### What changes were proposed in this pull request?
Port Koalas dependencies appropriately to PySpark dependencies.
### Why are the changes needed?
pandas-on-Spark has its own required dependency and optional dependencies.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual test.
Closes#32386 from xinrong-databricks/portDeps.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The parameter **no_implicit_optional** is defined twice in the mypy configuration, [ligne 20](https://github.com/apache/spark/blob/master/python/mypy.ini#L20) and ligne 105.
### Why are the changes needed?
We would like to keep the mypy configuration clean.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This patch can be tested with `dev/lint-python`
Closes#32418 from garawalid/feature/clean-mypy-config.
Authored-by: garawalid <gwalid94@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is rather a followup of https://github.com/apache/spark/pull/30518 that should be ported back to `branch-3.1` too.
`STOP_AT_DELIMITER` was mistakenly used twice. The duplicated `STOP_AT_DELIMITER` should be `SKIP_VALUE` in the documentation.
### Why are the changes needed?
To correctly document.
### Does this PR introduce _any_ user-facing change?
Yes, it fixes the user-facing documentation.
### How was this patch tested?
I checked them via running linters.
Closes#32423 from HyukjinKwon/SPARK-35250.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR corrects some exception type when the function input params are failed to validate due to TypeError.
In order to convenient to review, there are 3 commits in this PR:
- Standardize input validation error type on sql
- Standardize input validation error type on ml
- Standardize input validation error type on pandas
### Why are the changes needed?
As suggestion from Python exception doc [1]: "Raised when an operation or function is applied to an object of inappropriate type.", but there are many Value error are raised in some pyspark code, this patch fix them.
[1] https://docs.python.org/3/library/exceptions.html#TypeError
Note that: this patch only addresses the exsiting some wrong raise type for input validation, the input validation decorator/framework which mentioned in [SPARK-35176](https://issues.apache.org/jira/browse/SPARK-35176), would be submited in a speparated patch.
### Does this PR introduce _any_ user-facing change?
Yes, code can raise the right TypeError instead of ValueError.
### How was this patch tested?
Existing test case and UT
Closes#32368 from Yikun/SPARK-35176.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch adds a note for aarch64 user to install the specific pyarrow>=4.0.0.
### Why are the changes needed?
The pyarrow aarch64 support is [introduced](https://github.com/apache/arrow/pull/9285) in [PyArrow 4.0.0](https://github.com/apache/arrow/releases/tag/apache-arrow-4.0.0), and it has been published 27.Apr.2021.
See more in [SPARK-34979](https://issues.apache.org/jira/browse/SPARK-34979).
### Does this PR introduce _any_ user-facing change?
Yes, this doc can help user install arrow on aarch64.
### How was this patch tested?
doc test passed.
Closes#32363 from Yikun/SPARK-34979.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
There are two types of dense vectors:
* pyspark.ml.linalg.DenseVector
* pyspark.mllib.linalg.DenseVector
In spark-3.1.1, array_to_vector returns instances of pyspark.ml.linalg.DenseVector.
The documentation is ambiguous & can lead to the false conclusion that instances of
pyspark.mllib.linalg.DenseVector will be returned.
Conversion from ml versions to mllib versions can easly be achieved with
mlutils.convertVectorColumnsToML helper.
### What changes were proposed in this pull request?
Make documentation more explicit
### Why are the changes needed?
The documentation is a bit misleading and users can lose time investigating & realizing there are two DenseVector types.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No test were run as only the documentation was changed
Closes#32255 from jlafaye/master.
Authored-by: Julien Lafaye <jlafaye@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1, remove existing agg, and use a new agg supporting virtual centering
2, add related testsuites
### Why are the changes needed?
centering vectors should accelerate convergence, and generate solution more close to R
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
updated testsuites and added testsuites
Closes#32124 from zhengruifeng/svc_agg_refactor.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Removes PySpark version dependent codes from pyspark.pandas test codes.
### Why are the changes needed?
There are several places to check the PySpark version and switch the logic, but now those are not necessary.
We should remove them.
We will do the same thing after we finish porting tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32300 from xinrong-databricks/port.rmv_spark_version_chk_in_tests.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Consolidate PySpark testing utils by removing `python/pyspark/pandas/testing`, and then creating a file `pandasutils` under `python/pyspark/testing` for test utilities used in `pyspark/pandas`.
### Why are the changes needed?
`python/pyspark/pandas/testing` hold test utilites for pandas-on-spark, and `python/pyspark/testing` contain test utilities for pyspark. Consolidating them makes code cleaner and easier to maintain.
Updated import statements are as shown below:
- from pyspark.testing.sqlutils import SQLTestUtils
- from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils
(PandasOnSparkTestCase is the original ReusedSQLTestCase in `python/pyspark/pandas/testing/utils.py`)
Minor improvements include:
- Usage of missing library's requirement_message
- `except ImportError` rather than `except`
- import pyspark.pandas alias as `ps` rather than `pp`
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests under python/pyspark/pandas/tests.
Closes#32177 from xinrong-databricks/port.merge_utils.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Fixes incorrect return type for `rawPredictionUDF` in `OneVsRestModel`.
### Why are the changes needed?
Bugfix
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#32245 from harupy/SPARK-35142.
Authored-by: harupy <17039389+harupy@users.noreply.github.com>
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
### What changes were proposed in this pull request?
There are some more changes in Koalas such as [databricks/koalas#2141](c8f803d6be), [databricks/koalas#2143](913d68868d) after the main code porting, this PR is to synchronize those changes with the `pyspark.pandas`.
### Why are the changes needed?
We should port the whole Koalas codes into PySpark and synchronize them.
### Does this PR introduce _any_ user-facing change?
Fixed some incompatible behavior with pandas 1.2.0 and added more to the `to_markdown` docstring.
### How was this patch tested?
Manually tested in local.
Closes#32197 from itholic/SPARK-34995-fix.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas Index unit tests to PySpark.
### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable the Index unit tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enable Index unit tests.
Closes#32139 from xinrong-databricks/port.indexes_tests.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
There are some more changes in Koalas such as [databricks/koalas#2141](c8f803d6be), [databricks/koalas#2143](913d68868d) after the main code porting, this PR is to synchronize those changes with the `pyspark.pandas`.
### Why are the changes needed?
We should port the whole Koalas codes into PySpark and synchronize them.
### Does this PR introduce _any_ user-facing change?
Fixed some incompatible behavior with pandas 1.2.0 and added more to the `to_markdown` docstring.
### How was this patch tested?
Manually tested in local.
Closes#32154 from itholic/SPARK-34995.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to rename Koalas to pandas-on-Spark in main codes
### Why are the changes needed?
To have the correct name in PySpark. NOTE that the official name in the main documentation will be pandas APIs on Spark to be extra clear. pandas-on-Spark is not the official term.
### Does this PR introduce _any_ user-facing change?
No, it's master-only change. It changes the docstring and class names.
### How was this patch tested?
Manually tested via:
```bash
./python/run-tests --python-executable=python3 --modules pyspark-pandas
```
Closes#32166 from HyukjinKwon/rename-koalas.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas miscellaneous unit tests to PySpark.
### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable miscellaneous unit tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enable miscellaneous unit tests.
Closes#32152 from xinrong-databricks/port.misc_tests.
Lead-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Co-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch add `__version__` into pyspark.__init__.__all__ to make the `__version__` as exported explicitly, see more in https://github.com/apache/spark/pull/32110#issuecomment-817331896
### Why are the changes needed?
1. make the `__version__` as exported explicitly
2. cleanup `noqa: F401` on `__version`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Python related CI passed
Closes#32125 from Yikun/SPARK-34629-Follow.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: zero323 <mszymkiewicz@gmail.com>
### What changes were proposed in this pull request?
Removes PySpark version dependent codes from `pyspark.pandas` main codes.
### Why are the changes needed?
There are several places to check the PySpark version and switch the logic, but now those are not necessary.
We should remove them.
We will do the same thing after we finish porting tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32138 from ueshin/issues/SPARK-35039/pyspark_version.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas internal implementation unit tests to PySpark.
### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable the internal implementation unit tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enable internal implementation unit tests.
Closes#32137 from xinrong-databricks/port.test_internal_impl.
Lead-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Co-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas plot unit tests to PySpark.
### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable the plot unit tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enable plot unit tests.
Closes#32151 from xinrong-databricks/port.plot_tests.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add a new line to the `lineSep` parameter so that the doc renders correctly.
