pipx is a command-line tool that allows you to run Python applications in isolated environments. It's a great way to make VisiData available across your system without worrying that its dependencies will conflict with other Python projects. This short guide will show you how.
If you have have previously VisiData installed globally, uninstall it by running pip3 uninstall visidata.
Otherwise, you can skip this step.
| // Step 1: Go to http://craftcans.com/db.php?search=all&sort=beerid&ord=desc&view=text | |
| // Step 2: Open your browser's developer console | |
| // Step 3: Paste the code below into the console | |
| (function () { | |
| var getText = function (el) { | |
| return el.textContent.trim().replace(/\n/g, " "); | |
| }; | |
| // Get the last <table> on the page |
| /* OPTM Report -> TSV | |
| The script below takes reports generated by the Organ Procurement and Transplantation | |
| Network website and converts them into spreadsheet-friendly tab-separated values (TSV) files. | |
| Instructions: | |
| 1. Create an advanced data report at https://optn.transplant.hrsa.gov/converge/LatestData/advancedData.asp. In Step 4, keep the default options ("Counts" + "Portrait"). | |
| 2. On the results page, open up your browser's console. |
The Stockholm International Peace Research Institute's Arms Transfers Database provides a tool to download the data. By default, that output is an .rtf rich-text file — not so easy to analyze with your favorite spreadsheet or statistics software. Luckily, getting a CSV of the data isn't very difficult. Here's how.
To get all transfers for 2014, by seller, run this command in your terminal:
curl http://armstrade.sipri.org/armstrade/html/export_trade_register.php --compressed \
--data 'low_year=2014' \
--data 'high_year=2014' \
--data 'seller_country_code=' \
--data 'buyer_country_code=' \
I hereby claim:
To claim this, I am signing this object:
I'm setting up a new Mac. I'm using this Gist to keep track of the software I install (roughly in order, in case it's useful later.
In late October, the WSJ published Waste Lands, an interactive database I helped build and report. Waste Lands draws upon thousands of public records and other sources to trace the history of hundreds of factories and laboratories the government recruited to help develop nuclear weapons during the build-up to the Cold War.
Building the database took considerable time and effort. But it would have been immeasurably more difficult and time-consuming — if not nearly impossible — without the fistfuls of open-source code we were able to rely upon:
| var exemplify = function (fn, code_el) { | |
| fn.call(fn); | |
| var str = fn.toString(); | |
| var lines = str.split("\n").slice(1, -1); | |
| var start_index = lines[0].match(/[^\s].*$/).index; | |
| var trimmed = lines.map(function (x) { | |
| return x.slice(start_index); | |
| }).join("\n"); | |
| code_el.innerHTML = trimmed; | |
| }; |
| (function () { | |
| var root = this; | |
| var sum = function (arr) { | |
| return _.reduce(arr, function (m, x) { | |
| return m + x; | |
| }); | |
| }; | |
| // n: Size of the stack, i.e., how far to look back |