Bump pandas from 2.3.3 to 3.0.0 in the python-packages group

[dependabot]

Bumps the python-packages group with 1 update: pandas.

Updates pandas from 2.3.3 to 3.0.0

Release notes

Sourced from pandas's releases.

pandas 3.0.0

We are pleased to announce the release of pandas 3.0.0, a major release from the pandas 2.x series. This release includes various new features, bug fixes, and performance improvements, as well as possible breaking changes.

The pandas 3.0 release removed a functionality that was deprecated in previous releases. 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.

Highlights include:

• Dedicated string data type by default
• Consistent copy/view behaviour with Copy-on-Write (CoW) (a.k.a. getting rid of the SettingWithCopyWarning)
• New default resolution for datetime-like data
• Initial support for the new pd.col syntax

See the announcement blog post and the detailed release notes for a list of all the changes.

Pandas 3.0.0 supports Python 3.11 and higher. The release can be installed from PyPI

python -m pip install --upgrade pandas==3.0.*

Or from conda-forge

conda install -c conda-forge pandas=3.0

Please report any issues with the release on the pandas issue tracker.

Thanks to all the contributors who made this release possible.

Pandas 3.0.0rc2

No release notes provided.

Pandas 3.0.0rc1

No release notes provided.

Pandas 3.0.0rc0

We are pleased to announce a first release candidate for pandas 3.0.0. If all goes well, we'll release pandas 3.0.0 in a few weeks.

See the [whatsnew][0] for a list of all the changes.

The release is available on conda-forge and PyPI.

The release can be installed from PyPI

python -m pip install --upgrade --pre pandas==3.0.0rc0

Or from conda-forge

conda install -c conda-forge/label/pandas_rc pandas==3.0.0rc0

Please report any issues with the release candidate on the pandas issue tracker.

... (truncated)

Commits

• 366ccdf RLS: 3.0.0
• 139f4d0 DOC: split/reorder other enhancements section in 3.0.0 whatsnew notes (#63762)
• fd2a4f4 TST: assert reading of legacy pickles against current data (#61792)
• cc0bcaa DOC: update whatsnew section on datetimelike resolution (#63780)
• 3ddbb49 DOC: remove 2.3.4 whatsnew file from released 3.0 docs (#63779)
• ee63532 CLN: Create temporary HDF5 file path and HDFStore for pytables tests (#63492)
• 9512e99 CoW: better chained assignment warning message for update() method (#63500)
• 1db90d5 API: rename read_iceberg selected_fields keyword to columns (#63748)
• c9b51fa BUG: .str methods failing on PyArrow using regex with \Z (#63705)
• 7cff0f4 API: Period.to_timestamp default to microsecond unit (#63760)
• Additional commits viewable in compare view

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Summary by cubic

Upgrade pandas from 2.3.3 to 3.0.0 and widen our upper bound to <4. This brings pandas 3 defaults like string dtype and Copy‑on‑Write and may require small code updates.

• Dependencies

  • Set pandas constraint to >=2.1.3,<4 in pyproject.
  • Updated lockfile to pandas 3.0.0; added numpy 2.4.1 for Python 3.14+; dropped pytz with tzdata now optional on Windows/Emscripten.

• Migration

  • Run the full test suite and fix any breakages from pandas 3 changes.
  • Audit timezone code to use zoneinfo instead of pytz.
  • Review code that relied on SettingWithCopyWarning; Copy‑on‑Write is default.
  • Check assumptions about datetime precision; defaults changed in pandas 3.

^{Written for commit 5718500. Summary will update on new commits.}

[cubic-dev-ai]

No issues found across 2 files

Architecture diagram

sequenceDiagram
    participant App as Application Code
    participant Pandas as Pandas 3.0 Lib
    participant Mem as Memory/Buffer

    Note over App,Mem: Impact of Upgrade from v2.3 to v3.0

    %% 1. Data Loading / Types
    App->>Pandas: read_source() / DataFrame()
    Pandas->>Pandas: NEW: Default to "string" dtype (vs object)
    Pandas->>Pandas: CHANGED: Datetime resolution inference
    Pandas-->>App: DataFrame

    %% 2. Copy-on-Write (CoW) Flow - The major behavioral change
    App->>Pandas: Create Subset (df2 = df1[mask])
    Pandas-->>App: Return View (No data copy yet)

    App->>Pandas: Mutate Subset (df2.iloc[0] = x)
    alt NEW: Copy-on-Write Behavior
        Pandas->>Mem: CHANGED: Clone data on write
        Mem-->>Pandas: New Memory Reference
        Pandas->>Pandas: Update df2 only
        Note right of Pandas: Original df1 guaranteed unchanged<br/>SettingWithCopyWarning removed
    else Old Behavior (v2.x)
        Pandas->>Mem: Modify shared memory (potentially)
        Note right of Pandas: Risk of side-effects on df1
    end
    Pandas-->>App: Operation Complete

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[dependabot]

Looks like pandas is updatable in another way, so this is no longer needed.