Combine data in PySpark by matching multiple keys while keeping only one copy of columns with distinct names

Question

Combine data in PySpark by matching multiple keys while keeping only one copy of columns with distinct names

I need to perform an outer join on two dataframes using Spark:

Dataframe 1 columns: first_name, last, address 
Dataframe 2 columns: first_name, last_name, phone_number

The keys for joining are

first_name and df1.last==df2.last_name

The desired schema for the final dataset includes the following columns:

first_name, last, last_name, address, phone_number

This means that if column names match, I want them to be 'merged' in the output dataframe; otherwise, keep them separate.

Currently, I am achieving this with two join operations instead of one:

df1.join(df2,'first_name','outer').join(df2,[df1.last==df2.last_name],'outer')

python pyspark

Answer 1

Answer №1

To accomplish the join in one go, simply utilize a join condition with multiple elements:

join_condition = [df1.first_name == df2.first_name, df1.last == df2.last_name]
new_df = df1.join(df2, join_condition, 'outer')

Answer 2

To accomplish the join in one go, simply utilize a join condition with multiple elements:

join_condition = [df1.first_name == df2.first_name, df1.last == df2.last_name]
new_df = df1.join(df2, join_condition, 'outer')

Answer 3

Answer №2

Give this a shot:

import pyspark.sql.functions as f
join_condition = [dataframe1.first_name == dataframe2.first_name, dataframe1.last == dataframe2.last_name]
joined_dataframe = dataframe1.alias('l').join(dataframe2.alias('r'), join_condition, 'outer').select(f.col('l.first_name').alias('first_name'), f.col('l.last').alias('last'), f.col('r.last_name').alias('last_name'))

Answer 4

Give this a shot:

import pyspark.sql.functions as f
join_condition = [dataframe1.first_name == dataframe2.first_name, dataframe1.last == dataframe2.last_name]
joined_dataframe = dataframe1.alias('l').join(dataframe2.alias('r'), join_condition, 'outer').select(f.col('l.first_name').alias('first_name'), f.col('l.last').alias('last'), f.col('r.last_name').alias('last_name'))

Combine data in PySpark by matching multiple keys while keeping only one copy of columns with distinct names

Answer №1

Answer №2

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