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HyukjinKwon 66005a3236 [SPARK-31964][PYTHON][FOLLOW-UP] Use is_categorical_dtype instead of deprecated is_categorical 
### What changes were proposed in this pull request?

This PR is a small followup of https://github.com/apache/spark/pull/28793 and  proposes to use `is_categorical_dtype` instead of deprecated `is_categorical`.

`is_categorical_dtype` exists from minimum pandas version we support (https://github.com/pandas-dev/pandas/blob/v0.23.2/pandas/core/dtypes/api.py), and `is_categorical` was deprecated from pandas 1.1.0 (87a1cc21ca).

### Why are the changes needed?

To avoid using deprecated APIs, and remove warnings.

### Does this PR introduce _any_ user-facing change?

Yes, it will remove warnings that says `is_categorical` is deprecated.

### How was this patch tested?

By running any pandas UDF with pandas 1.1.0+:

```python
import pandas as pd
from pyspark.sql.functions import pandas_udf

def func(x: pd.Series) -> pd.Series:
    return x

spark.range(10).select(pandas_udf(func, "long")("id")).show()
```

Before:

```
/.../python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py:151: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead
...
```

After:

```
...
```

Closes #30114 from HyukjinKwon/replace-deprecated-is_categorical.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
2020-10-21 14:46:47 -07:00
..
docs [SPARK-32793][SQL] Add raise_error function, adds error message parameter to assert_true 2020-10-08 12:05:39 +09:00
lib [SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9 2020-02-20 09:09:30 -08:00
pyspark [SPARK-31964][PYTHON][FOLLOW-UP] Use is_categorical_dtype instead of deprecated is_categorical 2020-10-21 14:46:47 -07:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support [SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files 2018-04-29 11:25:31 +08:00
.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-33002][PYTHON] Remove non-API annotations 2020-10-07 19:53:59 +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-26252][PYTHON] Add support to run specific unittests and/or doctests in python/run-tests script 2018-12-05 15:22:08 +08:00
run-tests.py [SPARK-33189][PYTHON][TESTS] Add env var to tests for legacy nested timestamps in pyarrow 2020-10-21 09:13:33 +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-32982][BUILD] Remove hive-1.2 profiles in PIP installation option 2020-09-24 14:49:58 +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).