Dashing Up Your Python Code with NumPy – KDnuggets

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

There are a number of the explanation why NumPy might 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 hurries up the code course of. Let’s soar into it.

NumPy in Speed up Python Code Execution

 
The primary instance compares Python listing and NumPy array numerical operations, which purchase the article with the meant worth end result.

For instance, we wish 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()
end result = [(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()
end result = 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 possibly can see within the above output, the execution of NumPy arrays is quicker than that of the Python listing in buying the identical end result.

All through the instance, you’d see that the NumPy execution is quicker. Let’s see if we need 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 are able to see the execution time variations.

list_1 = listing(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 end result, Python’s in-built perform would take way more time than NumPy. If we had a a lot greater dataset, Python would take method longer to complete the NumPy.

One other instance is after we attempt to carry out in-place operations, we are able to 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 = listing(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 listing operation: 0.1937870979309082

The purpose of the instance is that you probably have 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 advanced implementation, utilizing matrix multiplication to see how briskly NumPy is in comparison with Python.

def python_matrix_multiply(A, B):
    end result = [[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 okay in vary(len(B)):
                end result[i][j] += A[i][k] * B[k][j]
    return end result

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 possibly can see, NumPy is even sooner in additional advanced actions, akin to Matrix Multiplication, which makes use of customary Python code.

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

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 an information science assistant supervisor and knowledge author. Whereas working full-time at Allianz Indonesia, he likes to share Python and knowledge ideas by way of social media and writing media. Cornellius writes on a wide range of AI and machine studying subjects.