Midterm_Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15-388 Final Project \n",
    "\n",
    "#### Group Members \n",
    "Stephen Chen, Sean Reidy, Roger Liu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our project, we want to discover the trends and underlying patterns of anime and the anime community. This community is particularly interesting because anime is a fairly niche genre, and over time certain patterns of shows, expectations, and tropes developed. Our analysis will be primarily concerned with discovering how certain types of shows are received and viewed by the community. We also want to try to discover shows that are “trope codifiers”, that is, which shows have had a heavy influence on the content that gets produced afterwards.\n",
    "\n",
    "To do this, we will be performing data analysis on the community of MyAnimeList (abbreviated MAL). MAL is a website where users can track and rate the anime that they have watched, and as such, it aggregates a lot of information about the shows themselves and how they are received. This provides many parameters for us to query on, such as the season the show aired, the studio that produced the show, and so on. We will begin by focusing on what factors influecne a shows rating. The site also contains a section where users can suggest related shows. We can use this section to try to find shows which may have spawned certain genres or aspects. \n",
    "\n",
    "Some problem we may have in the data is a recency bias, as the popularity of anime has sharply increased in recent years. To combat this we will look at summary statistics, and normalize the data so this effect is mitigated. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web Scraping \n",
    "\n",
    "The scraper we wrote currently pulls information from all shows from 1998 to 2015. We do this by iterating through each broadcasting season and pulling the relevant information from the back. This info is extracted by processing the html in beautiful soup. At the moment, the scraper only grabs top level categorical information, such the studios who produced the show, and various quantitative metrics, such as the show’s rating, the number of people who watched the show, and the show’s duration. We can expend this in the future by including the cast who worked on a show, as well as link shows in the same series together.\n",
    "\n",
    "The scraper is not included in this notebook, but the code for both it and other aspects of this notebook can be found here.\n",
    "(INSERT LINK TO GIT HUB PAGE LATTER)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Frame "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "After scraping is completed, the data is represented by a collection of JSON files, each representing an entry from MAL.  From here we programmatically read in each JSON file and parse them into a pandas dataframe. Since we are interested in the genre tags associated with each anime, we create a series of dummy variables for each genre tag represented in the data set. We also have the option of saving the dataframe as a CSV to be used in other applications like R or Excel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import *\n",
    "\n",
    "import AssembleData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Build Dataframe \n",
    "MAL_df, all_genre  = AssembleData.read_files_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5628\n"
     ]
    }
   ],
   "source": [
    "print len(MAL_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>action</th>\n",
       "      <th>adventure</th>\n",
       "      <th>aired_end</th>\n",
       "      <th>aired_start</th>\n",
       "      <th>broadcast</th>\n",
       "      <th>cars</th>\n",
       "      <th>category</th>\n",
       "      <th>comedy</th>\n",
       "      <th>dementia</th>\n",
       "      <th>demons</th>\n",
       "      <th>...</th>\n",
       "      <th>super power</th>\n",
       "      <th>supernatural</th>\n",
       "      <th>synopsis</th>\n",
       "      <th>thriller</th>\n",
       "      <th>title</th>\n",
       "      <th>type</th>\n",
       "      <th>url</th>\n",
       "      <th>vampire</th>\n",
       "      <th>yaoi</th>\n",
       "      <th>yuri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-04-04T00:00:00</td>\n",
       "      <td>2007-10-18T00:00:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>anime</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>In an isolated region of Kyushu lies the town ...</td>\n",
       "      <td>0</td>\n",
       "      <td>Shinreigari: Ghost Hound</td>\n",
       "      <td>TV</td>\n",
       "      <td>https://myanimelist.net/anime/2596/Shinreigari...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-06-25T00:00:00</td>\n",
       "      <td>2008-06-25T00:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>anime</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Special episode based on manga Navigation 39 e...</td>\n",
       "      <td>0</td>\n",
       "      <td>Aria The Origination: Sono Choppiri Himitsu no...</td>\n",
       "      <td>Special</td>\n",
       "      <td>https://myanimelist.net/anime/4772/Aria_The_Or...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-09-16T00:00:00</td>\n",
       "      <td>2008-07-01T00:00:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>anime</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>044 becomes the strongest female soldier excel...</td>\n",
       "      <td>0</td>\n",
       "      <td>Ultraviolet: Code 044</td>\n",
       "      <td>TV</td>\n",
       "      <td>https://myanimelist.net/anime/4264/Ultraviolet...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-09-29T00:00:00</td>\n",
       "      <td>2008-04-07T00:00:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>anime</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Silent, apathetic, yet mischievous, 14-year-ol...</td>\n",
       "      <td>0</td>\n",
       "      <td>Nabari no Ou</td>\n",
       "      <td>TV</td>\n",
       "      <td>https://myanimelist.net/anime/3655/Nabari_no_Ou</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2008-12-19T00:00:00</td>\n",
       "      <td>2008-12-19T00:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>anime</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A special Christmas episode.</td>\n",
       "      <td>0</td>\n",
       "      <td>Sore Ike! Anpanman: Franken-Robo-kun's Surpris...</td>\n",
       "      <td>Special</td>\n",
       "      <td>https://myanimelist.net/anime/17004/Sore_Ike_A...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   action  adventure            aired_end          aired_start broadcast  \\\n",
       "0       0          0  2008-04-04T00:00:00  2007-10-18T00:00:00   Unknown   \n",
       "0       0          0  2008-06-25T00:00:00  2008-06-25T00:00:00      None   \n",
       "0       1          0  2008-09-16T00:00:00  2008-07-01T00:00:00   Unknown   \n",
       "0       1          0  2008-09-29T00:00:00  2008-04-07T00:00:00   Unknown   \n",
       "0       0          0  2008-12-19T00:00:00  2008-12-19T00:00:00      None   \n",
       "\n",
       "   cars category  comedy  dementia  demons  ...   super power  supernatural  \\\n",
       "0     0    anime       0         0       0  ...             0             1   \n",
       "0     0    anime       1         0       0  ...             0             0   \n",
       "0     0    anime       0         0       0  ...             0             0   \n",
       "0     0    anime       1         0       0  ...             0             1   \n",
       "0     0    anime       1         0       0  ...             1             0   \n",
       "\n",
       "                                            synopsis  thriller  \\\n",
       "0  In an isolated region of Kyushu lies the town ...         0   \n",
       "0  Special episode based on manga Navigation 39 e...         0   \n",
       "0  044 becomes the strongest female soldier excel...         0   \n",
       "0  Silent, apathetic, yet mischievous, 14-year-ol...         0   \n",
       "0                       A special Christmas episode.         0   \n",
       "\n",
       "                                               title     type  \\\n",
       "0                           Shinreigari: Ghost Hound       TV   \n",
       "0  Aria The Origination: Sono Choppiri Himitsu no...  Special   \n",
       "0                              Ultraviolet: Code 044       TV   \n",
       "0                                       Nabari no Ou       TV   \n",
       "0  Sore Ike! Anpanman: Franken-Robo-kun's Surpris...  Special   \n",
       "\n",
       "                                                 url vampire  yaoi  yuri  \n",
       "0  https://myanimelist.net/anime/2596/Shinreigari...       0     0     0  \n",
       "0  https://myanimelist.net/anime/4772/Aria_The_Or...       0     0     0  \n",
       "0  https://myanimelist.net/anime/4264/Ultraviolet...       0     0     0  \n",
       "0    https://myanimelist.net/anime/3655/Nabari_no_Ou       0     0     0  \n",
       "0  https://myanimelist.net/anime/17004/Sore_Ike_A...       0     0     0  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAL_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "Name: action, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Genra tag extractor test \n",
    "test = MAL_df[MAL_df['id'] == 4264]\n",
    "test['action']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>action</th>\n",
       "      <th>adventure</th>\n",
       "      <th>cars</th>\n",
       "      <th>comedy</th>\n",
       "      <th>dementia</th>\n",
       "      <th>demons</th>\n",
       "      <th>drama</th>\n",
       "      <th>duration</th>\n",
       "      <th>ecchi</th>\n",
       "      <th>episodes</th>\n",
       "      <th>...</th>\n",
       "      <th>shounen ai</th>\n",
       "      <th>slice of life</th>\n",
       "      <th>space</th>\n",
       "      <th>sports</th>\n",
       "      <th>super power</th>\n",
       "      <th>supernatural</th>\n",
       "      <th>thriller</th>\n",
       "      <th>vampire</th>\n",
       "      <th>yaoi</th>\n",
       "      <th>yuri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.00000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "      <td>5628.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.314144</td>\n",
       "      <td>0.201848</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.502665</td>\n",
       "      <td>0.004797</td>\n",
       "      <td>0.025586</td>\n",
       "      <td>0.189055</td>\n",
       "      <td>27.289380</td>\n",
       "      <td>0.087242</td>\n",
       "      <td>17.719616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006752</td>\n",
       "      <td>0.131841</td>\n",
       "      <td>0.029495</td>\n",
       "      <td>0.048152</td>\n",
       "      <td>0.067520</td>\n",
       "      <td>0.13344</td>\n",
       "      <td>0.013504</td>\n",
       "      <td>0.017946</td>\n",
       "      <td>0.004264</td>\n",
       "      <td>0.001244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.464215</td>\n",
       "      <td>0.401415</td>\n",
       "      <td>0.086072</td>\n",
       "      <td>0.500037</td>\n",
       "      <td>0.069103</td>\n",
       "      <td>0.157912</td>\n",
       "      <td>0.391587</td>\n",
       "      <td>24.086078</td>\n",
       "      <td>0.282215</td>\n",
       "      <td>58.476666</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081900</td>\n",
       "      <td>0.338348</td>\n",
       "      <td>0.169205</td>\n",
       "      <td>0.214107</td>\n",
       "      <td>0.250942</td>\n",
       "      <td>0.34008</td>\n",
       "      <td>0.115429</td>\n",
       "      <td>0.132767</td>\n",
       "      <td>0.065169</td>\n",
       "      <td>0.035248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1818.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            action    adventure         cars       comedy     dementia  \\\n",
       "count  5628.000000  5628.000000  5628.000000  5628.000000  5628.000000   \n",
       "mean      0.314144     0.201848     0.007463     0.502665     0.004797   \n",
       "std       0.464215     0.401415     0.086072     0.500037     0.069103   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     1.000000     0.000000   \n",
       "75%       1.000000     0.000000     0.000000     1.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "            demons        drama     duration        ecchi     episodes  \\\n",
       "count  5628.000000  5628.000000  5628.000000  5628.000000  5628.000000   \n",
       "mean      0.025586     0.189055    27.289380     0.087242    17.719616   \n",
       "std       0.157912     0.391587    24.086078     0.282215    58.476666   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000    18.000000     0.000000     1.000000   \n",
       "50%       0.000000     0.000000    24.000000     0.000000     6.500000   \n",
       "75%       0.000000     0.000000    25.000000     0.000000    15.250000   \n",
       "max       1.000000     1.000000   162.000000     1.000000  1818.000000   \n",
       "\n",
       "          ...        shounen ai  slice of life        space       sports  \\\n",
       "count     ...       5628.000000    5628.000000  5628.000000  5628.000000   \n",
       "mean      ...          0.006752       0.131841     0.029495     0.048152   \n",
       "std       ...          0.081900       0.338348     0.169205     0.214107   \n",
       "min       ...          0.000000       0.000000     0.000000     0.000000   \n",
       "25%       ...          0.000000       0.000000     0.000000     0.000000   \n",
       "50%       ...          0.000000       0.000000     0.000000     0.000000   \n",
       "75%       ...          0.000000       0.000000     0.000000     0.000000   \n",
       "max       ...          1.000000       1.000000     1.000000     1.000000   \n",
       "\n",
       "       super power  supernatural     thriller      vampire         yaoi  \\\n",
       "count  5628.000000    5628.00000  5628.000000  5628.000000  5628.000000   \n",
       "mean      0.067520       0.13344     0.013504     0.017946     0.004264   \n",
       "std       0.250942       0.34008     0.115429     0.132767     0.065169   \n",
       "min       0.000000       0.00000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000       0.00000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000       0.00000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000       0.00000     0.000000     0.000000     0.000000   \n",
       "max       1.000000       1.00000     1.000000     1.000000     1.000000   \n",
       "\n",
       "              yuri  \n",
       "count  5628.000000  \n",
       "mean      0.001244  \n",
       "std       0.035248  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       0.000000  \n",
       "max       1.000000  \n",
       "\n",
       "[8 rows x 52 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAL_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Count for Each Genre "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Count</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2829</td>\n",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1768</td>\n",
       "      <td>action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1206</td>\n",
       "      <td>fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1184</td>\n",
       "      <td>shounen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1136</td>\n",
       "      <td>adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1088</td>\n",
       "      <td>sci-fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1064</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>976</td>\n",
       "      <td>romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>890</td>\n",
       "      <td>school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>751</td>\n",
       "      <td>supernatural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>742</td>\n",
       "      <td>slice of life</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>491</td>\n",
       "      <td>ecchi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>477</td>\n",
       "      <td>mecha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>463</td>\n",
       "      <td>magic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>436</td>\n",
       "      <td>kids</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>391</td>\n",
       "      <td>seinen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>380</td>\n",
       "      <td>super power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>362</td>\n",
       "      <td>mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>361</td>\n",
       "      <td>shoujo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>303</td>\n",
       "      <td>historical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>290</td>\n",
       "      <td>military</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>271</td>\n",
       "      <td>sports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>261</td>\n",
       "      <td>harem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>236</td>\n",
       "      <td>parody</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>198</td>\n",
       "      <td>music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>173</td>\n",
       "      <td>horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>166</td>\n",
       "      <td>space</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>158</td>\n",
       "      <td>psychological</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>150</td>\n",
       "      <td>game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>145</td>\n",
       "      <td>martial arts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>144</td>\n",
       "      <td>demons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>111</td>\n",
       "      <td>police</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>101</td>\n",
       "      <td>vampire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>97</td>\n",
       "      <td>samurai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>76</td>\n",
       "      <td>thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>51</td>\n",
       "      <td>shoujo ai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>42</td>\n",
       "      <td>cars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38</td>\n",
       "      <td>shounen ai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>37</td>\n",
       "      <td>josei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>27</td>\n",
       "      <td>dementia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>24</td>\n",
       "      <td>yaoi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>7</td>\n",
       "      <td>yuri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0</td>\n",
       "      <td>no genres have been added yet.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Count                           Genre\n",
       "4    2829                          comedy\n",
       "10   1768                          action\n",
       "11   1206                         fantasy\n",
       "5    1184                         shounen\n",
       "18   1136                       adventure\n",
       "2    1088                          sci-fi\n",
       "12   1064                           drama\n",
       "19    976                         romance\n",
       "8     890                          school\n",
       "3     751                    supernatural\n",
       "6     742                   slice of life\n",
       "7     491                           ecchi\n",
       "23    477                           mecha\n",
       "27    463                           magic\n",
       "13    436                            kids\n",
       "9     391                          seinen\n",
       "14    380                     super power\n",
       "0     362                         mystery\n",
       "15    361                          shoujo\n",
       "29    303                      historical\n",
       "24    290                        military\n",
       "26    271                          sports\n",
       "28    261                           harem\n",
       "17    236                          parody\n",
       "36    198                           music\n",
       "21    173                          horror\n",
       "25    166                           space\n",
       "1     158                   psychological\n",
       "30    150                            game\n",
       "22    145                    martial arts\n",
       "35    144                          demons\n",
       "20    111                          police\n",
       "38    101                         vampire\n",
       "37     97                         samurai\n",
       "31     76                        thriller\n",
       "33     51                       shoujo ai\n",
       "39     42                            cars\n",
       "16     38                      shounen ai\n",
       "41     37                           josei\n",
       "40     27                        dementia\n",
       "34     24                            yaoi\n",
       "32      7                            yuri\n",
       "42      0  no genres have been added yet."
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_genre_counts = []\n",
    "for genre in all_genre:\n",
    "    all_genre_counts.append(len(MAL_df[(MAL_df[genre] == 1)]))\n",
    "\n",
    "# Display \n",
    "genre_df = pd.DataFrame(\n",
    "    {'Genre' : all_genre,\n",
    "     'Count' : all_genre_counts\n",
    "    \n",
    "    })\n",
    "genre_df.sort_values('Count', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comedy is clearly the most popular genre among our dataset, followed by Action and Fantasy.  What is clear from this is a very diverse data set with 43 different genres. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre Subsets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# lists of dataframes by Genra \n",
    "genre_subsets = [ MAL_df[MAL_df[genre] == 1] for genre in all_genre]\n",
    "\n",
    "# TODO: IMPROVE THIS CODE \n",
    "\n",
    "duration_mean = []\n",
    "episodes_mean = []\n",
    "favorites_mean = []\n",
    "members_mean = []\n",
    "popularity_mean = []\n",
    "rank_mean = []\n",
    "score_mean = []\n",
    "score_users_mean = []\n",
    "for df in genre_subsets:\n",
    "    duration_mean.append(df[\"duration\"].mean())\n",
    "    episodes_mean.append(df[\"episodes\"].mean())\n",
    "    favorites_mean.append(df[\"favorites\"].mean())\n",
    "    members_mean.append(df[\"members\"].mean())\n",
    "    popularity_mean.append(df[\"popularity\"].mean())\n",
    "    rank_mean.append(df[\"rank\"].mean())\n",
    "    score_mean.append(df[\"score\"].mean())\n",
    "    score_users_mean.append(df[\"score_users\"].mean())\n",
    "    \n",
    "#genre_df[\"duration_mean\"] = duration_mean\n",
    "#genre_df[\"episodes_mean\"] = episodes_mean\n",
    "#genre_df[\"favorites_mean\"] = favorites_mean\n",
    "#genre_df[\"members_mean\"] = members_mean\n",
    "genre_df[\"popularity_mean\"] = popularity_mean\n",
    "genre_df[\"rank_mean\"] = rank_mean\n",
    "genre_df[\"score_mean\"] = score_mean\n",
    "#genre_df[\"score_users_mean\"] = score_users_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Count</th>\n",
       "      <th>Genre</th>\n",
       "      <th>popularity_mean</th>\n",
       "      <th>rank_mean</th>\n",
       "      <th>score_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>362</td>\n",
       "      <td>mystery</td>\n",
       "      <td>2301.339779</td>\n",
       "      <td>2202.096685</td>\n",
       "      <td>7.455193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>158</td>\n",
       "      <td>psychological</td>\n",
       "      <td>1438.379747</td>\n",
       "      <td>1890.184713</td>\n",
       "      <td>7.534873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1088</td>\n",
       "      <td>sci-fi</td>\n",
       "      <td>3192.631434</td>\n",
       "      <td>3264.154554</td>\n",
       "      <td>7.088199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>751</td>\n",
       "      <td>supernatural</td>\n",
       "      <td>2031.584554</td>\n",
       "      <td>2502.302826</td>\n",
       "      <td>7.323302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2829</td>\n",
       "      <td>comedy</td>\n",
       "      <td>3448.318841</td>\n",
       "      <td>3532.246181</td>\n",
       "      <td>7.034196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Count          Genre  popularity_mean    rank_mean  score_mean\n",
       "0    362        mystery      2301.339779  2202.096685    7.455193\n",
       "1    158  psychological      1438.379747  1890.184713    7.534873\n",
       "2   1088         sci-fi      3192.631434  3264.154554    7.088199\n",
       "3    751   supernatural      2031.584554  2502.302826    7.323302\n",
       "4   2829         comedy      3448.318841  3532.246181    7.034196"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Genre</th>\n",
       "      <th>mean_members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mystery</td>\n",
       "      <td>76333.574586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>psychological</td>\n",
       "      <td>108807.303797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sci-fi</td>\n",
       "      <td>42466.105699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>supernatural</td>\n",
       "      <td>88034.639148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>comedy</td>\n",
       "      <td>49545.696359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Genre   mean_members\n",
       "0        mystery   76333.574586\n",
       "1  psychological  108807.303797\n",
       "2         sci-fi   42466.105699\n",
       "3   supernatural   88034.639148\n",
       "4         comedy   49545.696359"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_membership_df = pd.DataFrame({\n",
    "        'Genre' : all_genre,\n",
    "        \"mean_members\": members_mean\n",
    "    })\n",
    "genre_membership_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15,10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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So6zyuPj5AYQr32dU4nOKsxihA3oZ3wWCSne41VjPD3kAPdPa6OXn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\nA3s6HHib1rHCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f35bd64c810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genre_plot = genre_df.plot(kind = 'bar', x = 'Genre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1a9empqYmNTU15euw3r68OYsWLcqiRYvK78eNG5eqqqpd1iuVWi8RF8mXvvSl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f35bd5dddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## top 15 genre \n",
    "genre_df.sort_values('Count', ascending = False, inplace = True)\n",
    "genre_df_top = genre_df.head(15)\n",
    "genre_plot_2 = genre_df_top.plot(kind = 'bar', x = 'Genre')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For most genres the mean popularity and the mean rank among anime are similar, while others (like Romance ) have clear differences in the two values. This could be a metric to  indicate the level of engagement among the members of a show’s community ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MGTPyxBNP5Otf/3qmTJmSTZs2JUkOHDiQHTt2ZMWKFVm2bFnWrVuX/v7+JMm6dety9913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\n/cj/A0s0EeU5Uxn0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f35bcdb8990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genre_membership_df.sort_values('mean_members', ascending = False, inplace = True)\n",
    "genre_membership_df_top = genre_membership_df.head(15)\n",
    "genre_plot_3 = genre_membership_df_top.plot(kind = 'bar', x = 'Genre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qj6yq4S5oKfFjh9G0bUgulcfXc7ScuO+16MMy9/OlasnPhzqZls2/MRF5cwPv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\nAX9AwI/P1AaBYM+65nAdW2PTpc18IjbtxPknhixtwm63lhnY2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lzH7fex5KM35nKrxRpi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CPSvKZthaP1WY7uy3vg3KCvrfLkqfASZpaCv5+Mu8z7tbgg2QcphATj0R9NKAw/sz1fPVR6+V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\ngg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('Rcode/scatterplotMatrix.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Scatterplot Matrix demonstrates that trends exists among the different continuous variables in the data set. Some interesting relations include the positive linear relation between a shows score and its durations where the longer the show the higher the average score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fpqVdYLFkyA5iIHZ7js/nq6mpkR0ESAjUVgDAidXU1GzcuDEz8xrZQYxFVYcI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\nx377298ajfwqanRTROyboVVUVJz52GAwyLLMOw43ndForKmpEV0RNQI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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"Rcode/scoreDen.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here, our first step is to build multivariate linear regression models focusing on an anime’s score. With this model we should hopefully be able to extrapolate what features in the dataset are the most important, where more advanced methods could be used.  One proposed Idea is to build a linear classifier to identify different genres and tropes.  We can also bring in more information that was scripted, like the lists of producers and licensors of each show. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}