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BUG: fillna with DataFrame input should preserve dtype when possible #61742

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BUG: fillna with DataFrame input should preserve dtype when possible #61742

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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v3.0.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -748,7 +748,7 @@ Indexing
Missing
^^^^^^^
- Bug in :meth:`DataFrame.fillna` and :meth:`Series.fillna` that would ignore the ``limit`` argument on :class:`.ExtensionArray` dtypes (:issue:`58001`)
-
- Bug in :meth:`DataFrame.fillna` where filling from another ``DataFrame`` of the same dtype could incorrectly cast the result to ``object`` dtype. (:issue:`61568`)

MultiIndex
^^^^^^^^^^
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19 changes: 18 additions & 1 deletion pandas/core/generic.py
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Expand Up @@ -7145,7 +7145,24 @@ def fillna(
else:
new_data = self._mgr.fillna(value=value, limit=limit, inplace=inplace)
elif isinstance(value, ABCDataFrame) and self.ndim == 2:
new_data = self.where(self.notna(), value)._mgr
filled_columns = {}
for col in self.columns:
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doing this column-by-column is going to mean a performance hit for non-object cases. i suspect we need to do this at the Block level in order to avoid that

lhs = self[col]
if col in value.columns:
rhs = value[col]
filled = lhs.where(notna(lhs), rhs)

# restore original dtype if fallback to object occurred
if lhs.dtype == rhs.dtype and filled.dtype == object:
try:
filled = filled.astype(lhs.dtype)
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id expect this to be handled by Series.where. is it not?

except Exception:
pass
else:
filled = lhs
filled_columns[col] = filled

new_data = type(self)(filled_columns, index=self.index)._mgr
else:
raise ValueError(f"invalid fill value with a {type(value)}")

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19 changes: 19 additions & 0 deletions pandas/tests/frame/methods/test_fillna.py
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Expand Up @@ -795,3 +795,22 @@ def test_fillna_out_of_bounds_datetime():
msg = "Cannot cast 0001-01-01 00:00:00 to unit='ns' without overflow"
with pytest.raises(OutOfBoundsDatetime, match=msg):
df.fillna(Timestamp("0001-01-01"))


def test_fillna_dataframe_preserves_dtypes_mixed_columns():
# GH#61568
empty = DataFrame([[None] * 4] * 4, columns=list("ABCD"), dtype=np.float64)
full = DataFrame(
[
[1.0, 2.0, "3.0", 4.0],
[5.0, 6.0, "7.0", 8.0],
[9.0, 10.0, "11.0", 12.0],
[13.0, 14.0, "15.0", 16.0],
],
columns=list("ABCD"),
)
result = empty.fillna(full)
expected_dtypes = Series(
{"A": "float64", "B": "float64", "C": "object", "D": "float64"}
)
tm.assert_series_equal(result.dtypes, expected_dtypes)
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