rinze

FROM ubuntu:18.04

RUN apt-get update \
    && apt-get dist-upgrade -y \
    && apt-get install -y wget gcc g++ redis supervisor libgl1 sudo \
        vim less htop \
        webp libwebp-dev \
    && apt-get clean

RUN sudo sed -i '/^bind/s/bind.*/bind 0.0.0.0/' /etc/redis/redis.conf

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library(ggplot2)
theme_set(theme_bw(12))

data1 <- data.frame(location = c(rep(0, 10), 1:100, rep(100, 200), 100:1, rep(0, 10)),
                    style = 1)
#data1$style[(nrow(data1)-30):nrow(data1)] <- 2
data1$idx <- 1:nrow(data1)

plt1 <- ggplot(data1) + geom_line(aes(x = idx, y = location, 
                                      linetype = factor(style)),

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Overview

When you're working on multiple coding projects, you might want a couple different version of Python and/or modules installed. That way you can keep each project in its own sandbox instead of trying to juggle multiple projects (each with different dependencies) on your system's version of Python. This intermediate guide covers one way to handle multiple Python versions and Python environments on your own (i.e., without a package manager like conda). See the Using the workflow section to view the end result.

Use cases

1. Working on 2+ projects that each have their own dependencies; e.g., a Python 2.7 project and a Python 3.6 project, or developing a module that needs to work across multiple versions of Python. It's not reasonable to uninstall/reinstall modules every time you want to switch environments.
2. If you want to execute code on the cloud, you can set up a Python environment that mirrors the relevant

rinze

library(readr)
library(dplyr)
library(ggplot2)

get_first_digit <- function(x) {
    return(substr(x, 1, 1))
}

votes <- read_csv("https://github.com/Prooffreader/election_2016_data/raw/master/data/presidential_general_election_2016_by_county.csv")

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library(ggplot2)                                                                
                                                                                
NLINEAS <- 20                                                                   
                                                                                
coords <- data.frame(x1 = runif(NLINEAS, 0, 10),                                
                     x2 = runif(NLINEAS, 0, 10),                                
                     y1 = runif(NLINEAS, 0, 10),                                
                     y2 = runif(NLINEAS, 0, 10))                                
                                                                                
plt1 <- ggplot(coords) + geom_segment(aes(x = x1, xend = x2,                    

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library(C50)

# Test data
group <- c(rep('groupA', 10), rep('groupB', 10))
data <- data.frame(group = group, var = c(rep(0, 10), rep(0, 10)))

# Leave-one-out
probs <-  lapply(1:nrow(data), function(i) {
    train <- data[-i, ]
    test <- data[i, ]

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

import csv
import codecs
import cStringIO
import os
from collections import namedtuple

def getParties(parties_file):

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### Keybase proof

I hereby claim:

  * I am rinze on github.
  * I am rinzewind (https://keybase.io/rinzewind) on keybase.
  * I have a public key whose fingerprint is 67AF C4B2 E15C 4F4C 67BD  5F58 5ADF 9021 2630 80EC

To claim this, I am signing this object:

rinze

Keybase proof

I hereby claim:

• I am rinze on github.
• I am rinzewind (https://keybase.io/rinzewind) on keybase.
• I have a public key whose fingerprint is C7F0 E413 1FFF 47C6 A2A7 F76B F4FE 866D 2948 FA19

To claim this, I am signing this object:

rinze

localpath <- "/home/chema/tmp/kaggle/digits/"
if (Sys.info()["sysname"] == "Windows")
    localpath <- "C:/temp/kaggle/digits"

testFile <- file.path(localpath, "test.Rda")
trainFile <- file.path(localpath, "train.Rda")

if(!file.exists(testFile) && !file.exists(trainFile)) {
    testCSV <- file.path(localpath, "test.csv")
    trainCSV <- file.path(localpath, "train.csv")