I love pandas. It’s one of the most important tools for data processing, but sometimes it can be tricky to remember how to adjust settings on the fly. Sometimes, I just want to see all my data, but pandas truncates the view:
This talk presents the story and software architecture behind an experimentally tested, machine learning framework for robot contact classification and motion control using a KUKA LBR iiwa robot, gRPC, and Python.
Design that is backed by data proves that your work is on the right track. It reveals the pain points, flaws, and opportunities, while unearthing new trends and improves your designs by adding objectivity.
I have a confession to make: I’m a stickler for filenames. I wholeheartedly believe that all filenames and paths should be kebab case or snake case. Spaces, capitalization, and non-alphanumeric characters drive me nuts.
Data analysis of sensor data is a typical hardware engineering activity that often involves an Excel-based workflow. While Excel (and other spreadsheet tools) are great for quick and dirty, one-off analyses, a Python + pandas workflow can really takes things to the next level.
Once upon a time, at McGill University in Mechanical Engineering, I took a course called COMP 208 Computer Programming for Physical Sciences and Engineering. The goal was to give us future hardware engineers a foundation in programming, to allow us to automate, perform advanced calculations, and solve our engineering problems through code.
Infinite values can occur more often than people expect, especially for calculated data.
For example, in a recent post I calculated the Twitter Follower-Friend ratio by dividing the followers_count series by the friends_count series.
While scrolling through my Twitter feed recently, I began to get a little annoyed at the amount of content that I was simply not interested in. In engineering terms, my signal-to-noise ratio was way too low.