Polars Select Function

last modified March 1, 2025

Polars is a fast, efficient DataFrame library in Python. The select function is used to choose specific columns from a DataFrame. This tutorial covers how to use the select function with practical examples.

The select function is essential for data manipulation tasks like filtering columns, renaming, and applying transformations. Polars provides a simple and intuitive API for these operations.

Basic Column Selection

This example shows how to select specific columns from a DataFrame.

basic_select.py

import polars as pl

df = pl.DataFrame({ ‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9] })

selected = df.select([‘A’, ‘B’]) print(selected)

The select([‘A’, ‘B’]) selects columns ‘A’ and ‘B’ from the DataFrame. This is useful for focusing on specific data.

Select with Renaming

This example demonstrates renaming columns during selection.

select_rename.py

import polars as pl

df = pl.DataFrame({ ‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9] })

selected = df.select([ pl.col(‘A’).alias(‘Column1’), pl.col(‘B’).alias(‘Column2’) ]) print(selected)

The alias function renames columns during selection. This is useful for creating more readable column names.

Select with Expression

This example shows how to apply an expression during column selection.

select_expression.py

import polars as pl

df = pl.DataFrame({ ‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9] })

selected = df.select([ (pl.col(‘A’) + pl.col(‘B’)).alias(‘Sum’) ]) print(selected)

The pl.col(‘A’) + pl.col(‘B’) expression calculates the sum of columns ‘A’ and ‘B’. This is useful for creating derived columns.

Select with Filter

This example demonstrates filtering rows during column selection.

select_filter.py

import polars as pl

df = pl.DataFrame({ ‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9] })

selected = df.select([ pl.col(‘A’).filter(pl.col(‘A’) > 1) ]) print(selected)

The filter(pl.col(‘A’) > 1) filters rows where column ‘A’ is greater than 1. This is useful for conditional data selection.

Select with Aggregation

This example shows how to aggregate data during column selection.

select_aggregate.py

import polars as pl

df = pl.DataFrame({ ‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9] })

selected = df.select([ pl.col(‘A’).sum().alias(‘Total’) ]) print(selected)

The sum function calculates the total of column ‘A’. This is useful for summarizing data.

Select with Multiple Expressions

This example demonstrates selecting multiple columns with different expressions.

select_multiple.py

import polars as pl

df = pl.DataFrame({ ‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9] })

selected = df.select([ pl.col(‘A’).alias(‘Column1’), (pl.col(‘B’) * 2).alias(‘DoubleB’) ]) print(selected)

The pl.col(‘B’) * 2 expression doubles the values in column ‘B’. This is useful for applying multiple transformations.

Select with Conditional Logic

This example shows how to use conditional logic during column selection.

select_conditional.py

import polars as pl

df = pl.DataFrame({ ‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9] })

selected = df.select([ pl.when(pl.col(‘A’) > 1).then(pl.col(‘B’)).otherwise(0).alias(‘Result’) ]) print(selected)

The when and then functions apply conditional logic. This is useful for creating dynamic columns.

Select with String Operations

This example demonstrates string operations during column selection.

select_string.py

import polars as pl

df = pl.DataFrame({ ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’] })

selected = df.select([ pl.col(‘Name’).str.to_uppercase().alias(‘UppercaseName’) ]) print(selected)

The str.to_uppercase function converts names to uppercase. This is useful for text manipulation.

Select with Date Operations

This example shows how to perform date operations during column selection.

select_date.py

import polars as pl

df = pl.DataFrame({ ‘Date’: [‘2023-01-01’, ‘2023-02-01’, ‘2023-03-01’] })

selected = df.select([ pl.col(‘Date’).str.strptime(pl.Date, ‘%Y-%m-%d’).dt.month().alias(‘Month’) ]) print(selected)

The dt.month function extracts the month from the date. This is useful for time-based analysis.

Select with Nested Data

This example demonstrates selecting nested data from a DataFrame.

select_nested.py

import polars as pl

df = pl.DataFrame({ ‘A’: [1, 2, 3], ‘B’: [[4, 5], [6, 7], [8, 9]] })

selected = df.select([ pl.col(‘B’).arr.get(0).alias(‘FirstElement’) ]) print(selected)

The arr.get(0) function extracts the first element from nested lists. This is useful for working with complex data structures.

Best Practices for Using Select

• Plan Column Selection: Identify required columns before applying select.

• Use Aliases: Rename columns for better readability.

• Combine Expressions: Apply multiple transformations in a single select call.

• Optimize Performance: Avoid unnecessary column selections to improve speed.

Source

Polars Documentation

In this article, we have explored how to use the select function in Polars.

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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