Python Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis of Anime using Jupyter notebooks and Python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table of Contents:\n",
    "* Obtaining a List\n",
    "* Counting Unique Instances\n",
    "* Adding Matplotlib\n",
    "* Frequency of Anime Based on Genre\n",
    "* Creating a Bar Chart with the Data Given"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial, you should have some understanding of Python and Pandas, as well as different libraries and how they work. Basic coding experience is necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtaining a List\n",
    "In this tutorial we plan to find the number of anime by category using a public dataset retrieved from a [Kaggle](https://www.kaggle.com) competition.\n",
    "\n",
    "First, we import the libraries that we're going to use.  One popular data analysis library for Python is [pandas](https://pandas.pydata.org/), as well as [matplotlib](https://matplotlib.org/), which is a data visualization library.\n",
    "(Note: The percent sign behind the `matplotlib inline` is known as a [magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html) symbol, exclusive to Jupyter notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline     \n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   Next, we specify the dataframe `anime_df` in which we took the first 31 (don't forget 0 also includes as 1!) anime from this [dataset](https://www.kaggle.com/CooperUnion/anime-recommendations-database) and organized it in a table from highest rating to lowest. The table below shows all the columns taken from the first thirty-one rows of the original dataset. The point of writing the whole table out was to give a rough estimate of what the numbers should turn out to be, more or less, and if the output will seem reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anime_id</th>\n",
       "      <th>name</th>\n",
       "      <th>genre</th>\n",
       "      <th>type</th>\n",
       "      <th>episodes</th>\n",
       "      <th>rating</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32281</td>\n",
       "      <td>Kimi no Na wa.</td>\n",
       "      <td>Drama, Romance, School, Supernatural</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>9.37</td>\n",
       "      <td>200630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5114</td>\n",
       "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
       "      <td>Action, Adventure, Drama, Fantasy, Magic, Mili...</td>\n",
       "      <td>TV</td>\n",
       "      <td>64</td>\n",
       "      <td>9.26</td>\n",
       "      <td>793665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28977</td>\n",
       "      <td>Gintama°</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.25</td>\n",
       "      <td>114262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9253</td>\n",
       "      <td>Steins;Gate</td>\n",
       "      <td>Sci-Fi, Thriller</td>\n",
       "      <td>TV</td>\n",
       "      <td>24</td>\n",
       "      <td>9.17</td>\n",
       "      <td>673572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9969</td>\n",
       "      <td>Gintama&amp;#039;</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.16</td>\n",
       "      <td>151266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32935</td>\n",
       "      <td>Haikyuu!!: Karasuno Koukou VS Shiratorizawa Ga...</td>\n",
       "      <td>Comedy, Drama, School, Shounen, Sports</td>\n",
       "      <td>TV</td>\n",
       "      <td>10</td>\n",
       "      <td>9.15</td>\n",
       "      <td>93351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11061</td>\n",
       "      <td>Hunter x Hunter (2011)</td>\n",
       "      <td>Action, Adventure, Shounen, Super Power</td>\n",
       "      <td>TV</td>\n",
       "      <td>148</td>\n",
       "      <td>9.13</td>\n",
       "      <td>425855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>820</td>\n",
       "      <td>Ginga Eiyuu Densetsu</td>\n",
       "      <td>Drama, Military, Sci-Fi, Space</td>\n",
       "      <td>OVA</td>\n",
       "      <td>110</td>\n",
       "      <td>9.11</td>\n",
       "      <td>80679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15335</td>\n",
       "      <td>Gintama Movie: Kanketsu-hen - Yorozuya yo Eien...</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>9.10</td>\n",
       "      <td>72534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15417</td>\n",
