pandas-dev/pandas · error · TypeError

Cannot interpolate with

Error message

Cannot interpolate with {self.dtype} dtype

What it means

interpolate() requires numeric data: the method's math (pairwise_diff, divide_checked, or the missing.interpolate_2d_inplace fallback) is undefined for non-numeric dtypes. The guard at the top refuses any dtype whose _is_numeric is False, raising TypeError with the offending dtype.

Solutions

  1. Use .fillna(method='ffill')/.ffill() for non-numeric columns instead of .interpolate().
  2. Select only numeric columns: df.select_dtypes(include='number').interpolate().
  3. Cast the column to a numeric dtype if it actually contains numbers stored as strings.

Example fix

// before
s = pd.Series(["1", None, "3"], dtype="string[pyarrow]")
s.interpolate()
// after
s = pd.Series([1.0, None, 3.0], dtype="double[pyarrow]")
s.interpolate()
Defensive patterns

Strategy: validation

Validate before calling

def safe_interpolate(s, **kw):
    if not getattr(s.dtype, "_is_numeric", False):
        raise TypeError(f"interpolate needs numeric dtype, got {s.dtype}")
    return s.interpolate(**kw)

Type guard

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

Try / catch

try:
    s.interpolate()
except TypeError:
    s.ffill()  # non-numeric fallback

Prevention

When it happens

Trigger: Calling .interpolate() on a string[pyarrow], binary[pyarrow], bool[pyarrow], or categorical-backed-by-arrow Series.

Common situations: Applying interpolate() to a whole DataFrame containing string columns; expecting interpolate to forward-fill text data.

Related errors

AI-assisted analysis of pandas-dev/pandas@3b7651241d (2026-08-11). Data as JSON: /api/errors/e9049fb46c19a5f9. Report an issue: GitHub.

Appendix: source

Thrown at pandas/core/arrays/arrow/array.py:3177

    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":

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