{"id":46774,"date":"2023-08-30T00:00:00","date_gmt":"2023-08-30T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/superhero-showdown-analyzing-marvel-and-dc-data-with-griddb\/"},"modified":"2025-11-13T12:56:43","modified_gmt":"2025-11-13T20:56:43","slug":"superhero-showdown-analyzing-marvel-and-dc-data-with-griddb","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/superhero-showdown-analyzing-marvel-and-dc-data-with-griddb\/","title":{"rendered":"Superhero Showdown: Analyzing Marvel and DC Data with GridDB"},"content":{"rendered":"
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Packages required\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[1]:<\/div>\n
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<\/span>import<\/span> <\/span>os<\/span>\nimport<\/span> <\/span>pandas<\/span> <\/span>as<\/span> <\/span>pd<\/span>\nimport<\/span> <\/span>numpy<\/span> <\/span>as<\/span> <\/span>np<\/span>\nfrom<\/span> <\/span>IPython.display<\/span> <\/span>import<\/span> Image<\/span>\nimport<\/span> <\/span>jaydebeapi<\/span>\nimport<\/span> <\/span>urllib.parse<\/span>\nimport<\/span> <\/span>sys<\/span>\nimport<\/span> <\/span>matplotlib.pyplot<\/span> <\/span>as<\/span> <\/span>plt<\/span>\nimport<\/span> <\/span>seaborn<\/span> <\/span>as<\/span> <\/span>sns<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Overview\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Marvel and DC comics have been around for several decades. Both Marvel and DC are known for their iconic characters, super-heroes and supervillains. While Marvel produced Spider-Man, Iron Man and Hulk; DC produced Superman, Batman and the Wonder Woman. Both of these publishers have diversified their content and expanded beyond traditional comic books to encompass various forms of media and storytelling.<\/p>\n
DC and Marvel have created characters that transcend nationality and resonate with people worldwide. The iconic characters created by these brands have become a part of popular culture, prompting passionate debates among fans when comparing Marvel and DC heroes.<\/p>\n
We delve deeper into the Marvel and DC universe with the help of a public dataset from Data.World called the  'Comic Characters Dataset'. <\/a><\/p>\n
There are two data files within the dataset. Download each of them using the 'download' icon as shown below. Download the DC Dataset as shown below.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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In\u00a0[2]:<\/div>\n
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<\/span>##Specify the path to your image file<\/span>\nimage_path<\/span> =<\/span> 'dc-wikia-data_download_icon.png'<\/span>\n\nwidth<\/span> =<\/span> 500<\/span>\n\n## Display the image<\/span>\nImage<\/span>(<\/span>filename<\/span>=<\/span>image_path<\/span>,<\/span> width<\/span>=<\/span>width<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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\n---------------------------------------------------------------------------<\/span>\nFileNotFoundError<\/span>                         Traceback (most recent call last)\n<ipython-input-2-3581f17e51f6><\/span> in <module><\/span>\n      5<\/span> \n      6<\/span> ## Display the image<\/span>\n----> 7<\/span> <\/span>Image(<\/span>filename=<\/span>image_path,<\/span> width=<\/span>width)<\/span>\n\n~\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/display.py<\/span> in __init__<\/span>(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)<\/span>\n   1223<\/span>         self.<\/span>unconfined =<\/span> unconfined\n   1224<\/span>         super(Image, self).__init__(data=data, url=url, filename=filename, \n-> 1225<\/span>                 metadata=metadata)\n<\/span>   1226<\/span> \n   1227<\/span>         if<\/span> self.<\/span>width is<\/span> None<\/span> and<\/span> self.<\/span>metadata.<\/span>get(<\/span>'width'<\/span>,<\/span> {<\/span>}<\/span>)<\/span>:<\/span>\n\n~\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/display.py<\/span> in __init__<\/span>(self, data, url, filename, metadata)<\/span>\n    628<\/span>             self.<\/span>metadata =<\/span> {<\/span>}<\/span>\n    629<\/span> \n--> 630<\/span>         <\/span>self.<\/span>reload(<\/span>)<\/span>\n    631<\/span>         self.<\/span>_check_data(<\/span>)<\/span>\n    632<\/span> \n\n~\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/display.py<\/span> in reload<\/span>(self)<\/span>\n   1254<\/span>         \"\"\"Reload the raw data from file or URL.