zwegner

# Proof of optimality of 4-move solution to adversarial Wordle
# https://qntm.org/files/wordle/index.html
import array
import collections

with open('wordle-words.txt') as f:
    normal_words = [word.strip() for word in f]

with open('wordle-fake-words.txt') as f:
    all_words = [word.strip() for word in f] + normal_words

softwaredoug

I am working on a project contemplating the best use of notebooks in our search relevance workflow. We're a cross-disciplinary team of software engineers and data scientists. Recently, to decide best practices, I watched the two famous talks I don't like Notebooks by Joel Grus and I like notebooks by nbdev creator Jeremy Howard. As a senior dev, I want to have opinions for how my team should develop both the notebooks and any underlying libraries.

Positive things about nbdev and notebooks

• Writing docs leads to better code - I have written better code when I know its being consumed as documentation by others, and needs to be read. I fully agree with the amazing feedback loop between writing and coding that creates much better libraries
• Jupyter as a dev env - For some people, Jupyter is their preferred dev environment, and should be supported as such.
• Philosophy - I generally agree with the philo

russellsamora

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs: