Choosing Polars Over Pandas for High-Performance Data Analysis

As web applications scale, the volume and speed of data growth can quickly overwhelm existing systems. It was merely a matter of time before our data processes would run into resource limitations. While the performance gains were impressive, the journey and insights gained during the transition were even more valuable.

At Factspan, we believe in data-informed decision-making. A core part of that is our Arcus application (formerly known as the Resource Recommendation Engine). It allows us to perform various analyses, from marketing campaign performance, SoWs, employee skills, strengths and projects demand forecasting, analytics and so much more. The results from these analyses give us the incentives to improve existing features and create new ones. In this article, we will discuss what we like to refer to as the Polarification of the Arcus data stack. More specifically: how solving one problem with Polars led us to replace Pandas across the entire Arcus data stack.

We also highlight some challenges and learnings anyone can use should they consider the move over to Polars.

Migration from Pandas to Polars in Python:

Pandas is the preferred Python data manipulation library, providing simple DataFrame operations on in-memory data. It is frequently used for exploratory analysis for small-to-medium datasets. Polars, on the other hand, is a modern DataFrame library written in Rust and using Apache Arrow for memory management. While Pandas conducts operations quickly (each command runs instantly) and mostly single-threaded, Polars supports both eager and lazy execution and can automatically use many CPU cores in parallel. This difference in design gives Polars a performance edge for large datasets and complex transformations. Both libraries provide a high-level DataFrame API, and in fact Polars mirrors much of Pandas syntax to ease the learning curve.

Why Migrate from Pandas to Polars

Moving an existing codebase from Pandas to Polars has considerable advantages of scalability, multi-threading, memory efficiency, and speed.

• Superior speed: Polars can be 10–100 times faster than Pandas for many procedures.
• Improved memory efficiency: Utilizes less RAM for the same datasets.
• Parallelization: is enabled by default. Automatically utilizes numerous cores.
• Lazy evaluation: optimizes query execution plans prior to running them.

Naturally, Polars will not be necessary for all projects. Pandas is usually fast enough for tiny datasets or quick one-time analyses, and it offers the advantage of familiarity. Polars, on the other hand, is a good choice if you’re experiencing bottlenecks, such as waiting hours or minutes for Pandas jobs, running into memory issues, or consuming up all of one CPU core. For large-scale data tasks, it promises faster execution and more efficient resource utilization. The following part will go over how to migrate from Pandas to Polars, including examples and syntax changes.

Migrating to Polars from Pandas

1.Installation: Before we start install polars and import the librar

2.Data Input/Output

3.Creating Dataframes

4.Data Inspection Functions

5.Selecting Columns

6.Filtering Columns

7.Adding new columns

8.Sorting and Dropping

9.Merging DataFrames and handling null values

10.Grouping and Aggregations:

11.Conditional statements

12.Pivot and Melting

Challenges of Migrating from Pandas to Polars

Polars has several advantages over Pandas in terms of performance and expressiveness, but the migration process is not always smooth. Here are the most typical issues every one can face while converting from Pandas to Polars, as well as techniques for overcoming them.

1.Dealing with Missing Data

Pandas uses NaN, None, and NaT for missing values, while Polars uses null. As a result, polars may treat missing values slightly differently than Pandas. Use the .is_null() or is_nan() and .is_not_null() expressions when needed, and pay attention to how aggregations handle nulls.

2.Polars Doesn’t Support Indexes

Pandas has a concept of indexes and access the values using .loc and .iloc functions but Polars uses a columnar model without special index status. The codes written using indexes need to be rewritten with Polars’ columnar functions.

3.Polars have Stricter Type Consistency

Polars is much stricter than Pandas when it comes to data types, which can raise errors in code if you’re not handling them correctly.In Polars, each column has to stick to a single data type, and you can’t have a mix of strings and numbers in the same column. If you try, Polars will throw an error. So, it’s important to handle type conversions explicitly using .cast() function and make sure your data transformations are consistent from the start.

4.Row-wise Operations to Vectorized Approach

The apply() function is commonly used for row-wise operations in pandas, but Polars does not support it. The apply() method will be slow, and whenever possible, use vectorized expressions in place of row-wise operations. If the logic is difficult, break the logic into smaller sections. If you are working with a single column, utilize the Polars map_elements() function to apply custom logic quickly.

5.Using Pandas Ecosystem Libraries.

Polars doesn’t have as many supported libraries as pandas. To use such libraries, convert the Polars into a Pandas DataFrame by using the .to_pandas() function to convert it into Pandas, and after that, if needed, convert it into Polars again by using the pl.from_pandas() function.

Benchmark:

To ensure a fair comparison, we tested both implementations using the same dataset, logic, and infrastructure setup. As part of our migration, we benchmarked a key API that processes and aggregates monthly and quarterly revenue data. The original version, developed with Pandas, had an average response time of about 59.38 seconds.

After rebuilding the logic in Polars to replace heavy merge, group by, and apply operations, we reduced the average response time to 25.21 seconds. This resulted in approximately 50% lower latency, faster execution, and reduced CPU usage, making the backend more efficient.

The Verdict: Pandas vs. Polars

Polars adds performance, efficiency, and a modern design to the DataFrame world, whereas Pandas offers flexibility and a deep history. Choosing Polars does not require abandoning Pandas entirely—you can use both where appropriate. However, if you want faster performance, less memory usage, and automatic multi-threading, Polars is worth a try.

Many teams have observed considerable benefits after switching essential data transformations to Polars, which can reduce hours-long Pandas processes to minutes. With the recommendations and examples in this post, you now have a practical roadmap to begin the migration and unlock the advantages of Polars in your own Python projects.

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