Comixology Analysis Notebook - English.ipynb

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    "%matplotlib inline"
   ]
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    "# Digital Comics Data Analysis (Python) - Marvel or DC?\n",
    "\n",
    "## Introduction\n",
    "\n",
    "After having done the analysis of the website ([post here](http://felipegalvao.com.br/blog/2016/05/01/comixology-digital-comics-analysis-part-1-analysis-of-site-for-web-scraping/) and the web scraping of the data from the Comixology website ([post here](http://felipegalvao.com.br/blog/2016/05/24/web-scraping-with-python-scraping-digital-comics-information-from-comixology/), we will analyze the data that we got using Python (Pandas).\n",
    "\n",
    "Let's find out what publisher have the best average ratings and prices, the average page count of the Vamos descobrir quais editoras tem os melhores preços relativos à quantidade de páginas de seus comics, as editoras com as melhores avaliações médias, além de uma análise mais profunda do duelo das gigantes: Marvel x DC Comics. Vamos começar.\n",
    "\n",
    "## Initial Preparation\n",
    "\n",
    "First, as usual, let's import the packages we need. With the warning package we will ignore the eventual warning messages that Python / Pandas give to us, so the code in our notebook does not get very long. For the other packages, they are old friends: numpy, pandas, matplotlib and seaborn. Then, we will read the csv file with the read_csv function from Pandas."
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    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "comixology_df = pd.read_csv(\"comixology_comics_dataset_19.04.2016.csv\", \n",
    "                            encoding = \"ISO-8859-1\")"
   ]
  },
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   "source": [
    "Now, let's create a new column, price per page. This column will help us compare the price of comics that have a different number of pages, and, therefore, should have a bigger price. But how much bigger?\n",
    "\n",
    "For some comics, the page count information is not available, and so, for these cases, Pandas will return inf as the value of the column, representing an infinite value. For these comics, we will set the price per page as NaN:"
   ]
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    "# Create price per page column\n",
    "comixology_df['Price_per_page'] = pd.Series(comixology_df['Original_price'] / \n",
    "                                            comixology_df['Page Count'], \n",
    "                                            index=comixology_df.index)\n",
    "                                            \n",
    "# Define price_per_page as NaN for comics with no information about page count\n",
    "comixology_df.Price_per_page[comixology_df['Price_per_page'] == np.inf] = np.nan"
   ]
  },
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   "source": [
    "Now, let's use the iterrows() function of the DataFrame to extract the publishing year of the print version of the comic. This function creates a for loop that iterates over each row of the DataFrame. Let's use the split() function to turn the string that contains the print release date into a list of values, and the third one will be the year. In some cases, this will return a value bigger than 2016, and since this is impossible, we will define these cases as NaN:"
   ]
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    "# Extract the year of release for print version\n",
    "print_dates = []\n",
    "for index, row in comixology_df.iterrows():\n",
    "    if type(comixology_df.ix[index]['Print Release Date']) == float:\n",
    "        row_year = np.nan\n",
    "    else:        \n",
    "        row_year = int(comixology_df.ix[index]['Print Release Date'].split()[2])\n",
    "        if row_year > 2016:\n",
    "            row_year = np.nan\n",
    "    print_dates.append(row_year)\n",
    "\n",
    "comixology_df['Print_Release_Year'] = pd.Series(print_dates, \n",
    "                                                index=comixology_df.index)"
   ]
  },
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    "## To the analysis (and beyond!)\n",
    "\n",
    "The first analysis we'll do is the calculation of some average values of the website, like average price of comics, average page count, among others. We'll use the nanmean() function from numpy. This function calculates the mean of a series os values, not considering NaN cases."
   ]
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     "text": [
      "Average Price: 3.69789045383\n",
      "Average Page Count: 51.5862786864\n",
      "Average Rating: 4.26347617558\n",
      "Average Ratings Quantity: 51.5088270335\n",
      "Average Price Per Page: 0.0805173472536\n"
     ]
    }
   ],
   "source": [
    "# Calculate some average values of the site\n",
    "average_price = np.nanmean(comixology_df['Original_price'])\n",
    "average_page_count = np.nanmean(comixology_df['Page Count'])\n",
    "average_rating = np.nanmean(comixology_df['Rating'])\n",
    "average_rating_quantity = np.nanmean(comixology_df['Ratings_Quantity'])\n",
    "average_price_per_page = np.nanmean(comixology_df['Price_per_page'])\n",
    "\n",
    "print(\"Average Price: \" + str(average_price))\n",
    "print(\"Average Page Count: \" + str(average_page_count))\n",
    "print(\"Average Rating: \" + str(average_rating))\n",
    "print(\"Average Ratings Quantity: \" + \n",
    "      str(average_rating_quantity))\n",
    "print(\"Average Price Per Page: \" + str(average_price_per_page))"
   ]
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   "source": [
    "Now, we will define the maximum number of columns for each field in the printing of the table to 40 columns. We'll do that because the name of some comics is long, and the printing of the table can get a little strange. With this configuration we can see more information in one row.\n",
    "\n",
    "After that, let's list comics with an average rating of 5 stars, that have more than 20 ratings (to consider only the more representative comics; comics with an average rating of 5 stars but with only one rating are not a very good metric), and let's sort it by price per page. In the top, we will have some free comics (the 6 first ones). Then, we will have great comics, in the eyes of the users, that have a very good price.</p>"
   ]
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    "# Define number of columns for table printing\n",
    "pd.set_option('display.max_colwidth', 40)"
   ]
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      "                                          Name                     Publisher  \\\n",
      "44099               Left 4 Dead: The Sacrifice                         Valve   \n",
      "6143                      Looking For Group #1                 Blind Ferret    \n",
      "55253                      The Walking Dead #1              Image - Skybound   \n",
      "30295  FCBD 2014: Don Rosa's Uncle Scrooge ...                 Fantagraphics   \n",
      "8762                   Mother Russia #1 (of 3)  Alterna Comics - FUBAR Press   \n",
      "80022  Scott Pilgrim Vol. 1: Scott Pilgrim'...                     Oni Press   \n",
      "50270       Transformers: The Cover Collection                           IDW   \n",
      "42749                        American Elf 2001         Top Shelf Productions   \n",
      "42748                        American Elf 2000         Top Shelf Productions   \n",
      "7677           Cerebus Vol. 2 #2: High Society             Aardvark-Vanaheim   \n",
      "81547                     Heart of a Corpse #2                           SLG   \n",
      "7679           Cerebus Vol. 2 #4: High Society             Aardvark-Vanaheim   \n",
