new folder who dis

dwarves/mining_hq.py

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+# Instantiating Main Python Script File
+# Collects stuff from the rest of the scripts
+import pandas as pd
+import numpy as np
+import seaborn as sns
+# containment breach
+import scipy as scp
+import digger, gunner, scout
+
+# Instantiating globals to be used in other files
+global games_merged_dat
+global games_sales_split_pre
+global games_sales_split_dur
+global games_sales_split_pos
+
+games_review = pd.read_csv("datasets/videogames/Games.xls")
+games_sales = scout.cure_depression(pd.read_csv("datasets/videogames/vgsales-12-4-2019-short.csv"))
+
+print(games_review.count())
+print(games_sales.count())
+
+games_review_phase1 = digger.slice_column(games_review, "GameName", "Review")
+games_review_final = digger.slice_column(games_review, "GameName", "(Import)")
+
+games_merged_dat = digger.write_joined_df(games_sales, games_review_final)
+
+# Acquisition of Merged dataset
+print(games_merged_dat.count())
+
+# Loading Crime Datasets
+crime_CA = pd.read_excel("datasets/crime/clean_crime_canada_dataset.xlsx")
+
+crime_US = pd.read_csv("datasets/crime/report.csv")
+
+print(crime_US.isnull())
+print(crime_CA.isnull())
+
+year_interval = gunner.year_interval(crime_US, crime_CA, "report_year", "year")
+
+year_max = year_interval[0]
+year_min = year_interval[1]
+
+crime_intersect = gunner.intersect_by_year(crime_US, crime_CA, "report_year", "year")
+
+crime_US_intersect = crime_intersect[0]
+crime_CA_intersect = crime_intersect[1]
+
+NA_col_list = [
+    "JP_Sales",
+    "Other_Sales",
+    "Global_Sales",
+    "PAL_Sales",
+    "GameName",
+    "Review",
+    "Console",
+    "Score",
+]
+GLO_col_list = [
+    "JP_Sales",
+    "Other_Sales",
+    "NA_Sales",
+    "PAL_Sales",
+    "GameName",
+    "Review",
+    "Console",
+    "Score",
+]
+
+# Splitting crime datasets
+# Collecting Split-Up Datasets
+games_merged_dat = gunner.drop_kick(NA_col_list, games_merged_dat)
+games_merged_dat.to_csv("datasets/videogames/games_merged.csv", index=False)
+
+sale_tri_split = gunner.trisect_by_year(games_merged_dat, "Year", year_interval)
+
+games_sales_split_pre = sale_tri_split[0]
+games_sales_split_dur = sale_tri_split[1]
+games_sales_split_pos = sale_tri_split[2]
+
+# Displaying Acquired Data
+print("Dataset Info:\n")
+games_sales_split_pre.info()
+games_sales_split_dur.info()
+games_sales_split_pos.info()
+
+print("Yer forsaken Statistical Description:\n", games_sales_split_dur.describe())
+
+print(
+    games_sales_split_pre.head(5),
+    games_sales_split_dur.head(5),
+    games_sales_split_pos.head(5),
+)
+
+# Load merged gammas
+
+# Required to use binning for cleaning, idk (DONE LESGOOOOOOOOOOOOOOOOOOOO)
+# https://towardsdatascience.com/data-preprocessing-with-python-pandas-part-5-binning-c5bd5fd1b950
+gammas = pd.read_csv("datasets/videogames/games_merged.csv")
+labels = ["smol", "epik", "larg"]
+gammas = digger.slam_dunk(gammas, "Critic_Score", labels=labels)
+# gammas = gammas[gammas["Genre"].isna() == False]
+# gammas = scout.cure_depression(gammas)
+
+# Also need to transform using Z-score (normal distr go brrrr lmao), or min-max
+# ah, scheiße
+# nvm, done, kekW
+gammas['Critic_Score_Norm'] = scout.scaling_zscore(gammas, 'Critic_Score')
+print(gammas['Critic_Score_Norm'].head(10))
+
+# Saving all into a file
+gammas.to_csv("datasets/videogames/games_cleanish.csv", index=False)
+
+# Need similarity and dissimialrity, scipy time
+# Selecting 5 random rows
+chosen_idx = np.random.choice(len(gammas), replace = False, size = 5)
+sample_rows = gammas.iloc[chosen_idx]
+print(sample_rows.head())
+
+scout.dissimilarity(sample_rows)