AlenkaF commented on code in PR #48648:
URL: https://github.com/apache/arrow/pull/48648#discussion_r2667991632
##########
python/pyarrow/_compute_docstrings.py:
##########
@@ -54,3 +54,105 @@
>>> modes[1]
<pyarrow.StructScalar: [('mode', 1), ('count', 2)]>
"""
+
+function_doc_additions["min"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min(arr1)
+ <pyarrow.Int64Scalar: 1>
+
+ Using `skip_nulls` to handle null values.
Review Comment:
```suggestion
Using ``skip_nulls`` to handle null values.
```
##########
python/pyarrow/tests/test_compute.py:
##########
@@ -883,6 +883,38 @@ def test_generated_docstrings():
Alternative way of passing options.
memory_pool : pyarrow.MemoryPool, optional
If not passed, will allocate memory from the default memory pool.
+
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min_max(arr1)
+ <pyarrow.StructScalar: [('min', 1), ('max', 3)]>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.min_max(arr2)
+ <pyarrow.StructScalar: [('min', 1.0), ('max', 3.0)]>
+ >>> pc.min_max(arr2, skip_nulls=False)
+ <pyarrow.StructScalar: [('min', None), ('max', None)]>
+
+ Using `ScalarAggregateOptions` to control minimum number of non-null
values.
Review Comment:
```suggestion
Using ``ScalarAggregateOptions`` to control minimum number of
non-null values.
```
##########
python/pyarrow/_compute_docstrings.py:
##########
@@ -54,3 +54,105 @@
>>> modes[1]
<pyarrow.StructScalar: [('mode', 1), ('count', 2)]>
"""
+
+function_doc_additions["min"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min(arr1)
+ <pyarrow.Int64Scalar: 1>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.min(arr2)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(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.min(arr3)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr3, options=pc.ScalarAggregateOptions(min_count=3))
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr3, options=pc.ScalarAggregateOptions(min_count=4))
+ <pyarrow.DoubleScalar: None>
+
+ This function also works with string values.
+
+ >>> arr4 = pa.array(["z", None, "y", "x"])
+ >>> pc.min(arr4)
+ <pyarrow.StringScalar: 'x'>
+ """
+
+function_doc_additions["max"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.max(arr1)
+ <pyarrow.Int64Scalar: 3>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.max(arr2)
+ <pyarrow.DoubleScalar: 3.0>
+ >>> pc.max(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.max(arr3)
+ <pyarrow.DoubleScalar: 3.0>
+ >>> pc.max(arr3, options=pc.ScalarAggregateOptions(min_count=3))
+ <pyarrow.DoubleScalar: 3.0>
+ >>> pc.max(arr3, options=pc.ScalarAggregateOptions(min_count=4))
+ <pyarrow.DoubleScalar: None>
+
+ This function also works with string values.
+
+ >>> arr4 = pa.array(["z", None, "y", "x"])
+ >>> pc.max(arr4)
+ <pyarrow.StringScalar: 'z'>
+ """
+
+function_doc_additions["min_max"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min_max(arr1)
+ <pyarrow.StructScalar: [('min', 1), ('max', 3)]>
+
+ Using `skip_nulls` to handle null values.
Review Comment:
```suggestion
Using ``skip_nulls`` to handle null values.
```
##########
python/pyarrow/_compute_docstrings.py:
##########
@@ -54,3 +54,105 @@
>>> modes[1]
<pyarrow.StructScalar: [('mode', 1), ('count', 2)]>
"""
+
+function_doc_additions["min"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min(arr1)
+ <pyarrow.Int64Scalar: 1>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.min(arr2)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr2, skip_nulls=False)
+ <pyarrow.DoubleScalar: None>
+
+ Using `ScalarAggregateOptions` to control minimum number of non-null
values.
Review Comment:
```suggestion
Using ``ScalarAggregateOptions`` to control minimum number of non-null
values.
```
##########
python/pyarrow/tests/test_compute.py:
##########
@@ -883,6 +883,38 @@ def test_generated_docstrings():
Alternative way of passing options.
memory_pool : pyarrow.MemoryPool, optional
If not passed, will allocate memory from the default memory pool.
+
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min_max(arr1)
+ <pyarrow.StructScalar: [('min', 1), ('max', 3)]>
+
+ Using `skip_nulls` to handle null values.
Review Comment:
```suggestion
Using ``skip_nulls`` to handle null values.
```
##########
python/pyarrow/_compute_docstrings.py:
##########
@@ -54,3 +54,105 @@
>>> modes[1]
<pyarrow.StructScalar: [('mode', 1), ('count', 2)]>
"""
+
+function_doc_additions["min"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min(arr1)
+ <pyarrow.Int64Scalar: 1>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.min(arr2)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(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.min(arr3)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr3, options=pc.ScalarAggregateOptions(min_count=3))
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr3, options=pc.ScalarAggregateOptions(min_count=4))
+ <pyarrow.DoubleScalar: None>
+
+ This function also works with string values.
+
+ >>> arr4 = pa.array(["z", None, "y", "x"])
+ >>> pc.min(arr4)
+ <pyarrow.StringScalar: 'x'>
+ """
+
+function_doc_additions["max"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.max(arr1)
+ <pyarrow.Int64Scalar: 3>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.max(arr2)
+ <pyarrow.DoubleScalar: 3.0>
+ >>> pc.max(arr2, skip_nulls=False)
+ <pyarrow.DoubleScalar: None>
+
+ Using `ScalarAggregateOptions` to control minimum number of non-null
values.
Review Comment:
```suggestion
Using ``ScalarAggregateOptions`` to control minimum number of non-null
values.
```
##########
python/pyarrow/_compute_docstrings.py:
##########
@@ -54,3 +54,105 @@
>>> modes[1]
<pyarrow.StructScalar: [('mode', 1), ('count', 2)]>
"""
+
+function_doc_additions["min"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min(arr1)
+ <pyarrow.Int64Scalar: 1>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.min(arr2)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(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.min(arr3)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr3, options=pc.ScalarAggregateOptions(min_count=3))
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr3, options=pc.ScalarAggregateOptions(min_count=4))
+ <pyarrow.DoubleScalar: None>
+
+ This function also works with string values.
+
+ >>> arr4 = pa.array(["z", None, "y", "x"])
+ >>> pc.min(arr4)
+ <pyarrow.StringScalar: 'x'>
+ """
+
+function_doc_additions["max"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.max(arr1)
+ <pyarrow.Int64Scalar: 3>
+
+ Using `skip_nulls` to handle null values.
Review Comment:
```suggestion
Using ``skip_nulls`` to handle null values.
```
##########
python/pyarrow/_compute_docstrings.py:
##########
@@ -54,3 +54,105 @@
>>> modes[1]
<pyarrow.StructScalar: [('mode', 1), ('count', 2)]>
"""
+
+function_doc_additions["min"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min(arr1)
+ <pyarrow.Int64Scalar: 1>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.min(arr2)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(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.min(arr3)
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr3, options=pc.ScalarAggregateOptions(min_count=3))
+ <pyarrow.DoubleScalar: 1.0>
+ >>> pc.min(arr3, options=pc.ScalarAggregateOptions(min_count=4))
+ <pyarrow.DoubleScalar: None>
+
+ This function also works with string values.
+
+ >>> arr4 = pa.array(["z", None, "y", "x"])
+ >>> pc.min(arr4)
+ <pyarrow.StringScalar: 'x'>
+ """
+
+function_doc_additions["max"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.max(arr1)
+ <pyarrow.Int64Scalar: 3>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.max(arr2)
+ <pyarrow.DoubleScalar: 3.0>
+ >>> pc.max(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.max(arr3)
+ <pyarrow.DoubleScalar: 3.0>
+ >>> pc.max(arr3, options=pc.ScalarAggregateOptions(min_count=3))
+ <pyarrow.DoubleScalar: 3.0>
+ >>> pc.max(arr3, options=pc.ScalarAggregateOptions(min_count=4))
+ <pyarrow.DoubleScalar: None>
+
+ This function also works with string values.
+
+ >>> arr4 = pa.array(["z", None, "y", "x"])
+ >>> pc.max(arr4)
+ <pyarrow.StringScalar: 'z'>
+ """
+
+function_doc_additions["min_max"] = """
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> import pyarrow.compute as pc
+ >>> arr1 = pa.array([1, 1, 2, 2, 3, 2, 2, 2])
+ >>> pc.min_max(arr1)
+ <pyarrow.StructScalar: [('min', 1), ('max', 3)]>
+
+ Using `skip_nulls` to handle null values.
+
+ >>> arr2 = pa.array([1.0, None, 2.0, 3.0])
+ >>> pc.min_max(arr2)
+ <pyarrow.StructScalar: [('min', 1.0), ('max', 3.0)]>
+ >>> pc.min_max(arr2, skip_nulls=False)
+ <pyarrow.StructScalar: [('min', None), ('max', None)]>
+
+ Using `ScalarAggregateOptions` to control minimum number of non-null
values.
Review Comment:
```suggestion
Using ``ScalarAggregateOptions`` to control minimum number of non-null
values.
```
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