Dashing Up Your Python Code with NumPy

NumPy is a Python bundle typically used for mathematical and statistical purposes. Nevertheless, some nonetheless didn’t know NumPy may assist velocity up our Python code execution.

There are a number of explanation why NumPy may speed up the Python code execution, together with:

• NumPy utilizing C Code as a substitute of Python throughout looping
• The higher CPU caching course of
• Environment friendly algorithms in mathematical operations
• Ready to make use of parallel operations
• Reminiscence-efficient in giant datasets and complicated computations

For a lot of causes, NumPy is efficient in bettering Python code execution. This tutorial will present examples of how NumPy accelerates the code course of. Let’s bounce into it.

NumPy in Speed up Python Code Execution

 
The primary instance compares Python checklist and NumPy array numerical operations, which purchase the article with the meant worth outcome.

For instance, we would like a listing of numbers from two lists we add collectively so we carry out the vectorized operation. We will attempt the experiment with the next code:

import numpy as np
import time

pattern = 1000000

list_1 = vary(pattern)
list_2 = vary(pattern)
start_time = time.time()
outcome = [(x + y) for x, y in zip(list_1, list_2)]
print("Time taken using Python lists:", time.time() - start_time)

array_1 = np.arange(pattern)
array_2 = np.arange(pattern)
start_time = time.time()
outcome = array_1 + array_2
print("Time taken using NumPy arrays:", time.time() - start_time)

Output>>
Time taken utilizing Python lists: 0.18960118293762207
Time taken utilizing NumPy arrays: 0.02495265007019043

As you may see within the above output, the execution of NumPy arrays is quicker than that of the Python checklist in buying the identical outcome.

All through the instance, you’ll see that the NumPy execution is quicker. Let’s see if we wish to carry out aggregation statistical evaluation.

array = np.arange(1000000)

start_time = time.time()
sum_rst = np.sum(array)
mean_rst = np.imply(array)
print("Time taken for aggregation functions:", time.time() - start_time)

Output>> 
Time taken for aggregation capabilities: 0.0029935836791992188

NumPy can course of the aggregation perform fairly quick. If we evaluate it with the Python execution, we will see the execution time variations.

list_1 = checklist(vary(1000000))

start_time = time.time()
sum_rst = sum(list_1)
mean_rst = sum(list_1) / len(list_1)
print("Time taken for aggregation functions (Python):", time.time() - start_time)

Output>>
Time taken for aggregation capabilities (Python): 0.09979510307312012

With the identical outcome, Python’s in-built perform would take far more time than NumPy. If we had a a lot larger dataset, Python would take means longer to complete the NumPy.

One other instance is after we attempt to carry out in-place operations, we will see that the NumPy could be a lot sooner than the Python instance.

array = np.arange(1000000)
start_time = time.time()
array += 1
print("Time taken for in-place operation:", time.time() - start_time)

list_1 = checklist(vary(1000000))
start_time = time.time()
for i in vary(len(list_1)):
    list_1[i] += 1
print("Time taken for in-place list operation:", time.time() - start_time)

Output>>
Time taken for in-place operation: 0.0010089874267578125
Time taken for in-place checklist operation: 0.1937870979309082

The purpose of the instance is that when you’ve got an choice to carry out with NumPy, then it’s a lot better as the method could be a lot sooner.

We will attempt a extra complicated implementation, utilizing matrix multiplication to see how briskly NumPy is in comparison with Python.

def python_matrix_multiply(A, B):
    outcome = [[0 for _ in range(len(B[0]))] for _ in vary(len(A))]
    for i in vary(len(A)):
        for j in vary(len(B[0])):
            for ok in vary(len(B)):
                outcome[i][j] += A[i][k] * B[k][j]
    return outcome

def numpy_matrix_multiply(A, B):
    return np.dot(A, B)

n = 200
A = [[np.random.rand() for _ in range(n)] for _ in vary(n)]
B = [[np.random.rand() for _ in range(n)] for _ in vary(n)]

A_np = np.array(A)
B_np = np.array(B)

start_time = time.time()
python_result = python_matrix_multiply(A, B)
print("Time taken for Python matrix multiplication:", time.time() - start_time)

start_time = time.time()
numpy_result = numpy_matrix_multiply(A_np, B_np)
print("Time taken for NumPy matrix multiplication:", time.time() - start_time)

Output>>
Time taken for Python matrix multiplication: 1.8010151386260986
Time taken for NumPy matrix multiplication: 0.008051872253417969

As you may see, NumPy is even sooner in additional complicated actions, corresponding to Matrix Multiplication, which makes use of normal Python code.

We will check out many extra examples, however NumPy needs to be sooner than Python’s built-in perform execution instances.
 

Conclusion

 
NumPy is a strong bundle for mathematical and numerical processes. In comparison with the usual Python in-built perform, NumPy execution time could be sooner than the Python counterpart. That’s the reason, attempt to use NumPy if it’s relevant to hurry up our Python code.
 
 

Cornellius Yudha Wijaya is a knowledge science assistant supervisor and information author. Whereas working full-time at Allianz Indonesia, he likes to share Python and information suggestions by way of social media and writing media. Cornellius writes on quite a lot of AI and machine studying subjects.