FinTech is a combination of the terms "finance" and "technology." It refers to any business that leverages technology to improve or automate financial services and operations. Python comes in handy in a broad range of FinTech use cases. Its clear programming language syntax and amazing ecosystem of tools make it one of the best technologies.
The finance sector is evolving daily, and now financial institutions are not only concerned with finance, but also with technology as an asset. Technology provides a competitive advantage as well as increased speed in the rate and frequency of financial transactions by financial institutions, among other things. Python is the most popular programming language in finance. Because it is an object-oriented and open-source language, it is used by many large corporations, including Google, for a variety of projects.
According to the Tiobe Index, Python is the second most popular programming language. As of 2021, Python grew by 11.87% percent, and its exponential growth is still continuing to become the most used programming language in the upcoming years. In this article, you will learn what companies use Python and what are their Python use cases.
Today we would like to switch gears a bit and get our feet wet with another BigData combo of Python and Impala. The reason for this is because there are some limitations that exist when using Hive that might prove a deal-breaker for your specific solution. Impala might be a better route to take instead.
If you hang around developers long enough you will start hearing the word REST very often and so we begin our adventure by giving a simple definition of the term. The REST acronym stands for Representational State Transfer, which is an architectural design. So it follows that when developers use the words RESTful, what is being referred to is an application that implements the REST architectural design.
Over the past few years, we have been hearing more about the wealth of data we humans generate. This has progressively grown into the concept that if you have enough of this data and you are able to piece together some meaning from it, then you can achieve everything from predicting the future to curing all human ills.
Every developer understands the advantages of having clean, easy to read and understand coding. Which is where tools like QuerySet and Manager come into play when using Django. They embed basic DRY and KISS principles that most forget about when using ORM in many frameworks. This has lead to the use of Fat Models which might seem fine in the first place but can lead to complications down the road.
When you hear the words "Server-side Programming" a number of languages are going to come to mind. But amongst that list are two that are very much loved and respected by programmers worldwide. These are obviously PHP and Python. The debate on which to use for any core web application has raged on since the very beginning.
The internet is filled with great codes. They form the bedrock on which various infrastructures are built. Even the very platform you are reading this on is built with a dedication to creating great codes. While the average user isn’t going to take note of this, good developers, on the other hand, are always seeking to make their applications (and hence, their coding) better.
Text data the most common form of information on the Internet, whether it be reviews, tweets or web pages. Natural Language Processing (NLP) is a powerful technology that helps you derive immense value from that data. In this article, we will look at the most popular Python NLP libraries, their features, pros, cons, and use cases.
Django describes itself as "the web framework for perfectionists with deadlines". It was designed to help Python developers take applications from concept to completion as quickly as possible. Learn advantages and disadvantages of Django.
Django is powerful web framework, but with power comes responsibility. In this article, we will cover common mistakes that are even seasoned Django developers make, yet most successful Django projects need to deal with these sooner or later. This checklist should useful even if you’re a skilled Django developer because mistakes, like not adding indexes on models or inconsistent data validation, aren’t just limited to new developers.
This year EuroPython featured 120 talks and 6 parallel tracks on almost every topic imaginable, including some that we wouldn’t have been able to imagine. In this post, I want to share with you my favorite 10 talks that I believe can make you a better Python developer.
Up until relatively recently, digital transformation struggled to penetrate the real estate sector in any meaningful way.In this post we will review what are the trends in PropTech and how Python fits into PropTech startup development techstack.
In times when computer programing is becoming more and more accessible due to the growing number of coding schools, online resources and bootcamps, this question seems to go viral - which computer language should I learn first or which language should I choose for my use case. This situation is no different for Ruby and Python.