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An interesting feature of NumPy arrays is that we can perform the same mathematical operation on every element with a single command.

Note:Both exponential and logarithmic operations are supported.

import numpy as np arr = np.array([[5, 10], [15, 20]]) # Add 10 to element values print("Adding 10: " + repr(arr + 10)) # Multiple elements by 5 print("Multiplying by 5: " + repr(arr * 5)) # Subtract 5 from elements print("Subtracting 5: " + repr(arr - 5)) # Matrix multiplication arr1 = np.array([[-8, 7], [17, 20], [8, -16], [11, 4]]) arr2 = np.array([[5, -5, 10, 20], [-8, 0, 13, 2]]) print("Multiplying two arrays: " + repr(np.matmul(arr1, arr2))) # Exponential arr3 = np.array([[1, 5], [2.5, 2]]) # Exponential of each element print("Taking the exponential: " + repr(np.exp(arr3))) # Cubing all elements print("Making each element a power of 3: " + repr(np.power(3, arr3)))

Since the goal is to produce something useful out of a dataset, NumPy offers several statistical tools such as `min`

, `max`

, `median`

, `mean`

and `sum`

.

import numpy as np arr = np.array([[18, 5, -25], [-10, 30, 7], [8, 16, -2]]) print "Min: ", arr.min() print "Max: ", arr.max() print "Sum: ", np.sum(arr) print "Mean: ", np.mean(arr) print "Median: ", np.median(arr) print "Variance: ", np.var(arr)