hg6HFYlE5PV6bc+LRCK28R/m3onMzClyOlOHtKDbSZ+UFr+dleXtZ2by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\nAMAw/xdkWVmKILzcpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113afb668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.rating_count.values, bins=100, alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatterplot in matplotlib of rating against year. By setting the alpha transparency low we can how the density of highly rated books on goodreads has changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tP/hQHr75tNo5lnPmO0/bTQLL9vdSSD72zuSVWCg3cYPnnHur8vLxnMvYbzOW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce467f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.year, df.rating, alpha=.08)\n",
    "plt.xlim([1900,2015])\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Rating\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alist = [1,2,3,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alist + alist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(alist) + np.array(alist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16, 25])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(alist)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  4  6  8 10]\n"
     ]
    }
   ],
   "source": [
    "a=np.array([1,2,3,4,5])\n",
    "b=np.array([1,2,3,4,5])\n",
    "print(a+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "print(a+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All Together - Hamlet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access Hamlet word by word - there are roughly 32,000 words in Hamlet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31659\n"
     ]
    }
   ],
   "source": [
    "with open(\"./data/hamlet.txt\") as hamletfile:\n",
    "    hamlettext=hamletfile.read()\n",
    "    hamlettokens=hamlettext.split()\n",
    "    print(len(hamlettokens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hamletlctokens=[word.lower() for word in hamlettokens]\n",
    "hamletlctokens.count(\"thou\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Find a unique set of words using python's 'set' data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "uniquelctokens=set(hamletlctokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tokendict={}\n",
    "for ut in uniquelctokens:\n",
    "    tokendict[ut]=hamletlctokens.count(ut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1136"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokendict['the']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the 15 most used words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 1136),\n",
       " ('and', 943),\n",
       " ('to', 720),\n",
       " ('of', 667),\n",
       " ('a', 527),\n",
       " ('my', 512),\n",
       " ('i', 510),\n",
       " ('in', 420),\n",
       " ('you', 412),\n",
       " ('ham.', 358),\n",
       " ('that', 337),\n",
       " ('it', 324),\n",
       " ('is', 320),\n",
       " ('his', 295),\n",
       " ('not', 270)]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqwords = sorted(tokendict.items(), key=lambda item: -item[1])[:15]\n",
    "freqwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5pdbue1sb2yZ8vvx6u/9eRic3A7yqPQ/ub+uftoDx/BzwjbbekH3xaHvnAp9s7d0L/OQC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ff1b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = np.arange(len(freqwords))\n",
    "plt.bar(pos, [e[1] for e in freqwords]);\n",
    "plt.xticks(pos, [e[0] for e in freqwords]);\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}