NumPy Mastery 150 Practical Examples in Python: A Comprehensive Guide to Mastering NumPy for Data Science, Machine Learning, and Scientific Computing by khonprakhon suchat

Author:khonprakhon, suchat
Language: eng
Format: epub, pdf
Published: 2023-09-11T00:00:00+00:00

97. Calculate the mean absolute error (MAE) between two arrays:

mae = np.mean(np.abs(predicted_values - actual_values))

98. Calculate the root mean squared error (RMSE) between two arrays:

rmse = np.sqrt(np.mean((predicted_values - actual_values) ** 2))

99. Perform a matrix-vector multiplication:

result_vector = np.matmul(matrix, vector)

100. Calculate the 1D discrete Fourier Transform (DFT):

dft = np.fft.fft(arr)

101. Create a 3x3 matrix with random integers between 0 and 9:

random_matrix = np.random.randint(0, 10, (3, 3))

print(random_matrix)

102. Reshape a 1D array into a 2D array with automatic calculation of dimensions:

arr = np.array([1, 2, 3, 4, 5, 6])

reshaped_arr = arr.reshape(-1, 2)

print(reshaped_arr)

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