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Prison Simulator

Prison Simulator is a brand new game developed by Baked Games.Take care about prisoners, trade with them or be strict and cruel. You decide.

manage the prison and fulfill your duties

deal with aggressive prisoners and the contraband

create personalities and style the prison

extend possibilities with downloadable content

Enjoy advanced plot and dialogues

Your life as a prison guard is going to end soon – your promotion is only 30 days away! However, the closer you get to this date, the harder your life is. rkprime jasmine sherni game day bump and ru fixed

prison administration imposes every day incriminating duties that you have to fulfill before the end of each day
on the other side of the barricade, there are unpredictable prisoners making your work annoying and, well, unattractive

Play the role of a prison guard, survive to your promotion, balancing on a thin line between the satisfaction of the prison management and dangerous convicts! rkprime jasmine sherni game day bump and ru fixed

Try a demo game and prove yourself!

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Keep control… or at least try

Prison Simulator is about to be available on Steam soon!

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Rkprime Jasmine Sherni Game Day Bump And Ru Fixed Link

print(f'Average views on game days: {game_day_views}') print(f'Average views on non-game days: {non_game_day_views}') This example is quite basic. Real-world analysis would involve more complex data manipulation, possibly natural language processing for content analysis, and machine learning techniques to model and predict user engagement based on various features.

# Assuming we have a DataFrame with dates, views, and a game day indicator df = pd.DataFrame({ 'Date': ['2023-01-01', '2023-01-05', '2023-01-08'], 'Views': [1000, 1500, 2000], 'Game_Day': [0, 1, 0] # 1 indicates a game day, 0 otherwise })

import pandas as pd

print(f'Average views on game days: {game_day_views}') print(f'Average views on non-game days: {non_game_day_views}') This example is quite basic. Real-world analysis would involve more complex data manipulation, possibly natural language processing for content analysis, and machine learning techniques to model and predict user engagement based on various features.

# Assuming we have a DataFrame with dates, views, and a game day indicator df = pd.DataFrame({ 'Date': ['2023-01-01', '2023-01-05', '2023-01-08'], 'Views': [1000, 1500, 2000], 'Game_Day': [0, 1, 0] # 1 indicates a game day, 0 otherwise })

import pandas as pd

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