Working whilst not at home is meh

py_scripts/digger.py

@@ -1 +1,3 @@
 # Getting and combining data
+import pandas as pd
+import numpy as np

py_scripts/engineer.py

@@ -1 +1,7 @@
 # Visualisations for Data
+import seaborn as sns
+import gunner
+
+games_vis = gunner.game_sales_NA
+
+sns.relplot(data = games_vis, x = "Year", y = "NA_Sales")

py_scripts/gunner.py

@@ -4,23 +4,45 @@
 import pandas as pd
 import numpy as np
 
+# Sharing the dataset variables
+# Games' data
+global games_dat
+# Sales in NA
+global game_sales_NA
+# Sales Globally
+global game_sales_GLO
+
 # Loading Datasets
 game_sales2019_dat = pd.read_csv('datasets/videogames/vgsales-12-4-2019-short.csv')
 games_dat = pd.read_csv('datasets/videogames/Games.xls')
 
 # Printing information regarding datasets
+print("Data Sets' Info:\n")
 game_sales2019_dat.info()
 games_dat.info()
 
 # Printing First n values (index start: 0)
-print(game_sales2019_dat.head(10))
+print("Game Sale Data:\n", game_sales2019_dat.head(10))
-print(games_dat.head(10))
+print("Game Scores:\n", games_dat.head(10))
 
+# 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_dat = games_dat[games_dat['Score'].notnull()]
 
+print("Game Scores (Cleaned):\n", games_dat.head())
 games_dat.info()
-print(games_dat.head())
+
+# Regarding the vgsales-12-4-2019 dataset
+# Considering we will be using a US (probs CA too) crime datasets
+# It wouldn't be that useful to have other columns regarding other regions
+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_sales2019_dat.drop(columns = NA_col_list, axis = 1)
+game_sales_GLO = game_sales2019_dat.drop(columns = GLO_col_list, axis = 1)
+
+print("Game Sales for NA:\n", game_sales_NA.head(10))
+print("Game Sales Globally:\n", game_sales_GLO.head(10))