How’d you create a sub dictionary from a dictionary where the keys of the sub-dict are provided as a list? I was reading a tweet1 by Ned Bachelder on this today and that made me realize that I usually solve it with O(DK) complexity, where K is the length of the sub-dict keys and D is the length of the primary dict. Here’s how I usually do that without giving it any thoughts or whatsoever: ...
I used to use Unittest’s self.assertTrue / self.assertFalse to check both literal booleans and truthy/falsy values in Unittest. Committed the same sin while writing tests in Django. I feel like assertTrue and assertFalse are misnomers. They don’t specifically check literal booleans, only truthy and falsy states respectively. Consider this example: # src.py import unittest class TestFoo(unittest.TestCase): def setUp(self): self.true_literal = True self.false_literal = False self.truthy = [True] self.falsy = [] def is_true(self): self.assertTrue(self.true_literal, True) def is_false(self): self.assertFalse(self.false_literal, True) def is_truthy(self): self.assertTrue(self.truthy, True) def is_falsy(self): self.assertFalse(self.falsy, True) if __name__ == "__main__": unittest.main() ...
To check whether an integer is a power of two, I’ve deployed hacks like this: def is_power_of_two(x: int) -> bool: return x > 0 and hex(x)[-1] in ("0", "2", "4", "8") ...
Django Rest Framework exposes a neat hook to customize the response payload of your API when errors occur. I was going through Microsoft’s REST API guideline1 and wanted to make the error response of my APIs more uniform and somewhat similar to this2. I’ll use a modified version of the quickstart example3 in the DRF docs to show how to achieve that. Also, we’ll need a POST API to demonstrate the changes better. Here’s the same example with the added POST API. Place this code in the project’s urls.py file. ...
If you want to define a variable that can accept values of multiple possible types, using typing.Union is one way of doing that: from typing import Union U = Union[int, str] However, there’s another way you can express a similar concept via constrained TypeVar. You’d do so as follows: ...
Recently, fell into this trap as I wanted to speed up a slow instance method by caching it. When you decorate an instance method with functools.lru_cache decorator, the instances of the class encapsulating that method never get garbage collected within the lifetime of the process holding them. Let’s consider this example: # src.py import functools import time from typing import TypeVar Number = TypeVar("Number", int, float, complex) class SlowAdder: def __init__(self, delay: int = 1) -> None: self.delay = delay @functools.lru_cache def calculate(self, *args: Number) -> Number: time.sleep(self.delay) return sum(args) def __del__(self) -> None: print("Deleting instance ...") # Create a SlowAdder instance. slow_adder = SlowAdder(2) # Measure performance. start_time = time.perf_counter() # ---------------------------------------------- result = slow_adder.calculate(1, 2) # ---------------------------------------------- end_time = time.perf_counter() print(f"Calculation took {end_time-start_time} seconds, result: {result}.") start_time = time.perf_counter() # ---------------------------------------------- result = slow_adder.calculate(1, 2) # ---------------------------------------------- end_time = time.perf_counter() print(f"Calculation took {end_time-start_time} seconds, result: {result}.") ...
Problem A common interview question that I’ve seen goes as follows: Write a function to crop a text corpus without breaking any word. Take the length of the text up to which character you should trim. Make sure that the cropped text doesn’t have any trailing space. Try to maximize the number of words you can pack in your trimmed text. Your function should look something like this: ...
While trying to avoid inheritance in an API that I was working on, I came across this neat trick to perform attribute delegation on composed classes. Let’s say there’s a class called Engine and you want to put an engine instance in a Car. In this case, the car has a classic ‘has a’ (inheritance usually refers to ‘is a’ relationships) relationship with the engine. So, composition makes more sense than inheritance here. Consider this example: ...
I was browsing through the source code of Tom Christie’s typesystem1 library and discovered that the shell scripts2 of the project don’t have any extensions attached to them. At first, I found it odd, and then it all started to make sense. Executable scripts can be written in any language and the users don’t need to care about that. Also, not gonna lie, it looks cleaner this way. ...
At my workplace, we have a fairly large Celery config file where you’re expected to subclass from a base class and extend that if there’s a new domain. However, the subclass expects the configuration in a specific schema. So, having a way to enforce that schema in the subclasses and raising appropriate runtime exceptions is nice. Wrote a fancy Python 3.6+ __init_subclasshook__ to validate the subclasses as below. This is neater than writing a metaclass. ...
Making tqdm play nice with multiprocessing requires some additional work. It’s not always obvious and I don’t want to add another third-party dependency just for this purpose. The following example attempts to make tqdm work with multiprocessing.imap_unordered. However, this should also work with similar mapping methods like—multiprocessing.map, multiprocessing.imap, multiprocessing.starmap, etc. """ Run `pip install tqdm` before running the script. The function `foo` is going to be executed 100 times across `MAX_WORKERS=5` processes. In a single pass, each process will get an iterable of size `CHUNK_SIZE=5`. So 5 processes each consuming 5 elements of an iterable will require (100 / (5*5)) 4 passes to finish consuming the entire iterable of 100 elements. Tqdm progress bar will be updated after every `MAX_WORKERS*CHUNK_SIZE` iterations. """ # src.py from __future__ import annotations import multiprocessing as mp from tqdm import tqdm import time import random from dataclasses import dataclass MAX_WORKERS = 5 CHUNK_SIZE = 5 @dataclass class StartEnd: start: int end: int def foo(start_end: StartEnd) -> int: time.sleep(0.2) return random.randint(start_end.start, start_end.end) def main() -> None: inputs = [ StartEnd(start, end) for start, end in zip( range(0, 100), range(100, 200), ) ] with mp.Pool(processes=MAX_WORKERS) as pool: results = tqdm( pool.imap_unordered(foo, inputs, chunksize=CHUNK_SIZE), total=len(inputs), ) # 'total' is redundant here but can be useful # when the size of the iterable is unobvious for result in results: print(result) if __name__ == "__main__": main() ...
One thing that came to me as news is that the command which—which is the de-facto tool to find the path of an executable—is not POSIX compliant. The recent Debian debacle1 around which brought it to my attention. The POSIX-compliant way of finding an executable program is command -v, which is usually built into most of the shells. So, instead of doing this: which python3.12 ...
Pasting shell commands can be a pain when they include hidden return \n characters. In such a case, your shell will try to execute the command immediately. To prevent that, use curly braces { <cmd> } while pasting the command. Your command should look like the following: { dig +short google.com } Here, the spaces after the braces are significant. ...
Use unofficial bash strict mode while writing scripts. Bash has a few gotchas and this helps you to avoid that. For example: #!/bin/bash set -euo pipefail echo "Hello" Where, -e Exit immediately if a command exits with a non-zero status. -u Treat unset variables as an error when substituting. -o pipefail The return value of a pipeline is the status of the last command to exit with a non-zero status, or zero if no command exited with a non-zero status. ...