topic Re: Union Multiple dataframes in loop, with different schema in Data Engineering

Union Multiple dataframes in loop, with different schema

KKo — Mon, 28 Mar 2022 19:47:46 GMT

With in a loop I have few dataframes created. I can union them with out an issue if they have same schema using (df_unioned = reduce(DataFrame.unionAll, df_list). Now my problem is how to union them if one of the dataframe in df_list has different number of columns? I thought, reduce(df_unioned=DataFrame.unionByName, df_list, allowMissingColumns=True) would solve the issue but it is giving me error: reduce() takes no keyword arguments. Thanks in advance. Let me know if you need any details in the question.

Re: Union Multiple dataframes in loop, with different schema

Anonymous — Tue, 29 Mar 2022 12:00:03 GMT

Union doesn't work if they have different schemas and columns. If you do need to union dataframes with different schemas, just add columns of nulls for anything missing to get them to the same schema.

Re: Union Multiple dataframes in loop, with different schema

KKo — Thu, 31 Mar 2022 15:19:04 GMT

@Joseph Kambourakis I found a way to achieve this. using the function

def union_all(dfs):

if len(dfs) > 1:

return dfs[0].unionByName(union_all(dfs[1:]), allowMissingColumns=True)

else:

return dfs[0]

Re: Union Multiple dataframes in loop, with different schema

anoopunni — Mon, 24 Jul 2023 03:58:13 GMT

Hi,
I have come across same scenario, using reduce() and unionByname we can implement the solution as below:

val lstDF: List[Datframe] = List(df1,df2,df3,df4,df5)

val combinedDF = lstDF.reduce((df1, df2) => df1.unionByName(df2, allowMissingColumns = true))

#Scala # Spark #multiple schema