It was quite an eventful week.
UPDATE Feb 17th, 2018: Though not a part of this article, it is worth noting that Facebook changed the licensing of React Native to MIT yesterday. This should come as a relief to many and would certainly increase the library’s commercial adoption even further:
The end of every year usually gets me thinking and recollecting, and 2017 makes no difference.
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.