### What changes were proposed in this pull request?
Introduces `InternalField` to manage dtypes and `StructField`s.
`InternalFrame` is already managing dtypes, but when it checks the Spark's data types, column names, and nullabilities, it tries to run the analysis phase each time it needs, which will cause a performance issue.
It will use `InternalField` class which stores the retrieved Spark's data types, column names, and nullabilities, and reuse them. Also, in case those can be known, just update and reuse them without asking Spark.
### Why are the changes needed?
Currently there are some performance issues in the pandas-on-Spark layer.
One of them is accessing Java DataFrame and run analysis phase too many times, especially just for retrieving the current column names or data types.
We should reduce the amount of unnecessary access.
### Does this PR introduce _any_ user-facing change?
Improves the performance in pandas-on-Spark layer:
```py
df = ps.read_parquet("/path/to/test.parquet") # contains ~75 columns
df = df[(df["col"] > 0) & (df["col"] < 10000)]
```
Before the PR, it took about **2.15 sec** and after **1.15 sec**.
### How was this patch tested?
Existing tests.
Closes#32775 from ueshin/issues/SPARK-35638/field.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Introduce BooleanExtensionOps in order to make boolean operators `and` and `or` data-type-based.
- Improve error messages for operators `and` and `or`.
### Why are the changes needed?
Boolean operators __and__, __or__, __rand__, and __ror__ should be data-type-based
BooleanExtensionDtypes processes these boolean operators differently from bool, so BooleanExtensionOps is introduced.
These boolean operators themselves are also bitwise operators, which should be able to apply to other data types classes later. However, this is not the goal of this PR.
### Does this PR introduce _any_ user-facing change?
Yes. Error messages for operators `and` and `or` are improved.
Before:
```
>>> psser = ps.Series([1, "x", "y"], dtype="category")
>>> psser | True
Traceback (most recent call last):
...
pyspark.sql.utils.AnalysisException: cannot resolve '(`0` OR true)' due to data type mismatch: differing types in '(`0` OR true)' (tinyint and boolean).;
'Project [unresolvedalias(CASE WHEN (isnull(0#9) OR isnull((0#9 OR true))) THEN false ELSE (0#9 OR true) END, Some(org.apache.spark.sql.Column$$Lambda$1442/17254916406fb8afba))]
+- Project [__index_level_0__#8L, 0#9, monotonically_increasing_id() AS __natural_order__#12L]
+- LogicalRDD [__index_level_0__#8L, 0#9], false
```
After:
```
>>> psser = ps.Series([1, "x", "y"], dtype="category")
>>> psser | True
Traceback (most recent call last):
...
TypeError: Bitwise or can not be applied to categoricals.
```
### How was this patch tested?
Unit tests.
Closes#32698 from xinrong-databricks/datatypeops_extension.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Make the conversion from/to pandas (for non-ExtensionDtype) data-type-based.
NOTE: Ops class per ExtensionDtype and its data-type-based from/to pandas will be implemented in a separate PR as https://issues.apache.org/jira/browse/SPARK-35614.
### Why are the changes needed?
The conversion from/to pandas includes logic for checking data types and behaving accordingly.
That makes code hard to change or maintain.
Since we have introduced the Ops class per non-ExtensionDtype data type, we ought to make the conversion from/to pandas data-type-based for non-ExtensionDtypes.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#32592 from xinrong-databricks/datatypeop_pd_conversion.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR proposes applying `black` to pandas API on Spark codes, for improving static analysis.
By executing the `./dev/reformat-python` in the spark home directory, all the code of the pandas API on Spark is fixed according to the static analysis rules.
### Why are the changes needed?
This can be reduces the cost of static analysis during development.
It has been used continuously for about a year in the Koalas project and its convenience has been proven.
### Does this PR introduce _any_ user-facing change?
No, it's dev-only.
### How was this patch tested?
Manually reformat the pandas API on Spark codes by running the `./dev/reformat-python`, and checked the `./dev/lint-python` is passed.
Closes#32779 from itholic/SPARK-35499.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to port Koalas documentation to PySpark documentation as its initial step.
It ports almost as is except these differences:
- Renamed import from `databricks.koalas` to `pyspark.pandas`.
- Renamed `to_koalas` -> `to_pandas_on_spark`
- Renamed `(Series|DataFrame).koalas` -> `(Series|DataFrame).pandas_on_spark`
- Added a `ps_` prefix in the RST file names of Koalas documentation
Other then that,
- Excluded `python/docs/build/html` in linter
- Fixed GA dependency installataion
### Why are the changes needed?
To document pandas APIs on Spark.
