[jira] [Commented] (SPARK-7035) Drop __getattr__ on pyspark.sql.DataFrame

["Reynold Xin (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-7035?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14505327#comment-14505327 ] 

Reynold Xin commented on SPARK-7035:
------------------------------------

Pandas also support this, doesn't it?


> Drop __getattr__ on pyspark.sql.DataFrame
> -----------------------------------------
>
>                 Key: SPARK-7035
>                 URL: https://issues.apache.org/jira/browse/SPARK-7035
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark
>    Affects Versions: 1.4.0
>            Reporter: Kalle Jepsen
>
> I think the {{\_\_getattr\_\_}} method on the DataFrame should be removed.
> There is no point in having the possibility to address the DataFrames columns as {{df.column}}, other than the questionable goal to please R developers. And it seems R people can use Spark from their native API in the future.
> I see the following problems with {{\_\_getattr\_\_}} for column selection:
> * It's un-pythonic: There should only be one obvious way to solve a problem, and we can already address columns on a DataFrame via the {{\_\_getitem\_\_}} method, which in my opinion is by far superior and a lot more intuitive.
> * It leads to confusing Exceptions. When we mistype a method-name the {{AttributeError}} will say 'No such column ... '.
> * And most importantly: we cannot load DataFrames that have columns with the same name as any attribute on the DataFrame-object. Imagine having a DataFrame with a column named {{cache}} or {{filter}}. Calling {{df.cache()}} will be ambiguous and lead to broken code.



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