### Why are the changes needed?
> <img width="608" alt="image" src="https://user-images.githubusercontent.com/8269566/114631408-5c608900-9c71-11eb-8ded-ae1e21ae48b2.png">
The first line of the description is part of the signature and is **bolded**.
### Does this PR introduce _any_ user-facing change?
Yes, it changes how the docs for `pyspark.sql.DataFrameWriter.json` are rendered.
### How was this patch tested?
I didn't test it; I don't have the doc rendering tool chain on my machine, but the change is obvious.
Closes#32153 from AlexMooney/patch-1.
Authored-by: Alex Mooney <alexmooney@fastmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas DataFrame-related unit tests to PySpark.
### Why are the changes needed?
Currently, the pandas-on-Spark modules are not fully tested. We should enable the DataFrame-related unit tests first.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enable DataFrame-related unit tests.
Closes#32131 from xinrong-databricks/port.test_dataframe_related.
Lead-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Co-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas Series related unit tests to PySpark.
### Why are the changes needed?
Currently, the pandas-on-Spark modules are not fully tested. We should enable the Series related unit tests first.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enable Series-related unit tests.
Closes#32117 from xinrong-databricks/port.test_series_related.
Lead-authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Co-authored-by: xinrong-databricks <47337188+xinrong-databricks@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas operations on different frames unit tests to PySpark.
### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested fully. We should enable the operations on different frames unit tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enable operations on different frames unit tests.
Closes#32133 from xinrong-databricks/port.test_ops_on_diff_frames.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fix type hints mismatches in pyspark.sql.*
### Why are the changes needed?
There were some mismatches in pyspark.sql.*
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
dev/lint-python passed.
Closes#32122 from Yikun/SPARK-35019.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to fix:
```python
import pyspark.pandas as pp
```
to
```python
import pyspark.pandas as ps
```
### Why are the changes needed?
`pp` might sound offensive in some contexts.
### Does this PR introduce _any_ user-facing change?
The change is in master only. We'll use `ps` as the short name instead of `pp`.
### How was this patch tested?
The CI in this PR will test it out.
Closes#32108 from LSturtew/renaming_pyspark.pandas.
Authored-by: Luka Sturtewagen <luka.sturtewagen@linkit.nl>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of #32069.
Makes some doctests which could be flaky skip.
### Why are the changes needed?
Some doctests in `pyspark.pandas` module enabled at #32069 could be flaky because the result row order is nondeterministic.
- groupby-apply with UDF which has a return type annotation will lose its index.
- `Index.symmetric_difference` uses `DataFrame.intersect` and `subtract` internally.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32116 from ueshin/issues/SPARK-34972/fix_flaky_tests.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds the typehint of pyspark.__version__, which was mentioned in [SPARK-34630](https://issues.apache.org/jira/browse/SPARK-34630).
### Why are the changes needed?
There were some short discussion happened in https://github.com/apache/spark/pull/31823#discussion_r593830911 .
After further deep investigation on [1][2], we can see the `pyspark.__version__` is added by [setup.py](c06758834e/python/setup.py (L201)), it makes `__version__` embedded into pyspark module, that means the `__init__.pyi` is the right place to add the typehint for `__version__`.
So, this patch adds the type hint `__version__` in pyspark/__init__.pyi.
[1] [PEP-396 Module Version Numbers](https://www.python.org/dev/peps/pep-0396/)
[2] https://packaging.python.org/guides/single-sourcing-package-version/
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
1. Disable the ignore_error on
ee7bf7d962/python/mypy.ini (L132)
2. Run mypy:
- Before fix
```shell
(venv) ➜ spark git:(SPARK-34629) ✗ mypy --config-file python/mypy.ini python/pyspark | grep version
python/pyspark/pandas/spark/accessors.py:884: error: Module has no attribute "__version__"
```
- After fix
```shell
(venv) ➜ spark git:(SPARK-34629) ✗ mypy --config-file python/mypy.ini python/pyspark | grep version
```
no output
Closes#32110 from Yikun/SPARK-34629.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into the PySpark code base (#32036), we should port the Koalas DataFrame unit test to PySpark.
### Why are the changes needed?
Currently, the pandas-on-Spark modules are not tested at all. We should enable the DataFrame unit test first.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enable the DataFrame unit test.
Closes#32083 from xinrong-databricks/port.test_dataframe.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now that we merged the Koalas main code into PySpark code base (#32036), we should enable doctests on the Spark's infrastructure.
