import dask.dataframe as dd
from functools import partial


def read_parquet_file(parquet_file_path, npartitions=50, top=None):

    print(f"Process parquet file from {parquet_file_path}")
    file_name = parquet_file_path.split("/")[-1]
    parquet_df = dd.read_parquet(parquet_file_path, engine="pyarrow")
    parquet_df = parquet_df.repartition(npartitions=npartitions)  # Smaller partitions

    if top:
        parquet_df = parquet_df.head(top, compute=False) # compute=False to keep it as DaskDataframe

    return parquet_df, file_name


def process_parquet_df(parquet_df, file_name, process_row_func, process_partition):

    # A new function of process_row_func to allow pre-defining parameters of process-row_func. 
    process_row_with_params = partial(process_row_func, parquet_file_name=file_name)
    result_df = parquet_df.map_partitions(process_partition, process_row_with_params)

    return result_df


def save_to_csv(df, final_path):
    # Save the processed DataFrame to csv
    df.to_csv(final_path, index=False, single_file=True)