       "      <td>Gintama&amp;#039;: Enchousen</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>13</td>\n",
       "      <td>9.11</td>\n",
       "      <td>81109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4181</td>\n",
       "      <td>Clannad: After Story</td>\n",
       "      <td>Drama, Fantasy, Romance, Slice of Life, Supern...</td>\n",
       "      <td>TV</td>\n",
       "      <td>24</td>\n",
       "      <td>9.06</td>\n",
       "      <td>456749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28851</td>\n",
       "      <td>Koe no Katachi</td>\n",
       "      <td>Drama, School, Shounen</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>9.05</td>\n",
       "      <td>102733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>918</td>\n",
       "      <td>Gintama</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>201</td>\n",
       "      <td>9.04</td>\n",
       "      <td>336376</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2904</td>\n",
       "      <td>Code Geass: Hangyaku no Lelouch R2</td>\n",
       "      <td>Action, Drama, Mecha, Military, Sci-Fi, Super ...</td>\n",
       "      <td>TV</td>\n",
       "      <td>25</td>\n",
       "      <td>8.98</td>\n",
       "      <td>572888</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>28891</td>\n",
       "      <td>Haikyuu!! Second Season</td>\n",
       "      <td>Comedy, Drama, School, Shounen, Sports</td>\n",
       "      <td>TV</td>\n",
       "      <td>25</td>\n",
       "      <td>8.93</td>\n",
       "      <td>179342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>199</td>\n",
       "      <td>Sen to Chihiro no Kamikakushi</td>\n",
       "      <td>Adventure, Drama, Supernatural</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>8.93</td>\n",
       "      <td>466254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23273</td>\n",
       "      <td>Shigatsu wa Kimi no Uso</td>\n",
       "      <td>Drama, Music, Romance, School, Shounen</td>\n",
       "      <td>TV</td>\n",
       "      <td>22</td>\n",
       "      <td>8.92</td>\n",
       "      <td>416397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24701</td>\n",
       "      <td>Mushishi Zoku Shou 2nd Season</td>\n",
       "      <td>Adventure, Fantasy, Historical, Mystery, Seine...</td>\n",
       "      <td>TV</td>\n",
       "      <td>10</td>\n",
       "      <td>8.88</td>\n",
       "      <td>75894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12355</td>\n",
       "      <td>Ookami Kodomo no Ame to Yuki</td>\n",
       "      <td>Fantasy, Slice of Life</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>8.84</td>\n",
       "      <td>226193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1575</td>\n",
       "      <td>Code Geass: Hangyaku no Lelouch</td>\n",
       "      <td>Action, Mecha, Military, School, Sci-Fi, Super...</td>\n",
       "      <td>TV</td>\n",
       "      <td>25</td>\n",
       "      <td>8.83</td>\n",
       "      <td>715151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>263</td>\n",
       "      <td>Hajime no Ippo</td>\n",
       "      <td>Comedy, Drama, Shounen, Sports</td>\n",
       "      <td>TV</td>\n",
       "      <td>75</td>\n",
       "      <td>8.83</td>\n",
       "      <td>157670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>44</td>\n",
       "      <td>Rurouni Kenshin: Meiji Kenkaku Romantan - Tsui...</td>\n",
       "      <td>Action, Drama, Historical, Martial Arts, Roman...</td>\n",
       "      <td>OVA</td>\n",
       "      <td>4</td>\n",
       "      <td>8.83</td>\n",
       "      <td>129307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>Cowboy Bebop</td>\n",
       "      <td>Action, Adventure, Comedy, Drama, Sci-Fi, Space</td>\n",
       "      <td>TV</td>\n",
       "      <td>26</td>\n",
       "      <td>8.82</td>\n",
       "      <td>486824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30276</td>\n",
       "      <td>One Punch Man</td>\n",
       "      <td>Action, Comedy, Parody, Sci-Fi, Seinen, Super ...</td>\n",
       "      <td>TV</td>\n",
       "      <td>12</td>\n",
       "      <td>8.82</td>\n",
       "      <td>552458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>164</td>\n",
       "      <td>Mononoke Hime</td>\n",
       "      <td>Action, Adventure, Fantasy</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>8.81</td>\n",
       "      <td>339556</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7311</td>\n",
       "      <td>Suzumiya Haruhi no Shoushitsu</td>\n",
