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PySpark – Merge Two DataFrames with Different Columns or Schema

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  • Last Updated : 27 Jan, 2022

In this article, we will discuss how to merge two dataframes with different amounts of columns or schema in PySpark in Python.

Let’s consider the first dataframe:

Here we are having 3 columns named id, name, and address for better demonstration purpose.

Python3




# importing module
import pyspark
  
# import when and lit function
from pyspark.sql.functions import when, lit
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of employee data
data = [["1", "sravan", "kakumanu"],
        ["2", "ojaswi", "hyd"],
        ["3", "rohith", "delhi"],
        ["4", "sridevi", "kakumanu"],
        ["5", "bobby", "guntur"]]
  
# specify column names
columns = ['ID', 'NAME', 'Address']
  
# creating a dataframe from the lists of data
dataframe1 = spark.createDataFrame(data, columns)
  
# display
dataframe1.show()

Output:

Let’s consider the second dataframe

Here we are going to create a dataframe with 2 columns.

Python3




# importing module
import pyspark
  
# import when and lit function
from pyspark.sql.functions import when, lit
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of employee data
data = [["1", 23],
        ["2", 21],
        ["3", 32],
        ]
  
# specify column names
columns = ['ID', 'Age']
  
# creating a dataframe from the lists of data
dataframe2 = spark.createDataFrame(data, columns)
  
# display
dataframe2.show()

Output:

We can not merge the data frames because the columns are different, so we have to add the missing columns. Here In first dataframe (dataframe1) , the columns  [‘ID’, ‘NAME’, ‘Address’] and second dataframe (dataframe2 ) columns are  [‘ID’,’Age’].

Now we have to add the Age column to the first dataframe and NAME and Address in the second dataframe, we can do this by using lit() function. This function is available in pyspark.sql.functions which are used to add a column with a value. Here we are going to add a value with None.

Syntax:

for column in [column for column in dataframe1.columns if column not in dataframe2.columns]:

   dataframe2 = dataframe2.withColumn(column, lit(None))

where,

  • dataframe1 is the firstdata frame
  • dataframe2 is the second dataframe

Add missing columns to both the dataframes

In both the data frames we are going to add the Age column to the first dataframe and NAME and Address in the second dataframe using the above syntax.

Finally, we are displaying the column names of both data frames.

Python3




# importing module
import pyspark
  
# import lit function
from pyspark.sql.functions import lit
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
  
# list  of employee data
data = [["1", "sravan", "kakumanu"],
        ["2", "ojaswi", "hyd"],
        ["3", "rohith", "delhi"],
        ["4", "sridevi", "kakumanu"],
        ["5", "bobby", "guntur"]]
  
# specify column names
columns = ['ID', 'NAME', 'Address']
  
# creating a dataframe from the lists of data
dataframe1 = spark.createDataFrame(data, columns)
  
# list  of employee data
data = [["1", 23],
        ["2", 21],
        ["3", 32],
        ]
  
# specify column names
columns = ['ID', 'Age']
  
# creating a dataframe from the lists of data
dataframe2 = spark.createDataFrame(data, columns)
  
# add columns in dataframe1 that are missing 
# from dataframe2
for column in [column for column in dataframe2.columns
               if column not in dataframe1.columns]:
    dataframe1 = dataframe1.withColumn(column, lit(None))
  
# add columns in dataframe2 that are missing 
# from dataframe1
for column in [column for column in dataframe1.columns
               if column not in dataframe2.columns]:
    dataframe2 = dataframe2.withColumn(column, lit(None))
  
# now see the columns of dataframe1
print(dataframe1.columns)
  
# now see the columns of dataframe2
print(dataframe2.columns)

Output:

['ID', 'NAME', 'Address', 'Age']
['ID', 'Age', 'NAME', 'Address']

Merging Dataframes

Method 1: Using union()

This will merge the data frames based on the position.

Syntax: 

dataframe1.union(dataframe2)

Example:

In this example, we are going to merge the two data frames using union() method after adding the required columns to both the data frames. Finally, we are displaying the dataframe that is merged.

