[jira] [Assigned] (SPARK-18069) Many examples in Python docstrings are incomplete
[
https://issues.apache.org/jira/browse/SPARK-18069?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-18069:
Assignee: Apache Spark
> Many examples in Python docstrings are incomplete
> -
>
> Key: SPARK-18069
> URL: https://issues.apache.org/jira/browse/SPARK-18069
> Project: Spark
> Issue Type: Documentation
> Components: Documentation
>Affects Versions: 2.0.1
>Reporter: Mortada Mehyar
>Assignee: Apache Spark
>Priority: Minor
>
> A lot of the python API functions show example usage that is incomplete. The
> docstring shows output without having the input DataFrame defined. It can be
> quite confusing trying to understand and/or follow the example.
> For instance, the docstring for `DataFrame.dtypes()` is currently
> {code}
> def dtypes(self):
> """Returns all column names and their data types as a list.
>
> >>> df.dtypes
> [('age', 'int'), ('name', 'string')]
> """
> {code}
> when it should really be
> {code}
> def dtypes(self):
> """Returns all column names and their data types as a list.
>
> >>> df = spark.createDataFrame([('Alice', 2), ('Bob', 5)], ['name',
> 'age'])
> >>> df.dtypes
> [('age', 'int'), ('name', 'string')]
> """
> {code}
> I have a pending PR for fixing many of these occurrences here:
> https://github.com/apache/spark/pull/15053
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[jira] [Assigned] (SPARK-18069) Many examples in Python docstrings are incomplete
[
https://issues.apache.org/jira/browse/SPARK-18069?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-18069:
Assignee: (was: Apache Spark)
> Many examples in Python docstrings are incomplete
> -
>
> Key: SPARK-18069
> URL: https://issues.apache.org/jira/browse/SPARK-18069
> Project: Spark
> Issue Type: Documentation
> Components: Documentation
>Affects Versions: 2.0.1
>Reporter: Mortada Mehyar
>Priority: Minor
>
> A lot of the python API functions show example usage that is incomplete. The
> docstring shows output without having the input DataFrame defined. It can be
> quite confusing trying to understand and/or follow the example.
> For instance, the docstring for `DataFrame.dtypes()` is currently
> {code}
> def dtypes(self):
> """Returns all column names and their data types as a list.
>
> >>> df.dtypes
> [('age', 'int'), ('name', 'string')]
> """
> {code}
> when it should really be
> {code}
> def dtypes(self):
> """Returns all column names and their data types as a list.
>
> >>> df = spark.createDataFrame([('Alice', 2), ('Bob', 5)], ['name',
> 'age'])
> >>> df.dtypes
> [('age', 'int'), ('name', 'string')]
> """
> {code}
> I have a pending PR for fixing many of these occurrences here:
> https://github.com/apache/spark/pull/15053
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