       "      <td>Comedy, Mystery, Romance, School, Sci-Fi, Supe...</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>8.81</td>\n",
       "      <td>240297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17074</td>\n",
       "      <td>Monogatari Series: Second Season</td>\n",
       "      <td>Comedy, Mystery, Romance, Supernatural, Vampire</td>\n",
       "      <td>TV</td>\n",
       "      <td>26</td>\n",
       "      <td>8.80</td>\n",
       "      <td>205959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>21939</td>\n",
       "      <td>Mushishi Zoku Shou</td>\n",
       "      <td>Adventure, Fantasy, Historical, Mystery, Seine...</td>\n",
       "      <td>TV</td>\n",
       "      <td>10</td>\n",
       "      <td>8.80</td>\n",
       "      <td>101351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>457</td>\n",
       "      <td>Mushishi</td>\n",
       "      <td>Adventure, Fantasy, Historical, Mystery, Seine...</td>\n",
       "      <td>TV</td>\n",
       "      <td>26</td>\n",
       "      <td>8.78</td>\n",
       "      <td>300030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2001</td>\n",
       "      <td>Tengen Toppa Gurren Lagann</td>\n",
       "      <td>Action, Adventure, Comedy, Mecha, Sci-Fi</td>\n",
       "      <td>TV</td>\n",
       "      <td>27</td>\n",
       "      <td>8.78</td>\n",
       "      <td>562962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>245</td>\n",
       "      <td>Great Teacher Onizuka</td>\n",
       "      <td>Comedy, Drama, School, Shounen, Slice of Life</td>\n",
       "      <td>TV</td>\n",
       "      <td>43</td>\n",
       "      <td>8.77</td>\n",
       "      <td>268487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    anime_id                                               name  \\\n",
       "0      32281                                     Kimi no Na wa.   \n",
       "1       5114                   Fullmetal Alchemist: Brotherhood   \n",
       "2      28977                                           Gintama°   \n",
       "3       9253                                        Steins;Gate   \n",
       "4       9969                                      Gintama&#039;   \n",
       "5      32935  Haikyuu!!: Karasuno Koukou VS Shiratorizawa Ga...   \n",
       "6      11061                             Hunter x Hunter (2011)   \n",
       "7        820                               Ginga Eiyuu Densetsu   \n",
       "8      15335  Gintama Movie: Kanketsu-hen - Yorozuya yo Eien...   \n",
       "9      15417                           Gintama&#039;: Enchousen   \n",
       "10      4181                               Clannad: After Story   \n",
       "11     28851                                     Koe no Katachi   \n",
       "12       918                                            Gintama   \n",
       "13      2904                 Code Geass: Hangyaku no Lelouch R2   \n",
       "14     28891                            Haikyuu!! Second Season   \n",
       "15       199                      Sen to Chihiro no Kamikakushi   \n",
       "16     23273                            Shigatsu wa Kimi no Uso   \n",
       "17     24701                      Mushishi Zoku Shou 2nd Season   \n",
       "18     12355                       Ookami Kodomo no Ame to Yuki   \n",
       "19      1575                    Code Geass: Hangyaku no Lelouch   \n",
       "20       263                                     Hajime no Ippo   \n",
       "21        44  Rurouni Kenshin: Meiji Kenkaku Romantan - Tsui...   \n",
       "22         1                                       Cowboy Bebop   \n",
       "23     30276                                      One Punch Man   \n",
       "24       164                                      Mononoke Hime   \n",
       "25      7311                      Suzumiya Haruhi no Shoushitsu   \n",
       "26     17074                   Monogatari Series: Second Season   \n",
       "27     21939                                 Mushishi Zoku Shou   \n",
       "28       457                                           Mushishi   \n",
       "29      2001                         Tengen Toppa Gurren Lagann   \n",
       "30       245                              Great Teacher Onizuka   \n",
       "\n",
       "                                                genre   type  episodes  \\\n",
       "0                Drama, Romance, School, Supernatural  Movie         1   \n",
       "1   Action, Adventure, Drama, Fantasy, Magic, Mili...     TV        64   \n",
       "2   Action, Comedy, Historical, Parody, Samurai, S...     TV        51   \n",
       "3                                    Sci-Fi, Thriller     TV        24   \n",
       "4   Action, Comedy, Historical, Parody, Samurai, S...     TV        51   \n",
       "5              Comedy, Drama, School, Shounen, Sports     TV        10   \n",
       "6             Action, Adventure, Shounen, Super Power     TV       148   \n",
       "7                      Drama, Military, Sci-Fi, Space    OVA       110   \n",
       "8   Action, Comedy, Historical, Parody, Samurai, S...  Movie         1   \n",
       "9   Action, Comedy, Historical, Parody, Samurai, S...     TV        13   \n",
       "10  Drama, Fantasy, Romance, Slice of Life, Supern...     TV        24   \n",
       "11                             Drama, School, Shounen  Movie         1   \n",
       "12  Action, Comedy, Historical, Parody, Samurai, S...     TV       201   \n",
       "13  Action, Drama, Mecha, Military, Sci-Fi, Super ...     TV        25   \n",
       "14             Comedy, Drama, School, Shounen, Sports     TV        25   \n",
       "15                     Adventure, Drama, Supernatural  Movie         1   \n",
       "16             Drama, Music, Romance, School, Shounen     TV        22   \n",
       "17  Adventure, Fantasy, Historical, Mystery, Seine...     TV        10   \n",
       "18                             Fantasy, Slice of Life  Movie         1   \n",
       "19  Action, Mecha, Military, School, Sci-Fi, Super...     TV        25   \n",
       "20                     Comedy, Drama, Shounen, Sports     TV        75   \n",
       "21  Action, Drama, Historical, Martial Arts, Roman...    OVA         4   \n",
       "22    Action, Adventure, Comedy, Drama, Sci-Fi, Space     TV        26   \n",
       "23  Action, Comedy, Parody, Sci-Fi, Seinen, Super ...     TV        12   \n",
       "24                         Action, Adventure, Fantasy  Movie         1   \n",
       "25  Comedy, Mystery, Romance, School, Sci-Fi, Supe...  Movie         1   \n",
       "26    Comedy, Mystery, Romance, Supernatural, Vampire     TV        26   \n",
       "27  Adventure, Fantasy, Historical, Mystery, Seine...     TV        10   \n",
       "28  Adventure, Fantasy, Historical, Mystery, Seine...     TV        26   \n",
       "29           Action, Adventure, Comedy, Mecha, Sci-Fi     TV        27   \n",
       "30      Comedy, Drama, School, Shounen, Slice of Life     TV        43   \n",
       "\n",
       "    rating  members  \n",
       "0     9.37   200630  \n",
       "1     9.26   793665  \n",
       "2     9.25   114262  \n",
       "3     9.17   673572  \n",
       "4     9.16   151266  \n",
       "5     9.15    93351  \n",
       "6     9.13   425855  \n",
       "7     9.11    80679  \n",
       "8     9.10    72534  \n",
       "9     9.11    81109  \n",
       "10    9.06   456749  \n",
       "11    9.05   102733  \n",
       "12    9.04   336376  \n",
       "13    8.98   572888  \n",
       "14    8.93   179342  \n",
       "15    8.93   466254  \n",
       "16    8.92   416397  \n",
       "17    8.88    75894  \n",
       "18    8.84   226193  \n",
       "19    8.83   715151  \n",
       "20    8.83   157670  \n",
       "21    8.83   129307  \n",
       "22    8.82   486824  \n",
       "23    8.82   552458  \n",
       "24    8.81   339556  \n",
       "25    8.81   240297  \n",
       "26    8.80   205959  \n",
       "27    8.80   101351  \n",
       "28    8.78   300030  \n",
       "29    8.78   562962  \n",
       "30    8.77   268487  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anime_df = pd.read_csv('anime.csv')\n",
    "#print(anime)\n",
    "#print(initials)\n",
    "anime_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `apply` method scans through each row, locates the `name` column, and extracts the first letter of the string. The `.print()` function isn't needed to print `anime_df` because it's a dataframe. I shortened the full list to just 3 shows for convenience; a full table would take up a lot of unnecessary space. However, if you want to see the full list, simply delete the `.head(3)`. What that method does is it selects the first n amount of things you tell it to, and displays only that. For example, if you were to change the 3 to a 5 or a 6, more and more of the list would show. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anime_id</th>\n",
       "      <th>name</th>\n",
       "      <th>genre</th>\n",
       "      <th>type</th>\n",
