When you perform a DataFrame join operation in Apache Spark, it’s common to end up with duplicate columns, especially when the columns you join on have the same name in both DataFrames. Removing these duplicate columns is a typical data cleaning task. Let’s discuss how you can handle this using PySpark, but the concept applies similarly in Scala and Java.
Removing Duplicate Columns After a DataFrame Join
Suppose you have two DataFrames (`df1` and `df2`) that you need to join, and both DataFrames have a column named “id”. Here’s how you can perform the join and remove the duplicate “id” column.
Step-by-Step Solution (PySpark)
1.
Perform the join operation.
2.
Select the necessary columns explicitly to avoid duplicates.
Example in PySpark
Let’s start by creating two sample DataFrames:
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
# Create a Spark session
spark = SparkSession.builder.appName("example").getOrCreate()
# Sample data
data1 = [(1, "Alice"), (2, "Bob")]
df1 = spark.createDataFrame(data1, ["id", "name"])
data2 = [(1, "Science"), (2, "Math"), (3, "History")]
df2 = spark.createDataFrame(data2, ["id", "subject"])
# Display initial DataFrames
df1.show()
df2.show()
+---+-----+
| id| name|
+---+-----+
| 1|Alice|
| 2| Bob|
+---+-----+
+---+-------+
| id|subject|
+---+-------+
| 1|Science|
| 2| Math|
| 3|History|
+---+-------+
Perform the join and remove the duplicate column:
# Perform the join operation
joined_df = df1.join(df2, "id")
# Remove duplicate columns by selecting necessary ones
result_df = joined_df.select(df1["id"], df1["name"], df2["subject"])
# Display the result DataFrame
result_df.show()
+---+-----+-------+
| id| name|subject|
+---+-----+-------+
| 1|Alice|Science|
| 2| Bob| Math|
+---+-----+-------+
In this example, we explicitly select the columns from each DataFrame that we want to include in the result `result_df`, thus avoiding duplicate columns.
Example in Scala
Here is the same example using Scala:
import org.apache.spark.sql.SparkSession
val spark = SparkSession.builder.appName("example").getOrCreate()
// Sample data
val data1 = Seq((1, "Alice"), (2, "Bob"))
val df1 = spark.createDataFrame(data1).toDF("id", "name")
val data2 = Seq((1, "Science"), (2, "Math"), (3, "History"))
val df2 = spark.createDataFrame(data2).toDF("id", "subject")
// Display initial DataFrames
df1.show(false)
df2.show(false)
+---+-----+
|id |name |
+---+-----+
|1 |Alice|
|2 |Bob |
+---+-----+
+---+-------+
|id |subject|
+---+-------+
|1 |Science|
|2 |Math |
|3 |History|
+---+-------+
Perform the join and remove the duplicate column:
// Perform the join operation
val joined_df = df1.join(df2, "id")
// Remove duplicate columns by selecting necessary ones
val result_df = joined_df.select(df1("id"), df1("name"), df2("subject"))
// Display the result DataFrame
result_df.show(false)
+---+-----+-------+
|id |name |subject|
+---+-----+-------+
|1 |Alice|Science|
|2 |Bob |Math |
+---+-----+-------+
In the Scala version, we also explicitly select the required columns to avoid duplicates.
In summary, by specifying the columns you want to keep in the result DataFrame, you can easily remove duplicate columns after a join operation in Apache Spark.