Unleashing the Power of Python: A Hosting Guide for Developers

Are you a Python enthusiast looking to take your projects to the next level? As a Python developer, the hosting environment plays a crucial role in the performance and scalability of your applications. In this guide, we’ll explore the intricacies of Python hosting, providing insights and tips to ensure your Python-powered projects thrive in the online world.

Decoding Python Hosting: Navigating the Technical Landscape

Before we dive into the hosting specifics, let’s unravel the technical aspects of Python hosting. From server configurations to compatibility considerations, understanding the essentials will empower you to make informed decisions that align with your development goals.

For a comprehensive understanding of Python hosting, check out this detailed hosting guide.

Python Hosting Providers: Finding the Right Match

Not all hosting providers are created equal, especially when it comes to catering to the unique requirements of Python developers. Explore a curated list of hosting providers that excel in supporting Python applications. Whether you’re working on a Django web app or a data science project, discover hosting solutions tailored to your Python-centric needs.

Discover top Python hosting providers that cater to a variety of Python applications.

Optimizing Performance with Python: Hosting Best Practices

Python’s versatility extends beyond coding practices; it influences how your applications perform in a hosting environment. Learn about best practices for optimizing Python applications on various hosting platforms. From efficient resource utilization to harnessing the power of cloud services, we’ll guide you through strategies to enhance your Python projects.

For actionable tips on optimizing Python performance in your hosting environment.

Securing Your Python Deployment: Hosting with Confidence

Security is paramount in the digital landscape. Gain insights into securing your Python deployments on hosting platforms. We’ll cover topics such as SSL implementation, firewall configurations, and other security measures to ensure your Python applications are robust and resilient.

Explore advanced security measures.

Connecting the Dots: Python Hosting and Your Development Journey

As we explore the dynamic realm of Python hosting, we’ll connect the dots between hosting choices and your overall development journey. Whether you’re a seasoned Python developer or just starting with the language, this guide aims to empower you with the knowledge to make hosting decisions that align with your unique Python projects.

Conclusion: Elevate Your Python Projects with Informed Hosting Choices

In conclusion, the right hosting environment can significantly impact the success of your Python projects. Armed with insights from this guide, you’re poised to make hosting decisions that amplify the capabilities of your Python applications. Here’s to a hosting journey that aligns seamlessly with your Python development endeavors!

Note: if you’re looking for a wordpress guide, drop by their wordpress hosting guide – I’ve learned quite a bit from it, and I believe so can others.

How to Enhance Your IPTV Experience with Python

As an IPTV enthusiast, I’ve been recently contributing to the IPTV community quite a bit through my engineering experience, and there’s quite a lack of innovation when it comes to IPTV, especially when you look at the IPTV players available.

I’ve spent some time looking at the current best iptv providers and found what I was looking for, no buffering issues, and the channels that I want to see – pretty much that’s all – but the experience is still a bit, lacking.

Anyhow, if you’re like me, you may find yourself constantly looking for ways to optimize your viewing experience. Fortunately, Python provides a wealth of tools and libraries that can help you do just that. Here are a few examples:

Scraping IPTV data with BeautifulSoup

BeautifulSoup is a popular Python library for web scraping, and it can be used to extract IPTV data from various sources. For example, the following code scrapes a website for links to M3U playlists:

import requests
from bs4 import BeautifulSoup

url = "https://example-iptv-site.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
links = soup.find_all('a')
for link in links:
    if link.get('href').endswith('.m3u'):

