This decorator caches a pandas.DataFrame returning function. It saves the pandas.DataFrame in a parquet file in the cache_dir.

pandas_caching_decorator.py

import pandas as pd
from pathlib import Path
from functools import wraps

def cache_pandas_result(cache_dir, hard_reset: bool):
    '''
    This decorator caches a pandas.DataFrame returning function.
    It saves the pandas.DataFrame in a parquet file in the cache_dir.
    It uses the following naming scheme for the caching files:

        cache_dir / function_name + '.trc.pqt'

    Parameters:
    cache_dir: a pathlib.Path object
    hard_reset: bool
    '''
    def build_caching_function(func):
        @wraps(func)
        def cache_function(*args, **kwargs):
            if not isinstance(cache_dir, Path):
                raise TypeError('cache_dir should be a pathlib.Path object')

            cache_file = cache_dir / (func.__name__ + '.trc.pqt')

            if hard_reset or (not cache_file.exists()):
                result = func(*args, **kwargs)
                if not isinstance(result, pd.DataFrame):
                    raise TypeError(f"The result of computing {func.__name__} is not a DataFrame")
                result.to_parquet(cache_file)
                return result
            result = pd.read_parquet(cache_file)
            return result
        return cache_function
    return build_caching_function

Really good !

Just need to import wraps:
functools import wraps

Thanks for letting me know!