BUG: Fixed TypeError for Series.isin() when large series and values contains NA (#60678)

[akj2018]

• closes BUG: boolean series .isin([pd.NA])] inconsistent for series length #60678
• Tests added and passed if fixing a bug or adding a new feature
• All code checks passed.
• Added type annotations to new arguments/methods/functions.
• Added an entry in the latest doc/source/whatsnew/v3.0.0.rst file if fixing a bug or adding a new feature.

Issue

Series.isin() raises TypeError: boolean value of NA is ambiguous when Series is large enough (>1_000_000) and values contains NA

Reason

• Series.isin() internally uses np.isin() for large series with smaller values to increase performance but it does not handles the case when values is of dtype=object and contains NA and passes it to np.isin

pandas/pandas/core/algorithms.py

Lines 543 to 568 in a4e8149

	# GH16012
	# Ensure np.isin doesn't get object types or it *may* throw an exception
	# Albeit hashmap has O(1) look-up (vs. O(logn) in sorted array),
	# isin is faster for small sizes
	if (
	len(comps_array) > _MINIMUM_COMP_ARR_LEN
	and len(values) <= 26
	and comps_array.dtype != object
	):
	# If the values include nan we need to check for nan explicitly
	# since np.nan it not equal to np.nan
	if isna(values).any():
	def f(c, v):
	return np.logical_or(np.isin(c, v).ravel(), np.isnan(c))
	else:
	f = lambda a, b: np.isin(a, b).ravel()
	else:
	common = np_find_common_type(values.dtype, comps_array.dtype)
	values = values.astype(common, copy=False)
	comps_array = comps_array.astype(common, copy=False)
	f = htable.ismember
	return f(comps_array, values)

• np.isin() internally calls in1d() which uses == sign with NA .

            mask = np.zeros(len(ar1), dtype=bool)
            for a in ar2:
                mask |= (ar1 == a)

• Using ar1 == NA raises a TypeError because the boolean value of pd.NA is ambiguous. refer docs.

Fix Implemented

Explicitly checking if values contains NA when large series and small number of values (<= 26) to avoid using np.isin in algorithms.py.

from pandas._libs.missing import NA

def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:

   
    # GH60678
    # Ensure values don't contain <NA>, otherwise it throws exception with np.in1d
    values_contains_NA = False
    
    if comps_array.dtype != object and len(values) <= 26:  
        values_contains_NA = any(v is NA for v in values)

    if (
        len(comps_array) > _MINIMUM_COMP_ARR_LEN
        and len(values) <= 26
        and comps_array.dtype != object
        and values_contains_NA == False
    ):

Testing

Successfully pass all existing test cases in test_isin.py with tests added for large series with dtype as boolean, Int64 and Float64 as follow:

1. Series dtype==boolean and values contain pd.NA
2. Series dtype==boolean and values contains mixed data with pd.NA
3. Series dtype==boolean and values empty
4. Series dtype==Int64 and values contains pd.NA
5. Series dtype==Float64 and values contains pd.NA