The latest Python 3.9.0 final version is out on the Monday, 2020–10–05
Just like most of the Python fans, I am super excited to explore and use the latest features. This article will provide an overview of the must-know features of Python 3.9.
This is again an exciting time for the Python programmers.
I read through the Python 3.9 release notes and the associated discussions. Based on the information, I wanted to write a comprehensive guide so everyone can get a glimpse of the features along with their detailed workings.
Before I begin, I have to say, I am very excited to explore version 3.9 as some of the features are definitely going to be used in my applications. …
One of the quickest and safest techniques to scale up the application is to launch multiple Python processes in an application. This also helps us by-pass the famous GIL issue.
The challenge of launching multiple processes is that it can cause unintended problems if the application is not designed appropriately.
I have written 12 top guidelines that I recommend everyone to follow. This article aims to outline the programming guidelines for multiple processing programming.
I recommend this article to everyone who is/or intends in using the Python programming language.
If you want to understand the Python programming language from the beginner to an advanced level then I highly recommend the article…
Most of the enterprise-level high-performant computationally-intensive applications are concurrent and parallel in nature. The advanced features of concurrency and parallelism are usually used to enhance the performance of an application.
One of the biggest challenges in the concurrent and parallel applications is to be able to share the data between threads, asyncio routines, and/or processes.
This is where we can use the synchronization primitives. This article will aim to explain what they are and when to use them.
This article will help us understand what Python Synchronisation Primitives are which can be used to share the data between processes/threads/tasks.
I will start by providing a brief overview of concurrency and parallelism concepts in general. I will then provide an overview of the basic synchronisation primitives along with the information on when to use them. …
All successful projects revolve around clean data. Google is by far the best search engine. Furthermore, Google News is a fantastic source that combines news from various media and publishers.
I will demonstrate how we can build data sets for data science projects by using news from Google News. I wanted to be able to fetch Google News via Python code so that I can get the latest news daily without any manual intervention.
This article will demonstrate the steps that are required to retrieve google news for our chosen topics, words, and locations via Python code.
I will present two methodologies that can be utilized to get news from Google News. …
Data Science is a hot topic nowadays. Organizations consider data scientists to be the Crème de la crème. Everyone in the industry is talking about the potential of data science and what data scientists can bring in their BigTech and FinTech organizations. It’s attractive to be a data scientist.
This article will outline everything we need to know to become an expert in the Data Science field.
During tough times, data scientists are required even more because it’s crucial to be able to work on projects that cut costs and generate revenue. …
Operating systems use signals to communicate with the processes. There is not a lot of content on Python signals on Medium, even though they are heavily used in enterprise-level applications. This article will demonstrate what signals are and how they can be used in Python applications.
This article will focus on signal driven programming. Signaling is used in a large number of Python packages such as Django, Flask, FastAPI etc.
The signaling concept is widely used in a number of enterprise-level industry applications and is a must-know topic for all programmers who want to become an expert in Python programing language. …
Caching is an important concept to understand for every Python programmer.
In a nutshell, the concept of caching revolves around utilising programming techniques to store data in a temporary location instead of retrieving it from the source each time.
Subsequently, caching can provide an application performance boost as it is faster to access data from the temporary location than it is to fetch the data from the source each time, such as from database, web service, etc.
This article aims to explain how caching works in Python.
This is an advanced level topic for Python developers and I recommend it to everyone who is/or intends in using the Python programming language. …
No one wants a slow data science application. Not when it is required to be consumed by high-demanding users who expect a quick turn around.
Profiling is one of those concepts that every Python programmer must be familiar with. It is required to become an expert in the programming field. This is an advanced level topic for Python developers and I recommend it to everyone who is/or intends in using the Python programming language.
We can learn the Python library and understand how to create objects and modules, but the true Python experts emerge when they encounter and fix tough technical issues. …
I haven’t watched the news on TV or read the news in newspapers for over 12 years.
On top, I strongly believe in continuous learning and staying up to date with world trends. Then how do I keep myself updated?
I use my Python applications.
One of the methodologies I follow is by coding Python applications to inform me with a short summary of the latest world news and trends on a timely basis. Today I will share the code and explain how it works.
I have coded multiple applications that get data from a number of sources. One of the important applications amongst them relies on sending me the summary of recent trends from Twitter. …
I have recently hosted a number of production-ready data science applications as Restful web services using the FastAPI web framework.
I found FastAPI to be stable and easy to use and for those reasons, I have decided to write an article on FastAPI library outlining the steps we can follow to host a data science application.
FastAPI was released in 2018 and is becoming the de facto choice for building high performant data science applications.
This article will explain what FastAPI is, why it’s superior to its competitors, along with a step by step guide on how to host a data science application using the FastAPI. …