Automating channel switching with PyAutoGUI

PyAutoGUI is a Python library for automating GUI tasks, such as clicking buttons or typing text. It can be used to automate the process of switching between IPTV channels. For example, the following code sets up coordinates for three channels and then switches between them:

import pyautogui
import time

# set up channel coordinates
channel1 = (100, 100)
channel2 = (200, 100)
channel3 = (300, 100)

# switch to channel 1

# switch to channel 2

# switch to channel 3

Creating personalized playlists with Pandas and NumPy

Pandas and NumPy are two popular Python libraries for data analysis and manipulation. They can be used to create personalized IPTV playlists based on your viewing history. For example, the following code reads in a CSV file containing viewing data, calculates channel preferences, and then creates a playlist of the top 10 channels:

import pandas as pd
import numpy as np

# read in viewing data
viewing_data = pd.read_csv('viewing_data.csv')

# calculate channel preferences
channel_preferences = np.sum(viewing_data, axis=0)

# create playlist
playlist = []
for channel in channel_preferences.nlargest(10).index:


Integrating with other services using Tweepy

Tweepy is a Python library for interacting with the Twitter API. It can be used to tweet about your IPTV viewing habits or integrate your IPTV service with other social media platforms. For example, the following code sets up Twitter API credentials and then creates a tweet about the current channel:

import tweepy

# set up Twitter API credentials
consumer_key = "your_consumer_key"
consumer_secret = "your_consumer_secret"
access_token = "your_access_token"
access_token_secret = "your_access_token_secret"

# authenticate with Twitter API
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)

# create tweet
tweet = "I'm watching " + channel_name + " on my IPTV service!"

Now that you’ve seen a few examples of how Python can enhance your IPTV experience, you may be wondering how to get started. Here are a few tips:

  • Start with simple projects and build up from there. For example, try scraping a website for M3U playlists before moving on to more complex tasks.
  • Join online communities such as forums, subreddits, and Discord servers to connect with other IPTV enthusiasts and share ideas.
  • Check out online tutorials and courses to learn more about Python and how it can be applied to IPTV.
  • Experiment with different Python libraries and tools to find the ones that work best for your IPTV setup and viewing preferences.

By using Python to enhance your IPTV experience, you can automate tedious tasks, create personalized playlists, and even integrate your IPTV service with other platforms. With a little bit of knowledge and creativity, the possibilities are endless.

Do you have any favorite Python libraries or tools that you use for IPTV? Share your thoughts in the comments below!

Hope you enjoyed the article on how to use Python for your IPTV needs – if you’d love to learn move about IPTV, I’d recommend jumping into this iptv guide, which has helped me greatly in learning about IPTV.

The Global Interpreter Lock in Python

Python is a popular programming language known for its simplicity and readability. However, one aspect of Python that can sometimes cause confusion is the Global Interpreter Lock (GIL). In this blog post, we’ll explain what the GIL is, why it exists, and how it affects performance in Python.

What is the Global Interpreter Lock in Python?

The GIL is a mechanism in the CPython implementation of Python that prevents multiple native threads from executing Python bytecodes simultaneously. This means that even on multi-core systems, only one thread can execute Python code at a time. This can lead to performance issues, especially in multi-threaded applications.

Why does the GIL exist?

The GIL was implemented to simplify memory management in the CPython interpreter. Without the GIL, it would be much more difficult to manage memory and prevent race conditions. Additionally, the GIL makes it easier to use Python extensions written in C, which are not thread-safe.

How does the GIL affect performance in Python?

The GIL can have a significant impact on the performance of multi-threaded Python applications. Since only one thread can execute Python bytecode at a time, other threads must wait their turn to run. This can lead to a reduction in performance, especially on multi-core systems. However, it’s worth noting that many Python applications are IO-bound and not CPU-bound, so the GIL may not have a big impact on performance in these cases.

How to work around the GIL?

Using multiple processes

One way to work around the GIL is to use multiple processes instead of threads. This allows for true parallelism on multi-core systems.

Using Python extension libraries

Another way is to use Python extension libraries that release the GIL, such as NumPy and pandas, which are designed to work with large data sets.


The Global Interpreter Lock is an important aspect of the CPython implementation of Python that can affect the performance of multi-threaded applications. Understanding the GIL and how to work around it can help you write more efficient and performant Python code.

