pandas-dev/pandas · error · TypeError

Invalid value ' ' for dtype ' '. Value should be a string…

Error message

Invalid value '{value}' for dtype '{self.dtype}'. Value should be a string or missing value, got '{type(value).__name__}' instead.

What it means

Thrown by StringArray._validate_scalar in pandas/core/arrays/string_.py:755 — used by NDArrayBackedExtensionIndex.insert. When a scalar being inserted is not NA/NaN and not a Python str, the method rejects it because StringArray can only hold strings plus its designated missing marker. The message names the offending value, the dtype, and the actual type received.

Solutions

Stringify the scalar before inserting: idx.insert(loc, str(5)).
Coerce missing sentinels explicitly: insert(loc, pd.NA) or insert(loc, np.nan) depending on the dtype's na_value.
If mixed types are legitimate, build the index with dtype='object' instead of 'string'.

Example fix

// before
idx = pd.Index(['a','b'], dtype='string')
idx.insert(1, 5)  # raises TypeError

// after
idx.insert(1, '5')

Defensive patterns

Strategy: validation

Validate before calling

import pandas as pd
def safe_insert(index_obj, loc, value):
    if isinstance(getattr(index_obj, 'dtype', None), pd.StringDtype):
        if not (pd.isna(value) or isinstance(value, str)):
            value = str(value)
    return index_obj.insert(loc, value)

Type guard

def is_string_scalar(v) -> bool:
    import pandas as pd
    return isinstance(v, str) or pd.isna(v)

Try / catch

null

Prevention

Stringify scalars before inserting into a string-dtype Index.
Use pd.NA (or np.nan for the NaN variant) when the intent is a missing entry.
When appending indices of different dtypes, normalize types upstream.

When it happens

Trigger: Calling .insert(loc, 5) on an Index backed by StringArray. Internally triggered by Index.insert / Index.append when concatenating indices that introduce a non-string scalar. Building a string index element-by-element with mixed types.

Common situations: Appending a numeric index level to a string index. insert() called by alignment/reindex machinery that picks up a stray int. Concatenating two differently-typed indices where pandas picks the string dtype but the other side contributes non-strings.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/39acc5d84f7c073d. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/string_.py:755

        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 "
                    "or NaN. Got '{self._ndarray.dtype}' dtype instead."
                )
            # TODO validate or force NA/None to NaN

    def _validate_scalar(self, value):
        # used by NDArrayBackedExtensionIndex.insert
        if isna(value):
            return self.dtype.na_value
        elif not isinstance(value, str):
            raise TypeError(
                f"Invalid value '{value}' for dtype '{self.dtype}'. Value should be a "
                f"string or missing value, got '{type(value).__name__}' instead."
            )
        return value

    @classmethod
    def _from_sequence(
        cls, scalars, *, dtype: Dtype | None = None, copy: bool = False
    ) -> Self:
        if dtype and not (isinstance(dtype, str) and dtype == "string"):
            dtype = pandas_dtype(dtype)
            assert isinstance(dtype, StringDtype) and dtype.storage == "python"
        elif using_string_dtype():
            dtype = StringDtype(storage="python", na_value=np.nan)
        else:
            dtype = StringDtype(storage="python")

        from pandas.core.arrays.masked import BaseMaskedArray

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