pandas-dev/pandas · error · ValueError

StringArray requires a sequence of strings or pandas.NA. Got

Error message

StringArray requires a sequence of strings or pandas.NA. Got '{self._ndarray.dtype}' dtype instead.

What it means

After the content check passes (or on an empty array), StringArray._validate (NA semantics) verifies the underlying ndarray has object dtype. StringArray stores Python str objects, so a typed ndarray (int64, float64, '<U...') is rejected with ValueError showing the offending dtype.

Source

Thrown at pandas/core/arrays/string_.py:727

            self._validate(dtype)
        NDArrayBacked.__init__(
            self,
            self._ndarray,
            dtype,
        )

    def _validate(self, dtype: StringDtype) -> None:
        """Validate that we only store NA or strings."""

        if dtype._na_value is libmissing.NA:
            if len(self._ndarray) and not lib.is_string_array(
                self._ndarray, skipna=True
            ):
                raise ValueError(
                    "StringArray requires a sequence of strings or pandas.NA"
                )
            if self._ndarray.dtype != "object":
                raise ValueError(
                    "StringArray requires a sequence of strings or pandas.NA. Got "
                    f"'{self._ndarray.dtype}' dtype instead."
                )
            # Check to see if need to convert Na values to pd.NA
            if self._ndarray.ndim > 2:
                # Ravel if ndims > 2 b/c no cythonized version available
                lib.convert_nans_to_NA(self._ndarray.ravel("K"))
            else:
                lib.convert_nans_to_NA(self._ndarray)
        else:
            # Validate that we only store NaN or strings.
            if len(self._ndarray) and not lib.is_string_array(
                self._ndarray, skipna=True
            ):
                raise ValueError("StringArray requires a sequence of strings or NaN")
            if self._ndarray.dtype != "object":
                raise ValueError(
                    "StringArray requires a sequence of strings "

View on GitHub (pinned to 71959b8cb9)

Solutions

  1. Create the ndarray with dtype=object: np.array(values, dtype=object).
  2. Use pd.array(values, dtype='string') which handles dtype correctly.
  3. Convert '<U' arrays with .astype(object) before passing.

Example fix

// before
arr = pd.arrays.StringArray(np.array(['a','b']))

// after
arr = pd.arrays.StringArray(np.array(['a','b'], dtype=object))
Defensive patterns

Strategy: validation

Validate before calling

import numpy as np
values = np.asarray(values)
if values.dtype != object:
    values = values.astype(object)
arr = pd.array(values, dtype='string')

Type guard

import numpy as np

def is_object_ndarray(values) -> bool:
    return isinstance(values, np.ndarray) and values.dtype == object

Prevention

When it happens

Trigger: Constructing StringArray(np.array([1, 2, 3])) (int64), StringArray(np.array([1.0, 2.0])) (float64), or StringArray(np.array(['a','b'])) which yields a '<U1' dtype rather than object.

Common situations: Passing numpy arrays created without dtype=object; assuming StringArray accepts unicode ('<U') ndarrays directly.

Related errors

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/3ec8e507a5945e5a. Report an issue: GitHub.