In PySpark, there are multiple ways to select columns from a DataFrame. Here are some common approaches:

Using the select() method:

The select() method allows you to specify the columns you want to select by passing the column names as arguments.

selected_df ="column1", "column2", "column3")

This selects the columns “column1”, “column2”, and “column3” from the DataFrame df and creates a new DataFrame selected_df containing only those columns.

Using dot notation:

You can use dot notation to select columns by referencing them directly on the DataFrame.

selected_df =, df.column2, df.column3)

This approach is useful when you want to perform operations or transformations on the selected columns within the select() method.

Using the selectExpr() method:

The selectExpr() method allows you to specify column expressions using SQL-like syntax.

selected_df = df.selectExpr("column1", "column2 * 2 as column2_doubled", "column3 + 1")

Here, you can perform arithmetic operations, apply functions, or create aliases for the selected columns using expressions.

we have discussed all examples for selectExpr() here

Using the col() function:

The col() function is useful when you want to select columns dynamically or perform operations based on column names provided as variables.

you need to import pyspark.sql.functions to use this function.

from pyspark.sql.functions import col

column_names = ["column1", "column2", "column3"]
selected_df =*[col(column_name) for column_name in column_names])

By using the col() function within a list comprehension, you can dynamically select columns based on the names provided in the column_names list.

These are some of the common ways to select columns in PySpark. Depending on your specific use case and preference, you can choose the approach that best suits your requirements.

for more hands on examples related to Apache PySpark you can visit our Examples page.