pandas-dev/pandas · error · TypeError

Cannot interpolate with {self.dtype} dtype

Error message

Cannot interpolate with {self.dtype} dtype

What it means

Raised by ArrowExtensionArray.interpolate when the dtype is not numeric (`self.dtype._is_numeric` is False). Interpolation only makes sense over a numeric domain, so non-numeric arrow dtypes are rejected upfront.

Source

Thrown at pandas/core/arrays/arrow/array.py:3152

    def interpolate(
        self,
        *,
        method: InterpolateOptions,
        axis: int,
        index,
        limit,
        limit_direction,
        limit_area,
        copy: bool,
        **kwargs,
    ) -> Self:
        """
        See NDFrame.interpolate.__doc__.
        """
        # NB: we return type(self) even if copy=False
        if not self.dtype._is_numeric:
            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")

        if (
            method == "linear"
            and limit_area is None
            and limit is None
            and limit_direction == "forward"
        ):
            values = self._pa_array.combine_chunks()
            na_value = pa.array([None], type=values.type)
            y_diff_2 = pc.fill_null_backward(pc.pairwise_diff_checked(values, period=2))
            prev_values = pa.concat_arrays([na_value, values[:-2], na_value])
            interps = pc.add_checked(prev_values, pc.divide_checked(y_diff_2, 2))
            return self._from_pyarrow_array(pc.coalesce(self._pa_array, interps))

        mask = self.isna()
        if self.dtype.kind == "f":
            data = self._pa_array.to_numpy()
        elif self.dtype.kind in "iu":

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Skip/Exclude non-numeric columns before calling interpolate (e.g. `df.select_dtypes('number').interpolate()`).
2. Convert the column to a numeric arrow dtype if its values are actually numeric: `s.astype('float64[pyarrow]').interpolate(...)`.
3. Use forward/backward fill (`.ffill()`) for non-numeric columns instead of interpolation.

Example fix

// before
s = pd.Series(["1", None, "3"], dtype="string[pyarrow]")
s.interpolate()

// after
s.astype("float64[pyarrow]").interpolate(method="linear")

def can_interpolate(arr) -> bool:
    return bool(getattr(arr.dtype, "_is_numeric", False))

def is_numeric_arrow_dtype(dtype) -> bool:
    return bool(getattr(dtype, "_is_numeric", False))

try:
    s.interpolate()
except TypeError as e:
    if "Cannot interpolate with" in str(e):
        s.astype("float64[pyarrow]").interpolate()
    else:
        raise

When it happens

Trigger: Calling `Series.interpolate()` (or DataFrame.interpolate on a column) backed by a non-numeric pyarrow dtype — e.g. `string[pyarrow]`, `bool[pyarrow]`, or binary arrow types.

Common situations: Applying interpolate to all columns indiscriminately, or after a column's dtype was inferred as string rather than numeric.

Related errors

• Cannot interpolate with {self.dtype} dtype
• bins argument only works with numeric data.
• Column {colname} must have a numeric dtype. Found '{dtype}'
• Invalid value '{value!s}' for dtype '{self.dtype}'
• operation '{name}' not supported for dtype '{self.dtype}'

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/e9049fb46c19a5f9. Report an issue: GitHub.