111 lines
2.9 KiB
Python
111 lines
2.9 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 scout
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import numpy as np
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import seaborn as sns
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import digger, 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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print(games_review.count())
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print(games_sales.count())
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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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print(games_merged_dat.count())
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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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print(crime_US.isnull())
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print(crime_CA.isnull())
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year_interval = gunner.year_interval(crime_US, crime_CA, "report_year", "year")
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year_max = year_interval[0]
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year_min = year_interval[1]
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crime_intersect = gunner.intersect_by_year(crime_US, crime_CA, "report_year", "year")
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crime_US_intersect = crime_intersect[0]
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crime_CA_intersect = crime_intersect[1]
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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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games_sales_split_pre = sale_tri_split[0]
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games_sales_split_dur = sale_tri_split[1]
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games_sales_split_pos = sale_tri_split[2]
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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), sale_tri_split[1].head(5), 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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print("Dataset Info:\n")
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games_sales_split_pre.info()
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games_sales_split_dur.info()
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games_sales_split_pos.info()
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print(games_sales_split_dur.describe())
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print(
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games_sales_split_pre.head(5),
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games_sales_split_dur.head(5),
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games_sales_split_pos.head(5),
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)
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# Required to use binning for cleaning, idk
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# https://towardsdatascience.com/data-preprocessing-with-python-pandas-part-5-binning-c5bd5fd1b950
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# Also need to transform using Z-score (normal distr go brrrr lmao), or min-max
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# Need similarity and dissimialrity, scipy time
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# Load merged gammas
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gammas = pd.read_csv("datasets/videogames/games_merged.csv")
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gammas["User_Score"] = scout.cure_depression(gammas, "User_Score")
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print(gammas["User_Score"])
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