A nice way to accept Pandas DataFrames as function arguments. Read more: https://brr.ai/posts/dataframe/

dataframe_requires.py

def dataframe_requires(column_names: List[str]):
    """
    Wraps a function that takes a pandas.DataFrame as its first argument.

    Ensures that functions wrapped by this can safely assume that the required
    columns are available.

    Also ensures that a copy of the given dataframe is used by the function to
    prevent modifying parent dataframes kept in memory.

    Meant to assist with documenting functions that require a dataframe type as
    a parameter.

    Sample usage:
    
    ```
    @dataframe_requires(["a", "b"])
    def example(big_data, another_argument, optional_argument=False):
        # `big_data` will always contain the "a" and "b" columns.
        # Feel free to modify the dataframe.
    ```
    """
    def decorator_require(func):
        @functools.wraps(func)
        def wrapper_require(df, *args, **kwargs):
            try:
                # Indexing this way returns a copy of the dataframe.
                df = df[list(set(column_names))]
            except KeyError as e:
                raise Exception(
                    f"@dataframe_requires couldn't find key in `{func.__name__}` input.\n"
                    f"Original exception: {e}"
                )
            return func(df, *rest, **kwargs)

        return wrapper_require

    return decorator_require