OneHotEncoder Encodes categorical integer features as a one-hot numeric array. Its Transform method returns a sparse matrix if sparse=True, otherwise it returns a 2-d array.
You can’t cast a 2-d array (or sparse matrix) into a Pandas Series. You must create a Pandas Serie (a column in a Pandas dataFrame) for each category.
I would recommend pandas.get_dummies instead:
data = pd.get_dummies(data,prefix=['Profession'], columns = ['Profession'], drop_first=True)
EDIT:
Using Sklearn OneHotEncoder:
transformed = jobs_encoder.transform(data['Profession'].to_numpy().reshape(-1, 1))
#Create a Pandas DataFrame of the hot encoded column
ohe_df = pd.DataFrame(transformed, columns=jobs_encoder.get_feature_names())
#concat with original data
data = pd.concat([data, ohe_df], axis=1).drop(['Profession'], axis=1)
Other Options: If you are doing hyperparameter tuning with GridSearch it’s recommanded to use ColumnTransformer and FeatureUnion with Pipeline or directly make_column_transformer