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Posted to reviews@spark.apache.org by "allisonwang-db (via GitHub)" <gi...@apache.org> on 2023/09/21 21:26:09 UTC
[GitHub] [spark] allisonwang-db opened a new pull request, #43039: [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join
allisonwang-db opened a new pull request, #43039:
URL: https://github.com/apache/spark/pull/43039
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### What changes were proposed in this pull request?
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This PR refines the docstring of `DataFrame.join` by adding more examples and explanations.
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To improve PySpark documentation.
### Does this PR introduce _any_ user-facing change?
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No
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doctest
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on PR #43039:
URL: https://github.com/apache/spark/pull/43039#issuecomment-1770017451
Merged to master.
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon closed pull request #43039: [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join
URL: https://github.com/apache/spark/pull/43039
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[GitHub] [spark] allisonwang-db commented on a diff in pull request #43039: [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join
Posted by "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
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[GitHub] [spark] cloud-fan commented on a diff in pull request #43039: [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1333755374
##########
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")
+-----+------+
| name|height|
+-----+------+
- | Tom| 80|
| Bob| 85|
|Alice| NULL|
+ | NULL| 80|
+-----+------+
- Outer join for both DataFrams with multiple columns.
+ Outer join on a single column with implicit join condition using column name
+
+ When you provide the column name directly as the join condition, Spark will treat
+ both name columns as one, and will not produce separate columns for `df.name` and
+ `df2.name`. This avoids having duplicate columns in the output.
+
+ >>> df.join(df2, "name", "outer").sort(sf.desc("name")).show()
+ +-----+----+------+
+ | name| age|height|
+ +-----+----+------+
+ | Tom|NULL| 80|
+ | Bob| 5| 85|
+ |Alice| 2| NULL|
+ +-----+----+------+
+
+ Outer join on multiple columns
+
+ >>> df.join(df3, ["name", "age"], "outer").show()
Review Comment:
does it output name, age columns from the left table?
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[GitHub] [spark] cloud-fan commented on a diff in pull request #43039: [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1333756115
##########
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")
+-----+------+
| name|height|
+-----+------+
- | Tom| 80|
| Bob| 85|
|Alice| NULL|
+ | NULL| 80|
+-----+------+
- Outer join for both DataFrams with multiple columns.
+ Outer join on a single column with implicit join condition using column name
+
+ When you provide the column name directly as the join condition, Spark will treat
+ both name columns as one, and will not produce separate columns for `df.name` and
+ `df2.name`. This avoids having duplicate columns in the output.
+
+ >>> df.join(df2, "name", "outer").sort(sf.desc("name")).show()
+ +-----+----+------+
+ | name| age|height|
+ +-----+----+------+
+ | Tom|NULL| 80|
+ | Bob| 5| 85|
+ |Alice| 2| NULL|
+ +-----+----+------+
+
+ Outer join on multiple columns
+
+ >>> df.join(df3, ["name", "age"], "outer").show()
+ +-----+----+------+
+ | name| age|height|
+ +-----+----+------+
+ | NULL|NULL| NULL|
+ |Alice| 2| NULL|
+ |Alice| 10| 80|
+ | Bob| 5| NULL|
+ | Tom|NULL| NULL|
+ +-----+----+------+
+
+ Left outer join on columns
+
+ >>> df.join(df2, "name", "left_outer").show()
+ +-----+---+------+
+ | name|age|height|
+ +-----+---+------+
+ |Alice| 2| NULL|
+ | Bob| 5| 85|
+ +-----+---+------+
+
+ Right outer join on columns
+
+ >>> df.join(df2, "name", "right_outer").show()
+ +----+----+------+
+ |name| age|height|
+ +----+----+------+
+ | Tom|NULL| 80|
+ | Bob| 5| 85|
+ +----+----+------+
+
+ Left semi join on columns
+
+ >>> df.join(df2, "name", "left_semi").show()
+ +----+---+
+ |name|age|
+ +----+---+
+ | Bob| 5|
+ +----+---+
+
+ Left anti join on columns
- >>> df.join(
- ... df3,
- ... [df.name == df3.name, df.age == df3.age],
- ... 'outer'
- ... ).select(df.name, df3.age).show()
+ >>> df.join(df2, "name", "left_anti").show()
+-----+---+
| name|age|
+-----+---+
|Alice| 2|
- | Bob| 5|
+-----+---+
+
+ Cross join
Review Comment:
do we really need to mention it? It's the same as inner join. We added cross join as we want to forbid inner join without join condition by default, but this restriction has been removed already.
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "beliefer (via GitHub)" <gi...@apache.org>.
beliefer commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1351192772
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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``.
Review Comment:
I am neutral on this change here.
Overall, LGTM.
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1353968058
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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()
Review Comment:
did we implement this `join` API differently in Spark Connect?
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1353967088
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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()
Review Comment:
It looks like this example does not work with spark connect:
```
pyspark.errors.exceptions.connect.AnalysisException: [UNRESOLVED_COLUMN.WITH_SUGGESTION] A column, variable, or function parameter with name `name` cannot be resolved. Did you mean one of the following? [`name`, `name`, `age`, `height`].;
'Sort ['name DESC NULLS LAST], true
+- Join FullOuter, (name#64 = name#78)
:- LocalRelation [name#64, age#65L]
+- LocalRelation [name#78, height#79L]
```
cc @zhengruifeng @HyukjinKwon
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on PR #43039:
URL: https://github.com/apache/spark/pull/43039#issuecomment-1753756796
cc @cloud-fan @HyukjinKwon the test failure seems unrelated.
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on PR #43039:
URL: https://github.com/apache/spark/pull/43039#issuecomment-1769337718
friendly ping @HyukjinKwon @zhengruifeng the test failures are not related.
