pandas-dev/pandas · error · TypeError

index must be an integer, got {type(index)}

Error message

index must be an integer, got {type(index)}

What it means

Raised by ExtensionArray.item(index) when the provided `index` is not an integer (e.g. a string, float, numpy float64, slice, or None passed positionally as something other than None). The method uses pandas.core.dtypes.common.is_integer to validate; only Python ints (and numpy integer scalars) pass. Reached via pd.array(...).item(index) or Series.array.item(index).

Source

Thrown at pandas/core/arrays/base.py:683

        >>> arr = pd.array([1], dtype="Int64")
        >>> arr.item()
        np.int64(1)

        >>> arr = pd.array([1, 2, 3], dtype="Int64")
        >>> arr.item(0)
        np.int64(1)
        >>> arr.item(2)
        np.int64(3)
        """
        if index is None:
            if len(self) != 1:
                raise ValueError(
                    "can only convert an array of size 1 to a Python scalar"
                )
            return self[0]
        else:
            if not is_integer(index):
                raise TypeError(f"index must be an integer, got {type(index)}")
            return self[index]

    def to_numpy(
        self,
        dtype: npt.DTypeLike | None = None,
        copy: bool = False,
        na_value: object = lib.no_default,
    ) -> np.ndarray:
        """
        Convert to a NumPy ndarray.

        This is similar to :meth:`numpy.asarray`, but may provide additional control
        over how the conversion is done.

        Parameters
        ----------
        dtype : str or numpy.dtype, optional
            The dtype to pass to :meth:`numpy.asarray`.

View on GitHub (pinned to 71959b8cb9)

Solutions

1. Coerce to int: `arr.item(int(index))`.
2. Use is_integer from pandas to validate before calling: `from pandas.api.types import is_integer`.
3. For slices/lists use `arr[key]` directly instead of item().
4. Sanitize upstream so indices are Python ints.

Example fix

# before
idx = np.float64(2)
arr.item(idx)  # TypeError: index must be an integer, got float64

# after
arr.item(int(idx))

from pandas.api.types import is_integer

def safe_item(arr, index):
    if not is_integer(index):
        raise TypeError(f"index must be int, got {type(index).__name__}")
    return arr.item(int(index))

import numbers
from pandas.api.types import is_integer

def is_valid_index(v) -> bool:
    return is_integer(v) or isinstance(v, numbers.Integral)

try:
    return arr.item(index)
except TypeError as e:
    if "index must be an integer" in str(e):
        return arr.item(int(index))
    raise

When it happens

Trigger: Calling `arr.item(0.0)`, `arr.item("0")`, or `arr.item(np.float64(2))`. Also when a slice or list is mistakenly passed to item() instead of an integer index.

Common situations: Index values coming from JSON/CSV as strings or floats; numpy operations returning float64 indices; dynamic indexing code that loses int typing.

Related errors

• {func_name} requires a Series, Index, ExtensionArray, np.nda
• cannot diff {type(arr).__name__} on axis={axis}
• {type(arr).__name__} has no 'diff' method. Convert to a suit
• Column {colname} is backed by an extension array, which is n
• func is expected but received {} in **kwargs.

AI-assisted analysis of pandas-dev/pandas@71959b8cb9 (2026-08-07). Data as JSON: /api/errors/c2348a99204b3374. Report an issue: GitHub.