Pandas vs Polars comparison table
| characteristic | Pandas | Polars |
|---|---|---|
| Development language | Python (Cython implementation core part) | Rust (High-performance system programming language) |
| performance | Slower, especially on large data sets (high memory usage and low computational efficiency) | Extremely fast, using multi-threading and vectorized operations, suitable for processing large-scale data |
| Memory management | High memory usage, easy to have memory bottlenecks | Better memory optimization, support zero-copy technology, reduce memory consumption |
| Multithreaded support | Mainly single threading, some operations support multi-threading (such as groupby), but performance improvement is limited | Natively support multi-threading, making full use of multi-core CPUs |
| Ease of use | API is simple and intuitive, rich in ecology, complete documents, and active community | APIs are similar to Pandas, with a low learning curve, but the ecosystem is not yet mature |
| Feature richness | Comprehensive functions, support complex data operations, time series analysis, statistical modeling, etc. | Relatively few functions, focusing on efficient data processing, some advanced functions are still under development |
| Extensibility | Supports seamless integration with NumPy, SciPy, Scikit-learn, etc. | Supports integration with Arrow, NumPy, etc., but has poor compatibility with tools such as SciPy |
| Lazy Evaluation | Lazy loading is not supported, all operations are executed immediately | Support lazy loading, delay calculations not executed until results are needed, improving performance |
| Applicable data size | Suitable for small and medium-sized data (usually less than 1GB) | Suitable for medium-to-large data (supports GB to TB level) |
| Installation and dependency | Easy to install, just pip install pandas | Installation is a bit complicated and requires compilation of the Rust library, which may require additional configuration |
| Community and support | The community is huge, the problem-solving resources are rich, and the plug-in ecosystem is mature | The community is smaller, but it is still growing rapidly, and the documents and tutorials are gradually improving |
Use scenario comparison
Pandas usage scenarios
Small and medium-sized data processing：
- The data volume is less than 1GB, suitable for rapid prototyping.
- For example: data analysis, data cleaning, simple statistical analysis.
Complex data operations：
- Rich data operation functions (such as time series analysis, grouping and aggregation, pivot tables, etc.) are required.
- For example: financial data analysis, marketing data processing.
Integrate with other Python toolchains：
- It requires seamless collaboration with machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch.
- For example: feature engineering, data preparation before model training.
Teaching and Introduction：
- Pandas is the first tool to get started with data science. The API is easy to learn and use, and the documentation is detailed.
Polars usage scenarios
Large-scale data processing：
- The data volume exceeds 1GB, and even reaches the GB to TB level.
- For example: log analysis, large-scale sensor data analysis.
High performance requirements：
- Data processing is required quickly, especially tasks running on multi-core CPUs.
- For example: real-time data stream processing, batch data conversion.
Lazy loading and optimization query：
- Delay calculations are required to optimize performance and avoid unnecessary intermediate calculations.
- For example: complex queries in ETL processes.
Memory sensitive scenarios：
- Memory resources are limited and memory is required to be efficiently utilized.
- For example: data analysis on embedded devices.
Cross-platform data exchange：
- Need to interact with Apache Arrow compatible toolchain.
- For example: data processing in distributed computing frameworks (such as Dask, Ray).
Summarize
Select Pandas：
- If your data is small (<1GB) and requires rich features and a mature ecosystem.
- If you need seamless integration with other tools in the Python ecosystem, such as Scikit-learn.
- If you are a beginner, you want to get started with data analysis quickly.
Select Polars：
- If your data is large (>1GB) and requires high performance.
- If you need to process real-time or streaming data, or need efficient memory management.
- If you are familiar with Rust or are willing to try emerging high-performance tools.
The above is personal experience. I hope you can give you a reference and I hope you can support me more.