In [1]:
%matplotlib inline
After having done the analysis of the website (post here and the web scraping of the data from the Comixology website (post here, we will analyze the data that we got using Python (Pandas).
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.
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.
In [2]:
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
comixology_df = pd.read_csv("comixology_comics_dataset_19.04.2016.csv",
encoding = "ISO-8859-1")
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?
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:
In [3]:
# Create price per page column
comixology_df['Price_per_page'] = pd.Series(comixology_df['Original_price'] /
comixology_df['Page Count'],
index=comixology_df.index)
# Define price_per_page as NaN for comics with no information about page count
comixology_df.Price_per_page[comixology_df['Price_per_page'] == np.inf] = np.nan
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:
In [4]:
# Extract the year of release for print version
print_dates = []
for index, row in comixology_df.iterrows():
if type(comixology_df.ix[index]['Print Release Date']) == float:
row_year = np.nan
else:
row_year = int(comixology_df.ix[index]['Print Release Date'].split()[2])
if row_year > 2016:
row_year = np.nan
print_dates.append(row_year)
comixology_df['Print_Release_Year'] = pd.Series(print_dates,
index=comixology_df.index)
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.
In [5]:
# Calculate some average values of the site
average_price = np.nanmean(comixology_df['Original_price'])
average_page_count = np.nanmean(comixology_df['Page Count'])
average_rating = np.nanmean(comixology_df['Rating'])
average_rating_quantity = np.nanmean(comixology_df['Ratings_Quantity'])
average_price_per_page = np.nanmean(comixology_df['Price_per_page'])
print("Average Price: " + str(average_price))
print("Average Page Count: " + str(average_page_count))
print("Average Rating: " + str(average_rating))
print("Average Ratings Quantity: " +
str(average_rating_quantity))
print("Average Price Per Page: " + str(average_price_per_page))
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.
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>
In [6]:
# Define number of columns for table printing
pd.set_option('display.max_colwidth', 40)
In [7]:
# List comics with 5 stars rating that have at least 20 ratings
comics_with_5_stars = comixology_df[comixology_df.Rating == 5]
comics_with_5_stars = comics_with_5_stars[comics_with_5_stars.Ratings_Quantity
> 20]
# Print comics sorted by price per page
print(comics_with_5_stars[['Name','Publisher','Price_per_page']].
sort_values(by='Price_per_page'))
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.
In [8]:
# Filter the original DataFrame for comics with more than 5 ratings
comics_more_than_5_ratings = comixology_df[comixology_df.Ratings_Quantity > 5]
# Create pivot table with average rating by publisher
publishers_avg_rating = pd.pivot_table(comics_more_than_5_ratings,
values=['Rating'],
index=['Publisher'],
aggfunc=[np.mean, np.count_nonzero])
# Filter for any Publisher that has more than 20 comics rated
main_pub_avg_rating = publishers_avg_rating[publishers_avg_rating.
count_nonzero.Rating > 20]
main_pub_avg_rating = main_pub_avg_rating.sort_values(by=('mean','Rating'),
ascending=False)
print(main_pub_avg_rating)
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.
To help in the visualization, let's create a matplotlib chart that represents the table above:
In [9]:
# Create chart with average ratings for the Publishers
plt.figure(figsize=(10, 6))
y_axis = main_pub_avg_rating['mean']['Rating']
x_axis = range(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis, tuple(main_pub_avg_rating.index),rotation=90)
plt.show()
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:
In [10]:
# Filter for Publishers that have more than 300 comics rated
big_pub_avg_rating = publishers_avg_rating[publishers_avg_rating.
count_nonzero.Rating > 300]
big_pub_avg_rating = big_pub_avg_rating.sort_values(by=('mean','Rating'),
ascending=False)
print(big_pub_avg_rating)
In [11]:
# Create chart with average ratings for Publishers with more than 300 comics
# rated
plt.figure(figsize=(10, 6))
y_axis = big_pub_avg_rating['mean']['Rating']
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.5, tuple(big_pub_avg_rating.index), rotation=90)
plt.show()
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:
In [12]:
# Create pivot table with Rating by Age Rating
rating_by_age = pd.pivot_table(comics_more_than_5_ratings,
values=['Rating'],
index=['Age Rating'],
aggfunc=[np.mean, np.count_nonzero])
print(rating_by_age)
And below, the corresponding chart:
In [13]:
# Bar Chart with rating by age rating
plt.figure(figsize=(10, 6))
y_axis = rating_by_age['mean']['Rating']
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.25, tuple(rating_by_age.index), rotation=45)
plt.show()
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.
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:
In [14]:
# Create pivot table with print releases per year
print_releases_per_year = pd.pivot_table(comixology_df,
values=['Name'],
index=['Print_Release_Year'],
aggfunc=[np.count_nonzero])
print_years = []
for index, row in print_releases_per_year.iterrows():
print_year = int(index)
print_years.append(print_year)
print_releases_per_year.index = print_years
print(print_releases_per_year)
And now let's create the cart to see the situation better:
In [15]:
# Create chart with print releases per year
y_axis = print_releases_per_year['count_nonzero']['Name']
x_axis = print_releases_per_year['count_nonzero']['Name'].index
plt.figure(figsize=(10, 6))
plt.plot(x_axis, y_axis)
plt.show()
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.
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.
In [16]:
# Sort the DataFrame by ratings quantity and show Name, Publisher and quantity
comics_by_ratings_quantity = comixology_df[['Name','Publisher',
'Ratings_Quantity']].sort_values(
by='Ratings_Quantity',
ascending=False)
print(comics_by_ratings_quantity.head(30))
And the chart with the most rated comics:
In [17]:
# Create chart with the previously sorted comics
plt.figure(figsize=(10, 6))
y_axis = comics_by_ratings_quantity.head(30)['Ratings_Quantity']
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.5, tuple(comics_by_ratings_quantity.head(30)['Name']),
rotation=90)
plt.show()
Walking Dead is by far the one with most ratings. After that, some Marvel and DC comics and then some varied ones.</p>
Now, let's make our detailed analysis on the giant publishers: Marvel and DC Comics.
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:
In [18]:
# Filter the DataFrame for comics from Marvel or DC Comics
marvel_dc_comics = comixology_df[(comixology_df.Publisher == 'Marvel') |
(comixology_df.Publisher == 'DC Comics')]
# Create pivot table with Primeiro, alguns valores médios de cada uma
marvel_dc_pivot_averages = pd.pivot_table(marvel_dc_comics,
values=['Rating','Original_price','Page Count',
'Price_per_page'],
index=['Publisher'],
aggfunc=[np.mean])
print(marvel_dc_pivot_averages)
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:
In [19]:
# Charts for average values for Marvel and DC
plt.figure(1,figsize=(10, 6))
plt.subplot(221) # Mean original price
y_axis = marvel_dc_pivot_averages['mean']['Original_price']
x_axis = np.arange(len(marvel_dc_pivot_averages['mean']['Original_price']))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4,
tuple(marvel_dc_pivot_averages['mean']['Original_price'].index))
plt.title('Mean Original Price')
plt.tight_layout()
plt.subplot(222) # Mean page count
y_axis = marvel_dc_pivot_averages['mean']['Page Count']
x_axis = np.arange(len(marvel_dc_pivot_averages['mean']['Page Count']))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4,
tuple(marvel_dc_pivot_averages['mean']['Page Count'].index))
plt.title('Mean Page Count')
plt.tight_layout()
plt.subplot(223) # Mean Price Per Page
y_axis = marvel_dc_pivot_averages['mean']['Price_per_page']
x_axis = np.arange(len(marvel_dc_pivot_averages['mean']['Price_per_page']))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4,
tuple(marvel_dc_pivot_averages['mean']['Price_per_page'].index))
plt.title('Mean Price Per Page')
plt.tight_layout()
plt.subplot(224) # Mean Comic Rating
y_axis = marvel_dc_pivot_averages['mean']['Rating']
x_axis = np.arange(len(marvel_dc_pivot_averages['mean']['Rating']))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4,
