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 ? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org