commit 912a16b959c6df4ec7198c9f8311cbc5d448ac43
Author: szymonj98 <szymonj98@wp.pl>
Date:   Sun Mar 20 17:35:12 2022 +0100

    zadanie 1

diff --git a/kagle.py b/kagle.py
new file mode 100644
index 0000000..1bf202e
--- /dev/null
+++ b/kagle.py
@@ -0,0 +1,79 @@
+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")
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