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MAJOR gunner refactor (sorry mark)

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LinlyBoi
2023-03-28 13:38:43 +02:00
parent 28e1c182ba
commit c8bffa8764
1 changed files with 21 additions and 47 deletions
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68
py_scripts/gunner.py
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@@ -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')
crime_US = pd.read_csv('datasets/crime/report.csv')
victim1 = pd.read_excel("datasets/crime/clean_crime_canada_dataset.xlsx")
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()
crime_CA.info()
victim2.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', '']
GLO_col_list = ['PAL_Sales', 'JP_Sales', 'Other_Sales', 'NA_Sales', 'User_Score', 'GameName', 'Review', '']
def drop_kick(col_list, dataframe):
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())
crime_year_max = min(crime_US['report_year'].max(), crime_CA['year'].max())
def year_interval(victim1, victim2, col1, col2):
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)]
game_sales_NA_pre = game_sales_NA[game_sales_NA['Year'] < crime_year_min]
game_sales_NA_pos = game_sales_NA[game_sales_NA['Year'] > crime_year_max]
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()}")
def trisect_by_year(victim1, col, interval):
victim1_pre = victim1[victim1[col] < interval[0]]
victim1_dur = victim1[(victim1[col] <= interval[1]) & (victim1[col] >= interval[0])]
victim1_pos = victim1[victim1[col] > interval[1]]
return (victim1_pre, victim1_dur, victim1_pos)