[jira] [Resolved] (SPARK-28189) Pyspark - df.drop() is Case Sensitive when Referring to Upstream Tables

["Dongjoon Hyun (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-28189?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dongjoon Hyun resolved SPARK-28189.
-----------------------------------
       Resolution: Fixed
         Assignee: Tony Zhang
    Fix Version/s: 3.0.0

This is resolved via https://github.com/apache/spark/pull/25055

> Pyspark  - df.drop() is Case Sensitive when Referring to Upstream Tables
> ------------------------------------------------------------------------
>
>                 Key: SPARK-28189
>                 URL: https://issues.apache.org/jira/browse/SPARK-28189
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.4.0
>            Reporter: Luke
>            Assignee: Tony Zhang
>            Priority: Minor
>             Fix For: 3.0.0
>
>
> Column names in general are case insensitive in Pyspark, and df.drop() in general is also case insensitive.
> However, when referring to an upstream table, such as from a join, e.g.
> {code:java}
> vals1 = [('Pirate',1),('Monkey',2),('Ninja',3),('Spaghetti',4)]
> df1 = spark.createDataFrame(vals1, ['KEY','field'])
> vals2 = [('Rutabaga',1),('Pirate',2),('Ninja',3),('Darth Vader',4)]
> df2 = spark.createDataFrame(vals2, ['KEY','CAPS'])
> df_joined = df1.join(df2, df1['key'] == df2['key'], "left")
> {code}
>  
> drop will become case sensitive. e.g.
> {code:java}
> # from above, df1 consists of columns ['KEY', 'field']
> # from above, df2 consists of columns ['KEY', 'CAPS']
> df_joined.select(df2['key']) # will give a result
> df_joined.drop('caps') # will also give a result
> {code}
> however, note the following
> {code:java}
> df_joined.drop(df2['key']) # no-op
> df_joined.drop(df2['caps']) # no-op
> df_joined.drop(df2['KEY']) # will drop column as expected
> df_joined.drop(df2['CAPS']) # will drop column as expected
> {code}
>  
>  
> so in summary, using df.drop(df2['col']) doesn't align with expected case insensitivity for column names, even though functions like select, join, and dropping a column generally are case insensitive.
>  



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