Pandas likes to throw cryptic errors when you feed its functions with empty DataFrames saying nothing that would help you to identify the root cause. In order to avoid this I used to write conditions like this one all over the place:

def normalize_null(data: pd.DataFrame) -> pd.DataFrame:
    """Replaces pd.NaT garbage with None."""

    if data.empty:
        return pd.DataFrame()

    return data.replace({pd.NaT: None})

I don't even remember if this particular operation fails on an empty DataFrame, but there are many that do so I just add it to every function just in case.

Then I thought why making it so ugly? Make it a decorator! So now I do this:

def normalize_null(data: pd.DataFrame) -> pd.DataFrame:
    """Replaces pd.NaT garbage with None."""
    return data.replace({pd.NaT: None})

with this decorator:

def skip_if_empty(default: Any | None):
    def decorate(decoratee):
        def decorator(*decoratee_args, **decoratee_kwargs):
            if len(decoratee_args) < 1:
                raise ValueError("The decorated function must have at least one argument.")

            if type(decoratee_args[0]) is not pd.DataFrame:
                raise ValueError("The first argument must be a DataFrame.")

            return default if decoratee_args[0].empty else decoratee(*decoratee_args, **decoratee_kwargs)

        decorator.__signature__ = inspect.signature(decoratee)
        return decorator

    return decorate

Now when I use several such methods that modify a DataFrame in a chain I don't have to worry about anything being empty:

data = normalize_null(data)
data = do_something_else(data)
data = ...

Would you say it's ok to use decorators this way or mabe there is something else about it that could be improved?


1 Answer 1



The triple-nested function confused me for a minute, looking at it as a stranger, particularly as the decorator is not documented. You may want to look into functools.wraps which is a convenience function which does what you want with preserving signature, docs, name, etc. while being a standardised method.


To me, default doesn't immediately tell me what it's doing, default_return would be more obvious to me. Also, skip_if_empty implies that this function would work for any type, but is explicitly limited by:

if type(decoratee_args[0]) is not pd.DataFrame

to pd.DataFrames. Perhaps skip_if_empty_df might be more clear. Either that, or you could make this more generically useful by allowing a second argument of permitted type:

def skip_if_empty(default: Any | None, allowed_types: tuple = (pd.DataFrame,)):
   if type(decoratee_args[0]) is not in allowed_types


if len(decoratee_args) < 1

unless len(tuple) is liable to return a negative number what you're actually asking is:

if len(decoratee_args) == 0

or more pythonically

if not decoratee_args


if type(decoratee_args[0]) is not pd.DataFrame

might want to be:

if not isinstance(decoratee_args[0], pd.DataFrame)

which would allow subclasses of pd.DataFrames to work as well.

Error messages

As a user, I would (and should) be unaware that any of the functions are in any way decorated, so seeing:

raise ValueError("The decorated function must have at least one argument.")

Is unhelpful. You might instead want:

raise ValueError(f"{decoratee.__name__} must have at least one argument.")
  • \$\begingroup\$ I like that and I have a question about this allowed_types: tuple = (pd.DataFrame,). Would this also work with a list or are tuples preferable in cases like this? I guess probably because they are immutable, right? \$\endgroup\$
    – t3chb0t
    Mar 29, 2023 at 10:55
  • 1
    \$\begingroup\$ It would work with a list and type annotations are just hints (by default), I would argue a tuple makes most sense here due to immutability and the fact that you will be defining it as a programmer rather than a user, but it could be any collection that supports in semantics. isinstance also recommends tuples \$\endgroup\$ Mar 29, 2023 at 10:59

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