UPDATE a fork of this gist has been used as a starting point for a community-maintained "awesome" list: machine-learning-with-ruby Please look here for the most up-to-date info!
- liblinear-ruby: Ruby interface to LIBLINEAR using SWIG
require "rubygems" | |
require "twitter" | |
require "json" | |
# things you must configure | |
TWITTER_USER = "your_username" | |
MAX_AGE_IN_DAYS = 1 # anything older than this is deleted | |
# get these from dev.twitter.com | |
CONSUMER_KEY = "your_consumer_key" |
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:
#!/usr/bin/env ruby | |
require "rubygems" | |
require "twitter" | |
require "json" | |
require "faraday" | |
# things you must configure | |
TWITTER_USER = "your_username" | |
# get these from dev.twitter.com |