MAJOR gunner refactor (sorry mark)

py_scripts/gunner.py

@@ -7,78 +7,52 @@ import mining_hq
 
 # Sharing the dataset variables
 # Games' data
-global games_dat
-# Sales in NA
-global game_sales_NA
-global game_sales_NA_dur
-global game_sales_NA_pre
-global game_sales_NA_pos
-# Sales Globally
-global game_sales_GLO
-
-# Crime Data
-# Crime Recorded in The US
-global crime_US
-# Crime Recorded in Canada
-global crime_CA
 
 # Loading Datasets
 games_merged = mining_hq.games_merged_dat
 
-crime_CA = pd.read_excel('datasets/crime/clean_crime_canada_dataset.xlsx')
+victim1 = pd.read_excel("datasets/crime/clean_crime_canada_dataset.xlsx")
-crime_US = pd.read_csv('datasets/crime/report.csv')
+victim2 = pd.read_csv("datasets/crime/report.csv")
 
 # Printing information regarding datasets
 print("Game Datasets' Info:\n")
 games_merged.info()
 
 print("Crime Datasets' Info:\n")
-crime_US.info()
+victim2.info()
-crime_CA.info()
+victim1.info()
 
-# Printing First n values (index start: 0)
-print("Game Sale Data:\n", games_merged.head(5))
-
-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_merged['Score'] = pd.to_numeric(games_merged['Score'], errors = 'coerce')
 
-games_merged = games_merged[games_merged['Score'].notnull()]
-
-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
 # It wouldn't be that useful to have other columns regarding other regions
-NA_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales', 'User_Score', 'GameName', 'Review', '']
+def drop_kick(col_list, dataframe):
-GLO_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'NA_Sales', 'User_Score', 'GameName', 'Review', '']
+    return dataframe.drop(columns=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(5)} \nWith minimum year being: {game_sales_NA['Year'].min()}")
-print(f"Game Sales Globally:\n{game_sales_GLO.head(5)}\nWith minimum year being: {game_sales_GLO['Year'].min()}")
 
 # Getting the range of years which both datasets share
-crime_year_min = max(crime_US['report_year'].min(), crime_CA['year'].min())
+def year_interval(victim1, victim2, col1, col2):
-crime_year_max = min(crime_US['report_year'].max(), crime_CA['year'].max())
+    return (
+        min(victim2[col2].max(), victim1[col1].max()),
+        max(victim2[col2].min(), victim1[col1].min()),
+    )
 
-crime_CA = crime_CA[(crime_CA['year'] >= crime_year_min) & (crime_CA['year'] <= crime_year_max)]
-crime_US = crime_US[(crime_US['report_year'] >= crime_year_min) & (crime_US['report_year'] <= crime_year_max)]
 
+def intersect_by_year(victim1, victim2, col1, col2):
+    interval = year_interval(victim1, victim2, col1, col2)
+    victim1 = victim1[(victim1[col1] >= interval[0]) & (victim1[col1] <= interval[1])]
+    victim2 = victim2[(victim2[col2] >= interval[0]) & (victim2[col2] <= interval[1])]
+    return (victim1, victim2)
 
 
 # Updating the NA game dataset to fit with the time ranges
-game_sales_NA_dur = game_sales_NA[(game_sales_NA['Year'] >= crime_year_min) & (game_sales_NA['Year'] <= crime_year_max)]
+def trisect_by_year(victim1, col, interval):
-
+    victim1_pre = victim1[victim1[col] < interval[0]]
-game_sales_NA_pre = game_sales_NA[game_sales_NA['Year'] < crime_year_min]
+    victim1_dur = victim1[(victim1[col] <= interval[1]) & (victim1[col] >= interval[0])]
-
+    victim1_pos = victim1[victim1[col] > interval[1]]
-game_sales_NA_pos = game_sales_NA[game_sales_NA['Year'] > crime_year_max]
+    return (victim1_pre, victim1_dur, victim1_pos)
-
-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()}")