itholic commented on code in PR #37948:
URL: https://github.com/apache/spark/pull/37948#discussion_r976058915
##########
python/pyspark/pandas/resample.py:
##########
@@ -412,21 +412,267 @@ def _handle_output(self, psdf: DataFrame) -> FrameLike:
pass
def min(self) -> FrameLike:
+ """
+ Compute max of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").min().sort_index()
+ A B
+ 2022-05-01 0.171162 0.338864
+ 2022-05-04 0.010527 0.561204
+ 2022-05-07 NaN NaN
+ 2022-05-10 0.813726 0.745100
+ """
return self._handle_output(self._downsample("min"))
def max(self) -> FrameLike:
+ """
+ Compute max of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").max().sort_index()
+ A B
+ 2022-05-01 0.420538 0.859182
+ 2022-05-04 0.270533 0.691041
+ 2022-05-07 NaN NaN
+ 2022-05-10 0.813726 0.745100
+ """
return self._handle_output(self._downsample("max"))
def sum(self) -> FrameLike:
+ """
+ Compute sum of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").sum().sort_index()
+ A B
+ 2022-05-01 0.800160 1.679727
+ 2022-05-04 0.281060 1.252245
+ 2022-05-07 0.000000 0.000000
+ 2022-05-10 0.813726 0.745100
+ """
return self._handle_output(self._downsample("sum").fillna(0.0))
def mean(self) -> FrameLike:
+ """
+ Compute mean of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").mean().sort_index()
+ A B
+ 2022-05-01 0.266720 0.559909
+ 2022-05-04 0.140530 0.626123
+ 2022-05-07 NaN NaN
+ 2022-05-10 0.813726 0.745100
+ """
return self._handle_output(self._downsample("mean"))
def std(self) -> FrameLike:
+ """
+ Compute mean of resampled values.
Review Comment:
mean -> std ?
##########
python/pyspark/pandas/resample.py:
##########
@@ -412,21 +412,267 @@ def _handle_output(self, psdf: DataFrame) -> FrameLike:
pass
def min(self) -> FrameLike:
+ """
+ Compute max of resampled values.
Review Comment:
max -> min ?
##########
python/pyspark/pandas/resample.py:
##########
@@ -412,21 +412,267 @@ def _handle_output(self, psdf: DataFrame) -> FrameLike:
pass
def min(self) -> FrameLike:
+ """
+ Compute max of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").min().sort_index()
+ A B
+ 2022-05-01 0.171162 0.338864
+ 2022-05-04 0.010527 0.561204
+ 2022-05-07 NaN NaN
+ 2022-05-10 0.813726 0.745100
+ """
return self._handle_output(self._downsample("min"))
def max(self) -> FrameLike:
+ """
+ Compute max of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").max().sort_index()
+ A B
+ 2022-05-01 0.420538 0.859182
+ 2022-05-04 0.270533 0.691041
+ 2022-05-07 NaN NaN
+ 2022-05-10 0.813726 0.745100
+ """
return self._handle_output(self._downsample("max"))
def sum(self) -> FrameLike:
+ """
+ Compute sum of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").sum().sort_index()
+ A B
+ 2022-05-01 0.800160 1.679727
+ 2022-05-04 0.281060 1.252245
+ 2022-05-07 0.000000 0.000000
+ 2022-05-10 0.813726 0.745100
+ """
return self._handle_output(self._downsample("sum").fillna(0.0))
def mean(self) -> FrameLike:
+ """
+ Compute mean of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").mean().sort_index()
+ A B
+ 2022-05-01 0.266720 0.559909
+ 2022-05-04 0.140530 0.626123
+ 2022-05-07 NaN NaN
+ 2022-05-10 0.813726 0.745100
+ """
return self._handle_output(self._downsample("mean"))
def std(self) -> FrameLike:
+ """
+ Compute mean of resampled values.
+
+ .. versionadded:: 3.4.0
+
+ See Also
+ --------
+ pyspark.pandas.Series.groupby
+ pyspark.pandas.DataFrame.groupby
+
+ Examples
+ --------
+ >>> np.random.seed(22)
+ >>> dates = [
+ ... datetime(2022, 5, 1, 4, 5, 6),
+ ... datetime(2022, 5, 3),
+ ... datetime(2022, 5, 3, 23, 59, 59),
+ ... datetime(2022, 5, 4),
+ ... pd.NaT,
+ ... datetime(2022, 5, 4, 0, 0, 1),
+ ... datetime(2022, 5, 11),
+ ... ]
+ >>> df = ps.DataFrame(
+ ... np.random.rand(len(dates), 2), index=pd.DatetimeIndex(dates),
columns=["A", "B"]
+ ... )
+ >>> df
+ A B
+ 2022-05-01 04:05:06 0.208461 0.481681
+ 2022-05-03 00:00:00 0.420538 0.859182
+ 2022-05-03 23:59:59 0.171162 0.338864
+ 2022-05-04 00:00:00 0.270533 0.691041
+ NaT 0.220405 0.811951
+ 2022-05-04 00:00:01 0.010527 0.561204
+ 2022-05-11 00:00:00 0.813726 0.745100
+ >>> df.resample("3D").std().sort_index()
+ A B
+ 2022-05-01 0.134509 0.268835
+ 2022-05-04 0.183852 0.091809
+ 2022-05-07 NaN NaN
+ 2022-05-10 NaN NaN
+ """
return self._handle_output(self._downsample("std"))
def var(self) -> FrameLike:
+ """
+ Compute mean of resampled values.
Review Comment:
mean -> var ?
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