How Do I Change DataFrame Data Type in PySpark?


Angela Bailey

Changing the data type of a DataFrame in PySpark can be a common requirement while working with large datasets. Whether you want to optimize memory usage or ensure compatibility with downstream operations, understanding how to change the data type of a DataFrame is essential. In this tutorial, we will explore different ways to achieve this using PySpark.

1. Using the `cast()` function

The simplest and most straightforward way to change the data type of a column in a DataFrame is by using the `cast()` function. This function allows you to explicitly specify the new data type for a column.

To use the `cast()` function, you need to import it from the `pyspark.sql.functions` module:

from pyspark.functions import col

Let’s assume we have a DataFrame called df with a column named age that we want to convert from an integer to a float:

df = df.withColumn("age", col("age").cast("float"))

The withColumn() function allows us to create a new DataFrame by replacing or adding columns. In this case, we are replacing the existing “age” column with the same name but with a different data type obtained using the `cast()` function.

2. Using SQL syntax

In addition to using functions, PySpark also allows you to change the data type of columns using SQL-like syntax. You can register your DataFrame as a temporary table and then execute SQL queries on it.

spark.sql("SELECT *, CAST(age AS float) AS new_age FROM temp_table")

In this example, we first create a temporary table called “temp_table” using the createOrReplaceTempView() function. Then, we use the spark.sql() function to execute our SQL query. The query selects all columns from the temporary table and casts the “age” column to a float, creating a new column called “new_age”.

3. Using `selectExpr()` function

The `selectExpr()` function offers another way to change the data type of a DataFrame column in PySpark. This function allows you to execute SQL-like expressions on your DataFrame.selectExpr(“*”, “CAST(age AS float) AS new_age”)

In this example, we use the * symbol to select all columns from our DataFrame and then add a new column called “new_age” by casting the existing “age” column to a float using the CAST() expression.

4. Using `withColumn()` and `lit()` functions

If you need to change the data type of a column in PySpark but also want to set specific values for each row, you can use the combination of withColumn() and lit().

from pyspark.functions import lit

df = df.withColumn("new_column", lit("default_value").cast("desired_type"))

In this example, we add a new column called “new_column” to our DataFrame and set the default value for each row using the lit() function. We then apply the desired data type to this new column using the cast() function.


Changing the data type of a DataFrame column in PySpark is a common operation that can be accomplished in multiple ways. Whether you prefer using functions like cast(), SQL-like syntax, or a combination of withColumn() and lit(), PySpark provides various options to meet your requirements. Experiment with these methods to find the one that best suits your needs.

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