import os
import pandas as pd

import warnings
warnings.filterwarnings('ignore')

def clean_and_analyze_csv_files(input_path, output_path):
    """ Data preprocessing """
    for filename in os.listdir(input_path):
        if filename.endswith('.csv'):
            file_path = os.path.join(input_path, filename)

            df = pd.read_csv(file_path)
            print(f"processing: {filename}")

            df = df.dropna(how='all', axis=0).dropna(how='all', axis=1)
            rows, columns = df.shape
            missing_rate = df.isnull().sum().sum() / (rows * columns)

            print(f"filename: {filename}")
            print(f"rows: {rows}")
            print(f"columns: {columns}")
            print(f"missing_rate: {missing_rate:.2%}")
            print("-" * 30)

            path = os.path.join(output_path, filename)
            df.to_csv(path, index=False)


def analyze_csv_folder(folder_path):
    """ Data info """
    total_memory = 0
    total_rows = 0
    total_columns = 0

    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.endswith('.csv'):
                file_path = os.path.join(root, file)

                df = pd.read_csv(file_path)
                memory_usage = df.memory_usage(deep=True).sum()
                total_memory += memory_usage

                rows = df.shape[0]
                total_rows += rows

                columns = df.shape[1]
                total_columns += columns

    print(f"Total memory size occupied by all CSV files: {total_memory}")
    print(f"Total rows in all CSV files: {total_rows}")
    print(f"Total total_columns in all CSV files: {total_columns}")

if __name__ == '__main__':
    input_path = '../metadata/table_data/'
    output_path = './output'
    clean_and_analyze_csv_files(input_path, output_path)
    
    analyze_csv_folder('output')