itholic commented on a change in pull request #33752:
URL: https://github.com/apache/spark/pull/33752#discussion_r691700514
##########
File path: python/pyspark/pandas/series.py
##########
@@ -944,6 +944,57 @@ def between(self, left: Any, right: Any, inclusive: bool =
True) -> "Series":
return lmask & rmask
+ def cov(self, other: "Series", min_periods: int = 1) -> float:
+ """
+ Compute covariance with Series, excluding missing values.
+ Parameters
+ ----------
+ other : Series
+ Series with which to compute the covariance.
+ min_periods : int, default 1
+ Minimum number of observations needed to have a valid result. None
= 1.
+
+ Returns
+ -------
+ float
+ Covariance between Series and other
+
+ 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")
+ """
+
+ if min_periods is None:
+ min_periods = 1
+
+ if same_anchor(self, other):
+ self_column_label = verify_temp_column_name(other.to_frame(),
"__self_column__")
+ other_column_label = verify_temp_column_name(self.to_frame(),
"__other_column__")
+ combined = DataFrame(
+ self._internal.with_new_columns(
+ [self.rename(self_column_label),
other.rename(other_column_label)]
+ )
+ )
Review comment:
How about something like:
```python
if min_periods is None:
min_periods = 1
if same_anchor(self, other):
sdf = self._internal.spark_frame.select(self.spark.column,
other.spark.column)
else:
combined = combine_frames(self.to_frame(), other.to_frame())
sdf =
combined._internal.spark_frame.select(*combined._internal.data_spark_columns)
sdf = sdf.dropna()
if len(sdf.head(min_periods)) < min_periods:
return np.nan
covariance = sdf.select(F.covar_samp(*sdf.columns)).head(1)[0][0]
return covariance
```
We'd better to leverage PySpark API (DataFrame, Column,,,) as much as
possible.
And we should avoid compute entire length of pandas-on-Spark object as much
as possible, too.
In this case, I think we can use `head` with `min_periods` instead of `len`.
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