Numba – Compiler to make Python code 1000x Faster

Did you know that there’s a third most popular programming language after English and Chinese? That’s right! It’s Python. In this article, we will discuss the top 3 best Numba Python tutorials for beginners. But first… What exactly is Numba? Numba is an open-source LLVM compiler that specializes in NumPy array data structure. As such, it has the capability to perform fast computation on large arrays of data in limited memory without making code unreadable or rewriting it from scratch.

If you are just starting out as a computer programmer or developer, picking up new languages can be quite challenging. So much so that some programmers will have difficulty determining which language they should begin with. The truth is that there are many programming languages out there and each one has different uses. Some are strictly used for game development while others are perfect for making websites or mobile applications. Whether you’re creating simple programs or building video games, understanding Python can open up many career opportunities. Let’s take a look at our best Numba Python tutorials for beginners:

What is Numba?

Numba is a Python-based software that allows programmer to speed up their code. It basically compiles a user’s Python code into native machine code. This means that the Numba software is able to turn Python code into something that can run at the same speed as C or C++ code. The Numba software can work with any Python library, including SciPy, NumPy, Pandas, and many others. It can also work with both Python 2 and Python 3.

Why learn Numba?

There are many reasons why you should learn Numba. First, it’s important to note that Numba is not an actual programming language. Instead, it’s a compiler that can take Python code and turn it into native machine code. The main reason you should learn Numba is that it can speed up your code. Whenever you write code, you want the code to be as fast as possible. This is because if the code takes a long time to run, then you’re wasting time. You could be doing something else while your code runs.

Also, if your code takes a long time to run, then it could also disrupt the workflow of other people that you share the computer with. Another reason you should learn Numba is that it supports many different libraries.

What are the benefits of using Numba?

There are many benefits of using Numba for your code. First, it will speed up your code. If your code is sped up, then it means that it’s going to take less time to run, which means that it’s also going to take less time to write. A big advantage of using Numba is that you can use it with many different libraries. Another benefit of using Numba is that it supports many different types of code, including C and C++ code.

Numba usage in real-world applications

One of the best ways to learn more about Numba is to look at the types of real-world applications that people use this software. One popular application of Numba is big data analytics. Big data analytics is when you take large amounts of data and then analyze it to find insights and patterns in it. Numba is also used in machine learning. Machine learning is a type of artificial intelligence. Numba is also used in cryptocurrency mining.

Python vs. Numba

This is a very common question: what is the difference between Python and Numba? The main difference is that Numba allows you to compile your Python code into C++ code. This means that Numba takes your Python code and compiles it into something that can be run at the speed of C code. This is an important difference because it allows you to write Python code with fewer lines of code. This is beneficial because it makes the code shorter and easier to read.

Perform fast computation on NumPy arrays

If you want to perform fast computation on NumPy arrays, then one of the best ways to do that is by using Numba. There are many different ways that you can use Numba to speed up your code. One of the best ways is to use the Numba array function. This function allows you to convert a Python array into a native machine array. This means that your code will run faster.

Top 3 Best Numba Python Tutorials for Beginners

Beginner’s Guide to the Numba Compiler: Numba is one of the most interesting Python tools out there. It allows you to compile Python code into C++ code, which makes it run much faster. This article is a complete beginner’s guide to Numba. Here, you’ll learn everything you need to know about this useful compiler. This article is all about installing and using the Numba Python compiler. It’s an excellent guide for beginners who want to learn everything about this new software.

Monte Carlo Method for Pi Generator

A highly beautiful Monte Carlo approach can be used to guess the value of pi.

Simply using a uniform probability distribution, we will shoot many spots in the square. An estimate of r is the percentage of the points that fall inside the circle.

Naturally, this estimator will be more accurate and can compute more decimals of pi if there are more points.

Let’s put this technique into practice and examine how the precision changes as the number of points increases.

