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Xinrong Meng 6e4e04f2a1 [SPARK-35615][PYTHON] Make unary and comparison operators data-type-based 
### 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>
2021-07-07 13:46:50 -07:00
..
docs [SPARK-35605][PYTHON] Move to_pandas_on_spark to the Spark DataFrame 2021-06-28 11:47:09 +09:00
lib [SPARK-34688][PYTHON] Upgrade to Py4J 0.10.9.2 2021-03-11 09:51:41 -06:00
pyspark [SPARK-35615][PYTHON] Make unary and comparison operators data-type-based 2021-07-07 13:46:50 -07:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support Spelling r common dev mlib external project streaming resource managers python 2020-11-27 10:22:45 -06:00
.coveragerc [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-32714][PYTHON] Initial pyspark-stubs port 2020-09-24 14:15:36 +09:00
mypy.ini [SPARK-35684][INFRA][PYTHON] Bump up mypy version in GitHub Actions 2021-07-07 13:26:28 +09:00
pylintrc [SPARK-32435][PYTHON] Remove heapq3 port from Python 3 2020-07-27 20:10:13 +09:00
README.md [SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9 2020-02-20 09:09:30 -08:00
run-tests [SPARK-29672][PYSPARK] update spark testing framework to use python3 2019-11-14 10:18:55 -08:00
run-tests-with-coverage [SPARK-34968][TEST][PYTHON] Add the -fr argument to xargs rm 2021-04-06 15:20:55 -07:00
run-tests.py [SPARK-32194][PYTHON] Use proper exception classes instead of plain Exception 2021-05-26 11:54:40 +09:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-35759][PYTHON] Remove the upperbound for numpy for pandas-on-Spark 2021-06-15 09:59:05 +09:00

README.md

Apache Spark

Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J, but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).