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new 8505084bc26 [SPARK-45003][PYTHON][DOCS] Refine docstring of `asc/desc`
8505084bc26 is described below
commit 8505084bc26533d3ee9b1b3cf1a7df1c52bbb04a
Author: yangjie01 <[email protected]>
AuthorDate: Tue Aug 29 08:32:33 2023 -0700
[SPARK-45003][PYTHON][DOCS] Refine docstring of `asc/desc`
### What changes were proposed in this pull request?
This pr refine docstring of `asc/desc ` 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 #42717 from LuciferYang/py-function-asc-desc.
Authored-by: yangjie01 <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>
---
python/pyspark/sql/functions.py | 149 +++++++++++++++++++++++++++++-----------
1 file changed, 108 insertions(+), 41 deletions(-)
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 43b82d31368..71671928f30 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -660,7 +660,8 @@ column = col
@try_remote_functions
def asc(col: "ColumnOrName") -> Column:
"""
- Returns a sort expression based on the ascending order of the given column
name.
+ Returns a sort expression for the target column in ascending order.
+ This function is used in `sort` and `orderBy` functions.
.. versionadded:: 1.3.0
@@ -670,42 +671,67 @@ def asc(col: "ColumnOrName") -> Column:
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
- target column to sort by in the ascending order.
+ Target column to sort by in the ascending order.
Returns
-------
:class:`~pyspark.sql.Column`
- the column specifying the order.
+ The column specifying the sort order.
Examples
--------
- Sort by the column 'id' in the descending order.
+ Example 1: Sort DataFrame by 'id' column in ascending order.
- >>> df = spark.range(5)
- >>> df = df.sort(desc("id"))
- >>> df.show()
- +---+
- | id|
- +---+
- | 4|
- | 3|
- | 2|
- | 1|
- | 0|
- +---+
+ >>> from pyspark.sql.functions import asc
+ >>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id',
'value'])
+ >>> df.sort(asc("id")).show()
+ +---+-----+
+ | id|value|
+ +---+-----+
+ | 2| C|
+ | 3| A|
+ | 4| B|
+ +---+-----+
- Sort by the column 'id' in the ascending order.
+ Example 2: Use `asc` in `orderBy` function to sort the DataFrame.
- >>> df.orderBy(asc("id")).show()
- +---+
- | id|
- +---+
- | 0|
- | 1|
- | 2|
- | 3|
- | 4|
- +---+
+ >>> from pyspark.sql.functions import asc
+ >>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id',
'value'])
+ >>> df.orderBy(asc("value")).show()
+ +---+-----+
+ | id|value|
+ +---+-----+
+ | 3| A|
+ | 4| B|
+ | 2| C|
+ +---+-----+
+
+ Example 3: Combine `asc` with `desc` to sort by multiple columns.
+
+ >>> from pyspark.sql.functions import asc, desc
+ >>> df = spark.createDataFrame([(2, 'A', 4),
+ ... (1, 'B', 3),
+ ... (3, 'A', 2)], ['id', 'group', 'value'])
+ >>> df.sort(asc("group"), desc("value")).show()
+ +---+-----+-----+
+ | id|group|value|
+ +---+-----+-----+
+ | 2| A| 4|
+ | 3| A| 2|
+ | 1| B| 3|
+ +---+-----+-----+
+
+ Example 4: Implement `asc` from column expression.
+
+ >>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id',
'value'])
+ >>> df.sort(df.id.asc()).show()
+ +---+-----+
+ | id|value|
+ +---+-----+
+ | 2| C|
+ | 3| A|
+ | 4| B|
+ +---+-----+
"""
return col.asc() if isinstance(col, Column) else _invoke_function("asc",
col)
@@ -713,7 +739,8 @@ def asc(col: "ColumnOrName") -> Column:
@try_remote_functions
def desc(col: "ColumnOrName") -> Column:
"""
- Returns a sort expression based on the descending order of the given
column name.
+ Returns a sort expression for the target column in descending order.
+ This function is used in `sort` and `orderBy` functions.
.. versionadded:: 1.3.0
@@ -723,27 +750,67 @@ def desc(col: "ColumnOrName") -> Column:
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
- target column to sort by in the descending order.
+ Target column to sort by in the descending order.
Returns
-------
:class:`~pyspark.sql.Column`
- the column specifying the order.
+ The column specifying the sort order.
Examples
--------
- Sort by the column 'id' in the descending order.
+ Example 1: Sort DataFrame by 'id' column in descending order.
- >>> spark.range(5).orderBy(desc("id")).show()
- +---+
- | id|
- +---+
- | 4|
- | 3|
- | 2|
- | 1|
- | 0|
- +---+
+ >>> from pyspark.sql.functions import desc
+ >>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id',
'value'])
+ >>> df.sort(desc("id")).show()
+ +---+-----+
+ | id|value|
+ +---+-----+
+ | 4| B|
+ | 3| A|
+ | 2| C|
+ +---+-----+
+
+ Example 2: Use `desc` in `orderBy` function to sort the DataFrame.
+
+ >>> from pyspark.sql.functions import desc
+ >>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id',
'value'])
+ >>> df.orderBy(desc("value")).show()
+ +---+-----+
+ | id|value|
+ +---+-----+
+ | 2| C|
+ | 4| B|
+ | 3| A|
+ +---+-----+
+
+ Example 3: Combine `asc` with `desc` to sort by multiple columns.
+
+ >>> from pyspark.sql.functions import asc, desc
+ >>> df = spark.createDataFrame([(2, 'A', 4),
+ ... (1, 'B', 3),
+ ... (3, 'A', 2)], ['id', 'group', 'value'])
+ >>> df.sort(desc("group"), asc("value")).show()
+ +---+-----+-----+
+ | id|group|value|
+ +---+-----+-----+
+ | 1| B| 3|
+ | 3| A| 2|
+ | 2| A| 4|
+ +---+-----+-----+
+
+ Example 4: Implement `desc` from column expression.
+
+ >>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id',
'value'])
+ >>> df.sort(df.id.desc()).show()
+ +---+-----+
+ | id|value|
+ +---+-----+
+ | 4| B|
+ | 3| A|
+ | 2| C|
+ +---+-----+
"""
return col.desc() if isinstance(col, Column) else _invoke_function("desc",
col)
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