pandas.api.extensions.ExtensionArray.take

ExtensionArray.take(indices, allow_fill=False, fill_value=None)

Take elements from an array.

Parameters:

indices : sequence of integers

Indices to be taken.

allow_fill : bool, default False

How to handle negative values in indices.

• False: negative values in indices indicate positional indices from the right (the default). This is similar to numpy.take().
• True: negative values in indices indicate missing values. These values are set to fill_value. Any other other negative values raise a ValueError.

fill_value : any, optional

Fill value to use for NA-indices when allow_fill is True. This may be None, in which case the default NA value for the type, self.dtype.na_value, is used.

For many ExtensionArrays, there will be two representations of fill_value: a user-facing “boxed” scalar, and a low-level physical NA value. fill_value should be the user-facing version, and the implementation should handle translating that to the physical version for processing the take if necessary.

Returns:

ExtensionArray

Raises:

IndexError

When the indices are out of bounds for the array.

ValueError

When indices contains negative values other than -1 and allow_fill is True.

See also

numpy.take, pandas.api.extensions.take

Notes

ExtensionArray.take is called by Series.__getitem__, .loc, iloc, when indices is a sequence of values. Additionally, it’s called by Series.reindex(), or any other method that causes realignment, with a fill_value.

Examples

Here’s an example implementation, which relies on casting the extension array to object dtype. This uses the helper method pandas.api.extensions.take().

def take(self, indices, allow_fill=False, fill_value=None):
    from pandas.core.algorithms import take

    # If the ExtensionArray is backed by an ndarray, then
    # just pass that here instead of coercing to object.
    data = self.astype(object)

    if allow_fill and fill_value is None:
        fill_value = self.dtype.na_value

    # fill value should always be translated from the scalar
    # type for the array, to the physical storage type for
    # the data, before passing to take.

    result = take(data, indices, fill_value=fill_value,
                  allow_fill=allow_fill)
    return self._from_sequence(result, dtype=self.dtype)