[GitHub] [spark] allisonwang-db commented on a diff in pull request #43039: [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join

["allisonwang-db (via GitHub)" <gi...@apache.org>]

allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1333620057


##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,68 +2647,154 @@ def join(
 
         Examples
         --------
-        The following performs a full outer join between ``df1`` and ``df2``.
+        The following examples demonstrate various join types between ``df1`` and ``df2``.
 
+        >>> import pyspark.sql.functions as sf
         >>> from pyspark.sql import Row
-        >>> from pyspark.sql.functions import desc
-        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")]).toDF("age", "name")
-        >>> df2 = spark.createDataFrame([Row(height=80, name="Tom"), Row(height=85, name="Bob")])
-        >>> df3 = spark.createDataFrame([Row(age=2, name="Alice"), Row(age=5, name="Bob")])
-        >>> df4 = spark.createDataFrame([
-        ...     Row(age=10, height=80, name="Alice"),
-        ...     Row(age=5, height=None, name="Bob"),
-        ...     Row(age=None, height=None, name="Tom"),
-        ...     Row(age=None, height=None, name=None),
+        >>> df = spark.createDataFrame([Row(name="Alice", age=2), Row(name="Bob", age=5)])
+        >>> df2 = spark.createDataFrame([Row(name="Tom", height=80), Row(name="Bob", height=85)])
+        >>> df3 = spark.createDataFrame([
+        ...     Row(name="Alice", age=10, height=80),
+        ...     Row(name="Bob", age=5, height=None),
+        ...     Row(name="Tom", age=None, height=None),
+        ...     Row(name=None, age=None, height=None),
         ... ])
 
         Inner join on columns (default)
 
-        >>> df.join(df2, 'name').select(df.name, df2.height).show()
-        +----+------+
-        |name|height|
-        +----+------+
-        | Bob|    85|
-        +----+------+
-        >>> df.join(df4, ['name', 'age']).select(df.name, df.age).show()
-        +----+---+
-        |name|age|
-        +----+---+
-        | Bob|  5|
-        +----+---+
-
-        Outer join for both DataFrames on the 'name' column.
-
-        >>> df.join(df2, df.name == df2.name, 'outer').select(
-        ...     df.name, df2.height).sort(desc("name")).show()
+        >>> df.join(df2, "name").show()
+        +----+---+------+
+        |name|age|height|
+        +----+---+------+
+        | Bob|  5|    85|
+        +----+---+------+
+
+        >>> df.join(df3, ["name", "age"]).show()
+        +----+---+------+
+        |name|age|height|
+        +----+---+------+
+        | Bob|  5|  NULL|
+        +----+---+------+
+
+        Outer join on a single column with an explicit join condition.
+
+        When the join condition is explicited stated: `df.name == df2.name`, this will
+        produce all records where the names match, as well as those that don't (since
+        it's an outer join). If there are names in `df2` that are not present in `df`,
+        they will appear with `NULL` in the `name` column of `df`, and vice versa for `df2`.
+
+        >>> joined = df.join(df2, df.name == df2.name, "outer").sort(sf.desc(df.name))
+        >>> joined.show()
+        +-----+----+----+------+
+        | name| age|name|height|
+        +-----+----+----+------+
+        |  Bob|   5| Bob|    85|
+        |Alice|   2|NULL|  NULL|
+        | NULL|NULL| Tom|    80|
+        +-----+----+----+------+
+
+        To select an output column, you must specify the dataframe along with the column
+        name to avoid ambiguous column references.
+
+        >>> joined.select(df.name, df2.height).show()
         +-----+------+
         | name|height|
         +-----+------+
         |  Bob|    85|
         |Alice|  NULL|
         | NULL|    80|
         +-----+------+
-        >>> df.join(df2, 'name', 'outer').select('name', 'height').sort(desc("name")).show()
+
+        A better approach is to assign aliases to the dataframes, and then reference
+        the ouptut columns from the join operation using these aliases:
+
+        >>> df.alias("df").join(df2.alias("df2"), df.name == df2.name, "outer") \\
+        ...     .sort(sf.desc("df.name")).select("df.name", "df2.height")
         +-----+------+

Review Comment:
   cc @cloud-fan please let me know if this makes sense to you



-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org


---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org