tuple(marvel_dc_pivot_averages['mean']['Rating'].index))
plt.title('Mean Comic Rating')
plt.tight_layout()
plt.show()
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:
In [20]:
# Calculate total number of comics for each Publisher, proportion of comics
# with rating 4 or bigger and proportion of comics with rating 2 or smaller
marvel_total = len(marvel_dc_comics[marvel_dc_comics['Publisher'] == 'Marvel'])
marvel_4_or_5 = len(marvel_dc_comics[(marvel_dc_comics['Publisher'] == 'Marvel')
& (marvel_dc_comics['Rating'] >= 4)])
marvel_proportion_4_or_5 = marvel_4_or_5 / marvel_total
marvel_1_or_2 = len(marvel_dc_comics[(marvel_dc_comics['Publisher'] == 'Marvel')
& (marvel_dc_comics['Rating'] <= 2)])
marvel_proportion_1_or_2 = marvel_1_or_2 / marvel_total
dc_total = len(marvel_dc_comics[marvel_dc_comics['Publisher'] == 'DC Comics'])
dc_4_or_5 = len(marvel_dc_comics[(marvel_dc_comics['Publisher'] == 'DC Comics')
& (marvel_dc_comics['Rating'] >= 4)])
dc_proportion_4_or_5 = dc_4_or_5 / dc_total
dc_1_or_2 = len(marvel_dc_comics[(marvel_dc_comics['Publisher'] == 'DC Comics')
& (marvel_dc_comics['Rating'] <= 2)])
dc_proportion_1_or_2 = dc_1_or_2 / dc_total
print("\n")
print("Marvel's Total Comics: " + str(marvel_total))
print("Marvel's comics with rating 4 or bigger: " +
str(marvel_4_or_5))
print("Proportion of Marvel's comics with rating 4 or bigger: " +
str("{0:.2f}%".format(marvel_proportion_4_or_5 * 100)))
print("Marvel's comics with rating 2 or smaller: " +
str(marvel_1_or_2))
print("Proportion of Marvel's comics with rating 2 or smaller: " +
str("{0:.2f}%".format(marvel_proportion_1_or_2 * 100)))
print("\n")
print("DC's Total Comics: " + str(dc_total))
print("DC's comics with rating 4 or bigger: " +
str(dc_4_or_5))
print("Proportion of DC's comics with rating 4 or bigger: " +
str("{0:.2f}%".format(dc_proportion_4_or_5 * 100)))
print("DC's comics with rating 2 or smaller: " +
str(dc_1_or_2))
print("Proportion of DC's comis with rating 2 or smaller: " +
str("{0:.2f}%".format(dc_proportion_1_or_2 * 100)))
print("\n")
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:
In [21]:
# Create charts with total comics and previously calculated proportions for
# Marvel and DC
plt.figure(2,figsize=(10, 6))
plt.subplot(221) # Total comics for Marvel and DC
y_axis = [dc_total, marvel_total]
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4, ('DC Comics','Marvel'))
plt.title('Total Comics')
plt.tight_layout()
plt.subplot(222) # Proportion of comics with rating 4 or 5
y_axis = [dc_proportion_4_or_5 * 100, marvel_proportion_4_or_5 * 100]
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4, ('DC Comics','Marvel'))
plt.title('Proportion of comics with rating 4 or 5')
plt.tight_layout()
plt.subplot(223) # Proportion of comics with rating 1 or 2
y_axis = [dc_proportion_1_or_2 * 100, marvel_proportion_1_or_2 * 100]
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4, ('DC Comics','Marvel'))
plt.title('Proportion of comics with rating 1 or 2')
plt.tight_layout()
plt.show()
Just as curiosity, let's check the number of ratings in comics of each publisher, through another pivot table:
In [22]:
# Create Pivot Table with quantity of ratings of each Publisher
marvel_dc_pivot_sums = pd.pivot_table(marvel_dc_comics,
values=['Ratings_Quantity'],
index=['Publisher'],
aggfunc=[np.sum])
print(marvel_dc_pivot_sums)
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.
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.
