and\or

&\|

for idx, group_by_cols in enumerate(group_by_combinations):

@dynamic
def d1(group_by_combinations: List[int]) ->
    for idx, group_by_cols in enumerate(group_by_combinations):

from flytekit import task, workflow, dynamic
import pandas as pd
from itertools import combinations
from typing import List

# Function to read CSV files and perform transformations
@task
def read_and_transform_csv(file_path: str, selected_columns: List[str]) -> pd.DataFrame:
    df = pd.read_csv(file_path, usecols=selected_columns)
    # Perform transformations here if needed
    return df

# Function to replace empty cells in specified columns
@task
def replace_empty_cells(df: pd.DataFrame, columns: List[str], replace_value='EMPTY') -> pd.DataFrame:
    for col in columns:
        df[col].fillna(replace_value, inplace=True)
    return df

# Function to generate combinations of group by parameters
@task
def generate_group_by_combinations(group_by_columns: List[str]) -> List[List[str]]:
    group_by_combinations = []
    for r in range(1, len(group_by_columns) + 1):
        group_by_combinations.extend(combinations(group_by_columns, r))
    return group_by_combinations

# Function to apply SQL-like transformations and aggregation
@task
def apply_sql_transformations(df: pd.DataFrame) -> pd.DataFrame:
    # Add savings_present column
    df['SAVINGS_PRESENT'] = df['SAVINGS_ACCOUNT_ID'].notnull().astype(int)
    # Extract year and month from transaction_date
    df['TRANSACTION_YEAR_MONTH'] = df['TRANSACTION_DATE'].str.slice(0, 7)
    return df

@workflow
# Main function
def wf1(csv1_path: str='/home/ubuntu/flyte/my_project/workflows/ODS_TRANSACTIONS.csv',    
        csv2_path: str='/home/ubuntu/flyte/my_project/workflows/ods_customers.csv', 
        csv1_columns: List[str]=['ACCOUNT_ID', 'PRODUCT_TYPE', 'TRANSACTION_DATE', 'CATEGORY_NAME', 'AMOUNT'], 
        csv2_columns: List[str]=['ACCOUNT_ID', 'SAVINGS_ACCOUNT_ID'], 
        group_by_columns: List[str]=['PRODUCT_TYPE', 'SAVINGS_PRESENT', 'CATEGORY_NAME', 'TRANSACTION_YEAR_MONTH']) -> dict:
    
    # Read and transform CSV files
    df1 = read_and_transform_csv(file_path=csv1_path, selected_columns=csv1_columns)
    df2 = read_and_transform_csv(file_path=csv2_path, selected_columns=csv2_columns)

    # Merge dataframes
    merged_df = pd.merge(df1, df2, on='ACCOUNT_ID', how='left')

    # Apply SQL-like transformations
    transformed_df = apply_sql_transformations(df=merged_df)

    # Replace empty cells with 'EMPTY'
    transformed_df = replace_empty_cells(df=transformed_df,columns=group_by_columns, replace_value='EMPTY')

    # Generate all possible combinations of group by parameters
    @dynamic
    def process_group_by_combinations(group_by_combinations: List[List[str]]):
        result_dfs = {}
        for idx, group_by_cols in enumerate(group_by_combinations):
            result_df = transformed_df.groupby(list(group_by_cols)).agg({
                'AMOUNT': ['sum', 'mean'],
                'ACCOUNT_ID': 'nunique'
            }).reset_index()

            # Rename columns as needed
            result_df.columns = ['_'.join(col).strip() for col in result_df.columns.values]

            # Name for the dataframe
            df_name = f"result{idx + 1}"

            # Store dataframe in the dictionary
            result_dfs[df_name] = result_df

        return result_dfs

    return process_group_by_combinations(group_by_combinations=generate_group_by_combinations(group_by_columns))

# Provide the values directly to the main_workflow function
result_dataframes = wf1()
# Now you can access each dataframe by its name in the result_dataframes dictionary
print(result_dataframes)