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Close #229 #233

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Close #229
PaulJonasJost Mar 4, 2026
50fdf42
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PaulJonasJost Mar 4, 2026
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57 changes: 55 additions & 2 deletions src/petab_gui/commands.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,38 @@ def redo(self):
if np.any(dtypes != df.dtypes):
for col, dtype in dtypes.items():
if dtype != df.dtypes[col]:
df[col] = df[col].astype(dtype)
is_pandas_nullable_int = isinstance(
dtype,
(
pd.Int64Dtype,
pd.Int32Dtype,
pd.Int16Dtype,
pd.Int8Dtype,
),
)

if is_pandas_nullable_int:
# Keep pandas nullable integer types as is
df[col] = df[col].astype(dtype)
# If column has NaN and dtype is integer, use nullable Int type
elif (
np.issubdtype(dtype, np.integer)
and df[col].isna().any()
):
# Convert numpy int types to pandas nullable Int types
if dtype == np.int64:
df[col] = df[col].astype("Int64")
elif dtype == np.int32:
df[col] = df[col].astype("Int32")
elif dtype == np.int16:
df[col] = df[col].astype("Int16")
elif dtype == np.int8:
df[col] = df[col].astype("Int8")
else:
# Fallback for other integer types
df[col] = df[col].astype("Int64")
else:
df[col] = df[col].astype(dtype)
self.model.endInsertRows()
else:
self.model.beginRemoveRows(
Expand Down Expand Up @@ -261,8 +292,30 @@ def _apply_changes(self, use_new: bool):
for col, dtype in original_dtypes.items():
if col not in update_df.columns:
continue
if np.issubdtype(dtype, np.number):
# Check if it's a pandas extension dtype (like Int64)
is_pandas_nullable_int = isinstance(
dtype,
(pd.Int64Dtype, pd.Int32Dtype, pd.Int16Dtype, pd.Int8Dtype),
)

if is_pandas_nullable_int:
# Keep pandas nullable integer types as is
df[col] = pd.to_numeric(df[col], errors="coerce")
df[col] = df[col].astype(dtype)
elif np.issubdtype(dtype, np.number):
df[col] = pd.to_numeric(df[col], errors="coerce")
# If original dtype was integer and column has NaN, use nullable Int type
if np.issubdtype(dtype, np.integer) and df[col].isna().any():
if dtype == np.int64:
df[col] = df[col].astype("Int64")
elif dtype == np.int32:
df[col] = df[col].astype("Int32")
elif dtype == np.int16:
df[col] = df[col].astype("Int16")
elif dtype == np.int8:
df[col] = df[col].astype("Int8")
else:
df[col] = df[col].astype("Int64")
else:
df[col] = df[col].astype(dtype)

Expand Down