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| author | Andrew Ray <ray.andrew@gmail.com> | 2015-12-07 15:01:00 -0800 |
|---|---|---|
| committer | Yin Huai <yhuai@databricks.com> | 2015-12-07 15:01:00 -0800 |
| commit | 36282f78b888743066843727426c6d806231aa97 (patch) | |
| tree | 896349b7ebd435e5226134cb7f7908f45b67a8ec /python | |
| parent | 84b809445f39b9030f272528bdaa39d1559cbc6e (diff) | |
| download | spark-36282f78b888743066843727426c6d806231aa97.tar.gz spark-36282f78b888743066843727426c6d806231aa97.tar.bz2 spark-36282f78b888743066843727426c6d806231aa97.zip | |
[SPARK-12184][PYTHON] Make python api doc for pivot consistant with scala doc
In SPARK-11946 the API for pivot was changed a bit and got updated doc, the doc changes were not made for the python api though. This PR updates the python doc to be consistent.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes #10176 from aray/sql-pivot-python-doc.
Diffstat (limited to 'python')
| -rw-r--r-- | python/pyspark/sql/group.py | 14 |
1 files changed, 9 insertions, 5 deletions
diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py index 1911588309..9ca303a974 100644 --- a/python/pyspark/sql/group.py +++ b/python/pyspark/sql/group.py @@ -169,16 +169,20 @@ class GroupedData(object): @since(1.6) def pivot(self, pivot_col, values=None): - """Pivots a column of the current DataFrame and perform the specified aggregation. + """ + Pivots a column of the current [[DataFrame]] and perform the specified aggregation. + There are two versions of pivot function: one that requires the caller to specify the list + of distinct values to pivot on, and one that does not. The latter is more concise but less + efficient, because Spark needs to first compute the list of distinct values internally. - :param pivot_col: Column to pivot - :param values: Optional list of values of pivot column that will be translated to columns in - the output DataFrame. If values are not provided the method will do an immediate call - to .distinct() on the pivot column. + :param pivot_col: Name of the column to pivot. + :param values: List of values that will be translated to columns in the output DataFrame. + // Compute the sum of earnings for each year by course with each course as a separate column >>> df4.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").collect() [Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)] + // Or without specifying column values (less efficient) >>> df4.groupBy("year").pivot("course").sum("earnings").collect() [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)] """ |