danilofreire

#!/usr/bin/env bash
sudo yum groups install -y Development\ tools
sudo yum install -y cmake
sudo yum install -y python34-{devel,pip}
sudo pip-3.4 install neovim --upgrade
(
cd "$(mktemp -d)"
git clone https://github.com/neovim/neovim.git
cd neovim
make CMAKE_BUILD_TYPE=Release

danilofreire

R to python data wrangling snippets

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:

danilofreire

/**
 * A simple theme for reveal.js presentations, derived from serif.css 
 * It's in the spirit of the Metropolis theme for beamer https://github.com/matze/mtheme
 *
 * This theme is Copyright (C) 2016 Vince Hodges, http://sourdoughlabs.com - it is MIT licensed.
 */

@import url('https://fonts.googleapis.com/css?family=Fira+Sans');

.reveal a {

danilofreire

# Delete all containers
docker rm $(docker ps -aq)
# Delete all images
docker rmi $(docker images -q)
# Delete all untagged images
docker rmi $(docker images -q --filter "dangling=true")

References:

danilofreire

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

danilofreire

library(devtools)
devtools::install_github('RobertMyles/congressbr')
library(congressbr)
library(tidyverse)
library(stringr)
library(lubridate)

`%p%` <- function(e1,e2) return(paste0(e1,e2))
erros <- c()
download_year <- function(year) {

danilofreire

1. Clone your fork:

git clone git@github.com:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream