HyukjinKwon commented on a change in pull request #34210:
URL: https://github.com/apache/spark/pull/34210#discussion_r723891511
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File path: python/docs/source/user_guide/pandas_on_spark/typehints.rst
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@@ -60,10 +60,10 @@ it as a Spark schema. As an example, you can specify the
return type hint as bel
>>> df = ps.DataFrame({'A': ['a', 'a', 'b'], 'B': [1, 2, 3], 'C': [4, 6,
5]})
>>> df.groupby('A').apply(pandas_div)
-The function ``pandas_div`` actually takes and outputs a pandas DataFrame
instead of pandas-on-Spark :class:`DataFrame`.
-However, pandas API on Spark has to force to set the mismatched type hints.
+Notice that the function ``pandas_div`` actually takes and outputs a pandas
DataFrame instead of
+pandas-on-Spark :class:`DataFrame`. So, technically the correct types should
be pandas'.
Review comment:
I think it's right one :-). I meant the correct types should be pandas'
(like of pandas). actually let me write it as of pandas since you pointed out.
##########
File path: python/docs/source/user_guide/pandas_on_spark/typehints.rst
##########
@@ -139,7 +139,8 @@ programmatically generate the return type and schema.
.. code-block:: python
- >>> def transform(pdf) -> pd.DataFrame[zip(pdf.columns, pdf.dtypes)]:
+ >>> def transform(pdf) -> pd.DataFrame[
+ .. zip(sample.columns, sample.dtypes)]:
Review comment:
We should .. but this and the below examples don't have the definition
for `psdf` too .. let's leave it in this PR for now.
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