Use pandas type checking for numeric dtype detection in Altair backend #2917
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Replace np.issubdtype() with pandas.api.types.is_numeric_dtype() to fix compatibility with pandas 3.0's new StringDtype. NumPy's np.issubdtype() cannot handle pandas ExtensionDtype objects, particularly the new StringDtype introduced as default in pandas 3.0. This caused TypeError when checking if color columns were numeric: "Cannot interpret '<StringDtype(storage='python', na_value=nan)>' as a data type" Using pandas' is_numeric_dtype() provides several benefits: - Correctly handles all pandas dtypes (native NumPy and extension types) - Works with numeric extension dtypes (Int64, Float64, Decimal, etc.) - Compatible with both pandas 2.x and 3.x - More semantically clear - checks if data is numeric rather than checking dtype inheritance hierarchy
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The CI is still failing on another, unrelated error: I will try to address that in another PR. |
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Replace
np.issubdtype()withpandas.api.types.is_numeric_dtype()to fix compatibility with pandas 3.0's newStringDtype.NumPy's
np.issubdtype()cannot handle pandasExtensionDtypeobjects, particularly the newStringDtypeintroduced as default in pandas 3.0. This caused TypeError when checking if color columns were numeric:Using pandas'
is_numeric_dtype()provides several benefits: