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[GitHub] [spark] zhengruifeng opened a new pull request, #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
zhengruifeng opened a new pull request, #37948:
URL: https://github.com/apache/spark/pull/37948
### What changes were proposed in this pull request?
Add resampling to API references
### Why are the changes needed?
Docs for `resampling` is missing
### Does this PR introduce _any_ user-facing change?
yes, new docs
### How was this patch tested?
existing UT
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
Posted by GitBox <gi...@apache.org>.
HyukjinKwon commented on code in PR #37948:
URL: https://github.com/apache/spark/pull/37948#discussion_r977083499
##########
python/pyspark/pandas/resample.py:
##########
@@ -491,3 +737,37 @@ def __getattr__(self, item: str) -> Any:
def _handle_output(self, psdf: DataFrame) -> Series:
return first_series(psdf).rename(self._psser.name)
+
+
+def _test() -> None:
+ import os
+ import doctest
+ import sys
+ import numpy
+ from datetime import datetime
+ from pyspark.sql import SparkSession
+ import pyspark.pandas.resample
+
+ os.chdir(os.environ["SPARK_HOME"])
+
+ globs = pyspark.pandas.resample.__dict__.copy()
+ globs["np"] = numpy
Review Comment:
Let's probably import this within the example. `datetime` too.
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[GitHub] [spark] zhengruifeng commented on pull request #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on PR #37948:
URL: https://github.com/apache/spark/pull/37948#issuecomment-1253185396
```
Warning, treated as error:
/__w/spark/spark/python/docs/source/reference/pyspark.pandas/resampling.rst:2:Explicit markup ends without a blank line; unexpected unindent.
make: *** [Makefile:35: html] Error 2
------------------------------------------------
Jekyll 4.2.1 Please append `--trace` to the `build` command
for any additional information or backtrace.
------------------------------------------------
```
@itholic @HyukjinKwon @Yikun Do you have any ideas? It keep failing like this, even though I tried several changs
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[GitHub] [spark] itholic commented on pull request #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
Posted by GitBox <gi...@apache.org>.
itholic commented on PR #37948:
URL: https://github.com/apache/spark/pull/37948#issuecomment-1253238299
> ```
> Warning, treated as error:
> /__w/spark/spark/python/docs/source/reference/pyspark.pandas/resampling.rst:2:Explicit markup ends without a blank line; unexpected unindent.
> make: *** [Makefile:35: html] Error 2
> ------------------------------------------------
> Jekyll 4.2.1 Please append `--trace` to the `build` command
> for any additional information or backtrace.
> ------------------------------------------------
> ```
>
> @itholic @HyukjinKwon @Yikun Do you have any ideas? It keep failing like this, even though I tried several changs
Seems like this should be aligned.
```diff
.. Licensed to the Apache Software Foundation (ASF) under one
-or more contributor license agreements. See the NOTICE file
-distributed with this work for additional information
-regarding copyright ownership. The ASF licenses this file
-to you under the Apache License, Version 2.0 (the
-"License"); you may not use this file except in compliance
-with the License. You may obtain a copy of the License at
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
.. http://www.apache.org/licenses/LICENSE-2.0
.. Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an
-"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-KIND, either express or implied. See the License for the
-specific language governing permissions and limitations
-under the License.
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
```
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[GitHub] [spark] zhengruifeng closed pull request #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
Posted by GitBox <gi...@apache.org>.
zhengruifeng closed pull request #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
URL: https://github.com/apache/spark/pull/37948
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
Posted by GitBox <gi...@apache.org>.
HyukjinKwon commented on code in PR #37948:
URL: https://github.com/apache/spark/pull/37948#discussion_r975207820
##########
python/docs/source/reference/pyspark.pandas/resampling.rst:
##########
@@ -0,0 +1,42 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+.. http://www.apache.org/licenses/LICENSE-2.0
+
+.. Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+
+
+.. _api.resampling:
+
+==========
+Resampling
Review Comment:
(should add this page to https://github.com/apache/spark/blob/253627e4dbb62af9f94d35ee3140024754a942e9/python/docs/source/reference/pyspark.pandas/index.rst)
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[GitHub] [spark] zhengruifeng commented on pull request #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
Posted by GitBox <gi...@apache.org>.
zhengruifeng commented on PR #37948:
URL: https://github.com/apache/spark/pull/37948#issuecomment-1254535505
Merged into master, thank you guys!
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[GitHub] [spark] itholic commented on a diff in pull request #37948: [SPARK-40327][PS][DOCS] Add resampling to API references
Posted by GitBox <gi...@apache.org>.
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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