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

Thrown by StringArray._validate in pandas/core/arrays/string_.py:727 when na_value is pandas.NA and the backing ndarray's dtype is not 'object'. StringArray stores strings as Python objects in an object-dtype ndarray; a numeric/structured dtype means the data was not prepared correctly, so construction is rejected before any string operation runs.

Solutions

Use pd.array(numeric_arr, dtype='string') which handles conversion through _from_sequence.
Convert to object dtype first: pd.arrays.StringArray(numeric_arr.astype(object)) — but note non-string objects still trip error 452.
Cast numerics to strings explicitly: numeric_arr.astype(str).astype(object).

Example fix

// before
import pandas as pd, numpy as np
pd.arrays.StringArray(np.array([1,2,3]))  # raises ValueError

// after
pd.array([1,2,3], dtype='string')

Defensive patterns

Strategy: validation

Validate before calling

import numpy as np
def ensure_object_ndarray(values):
    arr = np.asarray(values)
    if arr.dtype != object:
        arr = arr.astype(object)
    return arr

Type guard

import numpy as np
def is_object_ndarray(arr) -> bool:
    return getattr(arr, 'dtype', None) == object

Try / catch

null

Prevention

Convert numeric ndarrays to strings via .astype(str) before handing to StringArray.
Use pd.array(..., dtype='string') which handles dtype conversion internally.
Do not bypass _from_sequence with the raw constructor unless data is already object-dtype strings.

When it happens

Trigger: Constructing pd.arrays.StringArray(np.array([1,2,3])) (int dtype ndarray) directly. Passing a float64 ndarray from a numeric column to the StringArray constructor without object conversion. Internal code that hands a non-object ndarray to StringArray.

Common situations: User assumes StringArray casts numeric arrays the way pd.Series(..., dtype='string') does — the low-level constructor does not. Reusing a numeric buffer for a string column without an intermediate astype(object).

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/3ec8e507a5945e5a. Report an issue: GitHub.

Appendix: 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 "

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