| [alias] | |
| co = checkout | |
| ci = commit | |
| st = status | |
| br = branch | |
| hist = log --pretty=format:'%h %ad | %s%d [%an]' --graph --date=short | |
| type = cat-file -t | |
| dump = cat-file -p | |
| publish = "!git push --set-upstream origin \"$(git rev-parse --abbrev-ref HEAD)\"" | |
| refresh = "!git pull origin \"$(git rev-parse --abbrev-ref HEAD)\"" |
Podman is such a cool project! However, there is no easy way to setup the nvidia-container-runtime and podman so that we can run unprivileged container in a gpu host. This is specially interesting in environments with multiple people accessing the same host (strong isolation between containers!!!).
Ubuntu: add-apt-repository -y ppa:projectatomic/ppa && apt install podman buildah skopeo
| [Unit] | |
| Description=Jupyter Notebook | |
| [Service] | |
| Type=simple | |
| ExecStart=/home/roeyd/Notebook/.env/bin/jupyter lab --port 9090 | |
| WorkingDirectory=/home/roeyd/Notebook | |
| [Install] | |
| WantedBy=default.target |
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:
| package main | |
| import ( | |
| "fmt" | |
| "os" | |
| "os/exec" | |
| "syscall" | |
| ) | |
| func main() { |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
