The Series.cbrt() function in Polars
Polars is a DataFrame library for Rust designed for fast, flexible, and expressive data manipulation and analysis. It provides similar functionalities to pandas in Python. Polars facilitate performance and ease of use, making them suitable for handling large datasets efficiently.
The Series.cbrt() function
The Series.cbrt() function in Polars computes the cube root of each element in a Series (column) of a DataFrame. This function is optimized for performance and can be used as a convenient way to apply cube root operations to numerical data in a DataFrame.
Syntax
Below is the syntax of the series.cbrt() function:
Series.cbrt()
Parameters
The series.cbrt() function takes no argument. This function is the same as calculating the cube root using the mathematical expression
import polars as pl# Create a Seriespol_series = pl.Series([15, 20, 25])# Compute the cube root of the Series using (1.0 / 3)cube_root = pol_series ** (1.0 / 3)# Print the resultprint(cube_root)
Code
Let’s take a look at an example of the series.cbrt() function in Polars. For this, we will take a series with some values and then calculate its cube root by using the built-in method in Polars:
import polars as pl# Create a seriespol_series = pl.Series([15, 20, 25])# Compute the cube root of the seriescube_root = pol_series.cbrt()# Print the resultprint(cube_root)
Explanation
Let’s discuss the code above. First we import the Polars library with the alias pl.
Line 4: We create a series
pol_seriescontaining the values[15, 20, 25].Line 7: We computes the cube root of each element in the series using the
cbrt()function, storing the result in thecube_rootvariable.Line 10: Finally, it prints the resulting series containing the cube root values.
Conclusion
The Series.cbrt() function in Polars provides a convenient way to compute the cube root of each element in a series. It offers optimized performance for numerical computations and is useful for various data analysis and manipulation tasks. With its intuitive syntax and powerful capabilities, Polars simplifies working with DataFrame-like structures by providing expressive and concise APIs, making it an excellent choice for data processing applications in Python.