      "8085                 Mishka & The Sea Devil #1                    Action Lab   \n",
      "68077                       Infinity #1 (of 6)                        Marvel   \n",
      "42872        Motorcycle Samurai #3: Peripeteia         Top Shelf Productions   \n",
      "8098                  Molly Danger: Digital #4                    Action Lab   \n",
      "42796                         Double Barrel #9         Top Shelf Productions   \n",
      "42798                        Double Barrel #10         Top Shelf Productions   \n",
      "7680           Cerebus Vol. 2 #5: High Society             Aardvark-Vanaheim   \n",
      "12142                           Batman '66 #46                     DC Comics   \n",
      "8097                  Molly Danger: Digital #3                    Action Lab   \n",
      "12123                           Batman '66 #27                     DC Comics   \n",
      "12116                           Batman '66 #20                     DC Comics   \n",
      "12136                           Batman '66 #40                     DC Comics   \n",
      "8096                  Molly Danger: Digital #2                    Action Lab   \n",
      "42788                         Double Barrel #1         Top Shelf Productions   \n",
      "12183    Batman '66 Meets The Green Hornet #12                     DC Comics   \n",
      "12157                           Batman '66 #61                     DC Comics   \n",
      "12175     Batman '66 Meets The Green Hornet #4                     DC Comics   \n",
      "12148                           Batman '66 #52                     DC Comics   \n",
      "...                                        ...                           ...   \n",
      "69969  Moon Girl and Devil Dinosaur (2015-) #3                        Marvel   \n",
      "35343                        Powers: Bureau #7                          Icon   \n",
      "6036                            War Stories #3                        Avatar   \n",
      "67289           Hawkeye vs. Deadpool #4 (of 4)                        Marvel   \n",
      "65902                   Elektra (2014-2015) #8                        Marvel   \n",
      "67027  Giant-Size Little Marvel: AvX (2015) #1                        Marvel   \n",
      "67028  Giant-Size Little Marvel: AvX (2015) #2                        Marvel   \n",
      "69193                Loki: Agent of Asgard #17                        Marvel   \n",
      "67062                        Guardians 3000 #6                        Marvel   \n",
      "67288           Hawkeye vs. Deadpool #3 (of 4)                        Marvel   \n",
      "69074  Journey to Star Wars: The Force Awak...                        Marvel   \n",
      "68330           Invincible Iron Man (2015-) #5                        Marvel   \n",
      "69950  Miles Morales: Ultimate Spider-Man (...                        Marvel   \n",
      "1712                  Batman: The Killing Joke                     DC Comics   \n",
      "76682                     Weirdworld (2015) #4                        Marvel   \n",
      "72510           Spider-Man/Deadpool (2016-) #3                        Marvel   \n",
      "61631  Amazing Spider-Man: Renew Your Vows ...                        Marvel   \n",
      "46810  The Sandman: Overture (2013-) #1 (of 6)                       Vertigo   \n",
      "69192                Loki: Agent of Asgard #16                        Marvel   \n",
      "30357                    I Killed Adolf Hitler                 Fantagraphics   \n",
      "42350            Elric Vol. 1: The Ruby Throne                         Titan   \n",
      "70993                 Old Man Logan (2016-) #1                        Marvel   \n",
      "61629  Amazing Spider-Man: Renew Your Vows ...                        Marvel   \n",
      "65446                 Deadpool (2012-2015) #44                        Marvel   \n",
      "72350                    Spider-Man (2016-) #2                        Marvel   \n",
      "69949  Miles Morales: Ultimate Spider-Man (...                        Marvel   \n",
      "67030  Giant-Size Little Marvel: AvX (2015) #4                        Marvel   \n",
      "69819       Master of Kung Fu (2015) #3 (of 4)                        Marvel   \n",
      "72166             Silver Surfer (2014-2015) #7                        Marvel   \n",
      "74037          Thanos: The Infinity Relativity                        Marvel   \n",
      "\n",
      "       Price_per_page  \n",
      "44099        0.000000  \n",
      "6143         0.000000  \n",
      "55253        0.000000  \n",
      "30295        0.000000  \n",
      "8762         0.000000  \n",
      "80022        0.000000  \n",
      "50270        0.007674  \n",
      "42749        0.007868  \n",
      "42748        0.009967  \n",
      "7677         0.011786  \n",
      "81547        0.011928  \n",
      "7679         0.012375  \n",
      "8085         0.012857  \n",
      "68077        0.014014  \n",
      "42872        0.014099  \n",
      "8098         0.014214  \n",
      "42796        0.014214  \n",
      "42798        0.014420  \n",
      "7680         0.014776  \n",
      "12142        0.014962  \n",
      "8097         0.015191  \n",
      "12123        0.015191  \n",
      "12116        0.015669  \n",
      "12136        0.015669  \n",
      "8096         0.016311  \n",
      "42788        0.016311  \n",
      "12183        0.016446  \n",
      "12157        0.016723  \n",
      "12175        0.016723  \n",
      "12148        0.017009  \n",
      "...               ...  \n",
      "69969        0.190000  \n",
      "35343        0.190000  \n",
      "6036         0.190000  \n",
      "67289        0.190000  \n",
      "65902        0.190000  \n",
      "67027        0.190000  \n",
      "67028        0.190000  \n",
      "69193        0.190000  \n",
      "67062        0.190000  \n",
      "67288        0.190000  \n",
      "69074        0.190000  \n",
      "68330        0.190000  \n",
      "69950        0.190000  \n",
      "1712         0.196818  \n",
      "76682        0.199500  \n",
      "72510        0.199500  \n",
      "61631        0.199500  \n",
      "46810        0.199500  \n",
      "69192        0.199500  \n",
      "30357        0.199800  \n",
      "42350        0.199846  \n",
      "70993        0.207917  \n",
      "61629        0.210000  \n",
      "65446        0.210000  \n",
      "72350        0.210000  \n",
      "69949        0.210000  \n",
      "67030        0.221667  \n",
      "69819        0.221667  \n",
      "72166        0.234706  \n",
      "74037        0.249900  \n",
      "\n",
      "[7006 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# List comics with 5 stars rating that have at least 20 ratings\n",
    "comics_with_5_stars = comixology_df[comixology_df.Rating == 5]\n",
    "comics_with_5_stars = comics_with_5_stars[comics_with_5_stars.Ratings_Quantity \n",
    "                                          > 20]\n",
    "# Print comics sorted by price per page\n",
    "print(comics_with_5_stars[['Name','Publisher','Price_per_page']].\n",
    "      sort_values(by='Price_per_page')) "
   ]
  },
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    "In the next analysis, we will use only comics with more than 5 ratings. For that, we will filter the DataFrame. Then, we'll create a Pandas pivot table, so that we can visualize the quantity of comics with ratings and the average rating of this publisher. Then, we will consider as representative publishers those that have at least 20 comics with ratings. To do that, we will filter the pivot table. And finally, we will sort this table by average rating, going from the highest to the lowest. This means that the publishers on the top of the table will be the ones that have the best average rating from its comics."
