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

Approach 1: You can use pandas' pd.get_dummies.

Example 1:

import pandas as pd
s = pd.Series(list('abca'))
pd.get_dummies(s)
Out[]: 
     a    b    c
0  1.0  0.0  0.0
1  0.0  1.0  0.0
2  0.0  0.0  1.0
3  1.0  0.0  0.0

Example 2:

The following will transform a given column into one hot. Use prefix to have multiple dummies.

import pandas as pd
        
df = pd.DataFrame({
          'A':['a','b','a'],
          'B':['b','a','c']
        })
df
Out[]: 
   A  B
0  a  b
1  b  a
2  a  c

# Get one hot encoding of columns B
one_hot = pd.get_dummies(df['B'])
# Drop column B as it is now encoded
df = df.drop('B',axis = 1)
# Join the encoded df
df = df.join(one_hot)
df  
Out[]: 
       A  a  b  c
    0  a  0  1  0
    1  b  1  0  0
    2  a  0  0  1

Approach 2: Use Scikit-learn

Using a OneHotEncoder has the advantage of being able to fit on some training data and then transform on some other data using the same instance. We also have handle_unknown to further control what the encoder does with unseen data.

Given a dataset with three features and four samples, we let the encoder find the maximum value per feature and transform the data to a binary one-hot encoding.

>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder()
>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])   
OneHotEncoder(categorical_features='all', dtype=<class 'numpy.float64'>,
   handle_unknown='error', n_values='auto', sparse=True)
>>> enc.n_values_
array([2, 3, 4])
>>> enc.feature_indices_
array([0, 2, 5, 9], dtype=int32)
>>> enc.transform([[0, 1, 1]]).toarray()
array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.]])

Here is the link for this example: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html