Python3




# importing module
import pyspark
  
# import lit function
from pyspark.sql.functions import lit
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
  
# list  of employee data
data = [["1", "sravan", "kakumanu"],
        ["2", "ojaswi", "hyd"],
        ["3", "rohith", "delhi"],
        ["4", "sridevi", "kakumanu"],
        ["5", "bobby", "guntur"]]
  
# specify column names
columns = ['ID', 'NAME', 'Address']
  
# creating a dataframe from the lists of data
dataframe1 = spark.createDataFrame(data, columns)
  
# list  of employee data
data = [["1", 23],
        ["2", 21],
        ["3", 32],
        ]
  
# specify column names
columns = ['ID', 'Age']
  
# creating a dataframe from the lists of data
dataframe2 = spark.createDataFrame(data, columns)
  
# add columns in dataframe1 that are missing 
# from dataframe2
for column in [column for column in dataframe2.columns
               if column not in dataframe1.columns]:
    dataframe1 = dataframe1.withColumn(column, lit(None))
  
# add columns in dataframe2 that are missing 
# from dataframe1
for column in [column for column in dataframe1.columns
               if column not in dataframe2.columns]:
    dataframe2 = dataframe2.withColumn(column, lit(None))
  
# perform union
dataframe1.union(dataframe2).show()

Output:

Method 2: Using unionByName()

This will merge the two data frames based on the column name.

Syntax:

dataframe1.unionByName(dataframe2)

Example:

In this example, we are going to merge the two data frames using unionByName() method after adding the required columns to both the dataframes. Finally, we are displaying the dataframe that is merged.

Python3




# importing module
import pyspark
  
# import lit function
from pyspark.sql.functions import lit
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
  
# list  of employee data
data = [["1", "sravan", "kakumanu"],
        ["2", "ojaswi", "hyd"],
        ["3", "rohith", "delhi"],
        ["4", "sridevi", "kakumanu"],
        ["5", "bobby", "guntur"]]
  
# specify column names
columns = ['ID', 'NAME', 'Address']
  
# creating a dataframe from the lists of data
dataframe1 = spark.createDataFrame(data, columns)
  
# list  of employee data
data = [["1", 23],
        ["2", 21],
        ["3", 32],
        ]
  
# specify column names
columns = ['ID', 'Age']
  
# creating a dataframe from the lists of data
dataframe2 = spark.createDataFrame(data, columns)
  
# add columns in dataframe1 that are missing 
# from dataframe2
for column in [column for column in dataframe2.columns
               if column not in dataframe1.columns]:
    dataframe1 = dataframe1.withColumn(column, lit(None))
  
# add columns in dataframe2 that are missing 
# from dataframe1
for column in [column for column in dataframe1.columns
               if column not in dataframe2.columns]:
    dataframe2 = dataframe2.withColumn(column, lit(None))
  
# perform unionByName
dataframe1.unionByName(dataframe2).show()

Output:

Method 3: Using unionAll()

Syntax

dataframe1.unionAll(dataframe2)

Example:

In this example, we are going to merge the two dataframes using unionAll() method after adding the required columns to both the dataframes. Finally, we are displaying the dataframe that is merged.

Python3




# importing module
import pyspark
  
# import lit function
from pyspark.sql.functions import lit
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
  
# list  of employee data
data = [["1", "sravan", "kakumanu"],
        ["2", "ojaswi", "hyd"],
        ["3", "rohith", "delhi"],
        ["4", "sridevi", "kakumanu"],
        ["5", "bobby", "guntur"]]
  
# specify column names
columns = ['ID', 'NAME', 'Address']
  
# creating a dataframe from the lists of data
dataframe1 = spark.createDataFrame(data, columns)
  
# list  of employee data
data = [["1", 23],
        ["2", 21],
        ["3", 32],
        ]
  
# specify column names
columns = ['ID', 'Age']
  
# creating a dataframe from the lists of data
dataframe2 = spark.createDataFrame(data, columns)
  
# add columns in dataframe1 that are missing
# from dataframe2
for column in [column for column in dataframe2.columns
               if column not in dataframe1.columns]:
    dataframe1 = dataframe1.withColumn(column, lit(None))
  
# add columns in dataframe2 that are missing
# from dataframe1
for column in [column for column in dataframe1.columns
               if column not in dataframe2.columns]:
    dataframe2 = dataframe2.withColumn(column, lit(None))
  
# perform unionAll
dataframe1.unionAll(dataframe2).show()

Output:


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