   ]
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      "                                        mean count_nonzero\n",
      "                                      Rating        Rating\n",
      "Publisher                                                 \n",
      "Cartoon Books                       4.875000          80.0\n",
      "Aardvark-Vanaheim                   4.800000          25.0\n",
      "Abstract Studio                     4.786517          89.0\n",
      "BOOM! - BOOM! Box                   4.763889          72.0\n",
      "Archie                              4.711656         326.0\n",
      "Icon                                4.585859         198.0\n",
      "Evil Twin Comics                    4.565217          23.0\n",
      "Udon                                4.561798          89.0\n",
      "MAX                                 4.546911         437.0\n",
      "Fantagraphics                       4.518182         110.0\n",
      "Image - Skybound                    4.504092         611.0\n",
      "ComixTribe                          4.500000          32.0\n",
      "Kodansha                            4.487179          39.0\n",
      "Delcourt                            4.463415          41.0\n",
      "Thrillbent                          4.444444          36.0\n",
      "Dark Horse                          4.440000         550.0\n",
      "Vertigo                             4.435793        2453.0\n",
      "Magnetic Press                      4.396226          53.0\n",
      "BOOM! - KaBOOM!                     4.373494          83.0\n",
      "BOOM! - Archaia                     4.355932         177.0\n",
      "Th3rd World Studios                 4.355556          45.0\n",
      "Action Lab                          4.346457         127.0\n",
      "Antarctic Press                     4.341176         170.0\n",
      "Top Shelf Productions               4.335526         152.0\n",
      "Image                               4.316908        3105.0\n",
      "Action Lab - Danger Zone            4.315789          95.0\n",
      "IDW                                 4.313492        2772.0\n",
      "Zenescope                           4.309711         381.0\n",
      "Oni Press                           4.305376         465.0\n",
      "Monkeybrain                         4.302405         291.0\n",
      "...                                      ...           ...\n",
      "DC Comics                           4.218644       13012.0\n",
      "Aftershock Comics                   4.190476          21.0\n",
      "keenspot                            4.189655          58.0\n",
      "Image - Top Cow                     4.176545         793.0\n",
      "Image - Todd McFarlane Productions  4.171429         350.0\n",
      "Marvel                              4.154245       11177.0\n",
      "Archie - Dark Circle Comics         4.147059          34.0\n",
      "Ishimori                            4.121951          41.0\n",
      "Red 5                               4.115385          78.0\n",
      "Dynamite                            4.110597        1944.0\n",
      "Creator Owned                       4.092857         140.0\n",
      "Aspen Comics                        4.089844         256.0\n",
      "Jet City                            4.058824          34.0\n",
      "SLG                                 4.058140          86.0\n",
      "Valiant Classic                     4.028777         139.0\n",
      "Boundless Comics                    4.000000          35.0\n",
      "Digital Webbing                     3.956522          23.0\n",
      "NBM                                 3.920000          75.0\n",
      "Papercutz                           3.909091         121.0\n",
      "Alterna Comics                      3.898876          89.0\n",
      "Asylum Press                        3.898305          59.0\n",
      "A Wave Blue World                   3.809524          21.0\n",
      "AAM-Markosia                        3.787097         155.0\n",
      "Alternative Comics                  3.764706          34.0\n",
      "Kickstart                           3.695652          23.0\n",
      "Arcana Comics                       3.650794         315.0\n",
      "DMP                                 3.636364          99.0\n",
      "Moonstone                           3.531915          47.0\n",
      "Harlequin/ SB Creative Corp.        3.525641          78.0\n",
      "Stormfront Comics                   3.333333          78.0\n",
      "\n",
      "[70 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Filter the original DataFrame for comics with more than 5 ratings\n",
    "comics_more_than_5_ratings = comixology_df[comixology_df.Ratings_Quantity > 5]\n",
    "\n",
    "# Create pivot table with average rating by publisher\n",
    "publishers_avg_rating = pd.pivot_table(comics_more_than_5_ratings, \n",
    "                                       values=['Rating'], \n",
    "                                       index=['Publisher'], \n",
    "                                       aggfunc=[np.mean, np.count_nonzero])\n",
    "\n",
    "# Filter for any Publisher that has more than 20 comics rated\n",
    "main_pub_avg_rating = publishers_avg_rating[publishers_avg_rating.\n",
    "                                            count_nonzero.Rating > 20]\n",
    "main_pub_avg_rating = main_pub_avg_rating.sort_values(by=('mean','Rating'), \n",
    "                                                      ascending=False)\n",
    "print(main_pub_avg_rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the giants, Marvel and DC Comics, are not among the ones in the top. If we see the complete table, they are between the middle and the bottom of the table.\n",
    "\n",
    "To help in the visualization, let's create a matplotlib chart that represents the table above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    {
     "data": {
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77dzJmDGjVTOXI0cOr/tDoHtDXFycRo8eLSl1f3bo0EFvvPGGJk+erCVLlqh+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8sL3t7eghIHiqA3FaqqqvD19cXZs2fh4OCgNM2bj0ZLg6khpaOjU2eTfLt27fD582ecP38enz9/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\nHIJDLx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c5125e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create chart with average ratings for the Publishers\n",
    "plt.figure(figsize=(10, 6))\n",
    "y_axis = main_pub_avg_rating['mean']['Rating']\n",
    "x_axis = range(len(y_axis))\n",
    "\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis, tuple(main_pub_avg_rating.index),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simplify and have a better table and chart, let's consider now only the publishers that have 300 comics with ratings. First, the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                        mean count_nonzero\n",
      "                                      Rating        Rating\n",
      "Publisher                                                 \n",
      "Archie                              4.711656         326.0\n",
      "MAX                                 4.546911         437.0\n",
      "Image - Skybound                    4.504092         611.0\n",
      "Dark Horse                          4.440000         550.0\n",
      "Vertigo                             4.435793        2453.0\n",
      "Image                               4.316908        3105.0\n",
      "IDW                                 4.313492        2772.0\n",
      "Zenescope                           4.309711         381.0\n",
      "Oni Press                           4.305376         465.0\n",
      "Valiant                             4.291022         323.0\n",
      "BOOM! Studios                       4.277219        1194.0\n",
      "Avatar                              4.234672         473.0\n",
      "DC Comics                           4.218644       13012.0\n",
      "Image - Top Cow                     4.176545         793.0\n",
      "Image - Todd McFarlane Productions  4.171429         350.0\n",
      "Marvel                              4.154245       11177.0\n",
      "Dynamite                            4.110597        1944.0\n",
      "Arcana Comics                       3.650794         315.0\n"
     ]
    }
   ],
   "source": [
    "# Filter for Publishers that have more than 300 comics rated\n",
    "big_pub_avg_rating = publishers_avg_rating[publishers_avg_rating.\n",
    "                                           count_nonzero.Rating > 300]\n",
    "big_pub_avg_rating = big_pub_avg_rating.sort_values(by=('mean','Rating'), \n",
    "                                                    ascending=False)\n",
    "print(big_pub_avg_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ot9sefPBBbd26VZK0Z88eVa1a1fZBAQAAOO2OV5JuXt0mSRMmTFDFihWNDA4AAMApdyxJ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\nGzM/uhTHejglAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c72da630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create chart with average ratings for Publishers with more than 300 comics \n",
