How to Add a New Column to a Spark DataFrame Using PySpark?

Adding a new column to a Spark DataFrame in PySpark is a common operation you might need in data processing. You can achieve this in several ways, depending on your specific needs. Below, I’ll explain a couple of methods, along with code snippets and their expected output.

Method 1: Using the `withColumn` Method

The `withColumn` method is one of the most straightforward ways to add a new column to a DataFrame. You can create a new column based on existing columns or provide a constant value.

Step-by-Step Explanation

  1. Import the necessary libraries.
  2. Create a sample DataFrame.
  3. Add a new column using the `withColumn` method.

Code Snippet


from pyspark.sql import SparkSession
from pyspark.sql.functions import lit

# Create a Spark session
spark = SparkSession.builder.appName("AddColumnExample").getOrCreate()

# Define a sample DataFrame
data = [("Alice", 34), ("Bob", 45), ("Catherine", 29)]
columns = ["Name", "Age"]
df = spark.createDataFrame(data, columns)

# Add a new column "Country" with a constant value "USA"
df_with_new_column = df.withColumn("Country", lit("USA"))

# Show the resulting DataFrame
df_with_new_column.show()

Output


+---------+---+-------+
|     Name|Age|Country|
+---------+---+-------+
|    Alice| 34|    USA|
|      Bob| 45|    USA|
| Catherine| 29|    USA|
+---------+---+-------+

Method 2: Using the `select` Method

If you need to add a new column based on some computation of existing columns, you can use the `select` method in combination with other PySpark SQL functions.

Step-by-Step Explanation

  1. Import the necessary libraries.
  2. Create a sample DataFrame.
  3. Use the `select` method to compute the new column.

Code Snippet


from pyspark.sql import functions as F

# Create a sample DataFrame
data = [("Alice", 34), ("Bob", 45), ("Catherine", 29)]
columns = ["Name", "Age"]
df = spark.createDataFrame(data, columns)

# Add a new column "Age_in_5_years" by adding 5 to the "Age" column
df_with_new_column = df.select("Name", "Age", (F.col("Age") + 5).alias("Age_in_5_years"))

# Show the resulting DataFrame
df_with_new_column.show()

Output


+---------+---+-------------+
|     Name|Age|Age_in_5_years|
+---------+---+-------------+
|    Alice| 34|           39|
|      Bob| 45|           50|
| Catherine| 29|           34|
+---------+---+-------------+

Conclusion

These are two common methods to add a new column to a Spark DataFrame using PySpark. The `withColumn` method is more straightforward for adding columns with constant values or simple expressions, while the `select` method is useful for more complex computations. Choose the method that best suits your use case.

About Editorial Team

Our Editorial Team is made up of tech enthusiasts who are highly skilled in Apache Spark, PySpark, and Machine Learning. They are also proficient in Python, Pandas, R, Hive, PostgreSQL, Snowflake, and Databricks. They aren't just experts; they are passionate teachers. They are dedicated to making complex data concepts easy to understand through engaging and simple tutorials with examples.

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