In [23]:
# Define list of characters and teams of DC and Marvel
main_dc_characters = ['Superman','Batman','Aquaman','Wonder Woman', 'Flash',
'Robin','Arrow', 'Batgirl', 'Bane', 'Harley Queen',
'Poison Ivy', 'Joker','Firestorm','Vixen',
'Martian Manhunter','Zod','Penguin','Lex Luthor',
'Green Lantern','Supergirl','Atom','Cyborg','Hawkgirl',
'Starfire','Jonah Hex','Booster Gold','Black Canary',
'Shazam','Catwoman','Nightwing','Zatanna','Hawkman',
'Power Girl','Rorschach','Doctor Manhattan',
'Blue Beetle','Batwoman','Darkseid','Vandal Savage',
"Ra's Al Ghul",'Riddler','Reverse Flash','Black Adam',
'Deathstroke','Brainiac','Sinestro','Two-Face']
main_marvel_characters = ['Spider-Man','Captain Marvel','Hulk','Thor',
'Iron Man','Luke Cage','Black Widow','Daredevil',
'Captain America','Jessica Jones','Ghost Rider',
'Spider-Woman','Silver Surfer','Beast','Thing',
'Kitty Pride','Doctor Strange','Black Panther',
'Invisible Woman','Nick Fury','Storm','Professor X',
'Cyclops','Jean Grey','Wolverine','Scarlet Witch',
'Gambit','Rogue','X-23','Iceman','She-Hulk',
'Iron Fist','Hawkeye','Quicksilver','Vision',
'Ant-Man','Cable','Bishop','Colossus','Deadpool',
'Human Torch','Mr. Fantastic','Nightcrawler','Nova',
'Psylocke','Punisher','Rocket Raccoon','Groot',
'Star-Lord','War Machine','Gamora','Drax','Venom',
'Carnage','Octopus','Green Goblin','Abomination',
'Enchantress','Sentinel','Viper','Lady Deathstrike',
'Annihilus','Ultron','Galactus','Kang','Bullseye',
'Juggernaut','Sabretooth','Mystique','Kingpin',
'Apocalypse','Thanos','Dark Phoenix','Loki',
'Red Skull','Magneto','Doctor Doom','Ronan']
dc_teams = ['Justice League','Teen Titans','Justice Society','Lantern Corps',
'Legion of Super-Heroes','All-Star Squadron','Suicide Squad',
'Birds of Prey','Gen13', 'The League of Extraordinary Gentlemen',
'Watchmen']
marvel_teams = ['X-Men','Avengers','Fantastic Four','Asgardian Gods','Skrulls',
'S.H.I.E.L.D.','Inhumans','A.I.M.','X-Factor','X-Force',
'Defenders','New Mutants','Brotherhood of Evil Mutants',
'Thunderbolts', 'Alpha Flight','Guardians of the Galaxy',
'Nova Corps','Illuminati']
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:
In [24]:
# Create empty list and dict to hold character info
character_row = {}
characters_dicts = []
for character in main_dc_characters:
character_df = comixology_df[(comixology_df['Name'].str.contains(character)) &
(comixology_df['Publisher'] == 'DC Comics')]
character_row['Character_Name'] = character
character_row['Quantity_of_comics'] = len(character_df)
character_row['Average_Rating'] = np.nanmean(character_df['Rating'])
character_row['Average_Price'] = np.nanmean(character_df['Original_price'])
character_row['Average_Pages'] = np.nanmean(character_df['Page Count'])
character_row['Publisher'] = "DC Comics"
characters_dicts.append(character_row)
character_row = {}
for character in main_marvel_characters:
character_df = comixology_df[(comixology_df['Name'].str.contains(character)) &
(comixology_df['Publisher'] == 'Marvel')]
character_row['Character_Name'] = character
character_row['Quantity_of_comics'] = len(character_df)
character_row['Average_Rating'] = np.nanmean(character_df['Rating'])
character_row['Average_Price'] = np.nanmean(character_df['Original_price'])
character_row['Average_Pages'] = np.nanmean(character_df['Page Count'])
character_row['Publisher'] = "Marvel"
characters_dicts.append(character_row)
character_row = {}
characters_df = pd.DataFrame(characters_dicts)
In [25]:
# Create empty list and dict to hold team info
team_row = {}
teams_dicts = []
for team in dc_teams:
team_df = comixology_df[(comixology_df['Name'].str.contains(team)) &
(comixology_df['Publisher'] == 'DC Comics')]
team_row['Team_Name'] = team
team_row['Quantity_of_comics'] = len(team_df)
team_row['Average_Rating'] = np.nanmean(team_df['Rating'])
team_row['Average_Price'] = np.nanmean(team_df['Original_price'])
