pandas-dev/pandas · error · TypeError

Invalid value ' ' for dtype

Error message

Invalid value '{value!s}' for dtype '{self.dtype}'

What it means

Raised by BaseMaskedArray._validate_setitem_value when a scalar value cannot be losslessly stored in the array's dtype. Integer masked arrays accept integers or integer-valued floats, float arrays accept any integer or float, and boolean arrays accept only bools; anything else (strings, non-integer floats into Int dtypes, None) is rejected to avoid silent coercion.

Solutions

  1. Convert the value to the array's native type before assignment (int(value), float(value), bool(value)).
  2. Change the array's dtype to one that can hold the value, e.g. arr = arr.astype('Float64').
  3. Sanitize incoming data so only values valid for self.dtype reach the setitem path.

Example fix

// before
arr = pd.array([1, 2, 3], dtype='Int64')
arr[0] = 1.5   # raises: 1.5 is not integer-valued
// after
arr = arr.astype('Float64')
arr[0] = 1.5
Defensive patterns

Strategy: validation

Validate before calling

def coerce_to_dtype(dtype, value):
    kind = dtype.kind
    if kind == 'b':
        return bool(value)
    if kind == 'f':
        return float(value)
    if value is not None and (isinstance(value, float) and not float(value).is_integer()):
        raise ValueError(f'{value!r} not losslessly storable in {dtype}')
    return int(value)

arr[i] = coerce_to_dtype(arr.dtype, value)

Type guard

def is_valid_for_dtype(dtype, value) -> bool:
    import numpy as np
    k = dtype.kind
    if k == 'b':
        return isinstance(value, (bool, np.bool_))
    if k == 'f':
        return isinstance(value, (int, float, np.integer, np.floating)) and not isinstance(value, bool)
    if k in 'iu':
        return isinstance(value, (int, np.integer)) or (isinstance(value, float) and value.is_integer())
    return False

Try / catch

try:
    arr[i] = value
except TypeError as e:
    if 'Invalid value' in str(e):
        arr = arr.astype('Float64')
        arr[i] = value
    else:
        raise

Prevention

When it happens

Trigger: Assigning arr[i] = value, arr.fillna(value), arr.insert(loc, item), or any setitem path where value is a scalar that fails the kind checks in _validate_setitem_value (e.g. arr[i] = 1.5 on an Int64 array, or arr[i] = 'x' on any numeric masked array).

Common situations: Mixing dtypes after refactoring a column from float to Int; loading CSV data typed as object then assigning string cells into a nullable Int column; passing user input without normalization; bugs where a column is typed Int64 but the upstream producer emits floats.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/16ec625874b9c560. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/masked.py:420

        TypeError
        """
        kind = self.dtype.kind
        # TODO: get this all from np_can_hold_element?
        if kind == "b":
            if lib.is_bool(value):
                return value

        elif kind == "f":
            if lib.is_integer(value) or lib.is_float(value):
                return value

        elif lib.is_integer(value) or (lib.is_float(value) and value.is_integer()):
            return value
            # TODO: unsigned checks

        # Note: without the "str" here, the f-string rendering raises in
        #  py38 builds.
        raise TypeError(f"Invalid value '{value!s}' for dtype '{self.dtype}'")

    def insert(self, loc: int, item) -> Self:
        if not is_valid_na_for_dtype(item, self.dtype):
            self._validate_setitem_value(item)
        return super().insert(loc, item)

    def _validate_listlike(self, value) -> tuple[np.ndarray, npt.NDArray[np.bool_]]:
        """
        Validate a non-scalar setitem value and return ``(data, mask)``.

        Raises
        ------
        TypeError
            If `value` cannot be losslessly stored in self.dtype.
        """
        kind = self.dtype.kind

        if hasattr(value, "dtype"):

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