Title: pandas 3.0.0 release candidate ready for testing! Date: 2025-12-12

pandas 3.0.0 release candidate ready for testing!

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 can use your help to test the release candidate now.

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 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 our release notes.

Important changes requiring code updates

As a major release, pandas 3.0 includes some breaking changes that may require updates to your code. The two most significant changes are:

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).

Example:

# Old behavior (pandas < 3.0)
>>> ser = pd.Series(["a", "b"])
>>> ser
0    a
1    b
dtype: object  # <-- numpy object dtype

# New behavior (pandas 3.0)
>>> ser = pd.Series(["a", "b"])
>>> ser.dtype
>>> ser
0    a
1    b
dtype: str  # <-- new string dtype

This change improves performance and type safety, but may require code updates, especially for library code that currently looks for "object" dtype when expecting string data.

For more details, see the migration guide for the new string data type.

2. Consistent copy/view behaviour with Copy-on-Write (CoW)

Copy-on-Write is now the default and only mode in pandas 3.0. This makes behavior more consistent and predictable, but requires updates to certain coding patterns.

The most impactfull change is that chained assignment will no longer work. As a result, the SettingWithCopyWarning is also removed (since there is no longer ambiguity whether it would work or not), and defensive .copy() calls to silence the warning are no longer needed.

Example:

# Old behavior (pandas < 3.0) - chained assignment
df["foo"][df["bar"] > 5] =   # This might modify df (unpredictable)

# New behavior (pandas 3.0) - must do the modification in one step (e.g. with .loc)
df.loc[df["bar"] > 5, "foo"] = 100

In general, any result of an indexing operation or method now always behaves as if it were a copy, so modifications of the result won't affect the original DataFrame.

For more details, see the Copy-on-Write migration guide.

Call to Action: test the Release Candidate

We need your help to ensure a smooth pandas 3.0 release!

Especially if you have pandas code in production or maintain a library with pandas as a dependency, it is strongly recommended to run your test suites with the release candidate, and report any issue to our issue tracker before the official 3.0.0 release.

How can you best test the release candidate?

1. First update to the latest released pandas 2.3 (if you are not already running that version) and test it with your codebase. It is recommended to resolve any deprecation warning before upgrading to pandas 3.0.
2. Optionally, you can already enable the new string dtype and Copy-on-Write mode using pandas 2.3 (pd.options.future.infer_string = True and pd.options.mode.copy_on_write = True).
3. Install the release candidate (see below) and test it with your codebase
4. Run your existing code to identify any issues or needed updates
5. Report any problems you encounter on our GitHub repository issue tracker

The more testing we get now, the smoother the final pandas 3.0 release will be for everyone. Your feedback is crucial for making this a successful release!

Getting the Release Candidate

You can install the latest pandas 3.0 release candidate from PyPI:

python -m pip install --upgrade --pre pandas==3.*

Or from conda-forge using conda/mamba:

conda install -c conda-forge/label/pandas_rc pandas=3