plembo

Using GitHub Pages with a custom domain: Namecheap Edition

As often happens, I found the official documentation and forum answers to be "close, but no cigar", and so had to experiment a little to get things working.

The main problem for me was a lack of concrete configuration examples. That's not entirely GitHub's fault: having migrated from Google Domains to Namecheap in the middle of this project, I was once again reminded of how many different ways there are to do things in the name service universe [1].

Although you'd think the simplest setup would be to merely configure for the subdomain case (https://www.example.com), in my experience using the apex domain (https://example.com) instead resulted in fewer complications.

Procedure

So here's my recipe for using a custom domain with GitHub pages where Namecheap is the DNS provider:

Thiagobc23

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from pandas import Timestamp

##### DATA #####
data = {'Task': {0: 'TSK M',
                 1: 'TSK N',
                 2: 'TSK L',

DannyQuah

Managing Gists Locally

by Danny Quah, May 2020 (revised Jan 2022)

Through the Embed instruction or plugin, Gist snippets on GitHub can conveniently provide posts on Medium, WordPress, and elsewhere supplementary information (lines of code, images, Markdown-created tables, and so on). But while Gist snippets on GitHub can be managed directly via browser or through something like [Gisto][], a user might also wish to manipulate them offline. This last is for many of the same reasons that a user seeks to clone a git repo to their local filesystem, modify it locally, and then only subsequently push changes back up to GitHub.

Here's how to do this:

Create the gist on GitHub and then clone it to your local filesystem:

ThuyNT13

Big O notation is a way for us to describe how long it takes for an algorithm to run. We can use Big O notation to compare how efficient different approaches to solving a problem are. In big O notation we describe the runtime of an algorithm in terms of how quickly the runtime grows as the input to the algorithm gets very, very large. Let’s break down the definition a bit:

• How quickly the runtime grows:

  • We can’t give an exact amount of time that an algorithm will run for because it depends on too many things. Is it running on a supercomputer, a really old desktop, or a calculator? Which language is it written in? Is the computer running anything else at the same time? All of these can affect the runtime, so we remove these variables by focusing on how the runtime grows.

• Based on the input:

• Since we are removing runtime from the description we need another way to express the speed - we can’t use seconds anymore. Instead we’ll use the size of the input to describe it. We will use

TomAugspurger

nicolasdao

Exhaustive list of SPDX (Software Package Data Exchange) licenses: https://spdx.org/licenses/

Table of Contents

• TL;DR - The BSD 3 Clause License Should Do
• In a Nutshell
• 3 Main Types of Open Source Licenses
  • 1. MIT
  • 2. Apache
  • 3. GPL
• The BSD License Case

artisanalcode

WHAT DID I ACCOMPLISH?

Item on PR or CR. 🔬 Item currently on V&V/Q&A. ✅ Item done (as per definition of done). 🔹 Other item.

WHAT WILL I DO?

🔛 Currently working on.

jlln

def splitDataFrameList(df,target_column,separator):
    ''' df = dataframe to split,
    target_column = the column containing the values to split
    separator = the symbol used to perform the split

    returns: a dataframe with each entry for the target column separated, with each element moved into a new row. 
    The values in the other columns are duplicated across the newly divided rows.
    '''
    def splitListToRows(row,row_accumulator,target_column,separator):
        split_row = row[target_column].split(separator)

mbostock

license: gpl-3.0

hrldcpr

One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:

def tree(): return defaultdict(tree)

That's it!