       "      <th>episodes</th>\n",
       "      <th>rating</th>\n",
       "      <th>members</th>\n",
       "      <th>Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32281</td>\n",
       "      <td>Kimi no Na wa.</td>\n",
       "      <td>Drama, Romance, School, Supernatural</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>9.37</td>\n",
       "      <td>200630</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5114</td>\n",
       "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
       "      <td>Action, Adventure, Drama, Fantasy, Magic, Mili...</td>\n",
       "      <td>TV</td>\n",
       "      <td>64</td>\n",
       "      <td>9.26</td>\n",
       "      <td>793665</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28977</td>\n",
       "      <td>Gintama°</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.25</td>\n",
       "      <td>114262</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anime_id                              name  \\\n",
       "0     32281                    Kimi no Na wa.   \n",
       "1      5114  Fullmetal Alchemist: Brotherhood   \n",
       "2     28977                          Gintama°   \n",
       "\n",
       "                                               genre   type  episodes  rating  \\\n",
       "0               Drama, Romance, School, Supernatural  Movie         1    9.37   \n",
       "1  Action, Adventure, Drama, Fantasy, Magic, Mili...     TV        64    9.26   \n",
       "2  Action, Comedy, Historical, Parody, Samurai, S...     TV        51    9.25   \n",
       "\n",
       "   members Initials  \n",
       "0   200630        K  \n",
       "1   793665        F  \n",
       "2   114262        G  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initials = anime_df.apply(lambda my_current_row: my_current_row['name'][:1], axis=1)\n",
    "anime_df[\"Initials\"] = initials\n",
    "#type(initials)\n",
    "anime_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting Unique Instances\n",
    "As a quick side project, it might be intriguing to know what initial was most commonly used in the top rated anime titles, so we can use the code below to derive a list from our previous table. I rewrote two lines of the code from the previous cell into this one to keep the relevant code together. A new method we haven't seen before shows up in this cell, `.unique()`. What it does is it filters through all of the elements it's given from the input and returns only unique outputs, ridding any repetition from the original input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "initials = anime_df.apply(lambda my_current_row: my_current_row['name'][:1], axis=1)\n",
    "anime_df[\"Initials\"] = initials\n",
    "#type(initials)\n",
    "#anime_df\n",
    "distinct = initials.unique()\n",
    "#len(distinct)\n",
    "print(distinct.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.groupby()` function is very useful when trying to place a value to its respective object. When we create the dataframe `count_anime_df`, we use the `Initials` as the objects and the `Initial Count` as the values. What the `.groupby()` function does is it takes from the total count and it matches each number to an initial according to `Initials`. The `.sum()` function adds up everything in the input. However, because of the previous `.groupby()`, the function itself runs several times, once for every distinct initials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Initial Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Initials</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Initial Count\n",
       "Initials               \n",
       "C                     4\n",
       "F                     1\n",
       "G                     7\n",
       "H                     4\n",
       "K                     2\n",
       "M                     5\n",
       "O                     2\n",
       "R                     1\n",
       "S                     4\n",
       "T                     1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_count = anime_df.apply(lambda my_current_row: 1, axis=1)\n",
    "anime_df[\"Initial Count\"] = initial_count\n",
    "count_anime_df = anime_df[['Initials', 'Initial Count']].groupby(['Initials']).sum()\n",
    "#anime_df.groupby(['Initials']).sum()\n",
    "#anime_df.columns\n",
    "count_anime_df\n",
    "#sort_anime_df = count_anime_df.sort_values(by = ['Initial Count'], ascending=False)\n",
    "#sort_anime_df.head(3)   ###   top n results\n",
    "#sort_anime_df.rank(axis=0, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Matplotlib\n",
    "We use matplotlib for the first time in the following cell. Matplotlib, as well as numpy, are libraries, each serving different functions. [Matplotlib](https://matplotlib.org/) is a plotting library, and [numpy](https://docs.scipy.org/doc/numpy-1.13.0/user/whatisnumpy.html) is a library that provides objects and arrays. We use the `.groupby` from `count_anime_df` encountered in the previous cell to graph each number to its corresponding initial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "objs = count_anime_df.index\n",
    "count_anime_df.columns\n",
    "type(objs)\n",
    "y_pos = np.arange(len(objs))\n",
    "#y_pos\n",
    "frequency = count_anime_df['Initial Count']\n",
    "\n",
    "plt.title('Frequency of Initials')\n",
    "plt.bar(y_pos, frequency, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objs)\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency of Anime Based on Genre\n",
    "First, the genres themselves must be separated in order for them to be counted individually. We can do this by selecting the `genre` column from each row and finding the comma in each list, isolating the elements from each other. The `split`method separates two pieces of the same string by whatever is in the single quotes in the parentheses. For example, in the cell below, in each `genre` section of the current column, the genres are split with a comma and a space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               [Drama, Romance, School, Supernatural]\n",
       "1    [Action, Adventure, Drama, Fantasy, Magic, Mil...\n",
       "2    [Action, Comedy, Historical, Parody, Samurai, ...\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_list = anime_df.apply(lambda my_current_row: my_current_row['genre'].split(', '), axis=1)\n",
    "#type(genre_list) #genre_list is a list of lists\n",
    "genre_list.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We combine all of the elements together by [flattening](https://softwareengineering.stackexchange.com/questions/70743/what-does-flatten-mean) the list, and obtain the number of elements in the new list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flattened_genre_list = [ g  for inner_list in genre_list   for g in inner_list ] #flattening a list of lists\n",
    "#flattened_genre_list[:10]\n",
    "len(flattened_genre_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "listof_1 = [1 for x in flattened_genre_list]\n",
    "#len(listof_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           frequency\n",
       "genre               \n",
       "Action            14\n",
       "Adventure          9\n",
       "Comedy            14\n",
       "Drama             14\n",
       "Fantasy            7"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_dict = {'genre': flattened_genre_list,\n",
    "            'frequency': listof_1}\n",
    "freq_df = pd.DataFrame(freq_dict)\n",
    "#freq_df.head()\n",
    "count_by_genre = freq_df.groupby(['genre']).sum()\n",
    "count_by_genre.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Bar Chart with the Data Given\n",
    "We create `gs`, an index for `count_by_genre` in order to graph the numbers given to us from the initials, as well as `frequency` and `y_pos`. `np.arange` is a function that crafts numeric sequences and is [important to know](https://www.sharpsightlabs.com/blog/numpy-arange/), as it can be used in many circumstances. The second half of the cell plots where each piece of information belongs on the graph. For example, `plt.title` places the title at the top, depending on what is written in the parentheses. `plt.bar` adjusts the bars themselves, and `plt.xticks` places the genres at a certain distance between each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = count_by_genre.index\n",
    "frequency = count_by_genre['frequency']\n",
    "y_pos = np.arange(len(frequency))\n",
    "\n",
    "fig = plt.figure(figsize=(20, 8))\n",
    "plt.title('Frequency of Genres')\n",
    "plt.bar(gs, frequency, align='center', alpha=0.5)\n",
    "plt.xticks(gs, gs, rotation = 'vertical')\n",
    "#plt.yticks(y_pos)\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800.725x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "sns.relplot(x=\"total_bill\", y=\"tip\", col=\"time\",\n",
    "            hue=\"smoker\", style=\"smoker\", size=\"size\",\n",
    "            data=tips);"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}