Explore >> Select a destination
|
You are here 
|
rgoswami.me | ||
| | | | |
vickiboykis.com
|
|
| | | | | When I'm working with Jupyter notebooks, I often want to work with them from within a virtual environment. The general best practice is that you should always use either virtual environments or Docker containers for working with Python, for reasons outlined in this post, or you're gonna have a bad time. I know I have. The workflow is a little long, so I thought I'd document it for future me here. | |
| | | | |
sqrtminusone.xyz
|
|
| | | | | Freedom is a state of mind | |
| | | | |
tomaugspurger.net
|
|
| | | | | Today I released stitch into the wild. If you haven't yet, check out the examples page to see an example of what stitch does, and the Github repo for how to install. I'm using this post to explain why I wrote stitch, and some issues it tries to solve. Why knitr / knitpy / stitch / RMarkdown? Each of these tools or formats have the same high-level goal: produce reproducible, dynamic (to changes in the data) reports. They take some source document (typically markdown) that's a mixture of text and code and convert it to a destination output (HTML, PDF, docx, etc.). | |
| | | | |
eerielinux.wordpress.com
|
|
| | | The previous part of this series left off with a running "baby daemon" example. It covered Python fundamentals, signal handling, logging as well as an init script to start the daemon. Daemonization with Python The outcome of part 1 was a program that needed external help actually to be daemonized. I used FreeBSD's handy daemon(8)... | ||
