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new 9bf5e82a5577 [SPARK-46551][PYTHON][DOCS] Refine docstring of
`flatten/sequence/shuffle`
9bf5e82a5577 is described below
commit 9bf5e82a5577313e48b2ef983d1bc4285682960a
Author: yangjie01 <yangji...@baidu.com>
AuthorDate: Tue Jan 2 10:12:46 2024 +0800
[SPARK-46551][PYTHON][DOCS] Refine docstring of `flatten/sequence/shuffle`
### What changes were proposed in this pull request?
This pr refine docstring of `flatten/sequence/shuffle` and add some new
examples.
### Why are the changes needed?
To improve PySpark documentation
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass Github Actions
### Was this patch authored or co-authored using generative AI tooling?
No
Closes #44548 from LuciferYang/SPARK-46551.
Authored-by: yangjie01 <yangji...@baidu.com>
Signed-off-by: yangjie01 <yangji...@baidu.com>
---
python/pyspark/sql/functions/builtin.py | 171 +++++++++++++++++++++++++-------
1 file changed, 134 insertions(+), 37 deletions(-)
diff --git a/python/pyspark/sql/functions/builtin.py
b/python/pyspark/sql/functions/builtin.py
index 458bb8c8feaf..7f5e90739507 100644
--- a/python/pyspark/sql/functions/builtin.py
+++ b/python/pyspark/sql/functions/builtin.py
@@ -15224,32 +15224,73 @@ def array_sort(
@_try_remote_functions
def shuffle(col: "ColumnOrName") -> Column:
"""
- Collection function: Generates a random permutation of the given array.
+ Array function: Generates a random permutation of the given array.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
- Notes
- -----
- The function is non-deterministic.
-
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
- name of column or expression
+ The name of the column or expression to be shuffled.
Returns
-------
:class:`~pyspark.sql.Column`
- an array of elements in random order.
+ A new column that contains an array of elements in random order.
+
+ Notes
+ -----
+ The `shuffle` function is non-deterministic, meaning the order of the
output array
+ can be different for each execution.
Examples
--------
- >>> df = spark.createDataFrame([([1, 20, 3, 5],), ([1, 20, None, 3],)],
['data'])
- >>> df.select(shuffle(df.data).alias('s')).collect() # doctest: +SKIP
- [Row(s=[3, 1, 5, 20]), Row(s=[20, None, 3, 1])]
+ Example 1: Shuffling a simple array
+
+ >>> import pyspark.sql.functions as sf
+ >>> df = spark.createDataFrame([([1, 20, 3, 5],)], ['data'])
+ >>> df.select(sf.shuffle(df.data)).show() # doctest: +SKIP
+ +-------------+
+ |shuffle(data)|
+ +-------------+
+ |[1, 3, 20, 5]|
+ +-------------+
+
+ Example 2: Shuffling an array with null values
+
+ >>> import pyspark.sql.functions as sf
+ >>> df = spark.createDataFrame([([1, 20, None, 3],)], ['data'])
+ >>> df.select(sf.shuffle(df.data)).show() # doctest: +SKIP
+ +----------------+
+ | shuffle(data)|
+ +----------------+
+ |[20, 3, NULL, 1]|
+ +----------------+
+
+ Example 3: Shuffling an array with duplicate values
+
+ >>> import pyspark.sql.functions as sf
+ >>> df = spark.createDataFrame([([1, 2, 2, 3, 3, 3],)], ['data'])
+ >>> df.select(sf.shuffle(df.data)).show() # doctest: +SKIP
+ +------------------+
+ | shuffle(data)|
+ +------------------+
+ |[3, 2, 1, 3, 2, 3]|
+ +------------------+
+
+ Example 4: Shuffling an array with different types of elements
+
+ >>> import pyspark.sql.functions as sf
+ >>> df = spark.createDataFrame([(['a', 'b', 'c', 1, 2, 3],)], ['data'])
+ >>> df.select(sf.shuffle(df.data)).show() # doctest: +SKIP
+ +------------------+
+ | shuffle(data)|
+ +------------------+
+ |[1, c, 2, a, b, 3]|
+ +------------------+
"""
return _invoke_function_over_columns("shuffle", col)
@@ -15289,7 +15330,7 @@ def reverse(col: "ColumnOrName") -> Column:
@_try_remote_functions
def flatten(col: "ColumnOrName") -> Column:
"""
- Collection function: creates a single array from an array of arrays.
+ Array function: creates a single array from an array of arrays.
If a structure of nested arrays is deeper than two levels,
only one level of nesting is removed.
@@ -15301,29 +15342,57 @@ def flatten(col: "ColumnOrName") -> Column:
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
- name of column or expression
+ The name of the column or expression to be flattened.
Returns
-------
:class:`~pyspark.sql.Column`
- flattened array.
+ A new column that contains the flattened array.
Examples
--------
- >>> df = spark.createDataFrame([([[1, 2, 3], [4, 5], [6]],), ([None, [4,
5]],)], ['data'])
- >>> df.show(truncate=False)
- +------------------------+
- |data |
- +------------------------+
- |[[1, 2, 3], [4, 5], [6]]|
- |[NULL, [4, 5]] |
- +------------------------+
- >>> df.select(flatten(df.data).alias('r')).show()
+ Example 1: Flattening a simple nested array
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.createDataFrame([([[1, 2, 3], [4, 5], [6]],)], ['data'])
+ >>> df.select(sf.flatten(df.data)).show()
+------------------+
- | r|
+ | flatten(data)|
+------------------+
|[1, 2, 3, 4, 5, 6]|
- | NULL|
+ +------------------+
+
+ Example 2: Flattening an array with null values
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.createDataFrame([([None, [4, 5]],)], ['data'])
+ >>> df.select(sf.flatten(df.data)).show()
+ +-------------+
+ |flatten(data)|
+ +-------------+
+ | NULL|
+ +-------------+
+
+ Example 3: Flattening an array with more than two levels of nesting
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.createDataFrame([([[[1, 2], [3, 4]], [[5, 6], [7, 8]]],)],
['data'])
+ >>> df.select(sf.flatten(df.data)).show(truncate=False)
+ +--------------------------------+
+ |flatten(data) |
+ +--------------------------------+
+ |[[1, 2], [3, 4], [5, 6], [7, 8]]|
+ +--------------------------------+
+
+ Example 4: Flattening an array with mixed types
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.createDataFrame([([['a', 'b', 'c'], [1, 2, 3]],)], ['data'])
+ >>> df.select(sf.flatten(df.data)).show()
+ +------------------+
+ | flatten(data)|
+ +------------------+
+ |[a, b, c, 1, 2, 3]|
+------------------+
"""
return _invoke_function_over_columns("flatten", col)
@@ -15718,9 +15787,9 @@ def sequence(
start: "ColumnOrName", stop: "ColumnOrName", step:
Optional["ColumnOrName"] = None
) -> Column:
"""
- Generate a sequence of integers from `start` to `stop`, incrementing by
`step`.
- If `step` is not set, incrementing by 1 if `start` is less than or equal
to `stop`,
- otherwise -1.
+ Array function: Generate a sequence of integers from `start` to `stop`,
incrementing by `step`.
+ If `step` is not set, the function increments by 1 if `start` is less than
or equal to `stop`,
+ otherwise it decrements by 1.
.. versionadded:: 2.4.0
@@ -15730,25 +15799,53 @@ def sequence(
Parameters
----------
start : :class:`~pyspark.sql.Column` or str
- starting value (inclusive)
+ The starting value (inclusive) of the sequence.
stop : :class:`~pyspark.sql.Column` or str
- last values (inclusive)
+ The last value (inclusive) of the sequence.
step : :class:`~pyspark.sql.Column` or str, optional
- value to add to current to get next element (default is 1)
+ The value to add to the current element to get the next element in the
sequence.
+ The default is 1 if `start` is less than or equal to `stop`, otherwise
-1.
Returns
-------
:class:`~pyspark.sql.Column`
- an array of sequence values
+ A new column that contains an array of sequence values.
Examples
--------
- >>> df1 = spark.createDataFrame([(-2, 2)], ('C1', 'C2'))
- >>> df1.select(sequence('C1', 'C2').alias('r')).collect()
- [Row(r=[-2, -1, 0, 1, 2])]
- >>> df2 = spark.createDataFrame([(4, -4, -2)], ('C1', 'C2', 'C3'))
- >>> df2.select(sequence('C1', 'C2', 'C3').alias('r')).collect()
- [Row(r=[4, 2, 0, -2, -4])]
+ Example 1: Generating a sequence with default step
+
+ >>> import pyspark.sql.functions as sf
+ >>> df = spark.createDataFrame([(-2, 2)], ['start', 'stop'])
+ >>> df.select(sf.sequence(df.start, df.stop)).show()
+ +---------------------+
+ |sequence(start, stop)|
+ +---------------------+
+ | [-2, -1, 0, 1, 2]|
+ +---------------------+
+
+ Example 2: Generating a sequence with a custom step
+
+ >>> import pyspark.sql.functions as sf
+ >>> df = spark.createDataFrame([(4, -4, -2)], ['start', 'stop', 'step'])
+ >>> df.select(sf.sequence(df.start, df.stop, df.step)).show()
+ +---------------------------+
+ |sequence(start, stop, step)|
+ +---------------------------+
+ | [4, 2, 0, -2, -4]|
+ +---------------------------+
+
+
+ Example 3: Generating a sequence with a negative step
+
+ >>> import pyspark.sql.functions as sf
+ >>> df = spark.createDataFrame([(5, 1, -1)], ['start', 'stop', 'step'])
+ >>> df.select(sf.sequence(df.start, df.stop, df.step)).show()
+ +---------------------------+
+ |sequence(start, stop, step)|
+ +---------------------------+
+ | [5, 4, 3, 2, 1]|
+ +---------------------------+
"""
if step is None:
return _invoke_function_over_columns("sequence", start, stop)
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