NumPy arrays are stored in contiguous blocks of memory, which allows NumPy to take advantage of vectorization and other optimization techniques. Python lists are stored as individual objects in memory, which makes them less efficient and performant than NumPy arrays for numerical data.
import numpy as np # create a NumPy array of integers from 1 to 10 arr = np.arange(1, 11) # compute the square of each element using vectorized operations squares = arr**2 # print the squares print(squares)
Use the np.arange() function to create a NumPy array of integers from 1 to 10. We then use vectorized operations provided by NumPy to compute the square of each element in the array. This is more efficient and performant than using a Python list and a for loop, or a Python list comprehension to compute the squares.
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