79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
|
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('Steam-200k.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")
|