Top 10 Python libraries of 2018
Like in 2015, 2016 and 2017, we’re thrilled to share our hand-picked selection of the top Python libraries (according to our most humble opinion) with you. If you can think of a library that is not mentioned here but you believe deserves to be on the list, please let us know in the comments section at the end of the post. Here we go! 1. slundberg/shap Along with the Deep Learning boom that we’re experiencing, a huge amount of new tools have seen the light.
Getting started with AWS: open source workshop
Introduction One of our strengths at Tryolabs is that we have people coming from diverse technological backgrounds. In order to make sure that everyone who joins the company, no matter their previous experience, can be up to speed with developing apps with the stack we usually use, we have an extensive onboarding process that involves the development of a real application (frontend, backend and some data science), with a coach, code reviews, and iterative improvements.
Top 10 Python libraries of 2017
Update December 2018: The edition with the top Python libraries 2018 has been published here. December is the time when you sit back and think about the accomplishments of the past year. For us programmers, this is often looking at the open source libraries that were either released this year (or close enough), or whose popularity has recently boomed because they are simply great tools to solve a particular problem.
Launching Requestium: An integration layer between Requests and Selenium for automation of web actions
Top 10 Python libraries of 2016
Update (12/28/2018): other editions of this post: 2018, 2017, 2015. Last year, we did a recap with what we thought were the best Python libraries of 2015, which was widely shared within the Python community (see post in r/Python). A year has gone by, and again it is time to give due credit for the awesome work that has been done by the open source community this year. Again, we try to avoid most established choices such as Django, Flask, etc.
Scalable infrastructure in AWS (Part I)
Let’s imagine you have an API running on a single node and you need to implement a new feature that requires some heavy task processing. Obviously you can’t simultaneously process the HTTP request and the task without blocking the web server. Although in Python we have a few alternatives like Celery (check our post on this) or Asyncio (when the heavy task processing is IO bound, future post 😃) to handle this situation, this time we’ll explore a new approach: take advantage of Amazon Web Services (AWS).
Building our site: From Django & Wordpress to a static generator (Part I)
We recently announced the redesign of our website and blog. So far, it has been a great success. The site is a lot faster, SEO is better than ever (signaled by the growth of organic traffic), user bounce rates are down, the amount of visited pages went up as did the duration of the sessions. Hurray! :-) However, the change was much deeper than just a visual revamp. We decided to revise the tech stack that has been powering the site for more than 6 years.
Raspberry Pi + Slack: Our humble contribution to the office's laziness
If there is one issue that permeates every culture, is not wanting to answer the door. We used to have countless hours of discussion about who would do it and led to a myriad of problems: relationships were broken, projects were lost, accidents happened on the way to answer the door phone! (Well, not really. Nothing of this happened, but IT COULD HAVE). This clearly had to be fixed. Thinking a bit, we realized we could ease the annoyance of this task if we could avoid having to do the enormous, unbearable exercise of standing up and walking to the phone.
Top 10 Python libraries of 2015
Update (12/28/2018): other editions of this post: 2018, 2017, 2016. As the new year approaches, we often sit back and think about what we have accomplished in 2015. Many of our projects would not have been as successful if it were not for the great work done by the open source community, providing some solid, bullet-proof libraries. Everyone and their grandma seems to be writing top 10 lists, so we couldn’t be less and compiled our own.
How to tame a frenzy of tasks with Celery
Using Celery in our projects is not a constant, but from time to time we need to deal with some kind of asynchronous tasks for example, sending emails, calling APIs, and such. But this time we faced another kind of challenge, we needed to implement a processing intensive pipeline to download tweets, un-short URLs, get the sentiment using MonkeyLearn among other tasks. Celery looks like a perfect tool but, as in other aspects of life itself, it all depends on how you use the tool.