101 NumPy Exercises for Data Analysis (Python) – Machine Learning Plus

The goal of the numpy exercises is to serve as a reference as well as to get you to apply numpy beyond the basics.

These are my links for July 20th through July 24th:

Ask HN: Best-architected open-source business applications worth studying? | Hacker News -
Monospaced Programming Fonts with Ligatures | Hacker News -
The language of choice - Propositional logic was discovered by Stoics around 300 B.C., only to be abandoned in later antiquity and rebuilt in the 19th century by George Boole’s successors. One of them, Charles Peirce, saw its significance for what we now call logic circuits, yet that discovery too was forgotten until the 1930s. In the ’50s John McCarthy invented conditional expressions, casting the logic into the form we’ll study here; then in 1986 Randal Bryant repeated one of McCarthy’s constructions with a crucial tweak that made his report “for many years the most cited paper in all of computer science, because it revolutionized the data structures used to represent Boolean functions” (Knuth).1 Let’s explore and code up some of this heritage of millennia, and bring it to bear on a suitable challenge: playing tic-tac-toe.
Then we’ll tackle a task that’s a little more practical: verifying a carry-lookahead adder circuit. Supposedly logic gets used all the time for all kinds of serious work, but for such you’ll have to consult the serious authors; what I can say myself, from working out the code to follow, is that the subject offers a fun playground plus the most primitive form of the pun between meaning and mechanism.

You’re encouraged to read with this article’s code cloned and ready

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