Learning Python

When you come from different programming languages like C and C++, where you are used to control very finely what the machine does, learning Python is quite disconcerting, especially when it comes to performance.

Without being exactly an optimization freak, I rather like to use the best algorithms, favoring algorithms over an naïve algorithms/implementations, so you can guess my surprise when after replacing the initial implementation by an implementation I did not observe the expected speed-up.

Initializing Arrays

Initializing large arrays of data before use is always cumbersome but it seems to be unavoidable.

The first types of solutions fill the array with a value that denotes an empty entry. While this sounds straightforward, the choice of that value is not always easy. For floating points numbers, zero may or may not be a good candidate. If convenient (as a zero floating point value is encoded as binary zeroes filling the variable) it may be difficult in some contexts to use because zero may be valid. Fortunately, there’s always the possibility to initialize the array with NaNs, which can be tested and used correctly (but you need the POSIX functions to do that).

The 10 (classes of) Algorithms Every Programmer Must Know About

In Tunnels of Doom!, I wrote that the disjoint sets algorithm is one of the very few algorithms every programmer should know. That got me thinking. Should? What about must? If everyone must know about disjoint sets, what other algorithms must every programmer know about?

I made a “top ten” list of algorithms and data structures every programmer must know about.