### Does this PR introduce _any_ user-facing change?
Yes, it adds new documentations.
### How was this patch tested?
Manually built the docs and checked the output.
Closes#32726 from HyukjinKwon/SPARK-35587.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes restoring `to_koalas` to keep the backward compatibility, with throwing deprecated warning.
### Why are the changes needed?
If we remove `to_koalas`, the existing Koalas codes that include `to_koalas` wouldn't work.
### Does this PR introduce _any_ user-facing change?
No. It's restoring the existing functionality.
### How was this patch tested?
Manually tested in local.
```shell
>>> sdf.to_koalas()
.../spark/python/pyspark/pandas/frame.py:4550: FutureWarning: DataFrame.to_koalas is deprecated as of DataFrame.to_pandas_on_spark. Please use the API instead.
warnings.warn(
```
Closes#32729 from itholic/SPARK-35539.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Support arithmetic operations against bool IndexOpsMixin.
### Why are the changes needed?
Existing binary operations of bool IndexOpsMixin in Koalas do not match pandas’ behaviors.
pandas take True as 1, False as 0 when dealing with numeric values, numeric collections, and numeric Series/Index; whereas Koalas raises an AnalysisException no matter what the binary operation is.
We aim to match pandas' behaviors.
### Does this PR introduce _any_ user-facing change?
Yes.
Before the change:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([True, True, False])
>>> psser + 1
Traceback (most recent call last):
...
TypeError: Addition can not be applied to booleans.
>>> 1 + psser
Traceback (most recent call last):
...
TypeError: Addition can not be applied to booleans.
>>> from pyspark.pandas.config import set_option
>>> set_option("compute.ops_on_diff_frames", True)
>>> psser + ps.Series([1, 2, 3])
Traceback (most recent call last):
...
TypeError: Addition can not be applied to booleans.
>>> ps.Series([1, 2, 3]) + psser
Traceback (most recent call last):
...
TypeError: addition can not be applied to given types.
```
After the change:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([True, True, False])
>>> psser + 1
0 2
1 2
2 1
dtype: int64
>>> 1 + psser
0 2
1 2
2 1
dtype: int64
>>> from pyspark.pandas.config import set_option
>>> set_option("compute.ops_on_diff_frames", True)
>>> psser + ps.Series([1, 2, 3])
0 2
1 3
2 3
dtype: int64
>>> ps.Series([1, 2, 3]) + psser
0 2
1 3
2 3
dtype: int64
```
### How was this patch tested?
Unit tests.
Closes#32611 from xinrong-databricks/datatypeop_arith_bool.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR proposes renaming the existing "Koalas Accessor" to "Pandas API on Spark Accessor".
### Why are the changes needed?
Because we don't use name "Koalas" anymore, rather use "Pandas API on Spark".
So, the related code bases are all need to be changed.
### Does this PR introduce _any_ user-facing change?
Yes, the usage of pandas API on Spark accessor is changed from `df.koalas.[...]`. to `df.pandas_on_spark.[...]`.
**Note:** `df.koalas.[...]` is still available but with deprecated warnings.
### How was this patch tested?
Manually tested in local and checked one by one.
Closes#32674 from itholic/SPARK-35453.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to fix and reenable `test_stats_on_non_numeric_columns_should_be_discarded_if_numeric_only_is_true` that was disabled when we upgrade Python 3.9 in CI at https://github.com/apache/spark/pull/32657.
Seems like this is because of the latest NumPy's behaviour change, see also `https://github.com/numpy/numpy/pull/16273#discussion_r641264085`.
pandas inherits this behaviour but it doesn't make sense when `numeric_only` is set to `True` in pandas. I will track and follow the status of the issue between pandas and NumPy.
For the time being, I propose to exclude boolean case alone in percentile/quartile test case
### Why are the changes needed?
To keep the test coverage.
### Does this PR introduce _any_ user-facing change?
No, test-only.
### How was this patch tested?
I roughly locally tested. But it should pass in CI.
Closes#32690 from HyukjinKwon/SPARK-35510.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Re-enable some pandas-on-Spark test cases.
### Why are the changes needed?
pandas version in GitHub Actions is upgraded now so we can re-enable some pandas-on-Spark test cases.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#32682 from xinrong-databricks/enable_tests.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Introduce a util function `spark_column_equals` to check the underlying expressions of columns are the same or not.
### Why are the changes needed?
In pandas on Spark, there are some places checking the underlying expressions of columns are the same or not, but it's done one-by-one.
We should introduce a util function for it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
The existing tests.
Closes#32680 from ueshin/issues/SPARK-35537/spark_column_equals.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
BinaryType, which represents byte sequence values in Spark, doesn't support data-type-based operations yet. We are going to introduce BinaryOps for it.
### Why are the changes needed?
The data-type-based-operations class should be set for each individual data type, including BinaryType.
In addition, BinaryType has its special way of addition, which means concatenation.
### Does this PR introduce _any_ user-facing change?
Yes.
Before the change:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([b'1', b'2', b'3'])
>>> psser + psser
Traceback (most recent call last):
...
TypeError: Type object was not understood.
>>> psser + b'1'
Traceback (most recent call last):
...
TypeError: Type object was not understood.
```
After the change:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([b'1', b'2', b'3'])
>>> psser + psser
0 [49, 49]
1 [50, 50]
2 [51, 51]
dtype: object
>>> psser + b'1'
0 [49, 49]
1 [50, 49]
2 [51, 49]
dtype: object
```
### How was this patch tested?
Unit tests.
Closes#32665 from xinrong-databricks/datatypeops_binary.
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?
The PR is proposed to introduce ArrayOps, MapOps and StructOps to handle data-type-based operations for StructType, ArrayType, and MapType separately.
### Why are the changes needed?
StructType, ArrayType, and MapType are not accepted by DataTypeOps now.
We should handle these complex types. Among them:
- ArrayType supports concatenation: for example, ps.Series([[1,2,3]]) + ps.Series([[4,5,6]]) should work the same as pd.Series([[1,2,3]]) + pd.Series([[4,5,6]]), as concatenation.
- StructOps will be helpful to make to/from pandas conversion data-type-based.
### Does this PR introduce _any_ user-facing change?
Yes.
Before the change:
```py
>>> import pyspark.pandas as ps
>>> from pyspark.pandas.config import set_option
>>> set_option("compute.ops_on_diff_frames", True)
>>> ps.Series([[1, 2, 3]]) + ps.Series([[0.4, 0.5]])
Traceback (most recent call last):
...
TypeError: Type object was not understood.
>>> ps.Series([[1, 2, 3]]) + ps.Series([[4, 5]])
Traceback (most recent call last):
...
TypeError: Type object was not understood.
>>> ps.Series([[1, 2, 3]]) + ps.Series([['x']])
Traceback (most recent call last):
...
TypeError: Type object was not understood.
```
After the change:
```py
>>> import pyspark.pandas as ps
>>> from pyspark.pandas.config import set_option
>>> set_option("compute.ops_on_diff_frames", True)
>>> ps.Series([[1, 2, 3]]) + ps.Series([[0.4, 0.5]])
0 [1.0, 2.0, 3.0, 0.4, 0.5]
dtype: object
>>> ps.Series([[1, 2, 3]]) + ps.Series([[4, 5]])
0 [1, 2, 3, 4, 5]
dtype: object
>>> ps.Series([[1, 2, 3]]) + ps.Series([['x']])
Traceback (most recent call last):
...
TypeError: Concatenation can only be applied to arrays of the same type
```
### How was this patch tested?
Unit tests.
Closes#32626 from xinrong-databricks/datatypeop_complex.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR enables GitHub Actions to test PySpark with Python 3.9.
### Why are the changes needed?
To verify the support of Python 3.9.
### Does this PR introduce _any_ user-facing change?
No, test-only.
### How was this patch tested?
Existing tests should cover.
Closes#32657 from HyukjinKwon/SPARK-35506.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Removes APIs which have been deprecated in Koalas.
### Why are the changes needed?
There are some APIs that have been deprecated in Koalas. We shouldn't have those in pandas APIs on Spark.
### Does this PR introduce _any_ user-facing change?
Yes, the APIs deprecated in Koalas will be no longer available.
### How was this patch tested?
Modified some tests which use the deprecated APIs, and the other existing tests should pass.
Closes#32656 from ueshin/issues/SPARK-35505/remove_deprecated.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR enables plot tests with plotly
```bash
./python/run-tests --python-executables=python3 --modules=pyspark-pandas
```
**Before**:
```
Traceback (most recent call last):
File "/.../miniconda3/envs/python3.8/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/.../miniconda3/envs/python3.8/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/.../pyspark/pandas/tests/plot/test_frame_plot_plotly.py", line 42, in <module>
plotly_requirement_message + " Or pandas<1.0; pandas<1.0 does not support latest plotly "
TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
```
**After**:
```
...
Starting test(python3): pyspark.pandas.tests.plot.test_series_plot_plotly
...
Finished test(python3): pyspark.pandas.tests.plot.test_series_plot_plotly (23s)
...
Tests passed in 1296 seconds
```
### Why are the changes needed?
For test coverage.
### Does this PR introduce _any_ user-facing change?
No, test-only.
### How was this patch tested?
By running the tests.
Closes#32649 from HyukjinKwon/SPARK-35497.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds more type annotations in the files:
- `python/pyspark/pandas/spark/accessors.py`
- `python/pyspark/pandas/typedef/typehints.py`
- `python/pyspark/pandas/utils.py`
and fixes the mypy check failures.
### Why are the changes needed?
We should enable more `disallow_untyped_defs` mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#32627 from ueshin/issues/SPARK-35467_35468_35477/disallow_untyped_defs.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Sets up the `mypy` configuration to enable `disallow_untyped_defs` check for pandas APIs on Spark module.
### Why are the changes needed?
Currently many functions in the main codes in pandas APIs on Spark module are still missing type annotations and disabled `mypy` check `disallow_untyped_defs`.
We should add more type annotations and enable the mypy check.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes#32614 from ueshin/issues/SPARK-35465/disallow_untyped_defs.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
There are still naming related to Koalas in test and function name. This PR addressed them to fit pandas-on-spark.
- kdf -> psdf
- kser -> psser
- kidx -> psidx
- kmidx -> psmidx
- to_koalas() -> to_pandas_on_spark()
### Why are the changes needed?
This is because the name Koalas is no longer used in PySpark.
### Does this PR introduce _any_ user-facing change?
`to_koalas()` function is renamed to `to_pandas_on_spark()`
### How was this patch tested?
Tested in local manually.
After changing the related naming, I checked them one by one.
Closes#32516 from itholic/SPARK-35364.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
The PR is proposed for **pandas APIs on Spark**, in order to separate arithmetic operations shown as below into data-type-based structures.
`__add__, __sub__, __mul__, __truediv__, __floordiv__, __pow__, __mod__,
__radd__, __rsub__, __rmul__, __rtruediv__, __rfloordiv__, __rpow__,__rmod__`
DataTypeOps and subclasses are introduced.
The existing behaviors of each arithmetic operation should be preserved.
### Why are the changes needed?
Currently, the same arithmetic operation of all data types is defined in one function, so it’s difficult to extend the behavior change based on the data types.
Introducing DataTypeOps would be the foundation for [pandas APIs on Spark: Separate basic operations into data type based structures.](https://docs.google.com/document/d/12MS6xK0hETYmrcl5b9pX5lgV4FmGVfpmcSKq--_oQlc/edit?usp=sharing).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tests are introduced under pyspark.pandas.tests.data_type_ops. One test file per DataTypeOps class.
Closes#32596 from xinrong-databricks/datatypeop_arith_fix.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
The PR is proposed for **pandas APIs on Spark**, in order to separate arithmetic operations shown as below into data-type-based structures.
`__add__, __sub__, __mul__, __truediv__, __floordiv__, __pow__, __mod__,
__radd__, __rsub__, __rmul__, __rtruediv__, __rfloordiv__, __rpow__,__rmod__`
DataTypeOps and subclasses are introduced.
The existing behaviors of each arithmetic operation should be preserved.
### Why are the changes needed?
Currently, the same arithmetic operation of all data types is defined in one function, so it’s difficult to extend the behavior change based on the data types.
Introducing DataTypeOps would be the foundation for [pandas APIs on Spark: Separate basic operations into data type based structures.](https://docs.google.com/document/d/12MS6xK0hETYmrcl5b9pX5lgV4FmGVfpmcSKq--_oQlc/edit?usp=sharing).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tests are introduced under pyspark.pandas.tests.data_type_ops. One test file per DataTypeOps class.
Closes#32469 from xinrong-databricks/datatypeop_arith.
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 `mypy` errors and enables `mypy` check for pandas-on-Spark.
### Why are the changes needed?
The `mypy` check for pandas-on-Spark was disabled when the initial porting.
It should be enabled again; otherwise we will miss type checking errors.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The enabled `mypy` check and existing unit tests should pass.
Closes#32540 from ueshin/issues/SPARK-34941/pandas_mypy.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### 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?
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?
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?
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?
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?
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?
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?
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>
