take(arr, indices: 'TakeIndexer', axis: 'int' = 0, allow_fill: 'bool' = False, fill_value=None)
When allow_fill
is False, :None:None:`indices` may be whatever dimensionality is accepted by NumPy for :None:None:`arr`.
When allow_fill
is True, :None:None:`indices` should be 1-D.
Non array-likes (sequences/scalars without a dtype) are coerced to an ndarray.
Indices to be taken.
The axis over which to select values.
How to handle negative values in :None:None:`indices`.
False: negative values in :None:None:`indices` indicate positional indices from the right (the default). This is similar to numpy.take
.
True: negative values in :None:None:`indices` indicate missing values. These values are set to :None:None:`fill_value`. Any other negative values raise a ValueError
.
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 multi-dimensional :None:None:`arr`, each element is filled with :None:None:`fill_value`.
When :None:None:`indices` is out of bounds for the array.
When the indexer contains negative values other than -1
and allow_fill
is True.
Same type as the input.
Take elements from an array.
numpy.take
Take elements from an array along an axis.
>>> from pandas.api.extensions import take
With the default allow_fill=False
, negative numbers indicate positional indices from the right.
>>> take(np.array([10, 20, 30]), [0, 0, -1]) array([10, 10, 30])
Setting allow_fill=True
will place :None:None:`fill_value` in those positions.
>>> take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True) array([10., 10., nan])This example is valid syntax, but we were not able to check execution
>>> take(np.array([10, 20, 30]), [0, 0, -1], allow_fill=True,See :
... fill_value=-10) array([ 10, 10, -10])
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pandas.core.algorithms.take
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