TST: fix remaining tests

[hagenw]

• Ensures audformat.core.index.to_timedelta() returns timedelta64[ns] format
• Simplify code of audformat.segmented_index()
• Fix audformat.testing.add_table() to preserve ns resolution when file_duration is given as a string
• Update tests/test_misc_table.py object/string handling
• Update tests/test_table.py timedelta handling
• Update tests/test_utils.py object/string handling
• Update tests/test_utils_concat.py object/string handling

[sourcery-ai]

Reviewer's Guide

Aligns index/timedelta handling and tests with pandas>=3.0 behavior by enforcing explicit index dtypes, normalizing timedelta units to ns, and updating misc/table utilities and tests to reflect the new typing semantics.

File-Level Changes

Change	Details	Files
Normalize timedelta handling to nanosecond precision and enforce string-typed file indices in segmented indexes and test helpers.	Change to_timedelta() helper to always return Timedelta values with ns resolution via .as_unit('ns') for both numeric and string inputs. Update segmented_index() to construct a MultiIndex with a string-typed file level and timedelta levels created directly from to_timedelta(), removing the previous set_index_dtypes() post-processing. Use the shared to_timedelta() helper in audformat.core.testing.add_table() when converting string file_duration values.	audformat/core/index.py audformat/core/testing.py tests/test_table.py
Adjust tests to use explicit Index dtypes (string/object/int/Int64/timedelta64[ns]) and align expectations with pandas 3.x index and column typing behavior.	Replace raw array inputs in MultiIndex.from_arrays() and Index() constructors with explicit pd.Index(...) instances carrying the desired dtype for object, string, int, and nullable Int64 levels. Update tests that rely on Timedelta creation and comparison to work with ns resolution, including explicit TimedeltaIndex dtype declarations and as_unit('ns') usage in expectations. Modify CSV reading and misc table/index tests to account for pandas>=3.0 behavior, including normalizing empty DataFrame column dtypes to string and distinguishing object vs string index dtypes in expectations. Simplify or adjust a few type mapping test cases (e.g., removing redundant object mapping, changing expected source dtype from None to 'object') to reflect the updated type inference.	tests/test_utils.py tests/test_misc_table.py tests/test_utils_concat.py tests/test_table.py

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[sourcery-ai]

Hey - I've found 1 issue, and left some high level feedback:

• In audformat/core/testing.py::add_table, to_timedelta is now referenced directly but is not imported into the module; either import it (e.g. from audformat.core.index) or fully qualify the call to avoid a NameError.
• The tests now mix dtype="string", dtype="str", and dtype="object" for similar string-like indices/series; consider standardizing on one representation where possible to avoid subtle differences in behavior across pandas versions.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- In `audformat/core/testing.py::add_table`, `to_timedelta` is now referenced directly but is not imported into the module; either import it (e.g. from `audformat.core.index`) or fully qualify the call to avoid a `NameError`.
- The tests now mix `dtype="string"`, `dtype="str"`, and `dtype="object"` for similar string-like indices/series; consider standardizing on one representation where possible to avoid subtle differences in behavior across pandas versions.

## Individual Comments

### Comment 1
<location> `audformat/core/index.py:29-31` </location>
<code_context>
     r"""Convert time value to pd.Timedelta."""
     try:
-        return pd.to_timedelta(times, unit="s")
+        return pd.to_timedelta(times, unit="s").as_unit("ns")
     except ValueError:  # catches values like '1s'
-        return pd.to_timedelta(times)
+        return pd.to_timedelta(times).as_unit("ns")


</code_context>

<issue_to_address>
**issue (bug_risk):** Using `.as_unit("ns")` assumes the return value always supports this method, which can break for Series inputs.

`pd.to_timedelta` may return a `Series`, `TimedeltaIndex`, scalar `Timedelta`, or ndarray. Only some of these support `.as_unit`, so passing a `Series` (e.g. a `pd.Series` of time strings) will now raise `AttributeError` where it previously worked.

To keep behavior consistent, consider normalizing via dtype instead, e.g. `result = pd.to_timedelta(...); result = result.astype('timedelta64[ns]')` for array-likes, and only calling `.as_unit('ns')` on scalar `Timedelta` values (or branching on `isinstance(result, pd.Timedelta)` / `Index` / `Series`).
</issue_to_address>

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 100.0%. Comparing base (639d29d) to head (7921cde).
⚠️ Report is 1 commits behind head on dev.

Additional details and impacted files

Files with missing lines	Coverage Δ
audformat/core/index.py	100.0% <100.0%> (ø)
audformat/core/testing.py	100.0% <100.0%> (ø)

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