85 lines
3.0 KiB
Python
85 lines
3.0 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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import mining_hq
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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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global game_sales_NA_dur
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global game_sales_NA_pre
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global game_sales_NA_pos
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# Sales Globally
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global game_sales_GLO
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# Crime Data
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# Crime Recorded in The US
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global crime_US
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# Crime Recorded in Canada
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global crime_CA
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# Loading Datasets
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games_merged = mining_hq.games_merged_dat
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crime_CA = pd.read_excel('datasets/crime/clean_crime_canada_dataset.xlsx')
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crime_US = pd.read_csv('datasets/crime/report.csv')
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# Printing information regarding datasets
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print("Game Datasets' Info:\n")
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games_merged.info()
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print("Crime Datasets' Info:\n")
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crime_US.info()
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crime_CA.info()
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# Printing First n values (index start: 0)
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print("Game Sale Data:\n", games_merged.head(5))
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print("US Crime Data:\n", crime_US.head(5))
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print("CA Crime Data:\n", crime_CA.head(5))
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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_merged['Score'] = pd.to_numeric(games_merged['Score'], errors = 'coerce')
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games_merged = games_merged[games_merged['Score'].notnull()]
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print("Game Scores (Cleaned):\n", games_merged.head())
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games_merged.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', 'User_Score', 'GameName', 'Review', '']
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GLO_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'NA_Sales', 'User_Score', 'GameName', 'Review', '']
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game_sales_NA = games_merged.drop(columns = NA_col_list, axis = 1)
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game_sales_GLO = games_merged.drop(columns = GLO_col_list, axis = 1)
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print(f"Game Sales for NA:\n{game_sales_NA.head(5)} \nWith minimum year being: {game_sales_NA['Year'].min()}")
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print(f"Game Sales Globally:\n{game_sales_GLO.head(5)}\nWith minimum year being: {game_sales_GLO['Year'].min()}")
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# Getting the range of years which both datasets share
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crime_year_min = max(crime_US['report_year'].min(), crime_CA['year'].min())
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crime_year_max = min(crime_US['report_year'].max(), crime_CA['year'].max())
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crime_CA = crime_CA[(crime_CA['year'] >= crime_year_min) & (crime_CA['year'] <= crime_year_max)]
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crime_US = crime_US[(crime_US['report_year'] >= crime_year_min) & (crime_US['report_year'] <= crime_year_max)]
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# Updating the NA game dataset to fit with the time ranges
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game_sales_NA_dur = game_sales_NA[(game_sales_NA['Year'] >= crime_year_min) & (game_sales_NA['Year'] <= crime_year_max)]
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game_sales_NA_pre = game_sales_NA[game_sales_NA['Year'] < crime_year_min]
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game_sales_NA_pos = game_sales_NA[game_sales_NA['Year'] > crime_year_max]
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print(f"Game Sales for NA:\n{game_sales_NA.head(10)}\nWith minimum year being: {game_sales_NA['Year'].min()}")
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print(f"Game Sales Globally:\n{game_sales_GLO.head(10)}\nWith minimum year being: {game_sales_GLO['Year'].min()}")
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