How to convert Categorical features to Numerical Features in Python?

How to convert Categorical features to Numerical Features in Python?

This recipe helps you convert Categorical features to Numerical Features in Python
In [1]:
## How to convert Categorical features to Numerical Features in Python 
def Kickstarter_Example_26():
    print()
    print(format('How to convert Categorical features to Numerical Features in Python',
                 '*^82'))
    import warnings
    warnings.filterwarnings("ignore")

    # Load libraries
    from sklearn import preprocessing
    import pandas as pd

    #Create DataFrame
    raw_data = {'patient': [1, 1, 1, 2, 2],
                'obs': [1, 2, 3, 1, 2],
                'treatment': [0, 1, 0, 1, 0],
                'score': ['strong', 'weak', 'normal', 'weak', 'strong']}
    df = pd.DataFrame(raw_data, columns = ['patient', 'obs', 'treatment', 'score'])

    # Fit The Label Encoder
    # Create a label (category) encoder object
    le = preprocessing.LabelEncoder()

    # Fit the encoder to the pandas column
    le.fit(df['score'])

    # View The Labels
    print(); print(list(le.classes_))

    # Transform Categories Into Integers
    # Apply the fitted encoder to the pandas column
    print(); print(le.transform(df['score']))

    # Transform Integers Into Categories
    # Convert some integers into their category names
    print(); print(list(le.inverse_transform([2, 2, 1, 0, 1, 2])))

Kickstarter_Example_26()
*******How to convert Categorical features to Numerical Features in Python********

['normal', 'strong', 'weak']

[1 2 0 2 1]

['weak', 'weak', 'strong', 'normal', 'strong', 'weak']


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