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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/09/21 06:29:46 UTC
[GitHub] [spark] itholic commented on a diff in pull request #37953: [SPARK-40510][PS] Implement `ddof` in `Series.cov`
itholic commented on code in PR #37953:
URL: https://github.com/apache/spark/pull/37953#discussion_r976082796
##########
python/pyspark/pandas/series.py:
##########
@@ -1002,19 +1007,23 @@ def cov(self, other: "Series", min_periods: Optional[int] = None) -> float:
Examples
--------
>>> from pyspark.pandas.config import set_option, reset_option
- >>> set_option("compute.ops_on_diff_frames", True)
>>> s1 = ps.Series([0.90010907, 0.13484424, 0.62036035])
>>> s2 = ps.Series([0.12528585, 0.26962463, 0.51111198])
- >>> s1.cov(s2)
- -0.016857626527158744
- >>> reset_option("compute.ops_on_diff_frames")
+ >>> with ps.option_context("compute.ops_on_diff_frames", True):
+ ... s1.cov(s2)
+ -0.016857...
+ >>> with ps.option_context("compute.ops_on_diff_frames", True):
+ ... s1.cov(s2, ddof=2)
+ -0.033715...
"""
if not isinstance(other, Series):
raise TypeError("unsupported type: %s" % type(other))
if not np.issubdtype(self.dtype, np.number): # type: ignore[arg-type]
raise TypeError("unsupported dtype: %s" % self.dtype)
if not np.issubdtype(other.dtype, np.number): # type: ignore[arg-type]
raise TypeError("unsupported dtype: %s" % other.dtype)
+ if not isinstance(ddof, int):
+ raise TypeError("ddof must be integer")
Review Comment:
Maybe do we need to add a negative test case?
##########
python/pyspark/pandas/series.py:
##########
@@ -993,6 +993,11 @@ def cov(self, other: "Series", min_periods: Optional[int] = None) -> float:
Series with which to compute the covariance.
min_periods : int, optional
Minimum number of observations needed to have a valid result.
+ ddof : int, default 1
+ Delta degrees of freedom. The divisor used in calculations
Review Comment:
nit: there are two spaces between sentences.
##########
python/pyspark/pandas/series.py:
##########
@@ -1029,7 +1038,9 @@ def cov(self, other: "Series", min_periods: Optional[int] = None) -> float:
if len(sdf.head(min_periods)) < min_periods:
return np.nan
else:
- return sdf.select(F.covar_samp(*sdf.columns)).head(1)[0][0]
+ return sdf.select(SF.covar(F.col(sdf.columns[0]), F.col(sdf.columns[1]), ddof)).head(1)[
+ 0
+ ][0]
Review Comment:
Can we make it a bit more prettier?
e.g.
```python
return sdf.select(
SF.covar(F.col(sdf.columns[0]), F.col(sdf.columns[1]), ddof)).head(1)[0][0]
```
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