One hot Encoding with nominal categorical features in Python?
DATA MUNGING

One hot Encoding with nominal categorical features in Python?

One hot Encoding with nominal categorical features in Python?

One hot Encoding with nominal categorical features in Python

3
This python source code does the following: 1. Converts categorical into numerical types. 2. Loads the important libraries and Implements label binarizer. 3. Creates your own numpy feature matrix.
In [1]:
## One hot Encoding with nominal categorical features in Python 
def Kickstarter_Example_37():
    print()
    print(format('How to One hot Encode with nominal categorical features in Python', '*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # Load libraries
    import numpy as np
    from sklearn.preprocessing import LabelBinarizer

    # Create Data With One Class Label
    # Create NumPy array
    x = np.array([['Texas'],
                  ['California'],
                  ['Texas'],
                  ['Delaware'],
                  ['Texas']])

    # One-hot Encode Data (Method 1)

    # Create LabelBinzarizer object
    one_hot = LabelBinarizer()

    # One-hot encode data
    print(); print(one_hot.fit_transform(x))

    # View Column Headers
    # View classes
    print(); print(one_hot.classes_)

Kickstarter_Example_37()
********How to One hot Encode with nominal categorical features in Python*********

[[0 0 1]
 [1 0 0]
 [0 0 1]
 [0 1 0]
 [0 0 1]]

['California' 'Delaware' 'Texas']

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