Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)
That's it!
Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)
That's it!
license: gpl-3.0 |
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) |
Exhaustive list of SPDX (Software Package Data Exchange) licenses: https://spdx.org/licenses/
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:
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
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:
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', |
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.
So here's my recipe for using a custom domain with GitHub pages where Namecheap is the DNS provider: