I was recently helping a friend who was transitioning from Matlab to Python. Giving him some tips, I realized that many of the cool nuances I learned in Python were taught me by someone in a “do you know that?” style or to solve a very specific problem that could be solved more simply. When helping this friend who is there on the other side of the world, I remembered the time when there was no one to teach me a cool trick and, in fact, I didn’t even know it could exist.
This is the fifth post on my internship on the Outreachy Program with Project Jupyter. The previous posts are available and should be read in order if you want to understand the big picture: Outreachy I Outreachy II Outreachy III Outreachy IV Increasing documentation Native Authenticator was pretty advanced, but we still needed more information available on the documentation. And this got me thinking: what is relevant to make a good documentation?
I started working with MongoDB for fun and for some side projects in the last year. The main idea of using MongoDB is its flexibility. The pymongo library is really nice for getting some information, but on a project more complex, we may need something a little more intense. A nice alternative is the MongoEngine library, which is an Object-Document Mapper (ODM), which treats MongoDB documents as a kind of ORM.
This week I ran into a case were I should run several scripts with analysis that could run simultaneously. The analysis results would then be used as basis for another analysis, that could only run after all other scripts ended.
Hello, everybody! :D
On the last post I told everyone a little bit about my story, but now let’s begin the fun part! As I am studying and becoming a programmer I will try my best to pass along the fun and good things I am learning in the process, ok!?
On November I discovered that I was selected for the Outreachy internship program for the batch of December 2018 to March 2019.
I started to study programming logic when I came across problems that required knowledge in Matlab. After a while studying Matlab I was suggested to switch to Python for its ease, simplicity and for being able to be applied to numerous areas (besides being free).
Most of all Django tutorials teach us how to return HTML as response to a request. Sometimes, it is useful to make it a little more RESTful. One option is to use Django REST Framework but sometimes you need something a little bit simpler. Then you have Restless. Restless is a miniframework made by Daniel Lindsley based on what he learned by making Tastypie and some other REST libraries.
In today’s development, tests are a fundamental tool for keeping things nice and easy and to keep programmer’s sanity. I’ve been using a set of tools for developing my web applications with Django and it is time for me to share a little bit about them.
This post could also be called what comes after the tutorials :)
In several Django tutorials, we learn how to receive requests and return responses with html pages having several information. This is very easy to start understanding the process that Django does: receiving requests and returning templates. But what happens after that?
As I talked to some people, few new about Django’s Generic Relation and Generic Foreign Key. And when I was studying it to apply on our system, I realised that the documentation can be kind of tricky and sparse. Nevertheless, Generic Relations helped us a lot, and so I decided to write about it in this blog post :)