For a more in-depth overview of the global interpreter lock within python, you can read more here.

Kotlin vs Python: Which Is Better?

When it comes to programming languages, two of the most popular options are Kotlin and Python. Both of these languages have their own unique features and advantages, making them suitable for different types of projects and use cases. One of the most common questions developers ask is “Kotlin vs Python: which is better?” In this article, we will explore the differences between Kotlin and Python, their strengths, and weaknesses, and help you determine which language is better for your specific needs. We will cover areas such as performance, ease of use, and versatility, to give you a comprehensive understanding of both languages and help you make an informed decision when choosing the right language for your project.

What is Kotlin?

Developed by JetBrains, Kotlin is a cross-platform, statically typed programming language that is fully interoperable with Java. It was designed to improve upon some of the shortcomings of Java, such as verbose syntax and null pointer exceptions.

Kotlin also offers features such as type inference, data classes, and coroutines, making it a powerful and versatile language.

Potential reasons to use Kotlin

  • Fully compatible with Java, meaning you can use any existing Java libraries and frameworks.
  • Improved performance and safety compared to Java, making it a great choice for developing large-scale applications.
  • A modern language that has been gaining popularity in recent years.
  • Officially supported by Google for android development

What is Python?

Python is a high-level, interpreted programming language that is known for its simplicity and readability. It has a large and active community, meaning there are plenty of resources and libraries available for developers.

Python is also a versatile language, and it’s used for a wide range of applications, from web development and data analysis to artificial intelligence and machine learning.

Reasons to Use Python

  • Its simplicity and readability, which makes it easy to learn and use, even for beginners.
  • A huge number of libraries and frameworks available, making it a great choice for tasks such as data analysis and machine learning.
  • Popular language in many industries, especially in the data science and AI field.

Performance Comparison

Performance is an important factor to consider when choosing a programming language. Kotlin and Python both have their own unique performance characteristics, so let’s take a look at how they compare.

In terms of raw performance, Kotlin is generally faster than Python. Kotlin is a statically typed language, which means that the type of a variable is known at compile time. This allows for more efficient memory usage and faster execution times. Additionally, Kotlin’s improved type inference and coroutines make it well-suited for large-scale, performance-critical applications.

On the other hand, Python is an interpreted language, which means that it runs slower than a compiled language like Kotlin. However, Python’s simplicity and readability make it a great choice for tasks that don’t require high performance, such as data analysis and machine learning. Additionally, Python’s large and active community has created a wide range of libraries and frameworks that can help boost performance for specific tasks.

Here’s an example code and execution time for each language:

[kotlin] // Kotlin val startTime = System.nanoTime() val list = (1..1000000).toList() val sum = list.sum() val endTime = System.nanoTime() println(“Time taken: ” + (endTime – startTime) + “ns”) // Output: Time taken: 4549132ns [/kotlin]

[python] # Python import time start_time = time.time_ns() int_list = range(1, 1000001) sum_list = sum(int_list) end_time = time.time_ns() print(“Time taken: ” + str(end_time – start_time) + “ns”) # Output: Time taken: 166613385ns [/python]

As we can see from this example, Kotlin’s performance is significantly faster than Python’s, although it should be noted that the example is not a representative of all use cases and the performance gap might vary depending on the specific task at hand.

Ease of Use: Kotlin vs Python

When it comes to ease of use, both Kotlin and Python have their own unique strengths and weaknesses. Kotlin, being fully interoperable with Java, is a great option for developers who are already familiar with the Java syntax. The language also has improved readability and conciseness, making it easier to write and understand code.

Python, on the other hand, is known for its simplicity and readability. Its syntax is often compared to that of a natural language, making it easy to learn and use, even for beginners. The large and active community behind Python also means that there are plenty of resources and tutorials available to help developers learn the language.

In terms of ease of use, it’s hard to say which language is better as it largely depends on the developer’s background and experience. If you’re already familiar with Java and the JVM ecosystem, Kotlin may be the easier option for you. However, if you’re new to programming, Python’s simplicity and readability may make it a better choice for you.

