72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
# Instantiating Main Python Script File
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# Collects stuff from the rest of the scripts
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import pandas as pd
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import numpy as np
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import seaborn as sns
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import digger
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import gunner
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# Instantiating globals to be used in other files
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global games_merged_dat
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global games_sales_split_pre
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global games_sales_split_dur
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global games_sales_split_pos
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games_review = pd.read_csv("datasets/videogames/Games.xls")
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games_sales = pd.read_csv("datasets/videogames/vgsales-12-4-2019-short.csv")
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games_review_phase1 = digger.slice_column(games_review, "GameName", "Review")
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games_review_final = digger.slice_column(games_review, "GameName", "(Import)")
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games_merged_dat = digger.write_joined_df(games_sales, games_review_final)
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# Acquisition of Merged dataset
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games_merged_dat.to_csv("datasets/videogames/games_merged.csv")
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# Loading Crime Datasets
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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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year_interval = gunner.year_interval(crime_US, crime_CA, "report_year", "year")
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print(year_interval[0], year_interval[1])
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crime_intersect = gunner.intersect_by_year(crime_US, crime_CA, "report_year", "year")
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NA_col_list = [
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"PAL_Sales",
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"JP_Sales",
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"Other_Sales",
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"Global_Sales",
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"User_Score",
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"GameName",
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"Review",
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]
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GLO_col_list = [
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"PAL_Sales",
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"JP_Sales",
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"Other_Sales",
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"NA_Sales",
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"User_Score",
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"GameName",
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"Review",
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]
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# Splitting crime datasets
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# Collecting Split-Up Datasets
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games_merged_dat = gunner.drop_kick(NA_col_list, games_merged_dat)
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sale_tri_split = gunner.trisect_by_year(games_merged_dat, 'Year', year_interval)
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# Displaying Acquired Data
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print("Acquired Datasets:\n")
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print(sale_tri_split[0].head(5),
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sale_tri_split[1].head(5),
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sale_tri_split[2].head(5))
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print("Dataset Info:\n")
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sale_tri_split[0].info()
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sale_tri_split[1].info()
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sale_tri_split[2].info()
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