pyarrow.compute.last#

pyarrow.compute.last(array, /, *, skip_nulls=True, min_count=1, options=None, memory_pool=None)#

Compute the first value in each group.

Null values are ignored by default. If skip_nulls = false, then this will return the first and last values regardless if it is null

Parameters:

arrayArray-like: Argument to compute function.
skip_nullsbool, default True: Whether to skip (ignore) nulls in the input. If False, any null in the input forces the output to null.
min_countint, default 1: Minimum number of non-null values in the input. If the number of non-null values is below min_count, the output is null.
optionspyarrow.compute.ScalarAggregateOptions, optional: Alternative way of passing options.
memory_poolpyarrow.MemoryPool, optional: If not passed, will allocate memory from the default memory pool.

See also

pyarrow.compute.first
pyarrow.compute.first_last

Examples

>>> import pyarrow as pa
>>> import pyarrow.compute as pc
>>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
>>> pc.last(arr1)
<pyarrow.Int64Scalar: 2>

Using skip_nulls to handle null values.

>>> arr2 = pa.array([1.0, 2.0, 3.0, None])
>>> pc.last(arr2)
<pyarrow.DoubleScalar: 3.0>
>>> pc.last(arr2, skip_nulls=False)
<pyarrow.DoubleScalar: None>

Using ScalarAggregateOptions to control minimum number of non-null values.

>>> arr3 = pa.array([1.0, None, float("nan"), 3.0])
>>> pc.last(arr3)
<pyarrow.DoubleScalar: 3.0>
>>> pc.last(arr3, options=pc.ScalarAggregateOptions(min_count=3))
<pyarrow.DoubleScalar: 3.0>
>>> pc.last(arr3, options=pc.ScalarAggregateOptions(min_count=4))
<pyarrow.DoubleScalar: None>