5t3ph

*[class],
*[id] {
  position: relative;
  outline: 2px dashed red;
}

*[class]::before, *[class]::after,
*[id]::before,
*[id]::after {
  position: absolute;

zaydek-old

/* debug.css | MIT License | zaydek.github.com/debug.css */ if (!("is_debugging" in window)) { is_debugging = false; var debug_el = document.createElement("style"); debug_el.append(document.createTextNode(`*:not(g):not(path) { color: hsla(210, 100%, 100%, 0.9) !important; background: hsla(210, 100%,  50%, 0.5) !important; outline: solid 0.25rem hsla(210, 100%, 100%, 0.5) !important; box-shadow: none !important; filter: none !important; }`)); } function enable_debugger() { if (!is_debugging) { document.head.appendChild(debug_el); is_debugging = true; } } function disable_debugger() { if (is_debugging) { document.head.removeChild(debug_el); is_debugging = false; } } !is_debugging ? enable_debugger() : disable_debugger();

chranderson

// check version
node -v || node --version

// list locally installed versions of node
nvm ls

// list remove available versions of node
nvm ls-remote

// install specific version of node

nepsilon

Need to make sense of large CSV files?

Often you are handed-out big CSV files and need to sort, filter, update some cells or other tedious work. You tried sed, jq, and maybe the mighty awk.

But at the end of the day: You’re using the wrong tool for the job.

Here come csvkit’s csvsql, a small Python script to parse your CSV files and create the corresponding database. You now have the best tool, SQL, to answer your data questions: