A simple application

Imagine that we want to add two vectors called a and b. The word vector is used here in the mathematical sense, which means a one-dimensional array. We will learn in Chapter 3, Statistics and Linear Algebra, about specialized NumPy arrays that represent matrices. The vector a holds the squares of integers 0 to n; for instance, if n is equal to 3, a contains 0, 1, or 4. The vector b holds the cubes of integers 0 to n, so if n is equal to 3, then the vector b is equal to 0, 1, or 8. How would you do that using plain Python? After we come up with a solution, we will compare it with the NumPy equivalent.

The following function solves the vector addition problem using pure Python without NumPy:

def pythonsum(n):
   a = range(n)
   b = range(n)
   c = []

   for i in range(len(a)):
       a[i] = i ** 2
       b[i] = i ** 3
       c.append(a[i] + b[i])

   return c

The following is a function that solves the vector addition problem with NumPy:

def numpysum(n):
  a = numpy.arange(n) ** 2
  b = numpy.arange(n) ** 3
  c = a + b
  return c

Notice that numpysum() does not need a for loop. Also, we used the arange() function from NumPy, which creates a NumPy array for us with integers from 0 to n. The arange() function was imported; that is why it is prefixed with numpy.

Now comes the fun part. Remember that it was mentioned in the Preface that NumPy is faster when it comes to array operations. How much faster is Numpy, though? The following program will show us by measuring the elapsed time in microseconds for the numpysum() and pythonsum() functions. It also prints the last two elements of the vector sum. Let's check that we get the same answers using Python and NumPy:

#!/usr/bin/env/python

import sys
from datetime import datetime
import numpy as np

"""
 This program demonstrates vector addition the Python way.
 Run from the command line as follows

  python vectorsum.py n

 where n is an integer that specifies the size of the vectors.

 The first vector to be added contains the squares of 0 up to n.
 The second vector contains the cubes of 0 up to n.
 The program prints the last 2 elements of the sum and the elapsed time.
"""

def numpysum(n):
   a = np.arange(n) ** 2
   b = np.arange(n) ** 3
   c = a + b

   return c

def pythonsum(n):
   a = range(n)
   b = range(n)
   c = []

   for i in range(len(a)):
       a[i] = i ** 2
       b[i] = i ** 3
       c.append(a[i] + b[i])

   return c

size = int(sys.argv[1])

start = datetime.now()
c = pythonsum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "PythonSum elapsed time in microseconds", delta.microseconds

start = datetime.now()
c = numpysum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "NumPySum elapsed time in microseconds", delta.microseconds

The output of the program for 1000, 2000, and 3000 vector elements is as follows:

$ python vectorsum.py 1000
The last 2 elements of the sum [995007996, 998001000]
PythonSum elapsed time in microseconds 707
The last 2 elements of the sum [995007996 998001000]
NumPySum elapsed time in microseconds 171

$ python vectorsum.py 2000
The last 2 elements of the sum [7980015996, 7992002000]
PythonSum elapsed time in microseconds 1420
The last 2 elements of the sum [7980015996 7992002000]
NumPySum elapsed time in microseconds 168

$ python vectorsum.py 4000
The last 2 elements of the sum [63920031996, 63968004000]
PythonSum elapsed time in microseconds 2829
The last 2 elements of the sum [63920031996 63968004000]
NumPySum elapsed time in microseconds 274

Clearly, NumPy is much faster than the equivalent normal Python code. One thing is certain; we get the same results whether we are using NumPy or not. However, the result that is printed differs in representation. Notice that the result from the numpysum() function does not have any commas. How come? Obviously, we are not dealing with a Python list but with a NumPy array. We will learn more about NumPy arrays in the next chapter, Chapter 2, NumPy Arrays.