Evaluate expressions on each series individually

Oct 13, 2025 by wbrum.org 92 views

Introduction

When working with dataframes in Python, Pandas provides a convenient method called eval() to evaluate string expressions that involve column names. However, when switching to Polars, a popular alternative to Pandas, you may find that the eval() method is not available. In this article, we will explore the series.eval() method in Polars and provide a step-by-step guide on how to replace the pandas.eval() method with its equivalent in Polars.

Understanding Pandas.eval()

Before diving into the Polars equivalent, let's take a closer look at the pandas.eval() method. This method allows you to evaluate a string expression that involves column names in a Pandas dataframe. The expression can be a simple arithmetic operation, a conditional statement, or even a more complex expression involving multiple columns.

For example, suppose you have a dataframe with three columns: col1, col2, and col3. You can use the eval() method to create a new column col3 by adding the values of col1 and col2 together.

import pandas as pd
df = pd.DataFrame(
'col1' [1, 2, 3],
'col2': [4, 5, 6]
)
df['col3'] = df.eval('col2 + col1')
print(df)

Replacing Pandas.eval() with Polars.series.eval()

Now that we have a good understanding of the pandas.eval() method, let's explore the equivalent method in Polars. In Polars, you can use the series.eval() method to evaluate a string expression that involves column names in a series.

However, unlike Pandas, Polars does not provide a direct equivalent to the eval() method for dataframes. Instead, you need to use the series.eval() method on each series individually.

Here's an example of how to replace the pandas.eval() method with its equivalent in Polars:

import polars as pl
df = pl.DataFrame(
'col1' [1, 2, 3],
'col2': [4, 5, 6]
)
df['col3'] = df['col2'].eval(' + ')
df['col3'] = df['col3'] + df['col1'].eval(' + ')
print(df)

As you can see, the Polars equivalent requires more code and involves using the eval() method on each series individually.

Tips and Tricks

Here are some additional tips and tricks to help you replace the pandas.eval() method with its equivalent in Polars:

Use the series.eval() method: As we've seen, the series.eval() method is the key to replacing the pandas.eval() method in Polars.
Evaluate expressions on each series individually: Unlike Pandas, Polars does not provide a direct equivalent to the eval() method for dataframes. Instead, you need to use the series.eval() method on each series individually.
Use the + operator to concatenate strings: When using the series.eval() method, you can use the + operator to concatenate strings. For example, df['col3'] = df['col2'].eval(' + ') + df['col1'].eval(' + ').
Use the str.replace() method to remove whitespace: If your expressions contain whitespace, you can use the str.replace() method to remove it. For example, df['col3'] = df['col2'].eval(' + ').str.replace(' ', '').

Conclusion

In this article, we've explored the pandas.eval() method and its equivalent in Polars, series.eval(). We've seen how to replace the pandas.eval() method with its equivalent in Polars and provided tips and tricks to help you make the transition.

By following the steps outlined in this article, you can easily replace the pandas.eval() method with its equivalent in Polars and take advantage of the performance and flexibility of Polars.

Example Use Cases

Here are some example use cases for replacing the pandas.eval() method with its equivalent in Polars:

Data cleaning and preprocessing: When working with large datasets, you may need to perform data cleaning and preprocessing tasks, such as removing duplicates, handling missing values, and transforming data types.
Data analysis and visualization: Once you've cleaned and preprocessed your data, you can use Polars to perform data analysis and visualization tasks, such as creating summary statistics, visualizing data distributions, and identifying trends and patterns.
Machine learning and modeling: Polars provides a range of machine learning and modeling tools, including regression, classification, clustering, and dimensionality reduction. You can use these tools to build predictive models and make informed decisions.

Future Development

Polars is a rapidly evolving library, and new features and improvements are being added regularly. Some potential future developments include:

Improved support for data manipulation: Polars already provides a range of data manipulation tools, but there is always room for improvement. Future developments may include new functions for handling missing values, transforming data types, and performing data aggregation.
Enhanced support for machine learning and modeling: Polars already provides a range of machine learning and modeling tools, but there is always room for improvement. Future developments may include new functions for building predictive models, handling imbalanced data, and optimizing model performance.
Better integration with other libraries: Polars is designed to work seamlessly with other popular Python libraries, including NumPy, Pandas, and Matplotlib. Future developments may include improved integration with these libraries, as well as new integrations with other popular libraries.

Conclusion

In conclusion, replacing the pandas.eval() method with its equivalent in Polars, series.eval(), requires a bit more code and involves using the eval() method on each series individually. However, with the tips and tricks outlined in this article, you can easily make the transition and take advantage of the performance and flexibility of Polars.

Q: What is the main difference between Pandas.eval() and Polars.series.eval()?

A: The main difference between Pandas.eval() and Polars.series.eval() is that Pandas.eval() can be used on a Pandas dataframe, while Polars.series.eval() can only be used on a Polars series.

Q: How do I use Polars.series.eval() to replace Pandas.eval() in my code?

A: To use Polars.series.eval() to replace Pandas.eval() in your code, you need to use the eval() method on each series individually. For example, if you have a dataframe with two columns, 'col1' and 'col2', you can use the following code to create a new column 'col3' by adding the values of 'col1' and 'col2' together:

import polars as pl
df = pl.DataFrame(
'col1' [1, 2, 3],
'col2': [4, 5, 6]
)
df['col3'] = df['col2'].eval(' + ')
df['col3'] = df['col3'] + df['col1'].eval(' + ')
print(df)

Q: Can I use Polars.series.eval() to perform more complex operations, such as conditional statements?

A: Yes, you can use Polars.series.eval() to perform more complex operations, such as conditional statements. For example, you can use the following code to create a new column 'col3' that is equal to the value of 'col1' if 'col2' is greater than 5, and equal to the value of 'col2' otherwise:

import polars as pl
df = pl.DataFrame(
'col1' [1, 2, 3],
'col2': [4, 5, 6]
)
df['col3'] = df['col2'].eval('if col2 > 5 then col1 else col2')
print(df)

Q: How do I handle missing values when using Polars.series.eval()?

A: When using Polars.series.eval(), you can handle missing values by using the na function to replace missing values with a specific value. For example, you can use the following code to replace missing values with 0:

import polars as pl
df = pl.DataFrame(
'col1' [1, 2, None],
'col2': [4, 5, 6]
)
df['col3'] = df['col2'].eval(' + ')
df['col3'] = df['col3'] + df['col1'].eval(' + ')
df['col3'] = df['col3'].na().replace(0)
print(df)

Q: Can I use Polars.series.eval() to perform operations on multiple columns at once?

A: Yes, you can use Polars.series.eval() to perform operations on multiple columns at once. For example, you can use the following code to create a new column 'col3' that is equal to the sum of 'col1' and 'col2':

import polars as pl
df = pl.DataFrame(
'col1' [1, 2, 3],
'col2': [4, 5, 6]
)
df['col3'] = df.eval('col1 + col2')
print(df)

Q: How do I optimize the performance of Polars.series.eval()?

A: To optimize the performance of Polars.series.eval(), you can use the following tips:

Use the na function to replace missing values with a specific value, rather than using the eval method to handle missing values.
Use the + operator to concatenate strings, rather than using the eval method to concatenate strings.
Use the str.replace method to remove whitespace from strings, rather than using the eval method to remove whitespace.
Use the series.eval method on each series individually, rather than using the eval method on the entire dataframe.

By following these tips, you can optimize the performance of Polars.series.eval() and make it more efficient for your use case.