Using Scikit-Learn OneHotEncoder with a Pandas DataFrame

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

So turned out that Scikit-Learns LabelBinarizer gave me better luck in converting the data to one-hot encoded format, with help from Amnie's solution, my final code is as follows

import pandas as pd
from sklearn.preprocessing import LabelBinarizer

jobs_encoder = LabelBinarizer()
jobs_encoder.fit(data['Profession'])
transformed = jobs_encoder.transform(data['Profession'])
ohe_df = pd.DataFrame(transformed)
data = pd.concat([data, ohe_df], axis=1).drop(['Profession'], axis=1)