diff --git a/jupyter-notes/Panda Bamboo.ipynb b/jupyter-notes/Panda Bamboo.ipynb
index 8b3f2db..8407d54 100644
--- a/jupyter-notes/Panda Bamboo.ipynb	
+++ b/jupyter-notes/Panda Bamboo.ipynb	
@@ -135,7 +135,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "merged2.to_csv('merged_games.csv')"
+    "merged.to_csv('merged_games.csv')"
    ]
   },
   {
diff --git a/py_scripts/digger.py b/py_scripts/digger.py
index 5e36271..1c07b55 100644
--- a/py_scripts/digger.py
+++ b/py_scripts/digger.py
@@ -2,33 +2,20 @@
 import pandas as pd
 import numpy as np
 
+global games_merged_dat
+
 # reading the data
 # -> MAKE SURE OF THE DATA FRAMES NAMES PEFORE YOU RUN IT
 
+games_dat = pd.read_csv("Games.xls")
+games_sales_dat = pd.read_csv("vgsales-12-4-2019-short.csv")
 
-df1 = pd.read_csv("output_6th_df.csv")
-df2 = pd.read_csv("vgsales-12-4-2019-short.csv")
-
-
-# ----------------------------------------------------------
-
-
-# print(pf1.head)
-# print(pf2.head)
-
-# ---------------------------------------------------------
-
-
-#                    merging
-
-combined_df = df1.merge(df2, left_on="Name", right_on="Name", how="left")
-print(combined_df)
-combined_df.to_csv("output_final_df.csv")
-df = combined_df
-
-# ---------------------------------------------------------
+games_merged_dat = games_dat.merge(games_sales_dat, left_on="Name", right_on="Name", how="left")
+print(games_merged_dat)
 
+games_merged_dat.to_csv("output_final_df.csv")
 
+# Defining useful Functions to be used later
 def slice_column(input_df, output_df, column, expression=" "):
     unclean = input_df[column].to_list()
     clean = list()
diff --git a/py_scripts/gunner.py b/py_scripts/gunner.py
index 509ec5f..238f872 100644
--- a/py_scripts/gunner.py
+++ b/py_scripts/gunner.py
@@ -22,37 +22,34 @@ global crime_US
 global crime_CA
 
 # Loading Datasets
-game_sales_dat = pd.read_csv('datasets/videogames/vgsales-12-4-2019-short.csv')
-games_dat = pd.read_csv('datasets/videogames/Games.xls')
+games_merged = pd.read_csv('datasets/videogames/merged_games.csv')
 
 crime_CA = pd.read_excel('datasets/crime/clean_crime_canada_dataset.xlsx')
 crime_US = pd.read_csv('datasets/crime/report.csv')
 
 # Printing information regarding datasets
 print("Game Datasets' Info:\n")
-game_sales_dat.info()
-games_dat.info()
+games_merged.info()
 
 print("Crime Datasets' Info:\n")
 crime_US.info()
 crime_CA.info()
 
 # Printing First n values (index start: 0)
-print("Game Sale Data:\n", game_sales_dat.head(10))
-print("Game Scores:\n", games_dat.head(10))
+print("Game Sale Data:\n", games_merged.head(5))
 
-print("US Crime Data:\n", crime_US.head(10))
-print("CA Crime Data:\n", crime_CA.head(10))
+print("US Crime Data:\n", crime_US.head(5))
+print("CA Crime Data:\n", crime_CA.head(5))
 
 # Regarding the Games.xls dataset:
 # Coercing the non-numeric values will result in NaN
 # thus allowing easier removal through `.notnull()`
-games_dat['Score'] = pd.to_numeric(games_dat['Score'], errors = 'coerce')
+games_merged['Score'] = pd.to_numeric(games_merged['Score'], errors = 'coerce')
 
-games_dat = games_dat[games_dat['Score'].notnull()]
+games_merged = games_merged[games_merged['Score'].notnull()]
 
-print("Game Scores (Cleaned):\n", games_dat.head())
-games_dat.info()
+print("Game Scores (Cleaned):\n", games_merged.head())
+games_merged.info()
 
 # Regarding the vgsales-12-4-2019 dataset
 # Considering we will be using a US (probs CA too) crime datasets
@@ -60,8 +57,8 @@ games_dat.info()
 NA_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales']
 GLO_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'NA_Sales']
 
-game_sales_NA = game_sales_dat.drop(columns = NA_col_list, axis = 1)
-game_sales_GLO = game_sales_dat.drop(columns = GLO_col_list, axis = 1)
+game_sales_NA = games_merged.drop(columns = NA_col_list, axis = 1)
+game_sales_GLO = games_merged.drop(columns = GLO_col_list, axis = 1)
 
 print(f"Game Sales for NA:\n{game_sales_NA.head(10)} \nWith minimum year being: {game_sales_NA['Year'].min()}")
 print(f"Game Sales Globally:\n{game_sales_GLO.head(10)}\nWith minimum year being: {game_sales_GLO['Year'].min()}")