In Pandas and Numpy, there are vectorized functions like np.isnan, np.isinf, and pd.isnull to check if the elements of an array, series, or dataframe are various kinds of missing/null/invalid. They do work on scalars. pd.isnull(None) simply returns True rather than pd.Series([True]), which is convenient. But let's say I want to know if any object is one of these null values; You can't do that with any of these functions! That's because they will happily vectorize over a variety of data structures. Carelessly using them will inevitably lead to the dreaded "The truth value of a Series is ambiguous" error. What I want is a function like this: assert not is_scalar_null(3) assert not is_scalar_null([1,2]) assert not is_scalar_null([None, 1]) assert not is_scalar_null(pd.Series([None, 1])) assert not is_scalar_null(pd.Series([None, None])) assert is_scalar_null(None) assert is_scalar_null(np.nan) Internally, the Pandas function pandas._lib.missing.checknull will do the right thing: import pandas._libs.missing as libmissing libmissing.checknull(pd.Series([1,2])) # correctly returns False But it's generally bad practice to use it; according to Python naming convention, _lib is private. I'm also not sure about the Numpy equivalents. Is there an "acceptable" but official way to use the same null-checking logic as NumPy and Pandas, but strictly for scalars? Continue reading...
