Yicong-Huang commented on code in PR #53035:
URL: https://github.com/apache/spark/pull/53035#discussion_r2558070258
##########
python/pyspark/sql/pandas/functions.py:
##########
@@ -301,6 +303,66 @@ def calculate(iterator: Iterator[pa.Array]) ->
Iterator[pa.Array]:
Therefore, mutating the input arrays is not allowed and will cause
incorrect results.
For the same reason, users should also not rely on the index of
the input arrays.
+ * Iterator of Arrays to Scalar
+ `Iterator[pyarrow.Array]` -> `Any`
+
+ The function takes an iterator of `pyarrow.Array` and returns a scalar
value. This is
+ useful for grouped aggregations where the UDF can process all batches
for a group
+ iteratively, which is more memory-efficient than loading all data at
once. The returned
+ scalar can be a python primitive type, a numpy data type, or a
`pyarrow.Scalar` instance.
+
+ >>> import pandas as pd
Review Comment:
removed
##########
python/pyspark/sql/pandas/functions.py:
##########
@@ -301,6 +303,66 @@ def calculate(iterator: Iterator[pa.Array]) ->
Iterator[pa.Array]:
Therefore, mutating the input arrays is not allowed and will cause
incorrect results.
For the same reason, users should also not rely on the index of
the input arrays.
+ * Iterator of Arrays to Scalar
+ `Iterator[pyarrow.Array]` -> `Any`
+
+ The function takes an iterator of `pyarrow.Array` and returns a scalar
value. This is
+ useful for grouped aggregations where the UDF can process all batches
for a group
+ iteratively, which is more memory-efficient than loading all data at
once. The returned
+ scalar can be a python primitive type, a numpy data type, or a
`pyarrow.Scalar` instance.
+
+ >>> import pandas as pd
+ >>> from typing import Iterator
+ >>> @arrow_udf("double")
+ ... def arrow_mean(it: Iterator[pa.Array]) -> float:
+ ... sum_val = 0.0
+ ... cnt = 0
+ ... for v in it:
+ ... assert isinstance(v, pa.Array)
+ ... sum_val += pa.compute.sum(v).as_py()
+ ... cnt += len(v)
+ ... return sum_val / cnt
+ ...
+ >>> df = spark.createDataFrame(
+ ... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id",
"v"))
+ >>> df.groupby("id").agg(arrow_mean(df['v'])).show() # doctest: +SKIP
Review Comment:
removed skips
##########
python/pyspark/sql/pandas/functions.py:
##########
@@ -301,6 +303,66 @@ def calculate(iterator: Iterator[pa.Array]) ->
Iterator[pa.Array]:
Therefore, mutating the input arrays is not allowed and will cause
incorrect results.
For the same reason, users should also not rely on the index of
the input arrays.
+ * Iterator of Arrays to Scalar
+ `Iterator[pyarrow.Array]` -> `Any`
+
+ The function takes an iterator of `pyarrow.Array` and returns a scalar
value. This is
+ useful for grouped aggregations where the UDF can process all batches
for a group
+ iteratively, which is more memory-efficient than loading all data at
once. The returned
+ scalar can be a python primitive type, a numpy data type, or a
`pyarrow.Scalar` instance.
+
+ >>> import pandas as pd
+ >>> from typing import Iterator
+ >>> @arrow_udf("double")
+ ... def arrow_mean(it: Iterator[pa.Array]) -> float:
+ ... sum_val = 0.0
+ ... cnt = 0
+ ... for v in it:
+ ... assert isinstance(v, pa.Array)
+ ... sum_val += pa.compute.sum(v).as_py()
+ ... cnt += len(v)
+ ... return sum_val / cnt
+ ...
+ >>> df = spark.createDataFrame(
+ ... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id",
"v"))
+ >>> df.groupby("id").agg(arrow_mean(df['v'])).show() # doctest: +SKIP
+ +---+---------------+
+ | id|arrow_mean(v) |
+ +---+---------------+
+ | 1| 1.5|
+ | 2| 6.0|
+ +---+---------------+
+
+ * Iterator of Multiple Arrays to Scalar
+ `Iterator[Tuple[pyarrow.Array, ...]]` -> `Any`
+
+ The function takes an iterator of a tuple of multiple `pyarrow.Array`
and returns a
+ scalar value. This is useful for grouped aggregations with multiple
input columns.
+
+ >>> from typing import Iterator, Tuple
+ >>> import numpy as np
+ >>> @arrow_udf("double")
+ ... def arrow_weighted_mean(it: Iterator[Tuple[pa.Array, pa.Array]])
-> float:
+ ... weighted_sum = 0.0
+ ... weight = 0.0
+ ... for v, w in it:
+ ... assert isinstance(v, pa.Array)
+ ... assert isinstance(w, pa.Array)
+ ... weighted_sum += np.dot(v, w)
+ ... weight += pa.compute.sum(w).as_py()
+ ... return weighted_sum / weight
+ ...
+ >>> df = spark.createDataFrame(
+ ... [(1, 1.0, 1.0), (1, 2.0, 2.0), (2, 3.0, 1.0), (2, 5.0, 2.0),
(2, 10.0, 3.0)],
+ ... ("id", "v", "w"))
+ >>> df.groupby("id").agg(arrow_weighted_mean(df["v"], df["w"])).show()
# doctest: +SKIP
Review Comment:
removed skips
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