| 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) | |