### Why are the changes needed?
Currently the pandas-on-Spark modules are not tested at all.
We should enable doctests first, and we will port other unit tests separately later.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Enabled the whole doctests.
Closes#32069 from ueshin/issues/SPARK-34972/pyspark-pandas_doctests.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch add the `-fr` argument to `xargs rm`.
### Why are the changes needed?
This cmd is unavailable in basic case. If the find command does not get any search results, the rm command is invoked with an empty argument list, and then we will get a `rm: missing operand` and break, then the coverage report does not generate.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
python/run-tests-with-coverage --testnames pyspark.sql.tests.test_arrow --python-executables=python
The coverage report result is generated without break.
Closes#32064 from Yikun/patch-1.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
As a first step of [SPARK-34849](https://issues.apache.org/jira/browse/SPARK-34849), this PR proposes porting the Koalas main code into PySpark.
This PR contains minimal changes to the existing Koalas code as follows:
1. `databricks.koalas` -> `pyspark.pandas`
2. `from databricks import koalas as ks` -> `from pyspark import pandas as pp`
3. `ks.xxx -> pp.xxx`
Other than them:
1. Added a line to `python/mypy.ini` in order to ignore the mypy test. See related issue at [SPARK-34941](https://issues.apache.org/jira/browse/SPARK-34941).
2. Added a comment to several lines in several files to ignore the flake8 F401. See related issue at [SPARK-34943](https://issues.apache.org/jira/browse/SPARK-34943).
When this PR is merged, all the features that were previously used in [Koalas](https://github.com/databricks/koalas) will be available in PySpark as well.
Users can access to the pandas API in PySpark as below:
```python
>>> from pyspark import pandas as pp
>>> ppdf = pp.DataFrame({"A": [1, 2, 3], "B": [15, 20, 25]})
>>> ppdf
A B
0 1 15
1 2 20
2 3 25
```
The existing "options and settings" in Koalas are also available in the same way:
```python
>>> from pyspark.pandas.config import set_option, reset_option, get_option
>>> ppser1 = pp.Series([1, 2, 3])
>>> ppser2 = pp.Series([3, 4, 5])
>>> ppser1 + ppser2
Traceback (most recent call last):
...
ValueError: Cannot combine the series or dataframe because it comes from a different dataframe. In order to allow this operation, enable 'compute.ops_on_diff_frames' option.
>>> set_option("compute.ops_on_diff_frames", True)
>>> ppser1 + ppser2
0 4
1 6
2 8
dtype: int64
```
Please also refer to the [API Reference](https://koalas.readthedocs.io/en/latest/reference/index.html) and [Options and Settings](https://koalas.readthedocs.io/en/latest/user_guide/options.html) for more detail.
**NOTE** that this PR intentionally ports the main codes of Koalas first almost as are with minimal changes because:
- Koalas project is fairly large. Making some changes together for PySpark will make it difficult to review the individual change.
Koalas dev includes multiple Spark committers who will review. By doing this, the committers will be able to more easily and effectively review and drive the development.
- Koalas tests and documentation require major changes to make it look great together with PySpark whereas main codes do not require.
- We lately froze the Koalas codebase, and plan to work together on the initial porting. By porting the main codes first as are, it unblocks the Koalas dev to work on other items in parallel.
I promise and will make sure on:
- Rename Koalas to PySpark pandas APIs and/or pandas-on-Spark accordingly in documentation, and the docstrings and comments in the main codes.
- Triage APIs to remove that don’t make sense when Koalas is in PySpark
The documentation changes will be tracked in [SPARK-34885](https://issues.apache.org/jira/browse/SPARK-34885), the test code changes will be tracked in [SPARK-34886](https://issues.apache.org/jira/browse/SPARK-34886).
### Why are the changes needed?
Please refer to:
- [[DISCUSS] Support pandas API layer on PySpark](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-Support-pandas-API-layer-on-PySpark-td30945.html)
- [[VOTE] SPIP: Support pandas API layer on PySpark](http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-SPIP-Support-pandas-API-layer-on-PySpark-td30996.html)
### Does this PR introduce _any_ user-facing change?
Yes, now users can use the pandas APIs on Spark
### How was this patch tested?
Manually tested for exposed major APIs and options as described above.
