pandas - Python Data Analysis Library

About us About pandas Project roadmap Governance Team Sponsors Citing and logo Getting started Documentation Community Blog Ask a question (StackOverflow) Code of conduct Ecosystem Benchmarks Contribute What's new in pandas 3 Source: datapythonista blog - pandas | Author: Marc Garcia | Published: Jan 25, 2026 pandas 3.0 has just been released. This article uses a real‑world example to explain the most important differences between pandas 2 and the new pandas 3 release, focusing on performance, syntax, and user experience. A note on pandas versioning Before diving into the technical details of pandas 3 … Read more pandas 3.0 released! Source: pandas blog | Author: pandas team | Published: Jan 21, 2026 We're excited to announce the release of pandas 3.0.0. This major long-awaited release brings significant improvements to pandas, but also features some potentially breaking changes. Highlights of pandas 3.0 pandas 3.0 introduces several major Read more pandas 3.0.0 release candidate ready for testing! Source: pandas blog | Author: pandas team | Published: Dec 12, 2025 We're excited to announce the release candidate for pandas 3.0. This major release brings significant improvements to pandas, but also features some potentially breaking changes. To ensure a smooth pandas 3.0 release, we Read more How fast can we process a CSV file Source: datapythonista blog - pandas | Author: Marc Garcia | Published: Feb 22, 2024 Introduction Comma-separated values (CSV) are an extremely popular format to store tabular data because of their simplicity and how easy is to write them. The file can be directly read by a human, as opposed to more efficient binary formats like parquet, for example: name,age Maryam,23 Mèng yáo … Read more What's new in pandas 2.2 Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Jan 25, 2024 The most interesting things about the new release pandas 2.2 was released on January 22nd 2024. Let’s take a look at the things this release introduces and how it will help us to improve our pandas workloads. It includes a bunch of improvements that will improve the user … Read more Deep dive into pandas Copy-on-Write mode - part III Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Sep 28, 2023 Explaining the migration path for Copy-on-Write Introduction The introduction of Copy-on-Write (CoW) is a breaking change that will have some impact on existing pandas-code. We will investigate how we can adapt our code to avoid errors when CoW will be enabled by default. This is currently planned for the pandas … Read more What's new in pandas 2.1 Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Sep 06, 2023 The most interesting things about the new release pandas 2.1 was released on August 30th 2023. Let’s take a look at the things this release introduces and how it will help us improving our pandas workloads. It includes a bunch of improvements and also a set of new … Read more Deep Dive into pandas Copy-on-Write Mode - Part II Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Aug 16, 2023 Explaining how Copy-on-Write optimizes performance Introduction The first post explained how the Copy-on-Write mechanism works. It highlights some ares where copies are introduced into the workflow. This post will focus on optimizations that ensure that this won't slow the average workflow down. We utilize a technique that pandas internals use … Read more Deep Dive into pandas Copy-on-Write Mode - Part I Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Aug 08, 2023 Explaining how Copy-on-Write works internally Introduction pandas 2.0 was released in early April and brought many improvements to the new Copy-on-Write (CoW) mode. The feature is expected to become the default in pandas 3.0, which is scheduled for April 2024 at the moment. There are no plans for … Read more pandas Internals Explained Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Jul 20, 2023 Explaining the pandas data model and its advantages Introduction pandas enables you to choose between different types of arrays to represent the data of your DataFrame. Historically, most DataFrames are backed by NumPy arrays. pandas 2.0 introduced the option to use PyArrow arrays as a storage format. There exists … Read more Dask performance benchmarking put to the test: Fixing a pandas bottleneck Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Jun 27, 2023 Getting notified of a significant performance regression the day before release sucks, but quickly identifying and resolving it feels great! We were getting set up at our booth at JupyterCon 2023 when we received a notification: An engineer on our team had spotted a significant performance regression in Dask. With … Read more Benchmarking pandas against Polars from a pandas PoV Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Jun 14, 2023 Or: How writing efficient pandas code matters Introduction I've regularly seen benchmarks that show how much faster Polars is compared to pandas. The fact that Polars is faster than pandas is not too surprising since it is multithreaded while pandas is mostly single-core. The big difference surprises me though. That's … Read more Utilizing PyArrow to improve pandas and Dask workflows Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Jun 04, 2023 Get the most out of PyArrow support in pandas and Dask right now Introduction This post investigates where we can use PyArrow to improve our pandas and Dask workflows right now. General support for PyArrow dtypes was added with pandas 2.0 to pandas and Dask. This solves a bunch … Read more Welcoming pandas 2.0 Source: Patrick Hoefler - pandas | Author: Patrick Hoefler | Published: Mar 22, 2023 How the API is changing and how to leverage new functionalities Introduction After 3 years of development, the second pandas 2.0 release candi