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

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

Luke created SPARK-28189:
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             Summary: 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: PySpark
    Affects Versions: 2.4.0
            Reporter: Luke


Column names in general are case insensitive in Pyspark, and df.drop("col") 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(valuesA, ['KEY','field'])

vals2 = [('Rutabaga',1),('Pirate',2),('Ninja',3),('Darth Vader',4)]
df2 = spark.createDataFrame(valuesB, ['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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