### Koalas contributors
Koalas would not have been possible without the following contributors:
ueshin
HyukjinKwon
rxin
xinrong-databricks
RainFung
charlesdong1991
harupy
floscha
beobest2
thunterdb
garawalid
LucasG0
shril
deepyaman
gioa
fwani
90jam
thoo
AbdealiJK
abishekganesh72
gliptak
DumbMachine
dvgodoy
stbof
nitlev
hjoo
gatorsmile
tomspur
icexelloss
awdavidson
guyao
akhilputhiry
scook12
patryk-oleniuk
tracek
dennyglee
athena15
gstaubli
WeichenXu123
hsubbaraj
lfdversluis
ktksq
shengjh
margaret-databricks
LSturtew
sllynn
manuzhang
jijosg
sadikovi
Closes#32036 from itholic/SPARK-34890.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR replaces the non-ASCII characters to ASCII characters when possible in PySpark documentation
### Why are the changes needed?
To avoid unnecessarily using other non-ASCII characters which could lead to the issue such as https://github.com/apache/spark/pull/32047 or https://github.com/apache/spark/pull/22782
### Does this PR introduce _any_ user-facing change?
Virtually no.
### How was this patch tested?
Found via (Mac OS):
```bash
# In Spark root directory
cd python
pcregrep --color='auto' -n "[\x80-\xFF]" `git ls-files .`
```
Closes#32048 from HyukjinKwon/minor-fix.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
As a followup for #29818, document caveats of using the Arrow selfDestruct option in toPandas, which include:
- toPandas() may be slower;
- the resulting dataframe may not support some Pandas operations due to immutable backing arrays.
### Why are the changes needed?
This will hopefully reduce user confusion as with SPARK-34463.
### Does this PR introduce _any_ user-facing change?
Yes - documentation is updated and a config setting description is updated to clearly indicate the config is experimental.
### How was this patch tested?
This is a documentation-only change.
Closes#31738 from lidavidm/spark-34463.
Authored-by: David Li <li.davidm96@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes an issue that `quoteIfNeeded` quotes a name only if it contains `.` or ``` ` ```.
This method should quote it if it contains non-word characters.
### Why are the changes needed?
It's a potential bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#31964 from sarutak/fix-quoteIfNeeded.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR implements the missing typehints as per SPARK-34630.
### Why are the changes needed?
To satisfy the aforementioned Jira ticket
### Does this PR introduce _any_ user-facing change?
No, just adding a missing typehint for Project Zen
### How was this patch tested?
No tests needed (just adding a typehint)
Closes#31823 from dannymeijer/feature/SPARK-34630.
Authored-by: Danny Meijer <danny.meijer@nike.com>
Signed-off-by: zero323 <mszymkiewicz@gmail.com>
### What changes were proposed in this pull request?
Pass the raised `ImportError` on failing to import pandas/pyarrow. This will help the user identify whether pandas/pyarrow are indeed not in the environment or if they threw a different `ImportError`.
### Why are the changes needed?
This can already happen in Pandas for example where it could throw an `ImportError` on its initialisation path if `dateutil` doesn't satisfy a certain version requirement https://github.com/pandas-dev/pandas/blob/0.24.x/pandas/compat/__init__.py#L438
### Does this PR introduce _any_ user-facing change?
Yes, it will now show the root cause of the exception when pandas or arrow is missing during import.
### How was this patch tested?
Manually tested.
```python
from pyspark.sql.functions import pandas_udf
spark.range(1).select(pandas_udf(lambda x: x))
```
Before:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/...//spark/python/pyspark/sql/pandas/functions.py", line 332, in pandas_udf
require_minimum_pyarrow_version()
File "/.../spark/python/pyspark/sql/pandas/utils.py", line 53, in require_minimum_pyarrow_version
raise ImportError("PyArrow >= %s must be installed; however, "
ImportError: PyArrow >= 1.0.0 must be installed; however, it was not found.
```
After:
```
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/pandas/utils.py", line 49, in require_minimum_pyarrow_version
import pyarrow
ModuleNotFoundError: No module named 'pyarrow'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/pandas/functions.py", line 332, in pandas_udf
require_minimum_pyarrow_version()
File "/.../spark/python/pyspark/sql/pandas/utils.py", line 55, in require_minimum_pyarrow_version
raise ImportError("PyArrow >= %s must be installed; however, "
ImportError: PyArrow >= 1.0.0 must be installed; however, it was not found.
```
Closes#31902 from johnhany97/jayad/spark-34803.
Lead-authored-by: John Ayad <johnhany97@gmail.com>
Co-authored-by: John H. Ayad <johnhany97@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to reorder the items in User Guide in PySpark documentation in order to place general guides first and advance ones later.
### Why are the changes needed?
For users to more easily follow.
### Does this PR introduce _any_ user-facing change?
Yes, it changes the order in the items in documentation .
### How was this patch tested?
Manually verified the documentation after building:
<img width="768" alt="Screen Shot 2021-03-22 at 2 38 41 PM" src="https://user-images.githubusercontent.com/6477701/111945072-5537d680-8b1c-11eb-9f43-02f3ad63a509.png">
FWIW, the current page: https://spark.apache.org/docs/latest/api/python/user_guide/index.htmlCloses#31922 from HyukjinKwon/SPARK-34818.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Document `mode` as a supported Imputer strategy in Pyspark docs.
### Why are the changes needed?
Support was added in 3.1, and documented in Scala, but some Python docs were missed.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#31883 from srowen/ImputerModeDocs.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR fixes an issue that `sql` method in the following classes which take qualified names don't quote the qualified names properly.
* UnresolvedAttribute
* AttributeReference
* Alias
One instance caused by this issue is reported in SPARK-34626.
```
UnresolvedAttribute("a" :: "b" :: Nil).sql
`a.b` // expected: `a`.`b`
```
And other instances are like as follows.
```
UnresolvedAttribute("a`b"::"c.d"::Nil).sql
a`b.`c.d` // expected: `a``b`.`c.d`
AttributeReference("a.b", IntegerType)(qualifier = "c.d"::Nil).sql
c.d.`a.b` // expected: `c.d`.`a.b`
Alias(AttributeReference("a", IntegerType)(), "b.c")(qualifier = "d.e"::Nil).sql
`a` AS d.e.`b.c` // expected: `a` AS `d.e`.`b.c`
```
### Why are the changes needed?
This is a bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#31754 from sarutak/fix-qualified-names.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR upgrade Py4J from 0.10.9.1 to 0.10.9.2 that contains some bug fixes and improvements.
* expose shell parameter in Popen inside launch_gateway. ([bartdag/py4j220efc3](220efc3716))
* fixed Flake8 errors ([bartdag/py4j6c6ee9a](6c6ee9aedc))
### Why are the changes needed?
To leverage fixes from the upstream in Py4J.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Jenkins build and GitHub Actions will test it out.
Closes#31796 from wankunde/py4j.
Authored-by: wankunde <wankunde@163.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
pyrolite 4.21 introduced and enabled value comparison by default (`valueCompare=true`) during object memoization and serialization: https://github.com/irmen/Pyrolite/blob/pyrolite-4.21/java/src/main/java/net/razorvine/pickle/Pickler.java#L112-L122
This change has undesired effect when we serialize a row (actually `GenericRowWithSchema`) to be passed to python: https://github.com/apache/spark/blob/branch-3.0/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala#L60. A simple example is that
```
new GenericRowWithSchema(Array(1.0, 1.0), StructType(Seq(StructField("_1", DoubleType), StructField("_2", DoubleType))))
```
and
```
new GenericRowWithSchema(Array(1, 1), StructType(Seq(StructField("_1", IntegerType), StructField("_2", IntegerType))))
```
are currently equal and the second instance is replaced to the short code of the first one during serialization.
### Why are the changes needed?
The above can cause nasty issues like the one in https://issues.apache.org/jira/browse/SPARK-34545 description:
```
>>> from pyspark.sql.functions import udf
>>> from pyspark.sql.types import *
>>>
>>> def udf1(data_type):
def u1(e):
return e[0]
return udf(u1, data_type)
>>>
>>> df = spark.createDataFrame([((1.0, 1.0), (1, 1))], ['c1', 'c2'])
>>>
>>> df = df.withColumn("c3", udf1(DoubleType())("c1"))
>>> df = df.withColumn("c4", udf1(IntegerType())("c2"))
>>>
>>> df.select("c3").show()
+---+
| c3|
+---+
|1.0|
+---+
>>> df.select("c4").show()
+---+
| c4|
+---+
| 1|
+---+
>>> df.select("c3", "c4").show()
+---+----+
| c3| c4|
+---+----+
|1.0|null|
+---+----+
```
This is because during serialization from JVM to Python `GenericRowWithSchema(1.0, 1.0)` (`c1`) is memoized first and when `GenericRowWithSchema(1, 1)` (`c2`) comes next, it is replaced to some short code of the `c1` (instead of serializing `c2` out) as they are `equal()`. The python functions then runs but the return type of `c4` is expected to be `IntegerType` and if a different type (`DoubleType`) comes back from python then it is discarded: https://github.com/apache/spark/blob/branch-3.0/sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvaluatePython.scala#L108-L113
After this PR:
```
>>> df.select("c3", "c4").show()
