bzhaoopenstack opened a new pull request, #37232:
URL: https://github.com/apache/spark/pull/37232
Pandas disallow conversion between datetime/timedelta and
conversions for any datetimelike to float.
This will raise error in PYSPARK, during we simply call a DatetimeIndex.
So we need to avoid to call astype with datetime64.
BTW, PYSPARK PANDAS announces that won't support DatetimeTZD type.
So lets skip datetime64 type only in base __repr__ func in Index.
<!--
Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, please read our contributor guidelines:
https://spark.apache.org/contributing.html
2. Ensure you have added or run the appropriate tests for your PR:
https://spark.apache.org/developer-tools.html
3. If the PR is unfinished, add '[WIP]' in your PR title, e.g.,
'[WIP][SPARK-XXXX] Your PR title ...'.
4. Be sure to keep the PR description updated to reflect all changes.
5. Please write your PR title to summarize what this PR proposes.
6. If possible, provide a concise example to reproduce the issue for a
faster review.
7. If you want to add a new configuration, please read the guideline first
for naming configurations in
'core/src/main/scala/org/apache/spark/internal/config/ConfigEntry.scala'.
8. If you want to add or modify an error type or message, please read the
guideline first in
'core/src/main/resources/error/README.md'.
-->
### What changes were proposed in this pull request?
<!--
Please clarify what changes you are proposing. The purpose of this section
is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR. See the examples below.
1. If you refactor some codes with changing classes, showing the class
hierarchy will help reviewers.
2. If you fix some SQL features, you can provide some references of other
DBMSes.
3. If there is design documentation, please add the link.
4. If there is a discussion in the mailing list, please add the link.
-->
Skip datetime64 type during exec astype to convert by pandas in __repr__
func.
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
Improve the experience of spark python developers
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such as
the documentation fix.
If yes, please clarify the previous behavior and the change this PR proposes
- provide the console output, description and/or an example to show the
behavior difference if possible.
If possible, please also clarify if this is a user-facing change compared to
the released Spark versions or within the unreleased branches such as master.
If no, write 'No'.
-->
No
### How was this patch tested?
<!--
If tests were added, say they were added here. Please make sure to add some
test cases that check the changes thoroughly including negative and positive
cases if possible.
If it was tested in a way different from regular unit tests, please clarify
how you tested step by step, ideally copy and paste-able, so that other
reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why
it was difficult to add.
If benchmark tests were added, please run the benchmarks in GitHub Actions
for the consistent environment, and the instructions could accord to:
https://spark.apache.org/developer-tools.html#github-workflow-benchmarks.
-->
```
Using Python version 3.8.13 (default, Jun 29 2022 11:50:19)
Spark context Web UI available at http://172.25.179.45:4042
Spark context available as 'sc' (master = local[*], app id =
local-1658283215853).
SparkSession available as 'spark'.
>>> from pyspark import pandas as ps
WARNING:root:'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It
is required to set this environment variable to '1' in both driver and executor
sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it
does not work if there is a Spark context already launched.
>>> ps.DatetimeIndex(['1970-01-01', '1970-01-01', '1970-01-01'])
/home/spark/spark/python/pyspark/pandas/internal.py:1573: FutureWarning:
iteritems is deprecated and will be removed in a future version. Use .items
instead.
fields = [
/home/spark/spark/python/pyspark/sql/pandas/conversion.py:486:
FutureWarning: iteritems is deprecated and will be removed in a future version.
Use .items instead.
for column, series in pdf.iteritems():
/home/spark/.pyenv/versions/3.8.13/lib/python3.8/site-packages/_pydevd_bundle/pydevd_utils.py:601:
FutureWarning: iteritems is deprecated and will be removed in a future
version. Use .items instead.
for item in s.iteritems():
/home/spark/.pyenv/versions/3.8.13/lib/python3.8/site-packages/_pydevd_bundle/pydevd_utils.py:601:
FutureWarning: iteritems is deprecated and will be removed in a future
version. Use .items instead.
for item in s.iteritems():
/home/spark/.pyenv/versions/3.8.13/lib/python3.8/site-packages/_pydevd_bundle/pydevd_utils.py:601:
FutureWarning: iteritems is deprecated and will be removed in a future
version. Use .items instead.
for item in s.iteritems():
/home/spark/.pyenv/versions/3.8.13/lib/python3.8/site-packages/_pydevd_bundle/pydevd_utils.py:601:
FutureWarning: iteritems is deprecated and will be removed in a future
version. Use .items instead.
for item in s.iteritems():
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/spark/spark/python/pyspark/pandas/indexes/base.py", line 2770,
in __repr__
pindex =
self._psdf._get_or_create_repr_pandas_cache(max_display_count).index
File "/home/spark/spark/python/pyspark/pandas/frame.py", line 12780, in
_get_or_create_repr_pandas_cache
self, "_repr_pandas_cache", {n: self.head(n + 1)._to_internal_pandas()}
File "/home/spark/spark/python/pyspark/pandas/frame.py", line 12775, in
_to_internal_pandas
return self._internal.to_pandas_frame
File "/home/spark/spark/python/pyspark/pandas/utils.py", line 589, in
wrapped_lazy_property
setattr(self, attr_name, fn(self))
File "/home/spark/spark/python/pyspark/pandas/internal.py", line 1056, in
to_pandas_frame
pdf = sdf.toPandas()
File "/home/spark/spark/python/pyspark/sql/pandas/conversion.py", line
248, in toPandas
series = series.astype(t, copy=False)
File "/home/spark/upstream/pandas/pandas/core/generic.py", line 6095, in
astype
new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
File "/home/spark/upstream/pandas/pandas/core/internals/managers.py", line
386, in astype
return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
File "/home/spark/upstream/pandas/pandas/core/internals/managers.py", line
308, in apply
applied = getattr(b, f)(**kwargs)
File "/home/spark/upstream/pandas/pandas/core/internals/blocks.py", line
526, in astype
new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
File "/home/spark/upstream/pandas/pandas/core/dtypes/astype.py", line 299,
in astype_array_safe
new_values = astype_array(values, dtype, copy=copy)
File "/home/spark/upstream/pandas/pandas/core/dtypes/astype.py", line 227,
in astype_array
values = values.astype(dtype, copy=copy)
File "/home/spark/upstream/pandas/pandas/core/arrays/datetimes.py", line
631, in astype
return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy)
File "/home/spark/upstream/pandas/pandas/core/arrays/datetimelike.py",
line 504, in astype
raise TypeError(msg)
TypeError: Cannot cast DatetimeArray to dtype datetime64
```
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
