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

# Create NumPy array
x = np.array([['Texas'], 
              ['California'], 
              ['Texas'], 
              ['Delaware'], 
              ['Texas']])

# Create LabelBinzarizer object
one_hot = LabelBinarizer()

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

array([[0, 0, 1],
       [1, 0, 0],
       [0, 0, 1],
       [0, 1, 0],
       [0, 0, 1]])

# View classes
one_hot.classes_

array(['California', 'Delaware', 'Texas'],
      dtype='<U10')

# Dummy feature
pd.get_dummies(x[:,0])