chore: handle pandas v2 and v3 by enforcing datetime freq of millisec

[haakonvt]

Quite a few changes in pandas v3, specifically around the inference of frequency for datetime-like data: https://pandas.pydata.org/docs/whatsnew/v3.0.0.html#datetime-timedelta-resolution-inference

This PR unifies frequency to always be milliseconds, matching the API closely.

[gemini-code-assist]

Summary of Changes

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This pull request addresses compatibility issues with Pandas v3 by unifying datetime frequency to milliseconds throughout the application. The changes ensure consistent and correct handling of time-series data, adapting to Pandas' updated inference mechanisms. Additionally, various project dependencies have been updated to their latest stable versions, improving overall stability and leveraging new features.

Highlights

• Pandas v3 Compatibility: Adapted the codebase to handle changes introduced in Pandas v3, particularly concerning datetime frequency inference.
• Datetime Frequency Unification: Standardized datetime handling to consistently use millisecond ('ms') frequency across relevant functions and data structures, aligning with the updated Pandas API.
• Dependency Updates: Updated numerous Python dependencies in poetry.lock to their latest versions, including anyio, charset-normalizer, importlib-metadata, jaraco-context, nh3, protobuf, pyjwt, python-discovery, and types-requests. This also includes conditional updates for pandas itself, supporting both v2.x and v3.x based on Python version.
• Test Adjustments: Modified unit tests to reflect the new millisecond datetime precision and updated Pandas DataFrame indexing syntax (e.g., using .iloc[0] instead of [0]).

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[gemini-code-assist]

Code Review

This pull request updates the handling of datetime units in pandas DataFrames from nanoseconds to milliseconds across the codebase, including the _pandas_helpers.py utility functions and various unit tests. Additionally, it includes numerous dependency updates in poetry.lock, notably upgrading anyio, charset-normalizer, identify, importlib-metadata, jaraco-context, nh3, protobuf, pyjwt, python-discovery, and types-requests. The dependency management for pandas has been refined to support different versions based on the Python environment, with a new entry for pandas 3.0.1 for Python >=3.11 and adjusted markers for pandas and pytz for Python 3.10. There are no review comments to address.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 93.44%. Comparing base (b201b22) to head (b6f0dd5).
⚠️ Report is 6 commits behind head on pysdk-release-v8.

Additional details and impacted files

@@                 Coverage Diff                  @@
##           pysdk-release-v8    #2528      +/-   ##
====================================================
- Coverage             93.44%   93.44%   -0.01%     
====================================================
  Files                   478      478              
  Lines                 48173    48218      +45     
====================================================
+ Hits                  45016    45058      +42     
- Misses                 3157     3160       +3     

Files with missing lines	Coverage Δ
cognite/client/utils/_pandas_helpers.py	87.65% <100.00%> (ø)
...tegration/test_api/test_datapoint_subscriptions.py	86.93% <100.00%> (ø)
...ests/tests_integration/test_api/test_datapoints.py	99.30% <100.00%> (+<0.01%)	⬆️
tests/tests_unit/test_api/test_datapoints.py	100.00% <100.00%> (ø)
tests/tests_unit/test_api/test_raw.py	100.00% <ø> (ø)
tests/tests_unit/test_api/test_sequences.py	100.00% <100.00%> (ø)
tests/tests_unit/test_api/test_time_series.py	100.00% <100.00%> (ø)
tests/tests_unit/test_base.py	98.93% <ø> (ø)
...a_classes/test_data_models/test_typed_instances.py	99.37% <ø> (ø)
...ts/tests_unit/test_data_classes/test_datapoints.py	100.00% <ø> (ø)
... and 1 more

... and 16 files with indirect coverage changes

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[MortGron]

I already made a PR for this: #2485
I will just quote from the description of that PR:

Currently when using to_pandas on list like objects a timestamp resolution of 'ns' is enforced for both Pandas v2 and v3. For single Cognite resources that have only timestamp attributes, resolution will be 'ns' in Pandas v2 and 'ms' in v3. For single Cognite resources that have at least on timestamp attribute and at least one non-timestamp attribute, time resolution will be 'ms' in both v2 and v3.

Are all these cases handled here?