### What changes were proposed in this pull request?
Reuse `IndexOpsMixin.isnull()` where the null check is needed.
### Why are the changes needed?
There are some places where we can reuse `IndexOpsMixin.isnull()` instead of directly using Spark `Column`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33562 from ueshin/issues/SPARK-36333/reuse_isnull.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 07ed82be0b)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Fix `IndexOpsMixin.hasnans` to use `IndexOpsMixin.isnull().any()`.
### Why are the changes needed?
`IndexOpsMixin.hasnans` has a potential issue to cause `a window function inside an aggregate function` error.
Also it returns a wrong value when the `Series`/`Index` is empty.
```py
>>> ps.Series([]).hasnans
None
```
whereas:
```py
>>> pd.Series([]).hasnans
False
```
`IndexOpsMixin.any()` is safe for both cases.
### Does this PR introduce _any_ user-facing change?
`IndexOpsMixin.hasnans` will return `False` when empty.
### How was this patch tested?
Added some tests.
Closes#33547 from ueshin/issues/SPARK-36310/hasnan.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit bcc595c112)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Use `Column.__getitem__` instead of `Column.getItem` to suppress warnings.
### Why are the changes needed?
In pandas API on Spark code base, there are some places using `Column.getItem` with `Column` object, but it shows a deprecation warning.
### Does this PR introduce _any_ user-facing change?
Yes, users won't see the warnings anymore.
- before
```py
>>> s = ps.Series(list("abbccc"), dtype="category")
>>> s.astype(str)
/path/to/spark/python/pyspark/sql/column.py:322: FutureWarning: A column as 'key' in getItem is deprecated as of Spark 3.0, and will not be supported in the future release. Use `column[key]` or `column.key` syntax instead.
warnings.warn(
0 a
1 b
2 b
3 c
4 c
5 c
dtype: object
```
- after
```py
>>> s = ps.Series(list("abbccc"), dtype="category")
>>> s.astype(str)
0 a
1 b
2 b
3 c
4 c
5 c
dtype: object
```
### How was this patch tested?
Existing tests.
Closes#33486 from ueshin/issues/SPARK-36265/getitem.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
(cherry picked from commit a76a087f7f)
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Make unary and comparison operators data-type-based. Refactored operators include:
- Unary operators: `__neg__`, `__abs__`, `__invert__`,
- Comparison operators: `>`, `>=`, `<`, `<=`, `==`, `!=`
Non-goal: Tasks below are inspired during the development of this PR.
[[SPARK-35997] Implement comparison operators for CategoricalDtype in pandas API on Spark](https://issues.apache.org/jira/browse/SPARK-35997)
[[SPARK-36000] Support creating a ps.Series/Index with `Decimal('NaN')` with Arrow disabled](https://issues.apache.org/jira/browse/SPARK-36000)
[[SPARK-36001] Assume result's index to be disordered in tests with operations on different Series](https://issues.apache.org/jira/browse/SPARK-36001)
[[SPARK-36002] Consolidate tests for data-type-based operations of decimal Series](https://issues.apache.org/jira/browse/SPARK-36002)
[[SPARK-36003] Implement unary operator `invert` of numeric ps.Series/Index](https://issues.apache.org/jira/browse/SPARK-36003)
### Why are the changes needed?
We have been refactoring basic operators to be data-type-based for readability, flexibility, and extensibility.
Unary and comparison operators are still not data-type-based yet. We should fill the gaps.
### Does this PR introduce _any_ user-facing change?
Yes.
- Better error messages. For example,
Before:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([b"2", b"3", b"4"])
>>> -psser
Traceback (most recent call last):
...
pyspark.sql.utils.AnalysisException: cannot resolve '(- `0`)' due to data type mismatch: ...
```
After:
```py
>>> import pyspark.pandas as ps
>>> psser = ps.Series([b"2", b"3", b"4"])
>>> -psser
Traceback (most recent call last):
...
TypeError: Unary - can not be applied to binaries.
>>>
```
- Support unary `-` of `bool` Series. For example,
Before:
```py
>>> psser = ps.Series([True, False, True])
>>> -psser
Traceback (most recent call last):
...
pyspark.sql.utils.AnalysisException: cannot resolve '(- `0`)' due to data type mismatch: ...
```
After:
```py
>>> psser = ps.Series([True, False, True])
>>> -psser
0 False
1 True
2 False
dtype: bool
```
### How was this patch tested?
Unit tests.
Closes#33162 from xinrong-databricks/datatypeops_refactor.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 6e4e04f2a1)
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Introduce `Name` and `Label` type aliases to distinguish what is expected instead of `Any` or `Union[Any, Tuple]`.
- `Label`: `Tuple[Any, ...]`
Internal expression for name-like metadata, like `index_names`, `column_labels`, and `column_label_names` in `InternalFrame`, and similar internal structures.
- `Name`: `Union[Any, Label]`
External expression for user-facing names, which can be scalar values or tuples.
