Look a key up in a Python dict with ten entries and it takes the same time as looking one up in a dict with ten million. That is not an accident of a fast language. Python’s dict, Java’s HashMap and JavaScript’s Map are all hash tables, and the whole design exists to make lookup, insert and delete run in O(1) on average. Big O notation gives that lookup the label “constant time”. Here is the mechanism that earns it. ...
<.+> against <b>bold</b> and <i>italic</i> doesn’t match <b>. It matches the whole string, from the first < to the very last >. Nothing is broken. That is what a greedy quantifier is supposed to do, and it is also the most common “regex is matching too much” bug there is. Once you know what the engine does while it reads your pattern, this bug and most of its relatives stop being mysteries. ...
A function that calls itself has a maximum depth, and it is smaller than most people expect: 1,000 calls in CPython by default, roughly 10,000-15,000 in Node and browser engines depending on how much each frame holds. Go past it and the program does not slow down gracefully. It crashes. Python raises RecursionError: maximum recursion depth exceeded; JavaScript raises RangeError: Maximum call stack size exceeded. Both errors have the same cause, and once you see it you will know when recursion is the right tool and when it is a liability. ...
Run 0.1 + 0.2 in a Python shell and it does not return 0.3. It returns 0.30000000000000004. Try it in JavaScript, Java, Go, C, or Rust and you get the same sixteen-digit tail. Every one of those languages is doing exactly what the hardware standard tells it to. A number that needs infinitely many binary digits is being squeezed into 64 bits, and something has to give. Why 0.1 can’t be stored exactly in binary Computers store floating-point numbers in base 2. In base 2, the fraction 1/10 is a repeating expansion, the same way 1/3 repeats forever in base 10 (0.3333...). Written out, 0.1 in binary starts: ...
A Python dictionary lookup takes about 100 nanoseconds whether the dict holds 1,000 keys or 1,000,000. A nested loop that checks a list for duplicates takes half a millisecond on 200 items and 136 milliseconds on 3,200. Sixteen times the input, nearly 280 times the wait. Same machine, same Python. Big O notation is the name for that difference. What Big O notation measures Big O describes how the running time (or memory use) of a piece of code grows as the input gets larger. It is not a stopwatch reading, and it does not care whether your laptop is fast or slow. It cares about the shape of the growth curve: when you double the input, does the time double, quadruple, or barely move? ...