Removing duplicate columns after DataFrame join in PySpark

In this article, we will discuss how to remove duplicate columns after a DataFrame join in PySpark.

Create the first dataframe for demonstration:

# Importing necessary libraries
from pyspark.sql import SparkSession

# Create a spark session
spark = SparkSession.builder.appName('pyspark \
- example join').getOrCreate()

# Create data in dataframe
data = [(('Ram'),1,'M'),
          (('Mike'),2,'M'),
          (('Rohini'),3,'M'),
          (('Maria'),4,'F'),
          (('Jenis'),5,'F')]

# Column names in dataframe
columns = ["Name","ID","Gender"]

# Create the spark dataframe
df1 = spark.createDataFrame(data=data, schema = columns)

# Print the dataframe
df1.show()

Output:

+------+---+------+
|  Name| ID|Gender|
+------+---+------+
|   Ram|  1|     M|
|  Mike|  2|     M|
|Rohini|  3|     M|
| Maria|  4|     F|
| Jenis|  5|     F|
+------+---+------+

Create a second dataframe for demonstration:

# Create data in dataframe
data2 = [(1,3000),
          (2,4000),
          (3,4000),
          (4,4000),
          (5, 1200)]

# Column names in dataframe
columns = ["ID","salary"]

# Create the spark dataframe
df2 = spark.createDataFrame(data=data2, 
                            schema = columns)

# Print the dataframe
df2.show()

Output:

+---+------+
| ID|salary|
+---+------+
|  1|  3000|
|  2|  4000|
|  3|  4000|
|  4|  4000|
|  5|  1200|
+---+------+

Using join()

This will join the two dataframes

Syntax: dataframe.join(dataframe1).show()

where,

dataframe is the first dataframe

dataframe1 is the second dataframe

Let's see the dataframe after join:

df = df1.join(df2, df1.ID==df2.ID)
df.show()

Output:

+------+---+------+---+------+
|  Name| ID|Gender| ID|salary|
+------+---+------+---+------+
| Jenis|  5|     F|  5|  1200|
|   Ram|  1|     M|  1|  3000|
|Rohini|  3|     M|  3|  4000|
|  Mike|  2|     M|  2|  4000|
| Maria|  4|     F|  4|  4000|
+------+---+------+---+------+

Here we see the ID and Salary columns are added to our existing article.

Now, let check the columns once:

Here we check gender columns which is unique so its work fine.

df.select('Gender').show()

Output:

+------+
|Gender|
+------+
|     F|
|     M|
|     M|
|     M|
|     F|
+------+

Now let check our Duplicates Columns:

Here it will produce errors because of duplicate columns.

df.select('ID').show()

Output:

AnalysisException: Reference 'ID' is ambiguous, could be: ID, ID.

Removing duplicate columns after join in PySpark

If we want to drop the duplicate column, then we have to specify the duplicate column in the join function. Here we are simply using join to join two dataframes and then drop duplicate columns.

Syntax: dataframe.join(dataframe1, ['column_name']).show()

where,

• dataframe is the first dataframe
• dataframe1 is the second dataframe
• column_name is the common column exists in two dataframes

new_df = df1.join(df2, ["id"])
new_df.show()

Output:

+---+------+------+------+
| ID|  Name|Gender|salary|
+---+------+------+------+
|  5| Jenis|     F|  1200|
|  1|   Ram|     M|  3000|
|  3|Rohini|     M|  4000|
|  2|  Mike|     M|  4000|
|  4| Maria|     F|  4000|
+---+------+------+------+