Whether new to Python, or simply coming from a different domain, the data science enthusiast’s foray into the field can be intimidating. From entering the door, one gets overwhelmed with a bunch of unfamiliar libraries, necessary for one’s daily work: NumPy, SciPy, SciKit, Matplotlib, Pandas, Theano, Tensorflow, Keras, CNTK, just to name a few …
NOTE: This post is an ongoing collection of tips and tricks I have learned around my work with Pandas. It is a live document, intended to remain in progress forever, as I keep-adding more and more things to it. You can share your personal tips and tricks in the comments below, or on my blog’s subreddit.
Tuesday Happy Halloween! Today and tomorrow are public holidays in Bavaria and we decided to use the time for a short day trip to the idyllic town of Regensburg, one and half hours north of Munich:
Swift provides several constructs which make writing code a more fluid experience, with less boilerplate. Sometimes this succinctness of syntax comes at a potential cost though. One such aspect are properties, and more specifically, property initialisation. A small difference in the syntax might result in unnecessary memory consumption, unexpected state inconsistencies, etc. Those might remain unnoticed when the project is still small and reappear at a later stage, when the project is large enough to mak...