team_row['Average_Pages'] = np.nanmean(team_df['Page Count'])
team_row['Publisher'] = "DC Comics"
teams_dicts.append(team_row)
team_row = {}
for team in marvel_teams:
team_df = comixology_df[(comixology_df['Name'].str.contains(team)) &
(comixology_df['Publisher'] == 'Marvel')]
team_row['Team_Name'] = team
team_row['Quantity_of_comics'] = len(team_df)
team_row['Average_Rating'] = np.nanmean(team_df['Rating'])
team_row['Average_Price'] = np.nanmean(team_df['Original_price'])
team_row['Average_Pages'] = np.nanmean(team_df['Page Count'])
team_row['Publisher'] = "Marvel"
teams_dicts.append(team_row)
team_row = {}
teams_df = pd.DataFrame(teams_dicts)
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:
In [26]:
# Filter characters and teams DataFrame for rows where there are more than 20
# comics where the character / team name is present on the title of the comics
characters_df = characters_df[characters_df['Quantity_of_comics'] > 20]
teams_df = teams_df[teams_df['Quantity_of_comics'] > 20]
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:
In [27]:
# Limit number of characters to 20
top_characters_by_quantity = characters_df.sort_values(by='Quantity_of_comics',
ascending=False)[['Character_Name',
'Average_Rating',
'Quantity_of_comics']].head(20)
top_characters_by_rating = characters_df.sort_values(by='Average_Rating',
ascending=False)[['Character_Name',
'Average_Rating',
'Quantity_of_comics']].head(20)
top_teams_by_quantity = teams_df.sort_values(by='Quantity_of_comics',
ascending=False)[['Team_Name',
'Average_Rating',
'Quantity_of_comics']]
top_teams_by_rating = teams_df.sort_values(by='Average_Rating',
ascending=False)[['Team_Name',
'Average_Rating',
'Quantity_of_comics']]
print(top_characters_by_quantity)
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.
In [28]:
print(top_characters_by_rating)
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:
In [29]:
print(top_teams_by_quantity)
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:
In [30]:
print(top_teams_by_rating)
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.
Below we plot the charts for these numbers for the characters:
In [31]:
# Create charts related to the characters information
plt.figure(3,figsize=(10, 6))
plt.subplot(121) # Characters by quantity of comics
y_axis = top_characters_by_quantity['Quantity_of_comics']
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4, tuple(top_characters_by_quantity['Character_Name']),
rotation=90)
plt.title('Characters by quantity of comics')
plt.tight_layout()
plt.subplot(122) # Characters by average rating
y_axis = top_characters_by_rating['Average_Rating']
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4, tuple(top_characters_by_rating['Character_Name']),
rotation=90)
plt.title('Characters by average ratings')
plt.tight_layout()
plt.show()
And below, the charts for the teams:
In [32]:
# Creation of charts related to teams
plt.figure(4,figsize=(10, 6))
plt.subplot(121) # Teams by quantity of comics
y_axis = top_teams_by_quantity['Quantity_of_comics']
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4, tuple(top_teams_by_quantity['Team_Name']), rotation=90)
plt.title('Teams by quantity of comics')
plt.tight_layout()
plt.subplot(122) # Teams by average ratings
y_axis = top_teams_by_rating['Average_Rating']
x_axis = np.arange(len(y_axis))
plt.bar(x_axis, y_axis)
plt.xticks(x_axis+0.4, tuple(top_teams_by_rating['Team_Name']), rotation=90)
plt.title('Teams by average ratings')
plt.tight_layout()
plt.show()
And with that, we conclude our series of 3 posts with the analysis of the website, web scraping and data analysis of digital comics, with information extracted from the Comixology website. 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.
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:
And with that, we finish our small project. Hope everyone liked. :)
Regards!