pandas-dev/pandas · error · ValueError

setting an array element with a sequence.

Error message

setting an array element with a sequence.

What it means

StringArray.__setitem__ raises ValueError('setting an array element with a sequence.') when a scalar key (single integer/position) is paired with a non-scalar value. You cannot store a list/array into a single slot of a 1-D string array.

Source

Thrown at pandas/core/arrays/string_.py:879

                value = np.asarray(value)
            if len(value) and not lib.is_string_array(value, skipna=True):
                raise TypeError(
                    "Invalid value for dtype 'str'. Value should be a "
                    "string or missing value (or array of those)."
                )
        return value

    def __setitem__(self, key, value) -> None:
        if self._readonly:
            raise ValueError("Cannot modify read-only array")

        value = self._validate_setitem_value(value)

        key = check_array_indexer(self, key)
        scalar_key = lib.is_scalar(key)
        scalar_value = lib.is_scalar(value)
        if scalar_key and not scalar_value:
            raise ValueError("setting an array element with a sequence.")

        if not scalar_value:
            if value.dtype == self.dtype:
                value = value._ndarray
            else:
                value = np.asarray(value)
                mask = isna(value)
                if mask.any():
                    value = value.copy()
                    value[isna(value)] = self.dtype.na_value

        super().__setitem__(key, value)

    def _putmask(self, mask: npt.NDArray[np.bool_], value) -> None:
        # the super() method NDArrayBackedExtensionArray._putmask uses
        # np.putmask which doesn't properly handle None/pd.NA, so using the
        # base class implementation that uses __setitem__
        ExtensionArray._putmask(self, mask, value)

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Use a slice or array key to place multiple values: arr[0:2] = ['a', 'b'].
2. Pass a scalar value for a scalar key: arr[0] = 'a'.
3. Check lib.is_scalar(value) and adjust the key accordingly.

Example fix

// before
string_array[0] = ['a', 'b']

// after
string_array[0:2] = ['a', 'b']

import numpy as np
from pandas._libs import lib

if lib.is_scalar(key) and not lib.is_scalar(value):
    key = slice(key, key + len(value)) if isinstance(value, (list, np.ndarray)) else key
string_array[key] = value

from pandas._libs import lib

def key_value_shapes_match(key, value) -> bool:
    return lib.is_scalar(key) == lib.is_scalar(value)

When it happens

Trigger: Calling string_array[0] = ['a', 'b'], string_array[0] = np.array(['a','b']), or any assignment that puts a sequence into one scalar index.

Common situations: Accidentally passing a list where a single value is expected; off-by-one in slicing that collapses to a scalar key while the value stays list-like.

Related errors

• Lengths of operands do not match: {len(self)} != {len(other)
• Invalid value for dtype 'str'. Value should be a string or m
• Cannot modify read-only array
• too many dims to broadcast
• cannot broadcast result

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/f71d5b7340c839fa. Report an issue: GitHub.