It’s also worth noting that both languages have a large and active community that supports and maintains a large number of libraries and frameworks. This makes it easy for developers to find and use pre-existing solutions to common problems, rather than having to write everything from scratch. This is a big plus for both languages in terms of ease of use.

In summary, both Kotlin and Python have their own unique strengths and weaknesses when it comes to ease of use. Kotlin may be a better option for developers already familiar with Java, while Python’s simplicity and readability may make it a better choice for beginners. The availability of resources and libraries also makes both languages easy to use.

If you’re interested in reading more about python, you might be interested in reading more about lists vs [].

Celery: Advanced Routing Techniques

Nowadays the microservice architecture is considered the most scalable approach for a wide range of business problems, in particular because it promises fast and lean development cycles.

The best case scenario for microservices is when the data entities that define our applications are completely decoupled. Unfortunately that is rarely the case, and managing the communication between microservices is far from the easiest task a team may encounter.

In the most simple use case, we can use plain HTTPS requests to send and receive messages from and to other services.

Unfortunately this methodology does in fact tend to couple the microservices and depending on scale, could deteriorate the performance of the application.

Use case: A simple ecommerce

As a case study we’ll draft out the architecture of a simple ecommerce, we start with these three microservices:

Order – Manages the orders and its lines (e.g. in review, dispatched).
Logistic – Manages the moving about of the items.
Billing – Manages the company general ledger.

When a customers fills his basket with whichever item he wants and completes the payment procedure, we’ll be generating an order.

The Order microservice may need to send the information to another microservice(s), for example to the Billing and the Logistic microservices.

In the HTTPS scenario, the Order microservice needs to know of the existence of those services, namely Billing and Logistics, and of their API structure. This poses the following problems:
If a third microservice needs to be added to the loop, the code of Order needs to be altered directly and API changes may need to cascade to other microservices.

Additionally we may have long chains of HTTP requests and an API gateway that needs to manage both internal and client generated traffic. This could slow down our application significantly.

Another way to manage the communication between microservices is by using asynchronous messaging; one of the benefits of using async is that it allows a microservice to extend the logic whilst not requiring any alterations in the producers’ source code, thereby following the open-closed principle.

Unfortunately using asynchronous messaging at scale may be quite the challenge on its own, and the python asynchronous ecosystem is unfortunately, still pretty immature leaving developers with little to no reference.

In this article I will present an example implemented using Celery, attrs, and cattrs, which tries to be as exhaustive as possible.

Asynchronous messaging using Celery

Albeit we can choose among various libraries like pika, I will implement it using Celery and Kombu.

In particular we will create specific Topic exchanges that will be named after our microservices, and let each interested microservice subscribe to the various events using routing_keys.

We will also define our events using attrs, it has all the features of python dataclasses plus some other candy, like validation and a wider compatibility, which includes python 2.7 and python 3.4+.

The event_manager common package

Now we will create a library that will be common among our microservices, we will call it event_manager, the scope of this package is to declare the Exchanges, the dataclasses,  eventually their versions, and some utility classes.

The Order object

We will represent Order and OrderLine as dataclasses using attrs, this is not an ORM representation but a minimal representation as a message:

import attr

class OrderLine:
    id: int
    quantity: int
    price: float

class Order:
    id: int = attr.ib()
    lines: Sequence[OrderLine] = attr.ib(default=list)

The event class

Now we will declare a topic exchange, this will allow us to bind it to multiple queues.

from kombu import Exchange

ORDER_EXCHANGE = Exchange('order', type='topic')

We also create a class, lets call it Event, that will help us with abstracting some of the complexity, the class will do a number of things:

  • register a number of callbacks which will be called when the message is received.
  • Use cattrs to de/serialize our dataclass
  • Create a shared task under the hood.