    "# rated\n",
    "plt.figure(figsize=(10, 6))\n",
    "y_axis = big_pub_avg_rating['mean']['Rating']\n",
    "x_axis = np.arange(len(y_axis))\n",
    "\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.5, tuple(big_pub_avg_rating.index), rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing that I believed that could make a difference in the ratings of a comic was the age classification. Were comics made to the adults rated better? Or worse? Let's check that making another pivot table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                mean count_nonzero\n",
      "              Rating        Rating\n",
      "Age Rating                        \n",
      "12+ Only    4.185380       28304.0\n",
      "15+ Only    4.218854        4487.0\n",
      "17+ Only    4.341259        9925.0\n",
      "18+ Only    4.143939         264.0\n",
      "9+ Only     4.360186        1502.0\n",
      "All Ages    4.395935        1230.0\n"
     ]
    }
   ],
   "source": [
    "# Create pivot table with Rating by Age Rating\n",
    "rating_by_age = pd.pivot_table(comics_more_than_5_ratings, \n",
    "                               values=['Rating'], \n",
    "                               index=['Age Rating'], \n",
    "                               aggfunc=[np.mean, np.count_nonzero])\n",
    "                               \n",
    "print(rating_by_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And below, the corresponding chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PzK1H5tYjc+v5I3NJbRaxNktUXl6eamtrtXHjRr388suy2Wx64IEH1NDQIKfTqUWLFmn2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c86dbc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar Chart with rating by age rating\n",
    "plt.figure(figsize=(10, 6))\n",
    "y_axis = rating_by_age['mean']['Rating']\n",
    "x_axis = np.arange(len(y_axis))\n",
    "\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.25, tuple(rating_by_age.index), rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the height of the bars is quite similar. It seems that the age classification does not make a significant effect on the ratings of a comic. If we see it with a purely mathematical view, comics with an age classification for 9+ years or for all ages get the best ratings, by a small margin. But it is not possible to view a strong relation, since it does not varies in the same way as the age classification increases or decreases.\n",
    "\n",
    "Our next step is to see how the release of comics evolved (considering print versions) over the years. Remember that we already created a column with the year of release of the print version of the comic. The next step is basically to count the occurrences of each year in this column. Let's make a list with the years and then count the releases per year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     count_nonzero\n",
      "              Name\n",
      "1900            14\n",
      "1938             1\n",
      "1939             6\n",
      "1940             7\n",
      "1941            21\n",
      "1942            22\n",
      "1943            18\n",
      "1944            13\n",
      "1945            13\n",
      "1946            15\n",
      "1947             4\n",
      "1948            11\n",
      "1949            14\n",
      "1950             7\n",
      "1951             2\n",
      "1952             4\n",
      "1953             3\n",
      "1954             6\n",
      "1955             9\n",
      "1956             8\n",
      "1957             9\n",
      "1958             6\n",
      "1959            13\n",
      "1960            17\n",
      "1961            29\n",
      "1962            45\n",
      "1963            74\n",
      "1964            71\n",
      "1965            58\n",
      "1966            63\n",
      "...            ...\n",
      "1987           243\n",
      "1988           281\n",
      "1989           248\n",
      "1990           302\n",
      "1991           353\n",
      "1992           402\n",
      "1993           537\n",
      "1994           630\n",
      "1995           599\n",
      "1996           499\n",
      "1997           605\n",
      "1998           664\n",
      "1999           727\n",
      "2000           771\n",
      "2001           676\n",
      "2002           714\n",
      "2003           876\n",
      "2004          1151\n",
      "2005          1365\n",
      "2006          1684\n",
      "2007          2165\n",
      "2008          2664\n",
      "2009          2920\n",
      "2010          3500\n",
      "2011          4217\n",
      "2012          4501\n",
      "2013          3446\n",
      "2014          5369\n",
      "2015          4413\n",
      "2016          2005\n",
      "\n",
      "[80 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create pivot table with print releases per year\n",
    "print_releases_per_year = pd.pivot_table(comixology_df, \n",
    "                                         values=['Name'], \n",
    "                                         index=['Print_Release_Year'], \n",
    "                                         aggfunc=[np.count_nonzero])\n",
    "print_years = []\n",
    "for index, row in print_releases_per_year.iterrows():    \n",
    "    print_year = int(index)\n",
    "    print_years.append(print_year)\n",
    "print_releases_per_year.index = print_years\n",
    "print(print_releases_per_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's create the cart to see the situation better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rC/P5fMrLy5PH4+kXuPpe9/l88Wt9g9tQiooSfy6SgzlPPeY89Zjz1GPOUy+T57yrZ4vx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\nAAAAFvD/AIgUqk07EU9HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c91c4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create chart with print releases per year\n",
    "y_axis = print_releases_per_year['count_nonzero']['Name']\n",
    "x_axis = print_releases_per_year['count_nonzero']['Name'].index\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x_axis, y_axis)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The numbers show that the growing was moderate, until the decade of 2000, when a boom happened, with a great increase in releases until 2012, when the release numbers started to oscillate. The fall shown in 2016 is because we are still in the middle of the year.\n",
    "\n",
    "Now we'll go on to make an evaluation of the most rated comics on the website. We can also probably say that these are the most read comics on the website. So, for this analysis, we will check the comics with most ratings, sorting the table and printing some columns. Let's see the 30 first ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          Name         Publisher  \\\n",
      "55253                      The Walking Dead #1  Image - Skybound   \n",
      "41479    Arrow (2012-2013) #1: Special Edition         DC Comics   \n",
      "53325                                  Saga #1             Image   \n",
      "11898                                 Bane 101         DC Comics   \n",
      "16638       Injustice: Gods Among Us (2013) #1         DC Comics   \n",
      "12709                        Batman (2011-) #1         DC Comics   \n",
      "17286                Justice League (2011-) #1         DC Comics   \n",
      "55228   The Walking Dead Vol. 1: Days Gone Bye  Image - Skybound   \n",
      "19435     Batman: Night of the Owls Booklet #1         DC Comics   \n",
      "549    Batman Black & White: A Black and Wh...         DC Comics   \n",
      "231                                 Batman 101         DC Comics   \n",
      "2246                         Blackest Night #0         DC Comics   \n",
      "61309       Amazing Spider-Man (1999-2013) #36            Marvel   \n",
      "49490          Teenage Mutant Ninja Turtles #1               IDW   \n",
      "933               Batman: Gotham Adventures #1         DC Comics   \n",
      "22805  Superman: War of the Supermen #0 (of 0)         DC Comics   \n",
      "13226             Death of the Family: Preview         DC Comics   \n",
      "12722                       Batman (2011-) #13         DC Comics   \n",
      "64194  Captain America: The First Avenger #...            Marvel   \n",
      "62884              Avengers: Heroes Welcome #1            Marvel   \n",
      "80024  Scott Pilgrim Free Comic Book Day Story         Oni Press   \n",
      "69771                Marvel's Jessica Jones #1            Marvel   \n",
      "49753       Transformers: All Hail Megatron #1               IDW   \n",
      "553        Batman Black & White: Two of A Kind         DC Comics   \n",
      "51029                                  Chew #1             Image   \n",
      "13749              Detective Comics (2011-) #1         DC Comics   \n",
      "51699                                 Girls #1             Image   \n",
