Comparing the runtime

Let's compare the runtime behavior of NumPy to normal Python lists. In the following code, we will calculate the sum of all squared numbers from 1 to 1,000 and see how much time it will take. We will perform it 10,000 times and report the total time so that our measurement is accurate enough:

import timeit

normal_py_sec = timeit.timeit('sum(x*x for x in range(1000))',
                              number=10000)
naive_np_sec = timeit.timeit('sum(na*na)',
                             setup="import numpy as np; na=np.arange(1000)", 
                             number=10000)
good_np_sec = timeit.timeit('na.dot(na)',
                            setup="import numpy as np; na=np.arange(1000)", 
                            number=10000)

print("Normal Python: %f sec" % normal_py_sec) 
print("Naive NumPy: %f sec" % naive_np_sec) 
print("Good NumPy: %f sec" % good_np_sec) 

Executing this will output

Normal Python: 1.571072 sec
Naive NumPy: 1.621358 sec 
Good NumPy: 0.035686 sec

We can make two interesting observations from this code. Firstly, just using NumPy as data storage (naive NumPy) takes longer, which is surprising since it seems it should be much faster, as it is written as a C extension. One reason for this increased processing time is that the access of individual elements from Python itself is rather costly. Only when we are able to apply algorithms inside the optimized extension code do we get speed improvements. The other observation is quite a tremendous one: using the dot() function of NumPy, which does exactly the same, allows us to be more than 44 times faster. In summary, in every algorithm we are about to implement, we should always look at how we can move loops over individual elements from Python to some of the highly optimized NumPy or SciPy extension functions.

However, this speed comes at a price. Using NumPy arrays, we no longer have the incredible flexibility of Python lists, which can hold basically anything. NumPy arrays always have only one data type:

>>> a = np.array([1,2,3])
>>> a.dtype 
dtype('int32')

If we try to use elements of different types, such as the ones shown in the following code, NumPy will do its best to correct them to be the most reasonable common data type:

>>> np.array([1, "stringy"]) 
array(['1', 'stringy'], dtype='<U11')
>>> np.array([1, "stringy", {1, 2, 3}])
array([1, 'stringy', {1, 2, 3}], dtype=object)