+---+---+
| c3| c4|
+---+---+
|1.0| 1|
+---+---+
```
### Does this PR introduce _any_ user-facing change?
Yes, fixes a correctness issue.
### How was this patch tested?
Added new UT + manual tests.
Closes#31682 from peter-toth/SPARK-34545-fix-row-comparison.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Fix a call to setParams in the Linear Regression docs example in Pyspark to avoid a TypeError.
### Why are the changes needed?
The example is slightly wrong and we should not show an error in the docs.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
Existing tests
Closes#31760 from srowen/SPARK-34642.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fixes a Python UDF `plus_one` used in `GroupedAggPandasUDFTests` to always return float (double) values.
### Why are the changes needed?
The Python UDF `plus_one` used in `GroupedAggPandasUDFTests` is always returning `v + 1` regardless of its type. The return type of the UDF is 'double', so if the input is int, the result will be `null`.
```py
>>> df = spark.range(10).toDF('id') \
... .withColumn("vs", array([lit(i * 1.0) + col('id') for i in range(20, 30)])) \
... .withColumn("v", explode(col('vs'))) \
... .drop('vs') \
... .withColumn('w', lit(1.0))
>>> udf('double')
... def plus_one(v):
... assert isinstance(v, (int, float))
... return v + 1
...
>>> pandas_udf('double', PandasUDFType.GROUPED_AGG)
... def sum_udf(v):
... return v.sum()
...
>>> df.groupby(plus_one(df.id)).agg(sum_udf(df.v)).show()
+------------+----------+
|plus_one(id)|sum_udf(v)|
+------------+----------+
| null| 2900.0|
+------------+----------+
```
This is meaningless and should be:
```py
>>> udf('double')
... def plus_one(v):
... assert isinstance(v, (int, float))
... return float(v + 1)
...
>>> df.groupby(plus_one(df.id)).agg(sum_udf(df.v)).sort('plus_one(id)').show()
+------------+----------+
|plus_one(id)|sum_udf(v)|
+------------+----------+
| 1.0| 245.0|
| 2.0| 255.0|
| 3.0| 265.0|
| 4.0| 275.0|
| 5.0| 285.0|
| 6.0| 295.0|
| 7.0| 305.0|
| 8.0| 315.0|
| 9.0| 325.0|
| 10.0| 335.0|
+------------+----------+
```
### Does this PR introduce _any_ user-facing change?
No, test-only.
### How was this patch tested?
Fixed the test.
Closes#31730 from ueshin/issues/SPARK-34610/test_pandas_udf_grouped_agg.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
`TaskContextTestsWithWorkerReuse.test_task_context_correct_with_python_worker_reuse` can be flaky and fails sometimes:
```
======================================================================
ERROR [1.798s]: test_task_context_correct_with_python_worker_reuse (pyspark.tests.test_taskcontext.TaskContextTestsWithWorkerReuse)
...
test_task_context_correct_with_python_worker_reuse
self.assertTrue(pid in worker_pids)
AssertionError: False is not true
----------------------------------------------------------------------
```
I suspect that the Python worker was killed for whatever reason and new attempt created a new Python worker.
This PR fixes the flakiness simply by retrying the test case.
### Why are the changes needed?
To make the tests more robust.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Manually tested it by controlling the conditions manually in the test codes.
Closes#31723 from HyukjinKwon/SPARK-34604.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### Why is this change being proposed?
This patch adds support for a new "product" aggregation function in `sql.functions` which multiplies-together all values in an aggregation group.
This is likely to be useful in statistical applications which involve combining probabilities, or financial applications that involve combining cumulative interest rates, but is also a versatile mathematical operation of similar status to `sum` or `stddev`. Other users [have noted](https://stackoverflow.com/questions/52991640/cumulative-product-in-spark) the absence of such a function in current releases of Spark.
