Python PySpark – Union and UnionAll

In this article, we will discuss Union and UnionAll in PySpark in Python.

Union in PySpark

The PySpark union() function is used to combine two or more data frames having the same structure or schema. This function returns an error if the schema of data frames differs from each other.

Syntax:

dataFrame1.union(dataFrame2)

Here,

• dataFrame1 and dataFrame2 are the dataframes

Example 1:

In this example, we have combined two data frames, data_frame1 and data_frame2. Note that the schema of both the data frames is the same.

Python3

# Python program to illustrate the
# working of union() function

import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('GeeksforGeeks.com').getOrCreate()

# Creating a dataframe
data_frame1 = spark.createDataFrame(
    [("Bhuwanesh", 82.98), ("Harshit", 80.31)],
    ["Student Name", "Overall Percentage"]
)

# Creating another dataframe
data_frame2 = spark.createDataFrame(
    [("Naveen", 91.123), ("Piyush", 90.51)],
    ["Student Name", "Overall Percentage"]
)

# union()
answer = data_frame1.union(data_frame2)

# Print the result of the union()
answer.show()

Output:

Example 2:

In this example, we have combined two data frames, data_frame1 and data_frame2. Note that the schema of both the data frames is different. Hence, the output is not the desired one as union() function is ideal for datasets having the same structure or schema. 

Python3

# Python program to illustrate the
# working of union() function

import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('GeeksforGeeks.com').getOrCreate()

# Creating a data frame
data_frame1 = spark.createDataFrame(
    [("Bhuwanesh", 82.98), ("Harshit", 80.31)],
    ["Student Name", "Overall Percentage"]
)

# Creating another data frame
data_frame2 = spark.createDataFrame(
    [(91.123, "Naveen"), (90.51, "Piyush"), (87.67, "Hitesh")],
    ["Overall Percentage", "Student Name"]
)

# Union both the dataframes using union() function
answer = data_frame1.union(data_frame2)

# Print the union of both the dataframes
answer.show()

Output:

UnionAll()  in PySpark

UnionAll() function does the same task as union() function but this function is deprecated since Spark “2.0.0” version. Hence, union() function is recommended.

Syntax:

dataFrame1.unionAll(dataFrame2)

Here,

• dataFrame1 and dataFrame2 are the dataframes

Example 1:

In this example, we have combined two data frames, data_frame1 and data_frame2. Note that the schema of both the data frames is the same.

Python3

# Python program to illustrate the
# working of unionAll() function

import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('GeeksforGeeks.com').getOrCreate()

# Creating a dataframe
data_frame1 = spark.createDataFrame(
    [("Bhuwanesh", 82.98), ("Harshit", 80.31)],
    ["Student Name", "Overall Percentage"]
)

# Creating another dataframe
data_frame2 = spark.createDataFrame(
    [("Naveen", 91.123), ("Piyush", 90.51)],
    ["Student Name", "Overall Percentage"]
)

# Union both the dataframes using unionAll() function
answer = data_frame1.unionAll(data_frame2)

# Print the union of both the dataframes
answer.show()

Output:

Example 2:

In this example, we have combined two data frames, data_frame1 and data_frame2. Note that the schema of both the data frames is different. Hence, the output is not the desired one as unionAll() function is ideal for datasets having the same structure or schema. 

Python3

# Python program to illustrate the
# working of union() function

import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('GeeksforGeeks.com').getOrCreate()

# Creating a data frame
data_frame1 = spark.createDataFrame(
    [("Bhuwanesh", 82.98), ("Harshit", 80.31)],
    ["Student Name", "Overall Percentage"]
)

# Creating another data frame
data_frame2 = spark.createDataFrame(
    [(91.123, "Naveen"), (90.51, "Piyush"), (87.67, "Hitesh")],
    ["Overall Percentage", "Student Name"]
)

# Union both the dataframes using unionAll() function
answer = data_frame1.unionAll(data_frame2)

# Print the union of both the dataframes
answer.show()

Output: