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new b0791b513da3 [SPARK-45223][PYTHON][DOCS] Refine docstring of
`Column.when`
b0791b513da3 is described below
commit b0791b513da3f0671417b9fbcd3a0caddbb45318
Author: Hyukjin Kwon <gurwls...@apache.org>
AuthorDate: Tue Nov 7 13:51:37 2023 -0800
[SPARK-45223][PYTHON][DOCS] Refine docstring of `Column.when`
### What changes were proposed in this pull request?
This PR proposes to improve the docstring of `Column.when`.
### Why are the changes needed?
For end users, and better usability of PySpark.
### Does this PR introduce _any_ user-facing change?
Yes, it fixes the user facing documentation.
### How was this patch tested?
Manually tested.
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #43688 from HyukjinKwon/SPARK-45223.
Authored-by: Hyukjin Kwon <gurwls...@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls...@apache.org>
---
python/pyspark/sql/column.py | 40 +++++++++++++++++++++++++++++++++++++---
1 file changed, 37 insertions(+), 3 deletions(-)
diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 203e53474f74..9357b4842bbd 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -1388,10 +1388,12 @@ class Column:
Examples
--------
+ Example 1: Using :func:`when` with conditions and values to create a
new Column
+
>>> from pyspark.sql import functions as sf
- >>> df = spark.createDataFrame(
- ... [(2, "Alice"), (5, "Bob")], ["age", "name"])
- >>> df.select(df.name, sf.when(df.age > 4, 1).when(df.age < 3,
-1).otherwise(0)).show()
+ >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age",
"name"])
+ >>> result = df.select(df.name, sf.when(df.age > 4, 1).when(df.age <
3, -1).otherwise(0))
+ >>> result.show()
+-----+------------------------------------------------------------+
| name|CASE WHEN (age > 4) THEN 1 WHEN (age < 3) THEN -1 ELSE 0 END|
+-----+------------------------------------------------------------+
@@ -1399,6 +1401,38 @@ class Column:
| Bob| 1|
+-----+------------------------------------------------------------+
+ Example 2: Chaining multiple :func:`when` conditions
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.createDataFrame([(1, "Alice"), (4, "Bob"), (6,
"Charlie")], ["age", "name"])
+ >>> result = df.select(
+ ... df.name,
+ ... sf.when(df.age < 3, "Young").when(df.age < 5,
"Middle-aged").otherwise("Old")
+ ... )
+ >>> result.show()
+
+-------+---------------------------------------------------------------------------+
+ | name|CASE WHEN (age < 3) THEN Young WHEN (age < 5) THEN
Middle-aged ELSE Old END|
+
+-------+---------------------------------------------------------------------------+
+ | Alice|
Young|
+ | Bob|
Middle-aged|
+ |Charlie|
Old|
+
+-------+---------------------------------------------------------------------------+
+
+ Example 3: Using literal values as conditions
+
+ >>> from pyspark.sql import functions as sf
+ >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age",
"name"])
+ >>> result = df.select(
+ ... df.name, sf.when(sf.lit(True), 1).otherwise(
+ ... sf.raise_error("unreachable")).alias("when"))
+ >>> result.show()
+ +-----+----+
+ | name|when|
+ +-----+----+
+ |Alice| 1|
+ | Bob| 1|
+ +-----+----+
+
See Also
--------
pyspark.sql.functions.when
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