Python 2 will lose support on January 1, 2020. The time is nigh to upgrade to Python 3.
But is there a way to do it without disrupting your application development and operation?
This guide will show you how to upgrade to Python 3 quickly, easily, and cost-effectively.
We’ll assume you already have the latest Python 2.7 and are targeting Python 3.6 or newer.
Anything lower than Python 3.5 would be ill-advised, since it is already the oldest version still being supported and its end-of-life is scheduled for September 13, 2020.
For the second year in a row, STX Next has ranked as one of Europe’s fastest-growing companies on the Financial Times 1000.
But ours is just one spot on the ranking among many others who have chosen Python as their programming language of choice to build rapidly scaling businesses.
Here are just a few of our favorite Python-powered companies among the FT1000 in 2019.
The position of a CTO comes with a number of expectations and responsibilities. There are many goals to accomplish, and one great way to do that is to attend technology conferences focused on your tech stack of choice.
The foundation of tech stack at the center of this article is Python, and here is a list of 8 must-attend Python conferences in 2019 to help you become a better CTO.
Quality vs. time. Time vs. cost. Cost vs. quality.
These concerns play a key role in choosing the programming language for your project, which is one of the first major decisions you have to make.
As a Python software house, we are intimately familiar with the challenge of contrasting Python with other languages:
Python vs. Golang. Python vs. Node.js. Python vs. Java.
In this article, we’ll focus on the last one.
Each software project comes with its own unique set of needs and requirements. What works for one may not work for another at all.
At STX Next, we use whatever tech stack fits a given project best. That being said, comparisons are inevitable.
One of the most frequently asked questions we’ve seen lately is Python vs. Node.js. We’re gonna shed some light on that.
Read on for our in-depth look at Python and Node.js to learn their differences and similarities, strengths and weaknesses, and most importantly: which is better?
If we suppose for a second that Batman is a Python user, I bet his favorite conference would be PyGotham.
On October 5 and 6, Pythonistas from all over the world will meet in New York at Hotel Pennsylvania to attend a conference “for developers and run by developers”.
Two of our engineers will be flying over to give their presentations at the conference.
They'll talk about refocusing on business needs... and building bartender bots!
Read on for a sneak peek at their presentations, and a special bonus from yours truly.
Since you’re reading this, it’s safe to assume you’re interested in taking up Python—or maybe you’ve already started learning this awesome language.
It doesn’t seem too daunting, right? You can code, after all, so it’s just a matter of grasping the differences in syntax.
So let’s take it up a notch and talk about collecting proper experience in Python.
Where do you start? With an idea, obviously, but that won’t be a problem.
What’s next, then? The choice of a framework.
Being a free, cross-platform, general-purpose and high-level programming language, Python has been widely adopted by the scientific community. Scientists value Python for its precise and efficient syntax, relatively flat learning curve and the fact that it integrates well with other languages (e.g. C/C++).
As a result of this popularity there are plenty of Python scientific packages for data visualization, machine learning, natural language processing, complex data analysis and more. Here’s our list of the most popular Python scientific libraries and tools.
At some point in the creation of your fintech start-up you will have to make decisions that are very hard to un-make. One such decision is your choice of tech stack. If you go wrong here, your costs may skyrocket down the line, putting you in the red despite best intentions.
Your fintech needs a programming language that is easy to handle, scalable, mature, high-performance and coupled with ready-made libraries and components.
Luckily, Python is there to deliver it all. Read on and you’ll find that it’s quite easy to make a case for Python in this industry.
Let’s be honest, when was the last time you were excited to purchase insurance? Or the last time you discussed openly with friends about your most recent coverage?
If you’re drawing a blank, I’m not surprised. Most people hate the idea of shopping for insurance. But this state of affairs may change very soon.
Data analytics, AI and machine learning are driving the emergence of insurtech.
And insurance being an antiquated industry is precisely why insurtech companies are worth your attention. The top players in the field are spurring big changes in an industry ripe for innovation.