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[GitHub] [spark] cloud-fan commented on a diff in pull request #43039: [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1333754878
##########
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:
It makes sense, but we should probably use self-join in the example, so that people can understand why it's better
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1353967855
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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 unambiguously select output columns, specify the dataframe along with the column name:
+
+ >>> 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()
- +-----+------+
- | name|height|
- +-----+------+
- | Tom| 80|
- | Bob| 85|
- |Alice| NULL|
- +-----+------+
- Outer join for both DataFrams with multiple columns.
+ However, in self-joins, direct column references can cause ambiguity:
+
+ >>> df.join(df, df.name == df.name, "outer").select(df.name).show() # doctest: +SKIP
+ Traceback (most recent call last):
+ ...
+ pyspark.errors.exceptions.captured.AnalysisException: Column name#0 are ambiguous...
Review Comment:
However, this works in Spark connect!
```
>>> df.join(df, df.name == df.name, "outer").select(df.name).show()
+-----+
| name|
+-----+
|Alice|
|Alice|
| Bob|
| Bob|
+-----+
```
also cc @cloud-fan
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1353964131
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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``.
Review Comment:
Good catch!
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1348055895
##########
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")
+-----+------+
| name|height|
+-----+------+
- | Tom| 80|
| Bob| 85|
|Alice| NULL|
+ | NULL| 80|
+-----+------+
- Outer join for both DataFrams with multiple columns.
+ Outer join on a single column with implicit join condition using column name
+
+ When you provide the column name directly as the join condition, Spark will treat
+ both name columns as one, and will not produce separate columns for `df.name` and
+ `df2.name`. This avoids having duplicate columns in the output.
+
+ >>> df.join(df2, "name", "outer").sort(sf.desc("name")).show()
+ +-----+----+------+
+ | name| age|height|
+ +-----+----+------+
+ | Tom|NULL| 80|
+ | Bob| 5| 85|
+ |Alice| 2| NULL|
+ +-----+----+------+
+
+ Outer join on multiple columns
+
+ >>> df.join(df3, ["name", "age"], "outer").show()
+ +-----+----+------+
+ | name| age|height|
+ +-----+----+------+
+ | NULL|NULL| NULL|
+ |Alice| 2| NULL|
+ |Alice| 10| 80|
+ | Bob| 5| NULL|
+ | Tom|NULL| NULL|
+ +-----+----+------+
+
+ Left outer join on columns
+
+ >>> df.join(df2, "name", "left_outer").show()
+ +-----+---+------+
+ | name|age|height|
+ +-----+---+------+
+ |Alice| 2| NULL|
+ | Bob| 5| 85|
+ +-----+---+------+
+
+ Right outer join on columns
+
+ >>> df.join(df2, "name", "right_outer").show()
+ +----+----+------+
+ |name| age|height|
+ +----+----+------+
+ | Tom|NULL| 80|
+ | Bob| 5| 85|
+ +----+----+------+
+
+ Left semi join on columns
+
+ >>> df.join(df2, "name", "left_semi").show()
+ +----+---+
+ |name|age|
+ +----+---+
+ | Bob| 5|
+ +----+---+
+
+ Left anti join on columns
- >>> df.join(
- ... df3,
- ... [df.name == df3.name, df.age == df3.age],
- ... 'outer'
- ... ).select(df.name, df3.age).show()
+ >>> df.join(df2, "name", "left_anti").show()
+-----+---+
| name|age|
+-----+---+
|Alice| 2|
- | Bob| 5|
+-----+---+
+
+ Cross join
Review Comment:
Probably not, as it's not really widely used. I will remove this example.
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1355911443
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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()
Review Comment:
Can we exclude those examples in this PRs, and mind filing JIRAs for both issues @allisonwang-db?
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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()
Review Comment:
Can we exclude those examples in this PR, and mind filing JIRAs for both issues @allisonwang-db?
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1354126980
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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()
Review Comment:
It should work as the column in spark connect contain dataframe id. @zhengruifeng can you take a look?
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "holdenk (via GitHub)" <gi...@apache.org>.
holdenk commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1351066467
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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``.
Review Comment:
nit: it's not just df1 and df2 (no need to fix unless other changes)
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1355919218
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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()
Review Comment:
Sounds good. Created [SPARK-45509](https://issues.apache.org/jira/browse/SPARK-45509)
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1355919218
##########
python/pyspark/sql/dataframe.py:
##########
@@ -2646,67 +2647,147 @@ 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()
Review Comment:
Sounds good.
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Re: [PR] [SPARK-45220][PYTHON][DOCS] Refine docstring of DataFrame.join [spark]
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #43039:
URL: https://github.com/apache/spark/pull/43039#discussion_r1348084036
##########
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")
+-----+------+
| name|height|
+-----+------+
- | Tom| 80|
| Bob| 85|
|Alice| NULL|
+ | NULL| 80|
+-----+------+
- Outer join for both DataFrams with multiple columns.
+ Outer join on a single column with implicit join condition using column name
+
+ When you provide the column name directly as the join condition, Spark will treat
+ both name columns as one, and will not produce separate columns for `df.name` and
+ `df2.name`. This avoids having duplicate columns in the output.
+
+ >>> df.join(df2, "name", "outer").sort(sf.desc("name")).show()
+ +-----+----+------+
+ | name| age|height|
+ +-----+----+------+
+ | Tom|NULL| 80|
+ | Bob| 5| 85|
+ |Alice| 2| NULL|
+ +-----+----+------+
+
+ Outer join on multiple columns
+
+ >>> df.join(df3, ["name", "age"], "outer").show()
Review Comment:
Yup. I can add another example to make it more clear.
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