Pandas works best for small or medium datasets with standard Python libraries.
Polars excels at large data with multi-core processing and lower memory use.
Combining both tools can maximize speed, efficiency, and library support.
Data tools are evolving, and the choice between Pandas and Polars is becoming a hot topic in 2025. Pandas has long been the go-to library for Python users. People prefer the library for its ease of use and wide support. Polars is gaining attention for its faster processing and lower memory use, making it strong for handling large datasets efficiently.
Pandas has been a key part of Python data work for over a decade. Its simple commands and large community make it easy to learn and accessible. Pandas works well for:
Small and Medium Datasets: Handles data that fits in memory easily.
Quick Analysis: Makes it easy to explore and test data.
Easy Integration: Works smoothly with other libraries like scikit-learn, matplotlib, and seaborn.
Single-Core Processing: Works on one CPU core, so large datasets can be slow.
Memory-Heavy: Large files can use a lot of memory.
Immediate Execution: Runs commands right away, which can slow complex workflows.
Also Read: Pandas vs NumPy: Best Python tool for Data Science
Polars is built in Rust and uses Apache Arrow for memory. This gives it several advantages:
Multi-Core Processing: Uses all CPU cores for faster work.
Lazy Evaluation: Plans tasks first and runs them only when needed.
Lower Memory Use: Handles large data efficiently.
Streaming Support: Works on big files in chunks, so memory-related issues don’t occur.
Polars has an edge over Pandas:
Speed: Can be up to 10 times faster than Pandas for tasks like joins and groupings.
Memory: A 1GB CSV file uses approximately 179MB in Polars versus 1.4GB in Pandas.
Pick Pandas if:
Datasets are small or medium-sized.
Quick testing and analysis are needed.
Other Python tools are part of the workflow.
Pick Polars if:
Data is large and needs fast processing.
Speed and efficiency matter.
Tasks can benefit from lazy execution and multi-core use.
Also Read: Master Data Cleaning: 10 Pandas One-Liners You Need To Know
Some analysts prefer a hybrid approach and utilize both tools:
Polars handles importing, cleaning, and transforming large data.
Pandas is used for tasks that need extra library support or features that Polars does not fully provide.
The choice between Pandas and Polars depends on the needs of the project. Pandas remains reliable and easy to use for everyday data tasks. Whereas Polars offers high speed and efficiency when working with large datasets. Knowing the advantages of each tool helps users select the one that fits their data workflow.
1. What is the main difference between Pandas and Polars in 2025?
Pandas is easy to use and works well for small data, while Polars is faster, memory-efficient, and handles large datasets.
2. When should analysts choose Pandas over Polars?
Pandas is best for small or medium datasets, quick testing, and workflows using other Python libraries.
3. How does Polars improve performance over Pandas?
Polars uses multi-core processing, lazy evaluation, lower memory use, and can process big files in chunks.
4. Can Pandas and Polars be used together in workflows?
Yes. Polars can handle cleaning and transforming large data, while Pandas supports extra library features.
5. How much faster is Polars compared to Pandas?
Tests show Polars can be up to 10 times faster for tasks like joins and groupings while using much less memory.