      "54956                            Invincible #1  Image - Skybound   \n",
      "40223                               52 Week #1         DC Comics   \n",
      "232                                 Batman 201         DC Comics   \n",
      "\n",
      "       Ratings_Quantity  \n",
      "55253             38841  \n",
      "41479              8885  \n",
      "53325              7747  \n",
      "11898              6749  \n",
      "16638              6608  \n",
      "12709              6305  \n",
      "17286              5483  \n",
      "55228              5408  \n",
      "19435              5091  \n",
      "549                4920  \n",
      "231                4710  \n",
      "2246               4644  \n",
      "61309              4436  \n",
      "49490              4408  \n",
      "933                4335  \n",
      "22805              4301  \n",
      "13226              3960  \n",
      "12722              3753  \n",
      "64194              3672  \n",
      "62884              3660  \n",
      "80024              3652  \n",
      "69771              3633  \n",
      "49753              3625  \n",
      "553                3489  \n",
      "51029              3434  \n",
      "13749              3303  \n",
      "51699              3279  \n",
      "54956              3275  \n",
      "40223              3248  \n",
      "232                3162  \n"
     ]
    }
   ],
   "source": [
    "# Sort the DataFrame by ratings quantity and show Name, Publisher and quantity\n",
    "comics_by_ratings_quantity = comixology_df[['Name','Publisher',\n",
    "                                            'Ratings_Quantity']].sort_values(\n",
    "                                            by='Ratings_Quantity', \n",
    "                                            ascending=False)\n",
    "print(comics_by_ratings_quantity.head(30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the chart with the most rated comics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Woif7a9GT/bXoyf5a9GR/LXr6decNXwcOHFCvXr00atQoNW/eXJLUq1cvjRw5UhEREfri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tLSAICSSjlb03Jfg+paIDw8HB4eHoiJiYGioiIrOQwbNgweHh4oKiqCr68vjIyMaMc6e/Ys2rVr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\nPS0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c9234898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create chart with the previously sorted comics\n",
    "plt.figure(figsize=(10, 6))\n",
    "y_axis = comics_by_ratings_quantity.head(30)['Ratings_Quantity']\n",
    "x_axis = np.arange(len(y_axis))\n",
    "\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.5, tuple(comics_by_ratings_quantity.head(30)['Name']), \n",
    "           rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Walking Dead is by far the one with most ratings. After that, some Marvel and DC comics and then some varied ones.</p>\n",
    "\n",
    "Now, let's make our detailed analysis on the giant publishers: Marvel and DC Comics.\n",
    "\n",
    "## Marvel vs DC Comics\n",
    "\n",
    "First, let's filter the DataFrame, so that only comics from these two remain. After that, we will calculate some average values of these two using a pivot table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    mean                                    \n",
      "          Original_price Page Count Price_per_page    Rating\n",
      "Publisher                                                   \n",
      "DC Comics       2.600034  35.318463       0.078356  4.233034\n",
      "Marvel          3.398555  41.344295       0.090946  4.191335\n"
     ]
    }
   ],
   "source": [
    "# Filter the DataFrame for comics from Marvel or DC Comics\n",
    "marvel_dc_comics = comixology_df[(comixology_df.Publisher == 'Marvel') | \n",
    "                                 (comixology_df.Publisher == 'DC Comics')]\n",
    " \n",
    "# Create pivot table with Primeiro, alguns valores médios de cada uma                                \n",
    "marvel_dc_pivot_averages = pd.pivot_table(marvel_dc_comics, \n",
    "                               values=['Rating','Original_price','Page Count',\n",
    "                                       'Price_per_page'], \n",
    "                               index=['Publisher'], \n",
    "                               aggfunc=[np.mean])\n",
    "print(marvel_dc_pivot_averages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, DC Comics has a lower average price and price per page, and an average rating slightly higher. The average page count is a little higher on Marvel. Below, the bar charts that represent these comparations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dj+iR4Q2R6GE1dEMkFsRERAZIq9UiIiICcrkcABATE4PQ0FD07t0bERERSE9Ph6enp55T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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c53a6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Charts for average values for Marvel and DC\n",
    "plt.figure(1,figsize=(10, 6))\n",
    "\n",
    "plt.subplot(221) # Mean original price\n",
    "y_axis = marvel_dc_pivot_averages['mean']['Original_price']\n",
    "x_axis = np.arange(len(marvel_dc_pivot_averages['mean']['Original_price']))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, \n",
    "           tuple(marvel_dc_pivot_averages['mean']['Original_price'].index))\n",
    "plt.title('Mean Original Price')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(222) # Mean page count\n",
    "y_axis = marvel_dc_pivot_averages['mean']['Page Count']\n",
    "x_axis = np.arange(len(marvel_dc_pivot_averages['mean']['Page Count']))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, \n",
    "           tuple(marvel_dc_pivot_averages['mean']['Page Count'].index))\n",
    "plt.title('Mean Page Count')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(223) # Mean Price Per Page\n",
    "y_axis = marvel_dc_pivot_averages['mean']['Price_per_page']\n",
    "x_axis = np.arange(len(marvel_dc_pivot_averages['mean']['Price_per_page']))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, \n",
    "           tuple(marvel_dc_pivot_averages['mean']['Price_per_page'].index))\n",
    "plt.title('Mean Price Per Page')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(224) # Mean Comic Rating\n",
    "y_axis = marvel_dc_pivot_averages['mean']['Rating']\n",
    "x_axis = np.arange(len(marvel_dc_pivot_averages['mean']['Rating']))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, \n",
    "           tuple(marvel_dc_pivot_averages['mean']['Rating'].index))\n",
    "plt.title('Mean Comic Rating')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step is to see some numbers related to the quantity of comics that each have. How many comics each publisher has, how many of them are good (4 or 5 stars rating), how many are bad (1 or 2 stars) and the proportion of these to the total. For this analysis, we will basically filter the DataFrame and count the number of rows of each filtered view. Simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Marvel's Total Comics: 18063\n",
      "Marvel's comics with rating 4 or bigger: 14791\n",
      "Proportion of Marvel's comics with rating 4 or bigger: 81.89%\n",
      "Marvel's comics with rating 2 or smaller: 95\n",
      "Proportion of Marvel's comics with rating 2 or smaller: 0.53%\n",
      "\n",
      "\n",
      "DC's Total Comics: 17440\n",
      "DC's comics with rating 4 or bigger: 15986\n",
      "Proportion of DC's comics with rating 4 or bigger: 91.66%\n",
      "DC's comics with rating 2 or smaller: 62\n",
      "Proportion of DC's comis with rating 2 or smaller: 0.36%\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculate total number of comics for each Publisher, proportion of comics \n",
    "# with rating 4 or bigger and proportion of comics with rating 2 or smaller\n",
    "marvel_total = len(marvel_dc_comics[marvel_dc_comics['Publisher'] == 'Marvel'])\n",
    "marvel_4_or_5 = len(marvel_dc_comics[(marvel_dc_comics['Publisher'] == 'Marvel')\n",
    "                                     & (marvel_dc_comics['Rating'] >= 4)])\n",
    "marvel_proportion_4_or_5 = marvel_4_or_5 / marvel_total\n",
    "marvel_1_or_2 = len(marvel_dc_comics[(marvel_dc_comics['Publisher'] == 'Marvel') \n",
    "                                     & (marvel_dc_comics['Rating'] <= 2)])\n",
    "marvel_proportion_1_or_2 = marvel_1_or_2 / marvel_total\n",
    "\n",
    "dc_total = len(marvel_dc_comics[marvel_dc_comics['Publisher'] == 'DC Comics'])\n",
    "dc_4_or_5 = len(marvel_dc_comics[(marvel_dc_comics['Publisher'] == 'DC Comics')\n",
    "                                 & (marvel_dc_comics['Rating'] >= 4)])\n",
    "dc_proportion_4_or_5 = dc_4_or_5 / dc_total\n",
    "dc_1_or_2 = len(marvel_dc_comics[(marvel_dc_comics['Publisher'] == 'DC Comics') \n",
    "                                 & (marvel_dc_comics['Rating'] <= 2)])\n",
    "dc_proportion_1_or_2 = dc_1_or_2 / dc_total\n",
    "\n",
    "print(\"\\n\")\n",
