pandas-dev/pandas · error · TypeError

bad operand type for unary +: '{self.dtype}'

Error message

bad operand type for unary +: '{self.dtype}'

What it means

Raised by ArrowStringArray.__pos__ (unary + operator). Applying unary plus to a string array has no meaningful numeric result, so pandas raises a TypeError mirroring Python's own 'bad operand type for unary +'. This mirrors numpy/object behavior and prevents silent no-ops when code written for numeric arrays is applied to strings.

Source

Thrown at pandas/core/arrays/string_arrow.py:657

    def _cmp_method(self, other, op):
        if (
            isinstance(other, (BaseStringArray, ArrowExtensionArray))
            and self.dtype.na_value is not libmissing.NA
            and other.dtype.na_value is libmissing.NA
        ):
            # NA has priority of NaN semantics
            return NotImplemented

        result = super()._cmp_method(other, op)
        if self.dtype.na_value is np.nan:
            if op == operator.ne:
                return result.to_numpy(np.bool_, na_value=True)
            else:
                return result.to_numpy(np.bool_, na_value=False)
        return result

    def __pos__(self) -> Self:
        raise TypeError(f"bad operand type for unary +: '{self.dtype}'")

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Remove the unary + operator on string columns; it has no effect you want.
2. If the intent was to coerce to numeric, use `s.astype('float64')` or `pd.to_numeric(s)` explicitly.
3. Branch on dtype before applying unary operators so string columns are skipped.

Example fix

# before
s = pd.Series(['1','2'], dtype='string[pyarrow]')
result = +s  # TypeError
# after
result = s.astype('int64')

import pandas as pd

def safe_unary_plus(s):
    if pd.api.types.is_string_dtype(s):
        raise TypeError('unary + not valid on string dtype')
    return +s

import pandas as pd
def supports_unary_plus(s) -> bool:
    return pd.api.types.is_numeric_dtype(s)

try:
    return +s
except TypeError as e:
    if 'bad operand type for unary' in str(e):
        return s.astype('float64')
    raise

When it happens

Trigger: Writing `+s` or `+df['col']` where s/col has dtype 'string[pyarrow]'. Also triggered by libraries (e.g. some expression engines) that apply unary plus generically to all columns.

Common situations: Generic vectorized pipelines that prefix + to 'ensure numeric'; copy-paste from numeric code into a string context; expression-tree evaluators that visit every column with unary operators.

Related errors

• Invalid value '{item}' for dtype 'str'. Value should be a st
• Invalid value '{value}' for dtype 'str'. Value should be a s
• Invalid value for dtype 'str'. Value should be a string or m
• Cannot perform reduction '{name}' with string dtype
• Unordered Categoricals can only compare equality or not

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/066a8d1b76c903b3. Report an issue: GitHub.