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new e683932 [SPARK-38487][PYTHON][DOC] Fix docstrings of
nlargest/nsmallest of DataFrame
e683932 is described below
commit e683932495fae444b2c17a755d9a660a6c2d63ef
Author: Xinrong Meng <xinrong.m...@databricks.com>
AuthorDate: Thu Mar 10 15:32:48 2022 +0900
[SPARK-38487][PYTHON][DOC] Fix docstrings of nlargest/nsmallest of DataFrame
Fix docstrings of nlargest/nsmallest of DataFrame
To make docstring less confusing.
No.
Manual test.
Closes #35793 from xinrong-databricks/frame.ntop.
Authored-by: Xinrong Meng <xinrong.m...@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls...@apache.org>
(cherry picked from commit c483e2977cbc6ae33d999c9c9d1dbacd9c53d85a)
Signed-off-by: Hyukjin Kwon <gurwls...@apache.org>
---
python/pyspark/pandas/frame.py | 12 +++++++-----
1 file changed, 7 insertions(+), 5 deletions(-)
diff --git a/python/pyspark/pandas/frame.py b/python/pyspark/pandas/frame.py
index e576789..efc677b 100644
--- a/python/pyspark/pandas/frame.py
+++ b/python/pyspark/pandas/frame.py
@@ -7198,7 +7198,7 @@ defaultdict(<class 'list'>, {'col..., 'col...})]
)
return internal
- # TODO: add keep = First
+ # TODO: add keep = First
def nlargest(self, n: int, columns: Union[Name, List[Name]]) ->
"DataFrame":
"""
Return the first `n` rows ordered by `columns` in descending order.
@@ -7255,7 +7255,7 @@ defaultdict(<class 'list'>, {'col..., 'col...})]
6 NaN 12
In the following example, we will use ``nlargest`` to select the three
- rows having the largest values in column "population".
+ rows having the largest values in column "X".
>>> df.nlargest(n=3, columns='X')
X Y
@@ -7263,12 +7263,14 @@ defaultdict(<class 'list'>, {'col..., 'col...})]
4 6.0 10
3 5.0 9
+ To order by the largest values in column "Y" and then "X", we can
+ specify multiple columns like in the next example.
+
>>> df.nlargest(n=3, columns=['Y', 'X'])
X Y
6 NaN 12
5 7.0 11
4 6.0 10
-
"""
return self.sort_values(by=columns, ascending=False).head(n=n)
@@ -7318,7 +7320,7 @@ defaultdict(<class 'list'>, {'col..., 'col...})]
6 NaN 12
In the following example, we will use ``nsmallest`` to select the
- three rows having the smallest values in column "a".
+ three rows having the smallest values in column "X".
>>> df.nsmallest(n=3, columns='X') # doctest: +NORMALIZE_WHITESPACE
X Y
@@ -7326,7 +7328,7 @@ defaultdict(<class 'list'>, {'col..., 'col...})]
1 2.0 7
2 3.0 8
- To order by the largest values in column "a" and then "c", we can
+ To order by the smallest values in column "Y" and then "X", we can
specify multiple columns like in the next example.
>>> df.nsmallest(n=3, columns=['Y', 'X']) # doctest:
+NORMALIZE_WHITESPACE
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