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# pandas.api.extensions.ExtensionArray.take¶

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

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. ExtensionArray 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.

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)

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