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@Susensio
Susensio / numpy_lru_cache.md
Last active November 6, 2024 13:25
Make function of numpy array cacheable

How to cache slow functions with numpy.array as function parameter on Python

TL;DR

from numpy_lru_cache_decorator import np_cache

@np_cache()
def function(array):
 ...
@jagrosh
jagrosh / Github Webhook Tutorial.md
Last active November 18, 2024 08:23
Simple Github -> Discord webhook

Step 1 - Make a Discord Webhook

  1. Find the Discord channel in which you would like to send commits and other updates

  2. In the settings for that channel, find the Webhooks option and create a new webhook. Note: Do NOT give this URL out to the public. Anyone or service can post messages to this channel, without even needing to be in the server. Keep it safe! WebhookDiscord

Step 2 - Set up the webhook on Github

  1. Navigate to your repository on Github, and open the Settings Settings
@conormm
conormm / r-to-python-data-wrangling-basics.md
Last active September 24, 2024 04:20
R to Python: Data wrangling with dplyr and pandas

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: