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new f03f3d01176d [SPARK-46560][PYTHON][DOCS] Refine docstring
`reverse/map_contains_key`
f03f3d01176d is described below
commit f03f3d01176d5c6aae606937e4a4b033ba1c3017
Author: panbingkun <panbing...@baidu.com>
AuthorDate: Thu Jan 4 20:10:56 2024 -0800
[SPARK-46560][PYTHON][DOCS] Refine docstring `reverse/map_contains_key`
### What changes were proposed in this pull request?
The pr aims to refine docstring of `reverse/map_contains_key`.
### Why are the changes needed?
To improve PySpark documentation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually test.
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #44561 from panbingkun/SPARK-46560.
Lead-authored-by: panbingkun <panbing...@baidu.com>
Co-authored-by: panbingkun <pbk1...@gmail.com>
Signed-off-by: Dongjoon Hyun <dh...@apple.com>
---
python/pyspark/sql/functions/builtin.py | 50 ++++++++++++++++++++++++---------
1 file changed, 37 insertions(+), 13 deletions(-)
diff --git a/python/pyspark/sql/functions/builtin.py
b/python/pyspark/sql/functions/builtin.py
index 4147e71f4bb4..50d3b6cc01ce 100644
--- a/python/pyspark/sql/functions/builtin.py
+++ b/python/pyspark/sql/functions/builtin.py
@@ -15303,7 +15303,7 @@ def shuffle(col: "ColumnOrName") -> Column:
@_try_remote_functions
def reverse(col: "ColumnOrName") -> Column:
"""
- Collection function: returns a reversed string or an array with reverse
order of elements.
+ Collection function: returns a reversed string or an array with elements
in reverse order.
.. versionadded:: 1.5.0
@@ -15313,21 +15313,38 @@ def reverse(col: "ColumnOrName") -> Column:
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
- name of column or expression
+ The name of the column or an expression that represents the element to
be reversed.
Returns
-------
:class:`~pyspark.sql.Column`
- array of elements in reverse order.
+ A new column that contains a reversed string or an array with elements
in reverse order.
Examples
--------
+ Example 1: Reverse a string
+
+ >>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('Spark SQL',)], ['data'])
- >>> df.select(reverse(df.data).alias('s')).collect()
- [Row(s='LQS krapS')]
+ >>> df.select(sf.reverse(df.data)).show()
+ +-------------+
+ |reverse(data)|
+ +-------------+
+ | LQS krapS|
+ +-------------+
+
+ Example 2: Reverse an array
+
+ >>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([2, 1, 3],) ,([1],) ,([],)], ['data'])
- >>> df.select(reverse(df.data).alias('r')).collect()
- [Row(r=[3, 1, 2]), Row(r=[1]), Row(r=[])]
+ >>> df.select(sf.reverse(df.data)).show()
+ +-------------+
+ |reverse(data)|
+ +-------------+
+ | [3, 1, 2]|
+ | [1]|
+ | []|
+ +-------------+
"""
return _invoke_function_over_columns("reverse", col)
@@ -15406,7 +15423,7 @@ def flatten(col: "ColumnOrName") -> Column:
@_try_remote_functions
def map_contains_key(col: "ColumnOrName", value: Any) -> Column:
"""
- Returns true if the map contains the key.
+ Map function: Returns true if the map contains the key.
.. versionadded:: 3.4.0
@@ -15416,9 +15433,9 @@ def map_contains_key(col: "ColumnOrName", value: Any)
-> Column:
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
- name of column or expression
+ The name of the column or an expression that represents the map.
value :
- a literal value
+ A literal value.
Returns
-------
@@ -15427,15 +15444,22 @@ def map_contains_key(col: "ColumnOrName", value: Any)
-> Column:
Examples
--------
- >>> from pyspark.sql.functions import map_contains_key
+ Example 1: The key is in the map
+
+ >>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
- >>> df.select(map_contains_key("data", 1)).show()
+ >>> df.select(sf.map_contains_key("data", 1)).show()
+-------------------------+
|map_contains_key(data, 1)|
+-------------------------+
| true|
+-------------------------+
- >>> df.select(map_contains_key("data", -1)).show()
+
+ Example 2: The key is not in the map
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
+ >>> df.select(sf.map_contains_key("data", -1)).show()
+--------------------------+
|map_contains_key(data, -1)|
+--------------------------+
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