import os

import zipfile

import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import MinMaxScaler

os.system("kaggle datasets download -d gpreda/covid-world-vaccination-progress")

with zipfile.ZipFile("covid-world-vaccination-progress.zip", "r") as zip_ref:

    zip_ref.extractall("country_vaccinations")

csv_file = None

for root, dirs, files in os.walk("country_vaccinations"):

    for file in files:

        if file.endswith(".csv"):
            csv_file = os.path.join(root, file)

            break

if csv_file is None:
    raise FileNotFoundError("CSV file not found in the extracted dataset")

data = pd.read_csv(csv_file)

train_data, temp_data = train_test_split(data, test_size=0.4, random_state=42)

dev_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)


def print_stats(df, name):
    print(f"\nStatystyki dla {name}:")

    print(f"Wielkość zbioru: {len(df)}")

    for col in df.columns:

        if df[col].dtype != "object":
            print(f"\nParametr: {col}")

            print(f"Minimum: {df[col].min()}")

            print(f"Maksimum: {df[col].max()}")

            print(f"Średnia: {df[col].mean()}")

            print(f"Odchylenie standardowe: {df[col].std()}")

            print(f"Mediana: {df[col].median()}")


print_stats(data, "Cały zbiór")

print_stats(train_data, "Zbiór treningowy")

print_stats(dev_data, "Zbiór walidacyjny")

print_stats(test_data, "Zbiór testowy")


def normalize_data(df, columns):
    scaler = MinMaxScaler()

    for col in columns:

        if df[col].dtype != "object":
            df[col] = scaler.fit_transform(df[[col]])


normalize_data(train_data, train_data.columns)

normalize_data(dev_data, dev_data.columns)

normalize_data(test_data, test_data.columns)


def clean_data(df):
    df.dropna(inplace=True)

    df.drop_duplicates(inplace=True)


clean_data(data)

clean_data(train_data)

clean_data(dev_data)

clean_data(test_data)