| require 'faraday' | |
| require 'uri' | |
| data = { | |
| :your_data => "YOUR DATA" | |
| } | |
| url = "some url" | |
| response = Faraday.post(url) do |req| |
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:
| So you've cloned somebody's repo from github, but now you want to fork it and contribute back. Never fear! | |
| Technically, when you fork "origin" should be your fork and "upstream" should be the project you forked; however, if you're willing to break this convention then it's easy. | |
| * Off the top of my head * | |
| 1. Fork their repo on Github | |
| 2. In your local, add a new remote to your fork; then fetch it, and push your changes up to it | |
| git remote add my-fork git@github...my-fork.git |