    "print(\"Marvel's Total Comics: \" + str(marvel_total))\n",
    "print(\"Marvel's comics with rating 4 or bigger: \" + \n",
    "      str(marvel_4_or_5))\n",
    "print(\"Proportion of Marvel's comics with rating 4 or bigger: \" + \n",
    "      str(\"{0:.2f}%\".format(marvel_proportion_4_or_5 * 100)))\n",
    "print(\"Marvel's comics with rating 2 or smaller: \" + \n",
    "      str(marvel_1_or_2))\n",
    "print(\"Proportion of Marvel's comics with rating 2 or smaller: \" + \n",
    "      str(\"{0:.2f}%\".format(marvel_proportion_1_or_2 * 100)))\n",
    "print(\"\\n\")\n",
    "print(\"DC's Total Comics: \" + str(dc_total))\n",
    "print(\"DC's comics with rating 4 or bigger: \" + \n",
    "      str(dc_4_or_5))\n",
    "print(\"Proportion of DC's comics with rating 4 or bigger: \" + \n",
    "      str(\"{0:.2f}%\".format(dc_proportion_4_or_5 * 100)))\n",
    "print(\"DC's comics with rating 2 or smaller: \" + \n",
    "      str(dc_1_or_2))\n",
    "print(\"Proportion of DC's comis with rating 2 or smaller: \" + \n",
    "      str(\"{0:.2f}%\".format(dc_proportion_1_or_2 * 100)))\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, here, DC Comics comes a little better. DC shows a bigger proportion of good comics and a smaller proportion of bad comics. DC scores one more. Below, the chart with the comparisons:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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6daoSExOVm5srHx8fTZo0SdWqVVN+fr5CQkJ03333af78+Vq0aJH+/ve/y9/fX6Ghoapc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\nnjdD3dQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c53f7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create charts with total comics and previously calculated proportions for \n",
    "# Marvel and DC\n",
    "plt.figure(2,figsize=(10, 6))\n",
    "\n",
    "plt.subplot(221) # Total comics for Marvel and DC\n",
    "y_axis = [dc_total, marvel_total]\n",
    "x_axis = np.arange(len(y_axis))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, ('DC Comics','Marvel'))\n",
    "plt.title('Total Comics')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(222) # Proportion of comics with rating 4 or 5\n",
    "y_axis = [dc_proportion_4_or_5 * 100, marvel_proportion_4_or_5 * 100]\n",
    "x_axis = np.arange(len(y_axis))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, ('DC Comics','Marvel'))\n",
    "plt.title('Proportion of comics with rating 4 or 5')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(223) # Proportion of comics with rating 1 or 2\n",
    "y_axis = [dc_proportion_1_or_2 * 100, marvel_proportion_1_or_2 * 100]\n",
    "x_axis = np.arange(len(y_axis))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, ('DC Comics','Marvel'))\n",
    "plt.title('Proportion of comics with rating 1 or 2')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as curiosity, let's check the number of ratings in comics of each publisher, through another pivot table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       sum\n",
      "          Ratings_Quantity\n",
      "Publisher                 \n",
      "DC Comics          1725344\n",
      "Marvel             1099324\n"
     ]
    }
   ],
   "source": [
    "# Create Pivot Table with quantity of ratings of each Publisher\n",
    "marvel_dc_pivot_sums = pd.pivot_table(marvel_dc_comics, \n",
    "                               values=['Ratings_Quantity'], \n",
    "                               index=['Publisher'], \n",
    "                               aggfunc=[np.sum])\n",
    "print(marvel_dc_pivot_sums)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting to note that even with Marvel having more comics, as we saw in the previous table, there quantity of ratings of DC's comics is way bigger, approximately 55% more. It seems that DC's fans are more propense to rate comics in Comixology than Marvel ones.\n",
    "\n",
    "Our next evaluation will be about characters and teams of heroes / villains. First, we need to create lists of characters and teams for each publisher. I created the lists by hand, doing some research. It didn't took very long."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define list of characters and teams of DC and Marvel\n",
    "main_dc_characters = ['Superman','Batman','Aquaman','Wonder Woman', 'Flash', \n",
    "                      'Robin','Arrow', 'Batgirl', 'Bane', 'Harley Queen', \n",
    "                      'Poison Ivy', 'Joker','Firestorm','Vixen',\n",
    "                      'Martian Manhunter','Zod','Penguin','Lex Luthor',\n",
    "                      'Green Lantern','Supergirl','Atom','Cyborg','Hawkgirl',\n",
    "                      'Starfire','Jonah Hex','Booster Gold','Black Canary',\n",
    "                      'Shazam','Catwoman','Nightwing','Zatanna','Hawkman',\n",
    "                      'Power Girl','Rorschach','Doctor Manhattan',\n",
    "                      'Blue Beetle','Batwoman','Darkseid','Vandal Savage', \n",
    "                      \"Ra's Al Ghul\",'Riddler','Reverse Flash','Black Adam',\n",
    "                      'Deathstroke','Brainiac','Sinestro','Two-Face']\n",
    "                      \n",
    "main_marvel_characters = ['Spider-Man','Captain Marvel','Hulk','Thor',\n",
    "                          'Iron Man','Luke Cage','Black Widow','Daredevil',\n",
    "                          'Captain America','Jessica Jones','Ghost Rider',\n",
    "                          'Spider-Woman','Silver Surfer','Beast','Thing',\n",
    "                          'Kitty Pride','Doctor Strange','Black Panther',\n",
    "                          'Invisible Woman','Nick Fury','Storm','Professor X',\n",
    "                          'Cyclops','Jean Grey','Wolverine','Scarlet Witch',\n",
    "                          'Gambit','Rogue','X-23','Iceman','She-Hulk',\n",
    "                          'Iron Fist','Hawkeye','Quicksilver','Vision',\n",
    "                          'Ant-Man','Cable','Bishop','Colossus','Deadpool',\n",
    "                          'Human Torch','Mr. Fantastic','Nightcrawler','Nova',\n",
    "                          'Psylocke','Punisher','Rocket Raccoon','Groot',\n",
    "                          'Star-Lord','War Machine','Gamora','Drax','Venom',\n",
    "                          'Carnage','Octopus','Green Goblin','Abomination',\n",
    "                          'Enchantress','Sentinel','Viper','Lady Deathstrike',\n",
    "                          'Annihilus','Ultron','Galactus','Kang','Bullseye',\n",
    "                          'Juggernaut','Sabretooth','Mystique','Kingpin',\n",
    "                          'Apocalypse','Thanos','Dark Phoenix','Loki',\n",
    "                          'Red Skull','Magneto','Doctor Doom','Ronan']\n",
    "                          \n",
    "dc_teams = ['Justice League','Teen Titans','Justice Society','Lantern Corps',\n",
    "            'Legion of Super-Heroes','All-Star Squadron','Suicide Squad',\n",
    "            'Birds of Prey','Gen13', 'The League of Extraordinary Gentlemen',\n",
    "            'Watchmen']\n",
    "            \n",
    "marvel_teams = ['X-Men','Avengers','Fantastic Four','Asgardian Gods','Skrulls',\n",
    "                'S.H.I.E.L.D.','Inhumans','A.I.M.','X-Factor','X-Force',\n",
    "                'Defenders','New Mutants','Brotherhood of Evil Mutants',\n",
    "                'Thunderbolts', 'Alpha Flight','Guardians of the Galaxy',\n",
    "                'Nova Corps','Illuminati']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to pass each name of character or team. First, let's define a DataFrame, and we'll filter so that the only rows that remain are the ones where the comic name includes the name of this character or team. Then, we'll extract some information from there. The quantity of comics will be the number of rows of the resulting DataFrame. Then, we will get the average price, rating and page count. All this information will be saved in a dictionary, and this dictionary will be appended to a character list, if it is a character, or a team list, if it is a team. In the end, we will have a list of dictionaries for characters and one for teams, and we will use them to create DataFrames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create empty list and dict to hold character info\n",
    "character_row = {}\n",
    "characters_dicts = []\n",
    "\n",
    "for character in main_dc_characters:\n",
    "    character_df = comixology_df[(comixology_df['Name'].str.contains(character)) & \n",
    "                                 (comixology_df['Publisher'] == 'DC Comics')]\n",
    "    character_row['Character_Name'] = character\n",
    "    character_row['Quantity_of_comics'] = len(character_df)\n",
    "    character_row['Average_Rating'] = np.nanmean(character_df['Rating'])\n",
    "    character_row['Average_Price'] = np.nanmean(character_df['Original_price'])\n",
    "    character_row['Average_Pages'] = np.nanmean(character_df['Page Count'])\n",