\"\"\"<\/span>\n   1255<\/span>         if<\/span> self.<\/span>embed:<\/span>\n-> 1256<\/span>             <\/span>super(<\/span>Image,<\/span>self)<\/span>.<\/span>reload(<\/span>)<\/span>\n   1257<\/span>             if<\/span> self.<\/span>retina:<\/span>\n   1258<\/span>                 self.<\/span>_retina_shape(<\/span>)<\/span>\n\n~\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/display.py<\/span> in reload<\/span>(self)<\/span>\n    653<\/span>         \"\"\"Reload the raw data from file or URL.\"\"\"<\/span>\n    654<\/span>         if<\/span> self.<\/span>filename is<\/span> not<\/span> None<\/span>:<\/span>\n--> 655<\/span>             <\/span>with<\/span> open(<\/span>self.<\/span>filename,<\/span> self.<\/span>_read_flags)<\/span> as<\/span> f:<\/span>\n    656<\/span>                 self.<\/span>data =<\/span> f.<\/span>read(<\/span>)<\/span>\n    657<\/span>         elif<\/span> self.<\/span>url is<\/span> not<\/span> None<\/span>:<\/span>\n\nFileNotFoundError<\/span>: [Errno 2] No such file or directory: 'dc-wikia-data_download_icon.png'<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Similarly download the Marvel Dataset.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span>##Specify the path to your image file<\/span>\nimage_path<\/span> =<\/span> 'marvel-wikia-data_download_icon.png'<\/span>\n\nwidth<\/span> =<\/span> 500<\/span>\n\n## Display the image<\/span>\nImage<\/span>(<\/span>filename<\/span>=<\/span>image_path<\/span>,<\/span> width<\/span>=<\/span>width<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Out[\u00a0]:<\/div>\n
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Each data file (.csv) can be downloaded using the 'Download' button as shown below -<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Do the same for the Marvel Dataset as well.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Loading the data into a dataframe and Data Cleaning\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Loading the DC Dataframe\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span>##Loading the data into a dataframe in Python<\/span>\ndc_df<\/span> =<\/span> pd<\/span>.<\/span>read_csv<\/span>(<\/span>'dc-wikia-data.csv'<\/span>,<\/span>sep<\/span>=<\/span>','<\/span>)<\/span> #Load the dataframe <\/span>\ndc_df<\/span>[<\/span>'YEAR'<\/span>]<\/span>=<\/span> dc_df<\/span>[<\/span>'YEAR'<\/span>]<\/span>.<\/span>fillna<\/span>(<\/span>0<\/span>)<\/span>.<\/span>astype<\/span>(<\/span>int<\/span>)<\/span> \ndc_df<\/span>[<\/span>'APPEARANCES'<\/span>]<\/span>=<\/span> dc_df<\/span>[<\/span>'APPEARANCES'<\/span>]<\/span>.<\/span>fillna<\/span>(<\/span>0<\/span>)<\/span>.<\/span>astype<\/span>(<\/span>int<\/span>)<\/span>\ndc_df<\/span> =<\/span> dc_df<\/span>.<\/span>rename<\/span>(<\/span>columns<\/span>=<\/span>{<\/span>'ID'<\/span>:<\/span> 'identity'<\/span>})<\/span>\ndc_df<\/span>.<\/span>fillna<\/span>(<\/span>''<\/span>,<\/span> inplace<\/span>=<\/span>True<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Loading the Marvel Dataframe\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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<\/span>##Loading the data into a dataframe in Python<\/span>\nmarvel_df<\/span> =<\/span> pd<\/span>.<\/span>read_csv<\/span>(<\/span>'marvel-wikia-data.csv'<\/span>,<\/span>sep<\/span>=<\/span>','<\/span>)<\/span> #Load the dataframe<\/span>\nmarvel_df<\/span>[<\/span>'Year'<\/span>]<\/span>=<\/span> marvel_df<\/span>[<\/span>'Year'<\/span>]<\/span>.<\/span>fillna<\/span>(<\/span>0<\/span>)<\/span>.<\/span>astype<\/span>(<\/span>int<\/span>)<\/span> \nmarvel_df<\/span>[<\/span>'APPEARANCES'<\/span>]<\/span>=<\/span> marvel_df<\/span>[<\/span>'APPEARANCES'<\/span>]<\/span>.<\/span>fillna<\/span>(<\/span>0<\/span>)<\/span>.<\/span>astype<\/span>(<\/span>int<\/span>)<\/span>\nmarvel_df<\/span> =<\/span> marvel_df<\/span>.<\/span>rename<\/span>(<\/span>columns<\/span>=<\/span>{<\/span>'ID'<\/span>:<\/span> 'identity'<\/span>})<\/span>\nmarvel_df<\/span>.<\/span>fillna<\/span>(<\/span>''<\/span>,<\/span> inplace<\/span>=<\/span>True<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Why GridDB?\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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GridDB is a suitable choice for this analysis for the below reasons -<\/p>\n