Hi! My name is Gabi Cavalcante. I’m a full-stack engineer and tech lead based in Brazil, with 9+ years of Python behind me, mostly Django plus the data platform and infrastructure around it.
Every Django project I have worked on reaches the same moment. A request is too slow, someone moves the slow part into a Celery task, and the page gets fast. Quietly, the function has stopped being a function. It became a message, handed to a broker you do not control, to be run by a worker that might die halfway through. This post is my study guide for everything that can go wrong in that handoff, and what to do about each case.
Using seaborn and matplotlib to make great plots isn’t easy. I had been looking on the internet for complete tutorials on how to set up a good plot, but I only found small pieces of code. So here are a few tips for plotting data with the Google Global Mobility Report and matplotlib.
A ideia aqui é juntar sugestões que podem te ajudar caso você queira/vá dar um curso/aula/palestra e esteja preocupada em como fazer isso. Juntei algumas coisas que observei ao longo desses anos trabalhando no PyLadies Brasil , e talvez você possa aproveitar um pouco disso :) A ideia do post surgiu de uma conversa no grupo do PyLadies Natal , então a própria construção dessas sugestões foi algo coletivo.
When I started at my new job, the first task was: reports. It was a good way to learn the codebase, but when I opened the file that generated the PDFs, I found something really messed up. Every element (columns, rows, title…) had its position defined by hand, in centimeters. So a task that should have been quick was going to take much longer.