Pandas 3.0

Summary

pandas 3.0 released! 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 enhancements: Dedicated string data type by default: string columns are now inferred as the new str dtype instead of object, providing better performance and type safety Consistent copy/view behaviour with Copy-on-Write (CoW) (a.k.a. getting rid of the SettingWithCopyWarning): more predictable and consistent behavior for all operations, with improved performance through avoiding unnecessary copies New default resolution for datetime-like data: no longer defaulting to nanoseconds, but generally microseconds (or the resolution of the input), when constructing datetime or timedelta data (avoiding out-of-bounds errors for dates with a year before 1678 or after 2262) New pd.col syntax: initial support for pd.col() as a simplified syntax for creating callables in DataFrame.assign Further, pandas 3.0 includes a lot of other improvements and bug fixes. You can find the complete list of changes in the release notes. Upgrading to pandas 3.0 The pandas 3.0 release removed functionality that was deprecated in previous releases (see here for an overview). It is recommended to first upgrade to pandas 2.3 and to ensure your code is working without warnings, before upgrading to pandas 3.0. Further, as a major release, pandas 3.0 includes some breaking changes that may require updates to your code. The two most significant changes are the new string dtype and the copy/view behaviour changes, detailed below. An overview of all potentially breaking changes can be found in the Backwards incompatible API changes section of the release notes. 1. Dedicated string data type by default Starting with pandas 3.0, string columns are automatically inferred as str dtype instead of the numpy object (which can store any Python object). E...