This function is both much more concise than an expression of the form `exp(sum(log(...)))`, and avoids awkward edge-cases associated with some values being zero or negative, as well as being less computationally costly.
### Does this PR introduce _any_ user-facing change?
No - only adds new function.
### How was this patch tested?
Built-in tests have been added for the new `catalyst.expressions.aggregate.Product` class and its invocation via the (scala) `sql.functions.product` function. The latter, and the PySpark wrapper have also been manually tested in spark-shell and pyspark sessions. The SparkR wrapper is currently untested, and may need separate validation (I'm not an "R" user myself).
An illustration of the new functionality, within PySpark is as follows:
```
import pyspark.sql.functions as pf, pyspark.sql.window as pw
df = sqlContext.range(1, 17).toDF("x")
win = pw.Window.partitionBy(pf.lit(1)).orderBy(pf.col("x"))
df.withColumn("factorial", pf.product("x").over(win)).show(20, False)
+---+---------------+
|x |factorial |
+---+---------------+
|1 |1.0 |
|2 |2.0 |
|3 |6.0 |
|4 |24.0 |
|5 |120.0 |
|6 |720.0 |
|7 |5040.0 |
|8 |40320.0 |
|9 |362880.0 |
|10 |3628800.0 |
|11 |3.99168E7 |
|12 |4.790016E8 |
|13 |6.2270208E9 |
|14 |8.71782912E10 |
|15 |1.307674368E12 |
|16 |2.0922789888E13|
+---+---------------+
```
Closes#30745 from rwpenney/feature/agg-product.
Lead-authored-by: Richard Penney <rwp@rwpenney.uk>
Co-authored-by: Richard Penney <rwpenney@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Code in the PR generates random parameters for hyperparameter tuning. A discussion with Sean Owen can be found on the dev mailing list here:
http://apache-spark-developers-list.1001551.n3.nabble.com/Hyperparameter-Optimization-via-Randomization-td30629.html
All code is entirely my own work and I license the work to the project under the project’s open source license.
### Why are the changes needed?
Randomization can be a more effective techinique than a grid search since min/max points can fall between the grid and never be found. Randomisation is not so restricted although the probability of finding minima/maxima is dependent on the number of attempts.
Alice Zheng has an accessible description on how this technique works at https://www.oreilly.com/library/view/evaluating-machine-learning/9781492048756/ch04.html
Although there are Python libraries with more sophisticated techniques, not every Spark developer is using Python.
### Does this PR introduce _any_ user-facing change?
A new class (`ParamRandomBuilder.scala`) and its tests have been created but there is no change to existing code. This class offers an alternative to `ParamGridBuilder` and can be dropped into the code wherever `ParamGridBuilder` appears. Indeed, it extends `ParamGridBuilder` and is completely compatible with its interface. It merely adds one method that provides a range over which a hyperparameter will be randomly defined.
### How was this patch tested?
Tests `ParamRandomBuilderSuite.scala` and `RandomRangesSuite.scala` were added.
`ParamRandomBuilderSuite` is the analogue of the already existing `ParamGridBuilderSuite` which tests the user-facing interface.
`RandomRangesSuite` uses ScalaCheck to test the random ranges over which hyperparameters are distributed.
Closes#31535 from PhillHenry/ParamRandomBuilder.
Authored-by: Phillip Henry <PhillHenry@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR proposes:
- Change http to https for better security
- Change http://apache-spark-developers-list.1001551.n3.nabble.com/ to official mailing list link (https://mail-archives.apache.org/mod_mbox/spark-dev/)
### Why are the changes needed?
For better security, and to use official link.
### Does this PR introduce _any_ user-facing change?
Yes, It exposes more secure and correct links to the PySpark end users in PySpark documentation.
### How was this patch tested?
I manually checked if each link works
Closes#31616 from HyukjinKwon/minor-https.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Xinrong Meng
garawalid
HyukjinKwon
Yikun Jiang
Ludovic Henry
Julien Lafaye
Ruifeng Zheng
harupy
itholic
xinrong-databricks
Takuya UESHIN
Alex Mooney
Luka Sturtewagen
David Li
Kousuke Saruta
Danny Meijer
John Ayad
Sean Owen
wankunde
Peter Toth
Richard Penney
Phillip Henry
