import numpy as np
"""
This script demonstrates three different methods to flatten a NumPy array:
1. `flatten()`: Returns a copy of the array collapsed into one dimension.
2. `reshape(-1)`: Reshapes the array into a one-dimensional array.
3. `ravel()`: Returns a flattened array; returns a view whenever possible.
The script uses a simulated exam scores array for three students across three subjects.
It prints the results of each flattening method and shows that modifying the copy returned by `flatten()` does not affect the original array.
"""
# Simulated exam scores for three students in three subjects
exam_scores = np.array([[75, 82, 90], [92, 88, 78], [60, 70, 85]])
# Use the flatten() method for flattening
flattened_exam_scores = exam_scores.flatten()
print(flattened_exam_scores)

# Use the reshape() method for flattening
exam_scores_reshaped = exam_scores.reshape(-1)
print(exam_scores_reshaped)

# Use the ravel() method for flattening
exam_scores_raveled = exam_scores.ravel()
print(exam_scores_raveled)  

# Set the first element of the flattened copy to 100
flattened_exam_scores[0] = 100
print(flattened_exam_scores)
print(exam_scores)  # Original array remains unchanged