diff --git a/py_scripts/engineer.py b/py_scripts/engineer.py
index ab22516..f3c089d 100644
--- a/py_scripts/engineer.py
+++ b/py_scripts/engineer.py
@@ -5,19 +5,30 @@ import mining_hq
 from numpy import count_nonzero
 
 sns.set()
-plt.xticks(rotation = 90)
 
 games_pre = mining_hq.games_sales_split_pre
 games_dur = mining_hq.games_sales_split_dur
 games_pos = mining_hq.games_sales_split_pos
 
-games_fig_pre = sns.barplot(data = games_pre, x = "Year", y = "NA_Sales", estimator = count_nonzero)
+crime_US = mining_hq.crime_US_intersect
+crime_CA = mining_hq.crime_CA_intersect
+
+plt.xticks(rotation = 90)
+games_fig_pre = sns.histplot(data = games_pre, x = "Year", palette = sns.color_palette("flare"), kde = True)
 plt.show()
 
 plt.xticks(rotation = 90)
-games_fig_dur = sns.barplot(data = games_dur, x = "Year", y = "NA_Sales", estimator = count_nonzero)
+games_fig2_pre = sns.histplot(data = games_pre, x = "Year", hue = "Genre", multiple = "stack", shrink = 0.65)
 plt.show()
 
 plt.xticks(rotation = 90)
-games_fig_pos = sns.barplot(data = games_pos, x = "Year", y = "NA_Sales", estimator = count_nonzero)
+games_fig_dur = sns.barplot(data = games_dur, x = "Year", y = "NA_Sales")
+plt.show()
+
+plt.xticks(rotation = 90)
+games_fig_pos = sns.barplot(data = games_pos, x = "Year", y = "NA_Sales")
+plt.show()
+
+plt.xticks(rotation = 90)
+crime_CA_fig = sns.barplot(data = crime_CA, x = "year", y = "incidents")
 plt.show()
diff --git a/py_scripts/mining_hq.py b/py_scripts/mining_hq.py
index 9ccbff3..b18449b 100644
--- a/py_scripts/mining_hq.py
+++ b/py_scripts/mining_hq.py
@@ -4,8 +4,7 @@ import pandas as pd
 import scout
 import numpy as np
 import seaborn as sns
-import digger
-import gunner
+import digger, gunner
 
 # Instantiating globals to be used in other files
 global games_merged_dat
@@ -16,12 +15,16 @@ global games_sales_split_pos
 games_review = pd.read_csv("datasets/videogames/Games.xls")
 games_sales = 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())
 games_merged_dat.to_csv("datasets/videogames/games_merged.csv")
 
 # Loading Crime Datasets
@@ -29,12 +32,19 @@ 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")
 
-print(year_interval[0], year_interval[1])
+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 = [
     "PAL_Sales",
     "JP_Sales",
@@ -60,6 +70,10 @@ games_merged_dat = gunner.drop_kick(NA_col_list, games_merged_dat)
 
 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("Acquired Datasets:\n")
 print(sale_tri_split[0].head(5), sale_tri_split[1].head(5), sale_tri_split[2].head(5))
@@ -69,6 +83,26 @@ sale_tri_split[0].info()
 sale_tri_split[1].info()
 sale_tri_split[2].info()
 
+
+print("Dataset Info:\n")
+games_sales_split_pre.info()
+games_sales_split_dur.info()
+games_sales_split_pos.info()
+
+print(games_sales_split_dur.describe())
+
+print(
+    games_sales_split_pre.head(5),
+    games_sales_split_dur.head(5),
+    games_sales_split_pos.head(5),
+)
+# Required to use binning for cleaning, idk
+# https://towardsdatascience.com/data-preprocessing-with-python-pandas-part-5-binning-c5bd5fd1b950
+
+# Also need to transform using Z-score (normal distr go brrrr lmao), or min-max
+
+# Need similarity and dissimialrity, scipy time
+
 # Load merged gammas
 
 gammas = pd.read_csv("datasets/videogames/games_merged.csv")