import os import pandas as pd from sklearn.model_selection import train_test_split #os.system("kaggle datasets download -d tamber/steam-video-games") #os.system("unzip -o steam-video-games.zip") steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed']) steam.isnull().values.any() steam['userId'] = steam.userId.astype(str) print("Zbior danych:") print(steam) print("Describe:") print(steam.describe(include='all'),"\n\n") print("Gracze z najwieksza aktywnoscia:") print(steam["userId"].value_counts(),"\n\n") print("Gracze z najwieksza liczba kupionych gier:") print(steam[steam["behavior"] != "play"]["userId"].value_counts()) print("Mediana:") print(steam[steam["behavior"] != "play"]["userId"].value_counts().median(),"\n\n") print("Gracze ktorzy zagrali w najwieksza liczbe gier:") print(steam[steam["behavior"] != "purchase"]["userId"].value_counts()) print("Mediana:") print(steam[steam["behavior"] != "purchase"]["userId"].value_counts().median(),"\n\n") print("Gry:") print(steam["game"].value_counts(),"\n\n") print("Sredni czas grania w grania w dana gre") print(steam[steam["behavior"] != "purchase"].groupby("game").mean().sort_values(by="hoursPlayed",ascending=False)) print("Mediana:") print(steam[steam["behavior"] != "purchase"].groupby("game").mean().sort_values(by="hoursPlayed",ascending=False).median(),"\n\n") print("Najczesciej kupowana gra") print(steam[steam["behavior"] != "play"]["game"].value_counts()) print("Mediana:") print(steam[steam["behavior"] != "play"]["game"].value_counts().median(),"\n\n") print("Gra w ktora zagralo najwiecej graczy") print(steam[steam["behavior"] != "purchase"]["game"].value_counts()) print("Mediana:") print(steam[steam["behavior"] != "purchase"]["game"].value_counts().median(),"\n\n") print("Liczba kupionych gier i liczba gier w ktore gracze zagrali") print(steam["behavior"].value_counts(),"\n\n") print("Gra z najwieksza liczba godzin dla jednego gracza") print(steam[steam["behavior"] != "purchase"][["userId","hoursPlayed","game"]].sort_values(by="hoursPlayed",ascending=False)) print("Mediana:") print(steam[steam["behavior"] != "purchase"]["hoursPlayed"].sort_values(ascending=False).median(),"\n\n") print("Suma rozegranych godzin dla danej gry") print(steam[steam["behavior"] != "purchase"].groupby("game").sum().sort_values(by="hoursPlayed",ascending=False)) print("Mediana:") print(steam[steam["behavior"] != "purchase"].groupby("game").sum().sort_values(by="hoursPlayed",ascending=False).median(),"\n\n") #odrzucenie gier dla których jest mniej niż 10 wierszy steam = steam.groupby("game").filter(lambda x: len(x)>10) #rozmiar zbioru testowego i dev proporcje 8:1:1 size=int(len(steam)/10) steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"]) steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"]) print("Zbior trenujacy") print(steam_train["game"].value_counts(),"\n") print("Zbior testujacy") print(steam_test["game"].value_counts(),"\n") print("Zbior dev") print(steam_dev["game"].value_counts(),"\n")