Data manipulation and analysis can be challenging and involve working with large datasets. Thankfully, a widely used Python library known as Pandas has become the go-to tool for processing and manipulating data. Pandas recently got an update, which is version 2.0. This article takes a closer look at what Pandas is, its success, and what the new version brings, including its ecosystem around Arrow, Polars, and DuckDB. By Simon Späti.
That makes it an excellent time to reflect on what Pandas is and why it’s successful. Further in the article:
- What is Pandas
- How does Pandas work?
- What are the highlights of version 2.0
- What changes code-wise?
- What is Apache Arrow?
- Why Apache Arrow?
- Interoperability
- When not to use Pandas
- The alternatives
- Polars: Riding the fast train of rust
- DuckDB: The SQL version
- What about Dask?
- Others: Koalas, Vaex, VertiPaq
Apache Arrow sets the open standard to exchange in a heterogeneous data pipeline, which needs to read and share data among different steps. Overall, this article provides insights into the benefits of using Pandas, particularly with its 2.0 version, and the exciting changes in its ecosystem around Arrow, Polars, and DuckDB. Excellent read!
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