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Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
I would like to break down a pandas column consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence).
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Python Code:
import pandas as pd
import numpy as np
import sklearn
df = load_data()
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
df_out = df.join(
    pd.DataFrame(
        mlb.fit_transform(df.pop('Col3')),
        index=df.index,
        columns=mlb.classes_))
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