    "    character_row['Publisher'] = \"DC Comics\"\n",
    "    characters_dicts.append(character_row)\n",
    "    character_row = {}\n",
    "    \n",
    "for character in main_marvel_characters:\n",
    "    character_df = comixology_df[(comixology_df['Name'].str.contains(character)) & \n",
    "                                 (comixology_df['Publisher'] == 'Marvel')]\n",
    "    character_row['Character_Name'] = character\n",
    "    character_row['Quantity_of_comics'] = len(character_df)\n",
    "    character_row['Average_Rating'] = np.nanmean(character_df['Rating'])\n",
    "    character_row['Average_Price'] = np.nanmean(character_df['Original_price'])\n",
    "    character_row['Average_Pages'] = np.nanmean(character_df['Page Count'])\n",
    "    character_row['Publisher'] = \"Marvel\"\n",
    "    characters_dicts.append(character_row)\n",
    "    character_row = {}\n",
    "    \n",
    "characters_df = pd.DataFrame(characters_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create empty list and dict to hold team info\n",
    "team_row = {}\n",
    "teams_dicts = []\n",
    "\n",
    "for team in dc_teams:\n",
    "    team_df = comixology_df[(comixology_df['Name'].str.contains(team)) & \n",
    "                                 (comixology_df['Publisher'] == 'DC Comics')]\n",
    "    team_row['Team_Name'] = team\n",
    "    team_row['Quantity_of_comics'] = len(team_df)\n",
    "    team_row['Average_Rating'] = np.nanmean(team_df['Rating'])\n",
    "    team_row['Average_Price'] = np.nanmean(team_df['Original_price'])\n",
    "    team_row['Average_Pages'] = np.nanmean(team_df['Page Count'])\n",
    "    team_row['Publisher'] = \"DC Comics\"\n",
    "    teams_dicts.append(team_row)\n",
    "    team_row = {}\n",
    "    \n",
    "for team in marvel_teams:\n",
    "    team_df = comixology_df[(comixology_df['Name'].str.contains(team)) & \n",
    "                                 (comixology_df['Publisher'] == 'Marvel')]\n",
    "    team_row['Team_Name'] = team\n",
    "    team_row['Quantity_of_comics'] = len(team_df)\n",
    "    team_row['Average_Rating'] = np.nanmean(team_df['Rating'])\n",
    "    team_row['Average_Price'] = np.nanmean(team_df['Original_price'])\n",
    "    team_row['Average_Pages'] = np.nanmean(team_df['Page Count'])\n",
    "    team_row['Publisher'] = \"Marvel\"\n",
    "    teams_dicts.append(team_row)\n",
    "    team_row = {}\n",
    "    \n",
    "teams_df = pd.DataFrame(teams_dicts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider only teams and characters that have more than 20 comics where their names are present on the title of the comic. So, let's make a filter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Filter characters and teams DataFrame for rows where there are more than 20\n",
    "# comics where the character / team name is present on the title of the comics\n",
    "characters_df = characters_df[characters_df['Quantity_of_comics'] > 20]\n",
    "teams_df = teams_df[teams_df['Quantity_of_comics'] > 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's check the biggest characters and teams in number of comics and average rating. For the characters, even considering the ones with more than 20 comics, there are still too many characters left. So, we'll limit the list to the top 20 characters. For the teams, there is no need, since there are already less than 20. Then, we'll print the tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Character_Name  Average_Rating  Quantity_of_comics\n",
      "1            Batman        4.218568                2459\n",
      "47       Spider-Man        4.335099                1680\n",
      "0          Superman        4.197286                1043\n",
      "55  Captain America        3.949602                 831\n",
      "51         Iron Man        4.083821                 744\n",
      "49             Hulk        4.098540                 707\n",
      "18    Green Lantern        4.132159                 694\n",
      "71        Wolverine        4.122517                 631\n",
      "4             Flash        4.206271                 616\n",
      "3      Wonder Woman        4.313629                 615\n",
      "50             Thor        4.251244                 597\n",
      "54        Daredevil        4.306867                 529\n",
      "86         Deadpool        4.319018                 504\n",
      "5             Robin        4.308235                 429\n",
      "6             Arrow        4.223214                 341\n",
      "19        Supergirl        4.205036                 296\n",
      "28         Catwoman        3.920635                 266\n",
      "2           Aquaman        4.153543                 261\n",
      "29        Nightwing        4.248869                 221\n",
      "7           Batgirl        4.307692                 195\n"
     ]
    }
   ],
   "source": [
    "# Limit number of characters to 20\n",
    "top_characters_by_quantity = characters_df.sort_values(by='Quantity_of_comics',\n",
    "                                         ascending=False)[['Character_Name',\n",
    "                                         'Average_Rating',\n",
    "                                         'Quantity_of_comics']].head(20)\n",
    "top_characters_by_rating = characters_df.sort_values(by='Average_Rating',\n",
    "                                         ascending=False)[['Character_Name',\n",
    "                                         'Average_Rating',\n",
    "                                         'Quantity_of_comics']].head(20)\n",
    "\n",
    "top_teams_by_quantity = teams_df.sort_values(by='Quantity_of_comics', \n",
    "                                             ascending=False)[['Team_Name',\n",
    "                                             'Average_Rating',\n",
    "                                             'Quantity_of_comics']]\n",
    "top_teams_by_rating = teams_df.sort_values(by='Average_Rating', \n",
    "                                           ascending=False)[['Team_Name',\n",
    "                                           'Average_Rating',\n",
    "                                           'Quantity_of_comics']]\n",
    "\n",
    "print(top_characters_by_quantity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the characters, we have Batman as the one with the biggest number of comics, followed by Spider-Man and Superman. After that, we have some other famous characters, like Captain America, Iron Man, Wolverine, Flash. Here, nothing surprising."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Character_Name  Average_Rating  Quantity_of_comics\n",
      "115           Mystique        4.666667                  27\n",
      "25        Booster Gold        4.633803                  83\n",
      "24           Jonah Hex        4.632911                  84\n",
      "14   Martian Manhunter        4.611111                  55\n",
      "35         Blue Beetle        4.542373                  59\n",
      "59       Silver Surfer        4.468750                  82\n",
      "64       Black Panther        4.418033                 150\n",
      "52           Luke Cage        4.388889                  29\n",
      "83               Cable        4.361111                 144\n",
      "81              Vision        4.352941                  56\n",
      "92            Punisher        4.351852                 164\n",
      "78           Iron Fist        4.348624                 114\n",
      "96         War Machine        4.347826                  29\n",
      "69             Cyclops        4.346154                  27\n",
      "20                Atom        4.336735                 119\n",
      "47          Spider-Man        4.335099                1680\n",
      "75                X-23        4.333333                  39\n",
      "122            Magneto        4.324324                  37\n",
      "86            Deadpool        4.319018                 504\n",
      "3         Wonder Woman        4.313629                 615\n"
     ]
    }
   ],
   "source": [
    "print(top_characters_by_rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have some surprises on the top. Even if the quantity of comics is not very big, few people would imagine that Mystique would be the character with the highest average rating, among all these extremely popular characters. On the next positions, more surprises, with Booster Gold in second, Jonah Hex in third, Blue Beetle in fifth. Of the most popular characters, we see Spider-Man, Deadpool and Wonder Woman, in the end of the top 20. Let's go to the teams:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Team_Name  Average_Rating  Quantity_of_comics\n",
      "11                    X-Men        4.117677                2025\n",
      "12                 Avengers        4.063710                1721\n",
      "0            Justice League        4.190608                 744\n",
      "13           Fantastic Four        4.469671                 632\n",
      "1               Teen Titans        4.341518                 457\n",
      "4    Legion of Super-Heroes        4.268966                 326\n",
      "19                 X-Factor        4.253521                 224\n",
      "7             Birds of Prey        4.167513                 198\n",
      "20                  X-Force        4.240838                 193\n",
      "6             Suicide Squad        4.006329                 159\n",
      "26  Guardians of the Galaxy        4.132812                 143\n",
      "3             Lantern Corps        4.125926                 136\n",
      "22              New Mutants        4.095238                 130\n",
      "24             Thunderbolts        4.431193                 127\n",
      "16             S.H.I.E.L.D.        4.053763                 123\n",
      "21                Defenders        4.066667                  87\n",
      "2           Justice Society        4.142857                  77\n",
      "17                 Inhumans        4.245902                  69\n",
      "10                 Watchmen        4.345455                  55\n",
      "5         All-Star Squadron        4.500000                  50\n",
      "8                     Gen13        4.175000                  40\n"
     ]
    }
   ],
   "source": [
    "print(top_teams_by_quantity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among the teams with most comics, nothing surprising either. X-Men in first, Avenger in second and Justice League in third. Then, the other teams, like Fantastic Four, Suicide Squad:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Team_Name  Average_Rating  Quantity_of_comics\n",
      "5         All-Star Squadron        4.500000                  50\n",
      "13           Fantastic Four        4.469671                 632\n",
      "24             Thunderbolts        4.431193                 127\n",
      "10                 Watchmen        4.345455                  55\n",
      "1               Teen Titans        4.341518                 457\n",
      "4    Legion of Super-Heroes        4.268966                 326\n",
      "19                 X-Factor        4.253521                 224\n",
      "17                 Inhumans        4.245902                  69\n",
      "20                  X-Force        4.240838                 193\n",
      "0            Justice League        4.190608                 744\n",
      "8                     Gen13        4.175000                  40\n",
      "7             Birds of Prey        4.167513                 198\n",
      "2           Justice Society        4.142857                  77\n",
      "26  Guardians of the Galaxy        4.132812                 143\n",
      "3             Lantern Corps        4.125926                 136\n",
      "11                    X-Men        4.117677                2025\n",
      "22              New Mutants        4.095238                 130\n",
      "21                Defenders        4.066667                  87\n",
      "12                 Avengers        4.063710                1721\n",
      "16             S.H.I.E.L.D.        4.053763                 123\n",
      "6             Suicide Squad        4.006329                 159\n"
     ]
    }
   ],
   "source": [
    "print(top_teams_by_rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the ratings, the top 3 is formed by the All-Star Squadron, from DC Comics, Fantastic Four and the Thunderbolts, from Marvel. X-Men, Avenger and Suicide Squad are in the end of the list.\n",
    "\n",
    "Below we plot the charts for these numbers for the characters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OsWVn9JPO9LF5XZ+T+wjqC259QfzsUp9z9bdjOhCHhYVp2LBhCgwM1M6dO/XA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\np6bT6tixoypXrqynnnpKTzz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izbi1kRIZGYmtW7eisLAQ586dY38u/7f+MVxc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\nFAqFQqFQPmn+P9pSKS53bUDjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c6c2a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create charts related to the characters information\n",
    "plt.figure(3,figsize=(10, 6))\n",
    "\n",
    "plt.subplot(121) # Characters by quantity of comics\n",
    "y_axis = top_characters_by_quantity['Quantity_of_comics']\n",
    "x_axis = np.arange(len(y_axis))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, tuple(top_characters_by_quantity['Character_Name']), \n",
    "                             rotation=90)\n",
    "plt.title('Characters by quantity of comics')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(122) # Characters by average rating\n",
    "y_axis = top_characters_by_rating['Average_Rating']\n",
    "x_axis = np.arange(len(y_axis))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, tuple(top_characters_by_rating['Character_Name']), \n",
    "                             rotation=90)\n",
    "plt.title('Characters by average ratings')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And below, the charts for the teams:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GQExEREREssZATERERESyxkBMRERERLLGQExEREREssZATERERESyZlAgPnPm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x75c72c1828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creation of charts related to teams\n",
    "plt.figure(4,figsize=(10, 6))\n",
    "\n",
    "plt.subplot(121) # Teams by quantity of comics\n",
    "y_axis = top_teams_by_quantity['Quantity_of_comics']\n",
    "x_axis = np.arange(len(y_axis))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, tuple(top_teams_by_quantity['Team_Name']), rotation=90)\n",
    "plt.title('Teams by quantity of comics')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.subplot(122) # Teams by average ratings\n",
    "y_axis = top_teams_by_rating['Average_Rating']\n",
    "x_axis = np.arange(len(y_axis))\n",
    "plt.bar(x_axis, y_axis)\n",
    "plt.xticks(x_axis+0.4, tuple(top_teams_by_rating['Team_Name']), rotation=90)\n",
    "plt.title('Teams by average ratings')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "And with that, we conclude our series of 3 posts with the [analysis of the website](http://felipegalvao.com.br/blog/2016/05/01/comixology-digital-comics-analysis-part-1-analysis-of-site-for-web-scraping/), [web scraping](http://felipegalvao.com.br/blog/2016/05/24/web-scraping-with-python-scraping-digital-comics-information-from-comixology/) and [data analysis of digital comics](http://felipegalvao.com.br/blog/2016/06/06/digital-comics-analysis-python-marvel-or-dc-and-some-other-conclusions/), with information extracted from the [Comixology website](https://www.comixology.com/). As the data is not always available in a simple and practical manner, like a database or a csv dataset, sometimes we have to get the data through web scraping, or some other more complex technique.\n",
    "\n",
    "In this analysis, we reached some conclusions related to the comics on the website. I made a summary of my conclusions on the list below:\n",
    "\n",
    "* Some smaller publishers have a good average rating, probably being a good option if you want to read something different than the big ones (Marvel, DC Comics, Image, etc)\n",
    "* Among the big ones (publishers with more than 300 comics rated on Comixology), Marvel and DC Comics are in the bottom of the ranking when it comes to average ratings of its comics. The three first ones are Archie (of Archie comics, Mega Man, Sonic, among others), MAX (focused in adult comics: Dexter, Jessica Jones, Deadpool) and Image - Skybound (mainly Walking Dead).\n",
    "* Age Classifications does not seems to affect the rating a comic receives significantly.\n",
    "* The number of releases of comics increased a lot in the decade of 2000, suffered a recent downfall and now seems to oscilate through the years.\n",
    "* The two comics with most ratings on Comixology are free. The third, maybe surprisingly, is the first issue of the Saga series, from the publisher Image.\n",
    "* In the private battle between Marvel and DC Comics, DC seems to have a small advantage. DC has a smaller average price per page and average price, while having a slightly higher average rating. The average page count is a little bigger on Marvel's comics. DC also has a bigger proportion of good comics (4 ou 5 stars rating) and a smaller proportion of bad comics (1 or 2 stars rating).\n",
    "* Batman is the character with most comics, followed by Spider-Man and Superman. The heroes with the highest average rating are, surprisingly, Mystique (from X-Men), Booster Gold and Jonah Hex.\n",
    "* Among the teams, the ones with most comics are the X-Men, Avengers and Justice League. On the podium for the highest average rating are All-Star Squadron from DC, Fantastic Four and Thunderbolts from Marvel.\n",
    "\n",
    "And with that, we finish our small project. Hope everyone liked. :)\n",
    "\n",
    "Regards!"
   ]
  }
 ],
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