Repository: pandas-dev/pandas
File: pandas/core/generic.py (Python)

Code before:
                    # test_fillna_nonscalar
                    if inplace:
                        return None
                    return self.copy(deep=False)
                from pandas import Series

                value = Series(value)
                value = value.reindex(self.index)
                value = value._values
            elif not is_list_like(value):
                pass
            else:
                raise TypeError(
                    '"value" parameter must be a scalar, dict '
                    "or Series, but you passed a "
                    f'"{type(value).__name__}"'
                )

            new_data = self._mgr.fillna(value=value, limit=limit, inplace=inplace)

        elif isinstance(value, (dict, ABCSeries)):
            result = self if inplace else self.copy(deep=False)
            if axis == 1:
                # Check that all columns in result have the same dtype
                # otherwise don't bother with fillna and losing accurate dtypes
                unique_dtypes = self.dtypes.unique()
                if len(unique_dtypes) > 1:
                    raise ValueError(
                        "All columns must have the same dtype, but got dtypes: "
                        f"{list(unique_dtypes)}"
                    )
                # Use the first column, which we have already validated has the
                # same dtypes as the other columns.
                if not can_hold_element(result.iloc[:, 0], value):
                    value_dtype = np.asarray(value).dtype
                    frame_dtype = unique_dtypes.item()
                    raise ValueError(
                        "Dtype mismatch for value "
                        f"(value.dtype={value_dtype} vs {frame_dtype})"
                    )
                result = result.T.fillna(value=value).T
            else:
                for k, v in value.items():
                    if k not in result:
                        continue

                    res_k = result[k].fillna(v, limit=limit)

                    if not inplace:
                        result[k] = res_k
                    else:

Review comment (suggestion):
```suggestion
                unique_dtypes = unique(self._mgr.get_dtypes())
```

You'll also need to import unique from `pandas.core.algorithms`. For context, `self._mgr.get_dtypes()` can potentially reduce the amount of redundant types

Code after:
                    # test_fillna_nonscalar
                    if inplace:
                        return None
                    return self.copy(deep=False)
                from pandas import Series

                value = Series(value)
                value = value.reindex(self.index)
                value = value._values
            elif not is_list_like(value):
                pass
            else:
                raise TypeError(
                    '"value" parameter must be a scalar, dict '
                    "or Series, but you passed a "
                    f'"{type(value).__name__}"'
                )

            new_data = self._mgr.fillna(value=value, limit=limit, inplace=inplace)

        elif isinstance(value, (dict, ABCSeries)):
            result = self if inplace else self.copy(deep=False)
            if axis == 1:
                # Check that all columns in result have the same dtype
                # otherwise don't bother with fillna and losing accurate dtypes
                unique_dtypes = algos.unique(self._mgr.get_dtypes())
                if len(unique_dtypes) > 1:
                    raise ValueError(
                        "All columns must have the same dtype, but got dtypes: "
                        f"{list(unique_dtypes)}"
                    )
                # Use the first column, which we have already validated has the
                # same dtypes as the other columns.
                if not can_hold_element(result.iloc[:, 0], value):
                    frame_dtype = unique_dtypes.item()
                    raise ValueError(
                        f"{value} not a suitable type to fill into {frame_dtype}"
                    )
                result = result.T.fillna(value=value).T
            else:
                for k, v in value.items():
                    if k not in result:
                        continue

                    res_k = result[k].fillna(v, limit=limit)

                    if not inplace:
                        result[k] = res_k
                    else:
                        # We can write into our existing column(s) iff dtype
                        #  was preserved.