### Why are the changes needed?
Currently `Any` or `Union[Any, Tuple]` is used for name-like types, but type aliases should be used to distinguish what is expected clearly.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33159 from ueshin/issues/SPARK-35944/name_and_label.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Introduces `Axis` type alias for `axis` argument to be consistent.
### Why are the changes needed?
There are many places to use `axis` argument. We should define `Axis` type alias and reuse it to be consistent.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33144 from ueshin/issues/SPARK-35943/axis.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Cleaning up the type hints in pandas-on-Spark.
- Use a single file `_typing.py` for type variables or aliases
- Rename `IndexOpsLike` to `SeriesOrIndex`.
- Rename `T_Frame` and `T_IndexOps` to `FrameLike` and `IndexOpsLike` respectively
- Introduce `DataFrameOrSeries` for `Union[DataFrame, Series]`
### Why are the changes needed?
This is a cleanup for the mypy check stuff series.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33117 from ueshin/issues/SPARK-35859/cleanup.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
The PR is proposed to support creating a Column of numpy literal value in pandas-on-Spark. It consists of three changes mainly:
- Enable the `lit` function defined in `pyspark.pandas.spark.functions` to support numpy literals input.
```py
>>> from pyspark.pandas.spark import functions as SF
>>> SF.lit(np.int64(1))
Column<'CAST(1 AS BIGINT)'>
>>> SF.lit(np.int32(1))
Column<'CAST(1 AS INT)'>
>>> SF.lit(np.int8(1))
Column<'CAST(1 AS TINYINT)'>
>>> SF.lit(np.byte(1))
Column<'CAST(1 AS TINYINT)'>
>>> SF.lit(np.float32(1))
Column<'CAST(1.0 AS FLOAT)'>
```
- Substitute `F.lit` by `SF.lit`, that is, use `lit` function defined in `pyspark.pandas.spark.functions` rather than `lit` function defined in `pyspark.sql.functions` to allow creating columns out of numpy literals.
- Enable numpy literals input in `isin` method
Non-goal:
- Some pandas-on-Spark APIs use PySpark column-related APIs internally, and these column-related APIs don't support numpy literals, thus numpy literals are disallowed as input (e.g. `to_replace` parameter in `replace` API). This PR doesn't aim to adjust all of them. This PR adjusts `isin` only, because the PR is inspired by that (as https://github.com/databricks/koalas/issues/2161).
- To complete mappings between all kinds of numpy literals and Spark data types should be a followup task.
### Why are the changes needed?
Spark (`lit` function defined in `pyspark.sql.functions`) doesn't support creating a Column out of numpy literal value.
So `lit` function defined in `pyspark.pandas.spark.functions` is adjusted in order to support that in pandas-on-Spark.
### Does this PR introduce _any_ user-facing change?
Yes.
Before:
```py
>>> a = ps.DataFrame({'source': [1,2,3,4,5]})
>>> a.source.isin([np.int64(1), np.int64(2)])
Traceback (most recent call last):
...
AttributeError: 'numpy.int64' object has no attribute '_get_object_id'
```
After:
```py
>>> a = ps.DataFrame({'source': [1,2,3,4,5]})
>>> a.source.isin([np.int64(1), np.int64(2)])
0 True
1 True
2 False
3 False
4 False
Name: source, dtype: bool
```
### How was this patch tested?
Unit tests.
Closes#32955 from xinrong-databricks/datatypeops_literal.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/series.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#33045 from ueshin/issues/SPARK-35476/disallow_untyped_defs_series.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/groupby.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#33032 from ueshin/issues/SPARK-35473/disallow_untyped_defs_groupby.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/base.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#32968 from ueshin/issues/SPARK-35470/disallow_untyped_defs_base.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Introduce a DecimalOps for DecimalType
- Make `isnull` method data-type-based
### Why are the changes needed?
Now DecimalType, DoubleType, and FloatType data share the FractionalOps class, but DecimalType behaves differently from FloatType and DoubleType (as https://github.com/apache/spark/blob/master/python/pyspark/pandas/base.py#L987-L990), so we propose to introduce DecimalOps. The behavior difference here is caused by DecimalType could not have NaN.
https://issues.apache.org/jira/browse/SPARK-35342
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- New added DecimalOpsTest passed
- Existing NumOpsTest passed
Closes#32821 from Yikun/SPARK-35342.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Make `astype` method data-type-based.
**Non-goal: Match pandas' `astype` TypeErrors.**
Currently, `astype` throws TypeError error messages only when the destination type is not recognized. However, for some destination types that don't make sense to the specific type of Series/Index, for example, `numeric Series/Index → bytes`, we don't have proper TypeError error messages.
Since the goal of the PR is refactoring mainly, the above issue might be resolved later if needed.
### Why are the changes needed?
There are many type checks in the `astype` method. Since `DataTypeOps` and its subclasses are introduced, we should refactor `astype` to make it data-type-based. In this way, code is cleaner, more maintainable, and more flexible.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#32847 from xinrong-databricks/datatypeops_astype.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### 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?
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?
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?
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?
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?
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?
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>