The class will implement the descriptor protocol so that we will be able to declare each event while building the class.

from ...

Message = TypeVar('Message')

class Event:

    def __init__(self, exchange: Exchange, routing_key: str):

    def __get__(self, instance: Message, owner: Type[Message]
    ) -> Union['Event', Task]:

    def register_callback(self, callback: Callable[[Message], Any]):

For a full implementation see the code on github.

We can now add a new line to the Order class, as you can see we are setting up a versioning:

class Order:
    # represent the submission of an order
    submit = Event(ORDER_EXCHANGE, 'order.v1.submit')
    # represent the cancellation of an order
    chargeback = Event(ORDER_EXCHANGE, 'order.v1.chargeback')
    # other events

An Order is submitted

Now the Order microservice will be able to create an order message and send events through it:

from event_manager.types.order import Order, OrderLine
order = Order(1, [
    OrderLine(1, 2, 3),
    OrderLine(2, 1, 4.5),

The Billing Microservice

In the billing microservice we will need to bind a queue, we will make sure that it will receive the message wathever the version, we will make sure that the message is received regardless of its version:

from kombu import Queue

from event_manager.exchanges import ORDER_EXCHANGE

Queue(f'billing_order', exchange=ORDER_EXCHANGE,

And register at least one callback:

from event_manager.types.order import Order

def billing_received(order: Order):
    print(f'billing received a task for order {order}')

You can go check my repository on github to find a complete example on how this will work.

All in all asynchronous messaging is, likely, the way to go when it comes to the communication between microservices. Unfortunately the ecosystem is still a bit lacking when talking about a framework able to painlessly help developers build and manage complex networks of microservices, on the other hand this means that it is, once again, the time for pioneering new solutions.

Licensed under: Attribution-ShareAlike 4.0 International

Do you know the difference between list() and [], if not; head to this article to read more.

What’s the difference between list() and []

What are the key differences between using list() and []?

The most obvious and visible key difference between [python]list()[/python] and [python][][/python] is the syntax. Putting the syntax aside for a minute here, someone whose new or intermediately exposed to python might argue that they’re both lists or derive from the same class; that is true. Which furthermore increases the importance of understanding the key differences of both, most of which are outlined below.

[python]list()[/python] is a function and [python][][/python] is literal syntax.

Literal syntax
Literal Syntax – src:excess.org
Function – src:excess.org

Let’s take a look at what happens when we call [python]list()[/python] and [python][][/python] respectively through the disassembler.

[python]>>> import dis >>> print(dis.dis(lambda: list())) 1 0 LOAD_GLOBAL 0 (list) 3 CALL_FUNCTION 0 (0 positional, 0 keyword pair) 6 RETURN_VALUE None >>> print(dis.dis(lambda: [])) 1 0 BUILD_LIST 0 3 RETURN_VALUE None[/python] The output from the disassembler above shows that the literal syntax version doesn’t require a global lookup, denoted by the op code LOAD_GLOBAL or a function call, denoted by the op code CALL_FUNCTION.

As a result, literal syntax is faster than it’s counterpart. – Let’s take a second and look at the timings below.

[python]import timeit >>> timeit.timeit(‘[]’, number=10**4) 0.0014592369552701712 >>> timeit.timeit(‘list()’, number=10**4) 0.0033833282068371773[/python] On another note it’s equally important and worth pointing out that literal syntax, [python][][/python] does not unpack values. An example of unpacking is shown below.

[python] >>> list(‘abc’) # unpacks value [‘a’, ‘b’, ‘c’] >>> [‘abc’] # value remains packed [‘abc’] [/python]

What’s a literal in python?

Literals are notations or a way of writing constant or raw variable values which python recognises as built-in types.

It has been fun and interesting to write the first of many to come PythonRight blog posts; in the next blog post we’ll be going over the beauty of unpacking , so stay tuned. 😉 – If you have any feedback or any other topics that you’d like to see explained in detail, do feel free to comment.