Statistics, Machine Learning, Data Science, Financial Planning, Python.
matplotlib.pyplot as plt # Sample data for demonstration np.random.seed(0) data = { 'y': np.random.randint(50, 100, 30), 'factor1': ['A', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'C'], 'factor2':