Risks

• astype(object) casts every column to object dtype. Most call sites feed straight into to_dict('records') or a tab-file write, but any downstream action that does something numerical on the returned DataFrame will see a dtype change.
• Two chained-assignment sites in convert_2_magic.py (lines 3602, 3718) were silently a no-op on pandas 3.x; the fix makes them actually replace NaN values, matching pandas 2.x behavior.

Review testing

• Run the full test suite on both pandas 2.x and 3.x
• Spot-check some lab-instrument converters end-to-end against the example data in data_files/ and diff the output against what the same call produces on pandas 2.x.
• Run example notebooks (PmagPy_calculations.ipynb, PmagPy_MagIC.ipynb) on pandas 3.x.

Test results

Tested by running MagIC_contribution.ipynb (which exercises ipmag.upload_magic, ipmag.combine_magic, and several magic_write calls — i.e. the main fillna call sites this PR touches) end-to-end in two fresh venvs:

Confirms the PR resolves the pandas-3.x regression while remaining compatible with pandas 2.x.

Spot-check: convert_2_magic with example data

Ran four converters touched by the PR end-to-end with each pandas version, using data_files/:

• convert._2g_bin ← data_files/convert_2_magic/2g_bin_magic/mn1/mn001-1a.dat
• convert._2g_asc ← data_files/convert_2_magic/2g_asc_magic/_2g_asc/DR3B.asc
• convert.iodp_srm_lore ← data_files/iodp_magic/U999A/SRM_archive_data/srmsection_17_5_2019.csv
• convert.iodp_dscr_lore ← data_files/iodp_magic/U999A/SRM_discrete_data/srmdiscrete_17_5_2019.csv

All four ran cleanly and produced identical record counts under both pandas 2.3.3 and pandas 3.0.2.

diff -r across the two output trees shows a single line difference: in _2g_bin/measurements.txt, an integer-valued temperature column stringifies as 273.0 under pandas 2.x and 273 under pandas 3.x. Functionally equivalent (both round-trip to 273 K) but driven by a behavior change in astype(object) for integer-valued floats. All other output files (specimens, samples, sites, locations, measurements across the four converters) are identical between the two pandas versions.