pandas.isna() vs pandas.DataFrame.isna() I've seen the two documentation pages for pandas.isna() and pandas.DataFrame.isna() but the difference is still unclear to me.  Could someone explain the difference to me using examples?
They call the same underlying method, so there is no functional difference.

Calling the dataframe member function is preferred for OOP patterns, but there are many redundancies/aliases in pandas and python in general.



In case you are curious, here is how the source code breaks down (it is a mess).

The DataFrame (pandas/core/frame.py) method is simply:

def isna(self):
    return super().isna()


Where DataFrame extends NDFrame (implemented in pandas/core/generic.py). NDFrame subsequently invokes:

def isna(self):
    return isna(self).__finalize__(self)


Which was imported here:

from pandas.core.dtypes.missing import isna, notna


In pandas/core/dtypes/missing.py:

def isna(obj):
    return _isna(obj)


The _isna function is later aliased as _isna = _isna_new because there is a deprecated method _isna_old(obj).

The _isna_new(obj) function then performs the logic operations:

def _isna_new(obj)
    if is_scalar(obj):
        return libmissing.checknull(obj)
    # hack (for now) because MI registers as ndarray
    elif isinstance(obj, ABCMultiIndex):
        raise NotImplementedError("isna is not defined for MultiIndex")
    elif isinstance(obj, type):
        return False
    elif isinstance(
        obj,
        (
            ABCSeries,
            np.ndarray,
            ABCIndexClass,
            ABCExtensionArray,
            ABCDatetimeArray,
            ABCTimedeltaArray,
        ),
    ):
        return _isna_ndarraylike(obj)
    elif isinstance(obj, ABCGeneric):
        return obj._constructor(obj._data.isna(func=isna))
    elif isinstance(obj, list):
        return _isna_ndarraylike(np.asarray(obj, dtype=object))
    elif hasattr(obj, "__array__"):
        return _isna_ndarraylike(np.asarray(obj))
    else:
        return obj is None


Ultimately, the DataFrame method passes itself as a parameter to the same function that you call with pandas.isna().