#importing all the necessary libraries
from numba import jit
import random
from time import perf_counter

#Defining the Monte Carlo Pi Generetor Function
def pi_generator(num):
    acc=0
    for i in range(num):
        a=random.random()
        b=random.random()
        if (a**2 + b**2)< 1.0:
            acc += 1
    return 4.0 * acc/num

#By setting Nopython = True will increase the speed the of function
pi_generator_jit = jit(nopython=True)(pi_generator)

#Speed of Python code 
print("Python's Monte Carlo Pi Generator:")

begin =perf_counter()
output = pi_generator(10_000_000)
end = perf_counter()
print (end-begin, output)

  
#Speed of Numba code
print("Numba's Monte Carlo Pi Generator:")
begin =perf_counter()
output = pi_generator_jit(10_000_000)
end = perf_counter()
print (end-begin, output)

Output:

A Number Generator in an array using Numba

#Defining all the necessary Libraries
from numba import jit
import numpy as np


x = np.arange(100).reshape(10, 10)

# Set "nopython" mode, which is same to @njit, for the optimal performance.
@jit(nopython=True)

# When a function is initially called, it is converted to machine code.
def speed_up(a): 
    locate = 0.0
    # Numba relishes loops.
    for i in range(a.shape[0]):

    # Numba likes NumPy functions       
        locate += np.tanh(a[i, i]) 

    # Numba likes NumPy broadcasting    
    return a + locate              

print(speed_up(x))

Output:

Conway’s Game of life

John Conway developed the cellular automation method known as Conway’s Game of Life. Although it was developed with biology in mind, this game has applications in a number of areas, including graphics and landscape generation.

The “game” is a zero-player game, which means that its development depends only on its initial condition and doesn’t require any additional input. The Game of Life can be played by setting up an initial configuration and seeing how it develops, or, for more experienced “players,” by designing patterns with specific characteristics.

Every cell in the Game of Life has eight adjacent cells because the grid is made up of nine squares, as can be seen in the attached diagram. On a grid with the coordinates [i][j], where I and j are the corresponding row and column indices, a given cell I j) in the simulation is accessible. The condition of a cell’s neighbors at a given time step determines the value of that cell at that time.

Python Code

import array 
import random 
from time import perf_counter

WIDTH=40 
HEIGHT=15 
world = [array.array("B",[0]* WIDTH*HEIGHT) for _ in range(2)] 
current_world =0

def life (worlds, c_w):
    current = worlds[c_w] 
    next = worlds[1-c_w]  
    r=0
    for y in range(HEIGHT): 
        for x in range(WIDTH):
            total = 0
            for y1 in range(y-1,y+2): 
                for x1 in range(x-1,x+2): 
                    if current [((y1 % HEIGHT)* WIDTH) + (x1 % WIDTH)]:
                        total +=1 
            total -= current[r] 
            next[r] = 1<total<4 if current[r] else total==3
            r+=1

def display(w):
    r=0
    for y in range(HEIGHT):
        for x in range(WIDTH):
            print("0" if  w[r] else " ", end="")
            r+=1
        print()

if __name__ == "__main__":
    w = world[current_world]
    for n in range (WIDTH*HEIGHT):
        w[n] = random.random()>.8                   
    
    while True:
        display(world[current_world]) 
        input(">") 
        t1 = perf_counter() 
        life(world, current_world) 
        print (f"render time: {perf_counter()-t1:.6f}")
        current_world =1 - current_world

Numba Code

from numba import njit
from numba.typed import List 
import array 
import random 
from time import perf_counter

WIDTH=40
HEIGHT=15 
world = [array.array("B",[0]* WIDTH*HEIGHT) for _ in range(2)] 
ww = List(world)
current_world =0

@njit
def life (worlds, c_w):
    current = worlds[c_w] 
    next = worlds[1-c_w]  
    r=0
    for y in range(HEIGHT): 
        for x in range(WIDTH):
            total = 0
            for y1 in range(y-1,y+2): 
                for x1 in range(x-1,x+2): 
                    if current [((y1 % HEIGHT)* WIDTH) + (x1 % WIDTH)]:
                        total +=1 
            total -= current[r] 
            next[r] = 1<total<4 if current[r] else total==3
            r+=1

def display(w):
    r=0
    for y in range(HEIGHT):
        for x in range(WIDTH):
            print("0" if  w[r] else " ", end="")
            r+=1
        print()

if __name__ == "__main__":
    w = world[current_world]
    for n in range (WIDTH*HEIGHT):
        w[n] = random.random()>.8                   
    
    while True:
        display(world[current_world]) 
        input(">") 
        t1 = perf_counter() 
        life(ww, current_world) 
        print (f"render time: {perf_counter()-t1:.6f}")
        current_world =1 - current_world

Conclusion

In this blog post, we have introduced you to the top best Numba Python tutorials for beginners so that you can learn everything about this new software and its usage in real-world applications today. Now, it’s time to put this information into practice. So, what are you waiting for? Head on over to this blog post and start reading. We hope that you enjoy reading these articles as much as we enjoyed writing them.

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