pandas-dev/pandas · error · TypeError

values should be boolean numpy array. Use the 'pd.array' fun

Error message

values should be boolean numpy array. Use the 'pd.array' function instead

What it means

BooleanArray.__init__ (boolean.py:340) requires `values` to be a numpy ndarray with dtype np.bool_; anything else (a list, an int array, a Python bool) raises TypeError directing the user to pd.array. The constructor is a low-level API; the two-array (data+mask) representation is an invariant that must be honored by callers.

Source

Thrown at pandas/core/arrays/boolean.py:340

    <BooleanArray>
    [True, False, <NA>]
    Length: 3, dtype: boolean
    """

    _TRUE_VALUES = {"True", "TRUE", "true", "1", "1.0"}
    _FALSE_VALUES = {"False", "FALSE", "false", "0", "0.0"}

    @classmethod
    def _simple_new(cls, values: np.ndarray, mask: npt.NDArray[np.bool_]) -> Self:
        result = super()._simple_new(values, mask)
        result._dtype = BooleanDtype()
        return result

    def __init__(
        self, values: np.ndarray, mask: np.ndarray, copy: bool = False
    ) -> None:
        if not (isinstance(values, np.ndarray) and values.dtype == np.bool_):
            raise TypeError(
                "values should be boolean numpy array. Use "
                "the 'pd.array' function instead"
            )
        self._dtype = BooleanDtype()
        super().__init__(values, mask, copy=copy)

    @property
    def dtype(self) -> BooleanDtype:
        return self._dtype

    @classmethod
    def _from_sequence_of_strings(
        cls,
        strings: list[str],
        *,
        dtype: ExtensionDtype,
        copy: bool = False,
        true_values: list[str] | None = None,

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Use the public constructor: pd.array([True, False, None], dtype='boolean').
2. If you must use BooleanArray directly, first convert: np.asarray(values, dtype=bool).
3. Provide a correctly-shaped mask matching the bool ndarray.
4. Avoid the low-level constructor in application code; it is intended for EA internals.

Example fix

# before
from pandas.arrays import BooleanArray
ba = BooleanArray([True, False], mask=[False, False])  # raises

# after
import numpy as np
ba = BooleanArray(np.array([True, False], dtype=bool), np.array([False, False], dtype=bool))
# or preferably
ba = pd.array([True, False], dtype="boolean")

def make_boolean_array(values, mask=None):
    import numpy as np
    values = np.asarray(values, dtype=bool)
    if mask is None:
        mask = np.zeros(values.shape, dtype=bool)
    else:
        mask = np.asarray(mask, dtype=bool)
    from pandas.core.arrays.boolean import BooleanArray
    return BooleanArray(values, mask)

def is_bool_ndarray(x) -> bool:
    import numpy as np
    return isinstance(x, np.ndarray) and x.dtype == np.bool_

try:
    from pandas.arrays import BooleanArray
    ba = BooleanArray(values, mask)
except TypeError as e:
    if "pd.array" in str(e):
        ba = pd.array(values, dtype="boolean")
    else:
        raise

When it happens

Trigger: Directly instantiating pd.arrays.BooleanArray([True, False], mask) with a Python list or a non-bool ndarray instead of using pd.array(...).

Common situations: Copy-pasted examples that call BooleanArray(...) directly; library code that tried to skip the public constructor; misunderstandings of the public API surface.

Related errors

• cannot pass mask for BooleanArray input
• Need to pass bool-like values
• values should be {descr} numpy array. Use the 'pd.array' fun
• Array with ndim > 2 is not supported.
• Unsupported type '{type(values)}' for ArrowExtensionArray

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/9976541a40e8865f. Report an issue: GitHub.