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Date: Tue, 12 May 2015 03:52:00 +0000 (UTC)
From: "Reynold Xin (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.12822693.1429628742000.81547.1431402720063@Atlassian.JIRA>
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 <JIRA.12822693.1429628742460@arcas>
Subject: [jira] [Closed] (SPARK-7035) Drop __getattr__ on
 pyspark.sql.DataFrame
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     [ https://issues.apache.org/jira/browse/SPARK-7035?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Reynold Xin closed SPARK-7035.
------------------------------
    Resolution: Won't Fix

I'm closing this one for now. We can continue the discussion.

> Drop __getattr__ on pyspark.sql.DataFrame
> -----------------------------------------
>
>                 Key: SPARK-7035
>                 URL: https://issues.apache.org/jira/browse/SPARK-7035
>             Project: Spark
>          Issue Type: Sub-task
>          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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