49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
# Cleaning of datasets
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# Somewhat main in the beninging
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import pandas as pd
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import numpy as np
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# Sharing the dataset variables
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# Games' data
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global games_dat
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# Sales in NA
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global game_sales_NA
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# Sales Globally
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global game_sales_GLO
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# Loading Datasets
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game_sales2019_dat = pd.read_csv('datasets/videogames/vgsales-12-4-2019-short.csv')
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games_dat = pd.read_csv('datasets/videogames/Games.xls')
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# Printing information regarding datasets
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print("Data Sets' Info:\n")
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game_sales2019_dat.info()
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games_dat.info()
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# Printing First n values (index start: 0)
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print("Game Sale Data:\n", game_sales2019_dat.head(10))
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print("Game Scores:\n", games_dat.head(10))
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# Regarding the Games.xls dataset:
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# Coercing the non-numeric values will result in NaN
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# thus allowing easier removal through `.notnull()`
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games_dat['Score'] = pd.to_numeric(games_dat['Score'], errors = 'coerce')
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games_dat = games_dat[games_dat['Score'].notnull()]
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print("Game Scores (Cleaned):\n", games_dat.head())
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games_dat.info()
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# Regarding the vgsales-12-4-2019 dataset
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# Considering we will be using a US (probs CA too) crime datasets
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# It wouldn't be that useful to have other columns regarding other regions
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NA_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales']
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GLO_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'NA_Sales']
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game_sales_NA = game_sales2019_dat.drop(columns = NA_col_list, axis = 1)
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game_sales_GLO = game_sales2019_dat.drop(columns = GLO_col_list, axis = 1)
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print("Game Sales for NA:\n", game_sales_NA.head(10))
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print("Game Sales Globally:\n", game_sales_GLO.head(10))
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