bearfrieze

Comprehensions in Python the Jedi way

by Bjørn Friese

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

jay-johnson

Format the Volume

driver@localhost:/var/lib$ sudo fdisk -l

Disk /dev/sda: 31.5 GB, 31515377664 bytes
255 heads, 63 sectors/track, 3831 cylinders, total 61553472 sectors
Units = sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes

mchirico

#!/usr/bin/env python
import tensorflow as tf
import numpy as np
from numpy import genfromtxt

# Build Example Data is CSV format, but use Iris data
from sklearn import datasets
from sklearn.model_selection import train_test_split
import sklearn
def buildDataFromIris():

jglick

node {
    // https://registry.hub.docker.com/_/maven/
    def maven32 = docker.image('maven:3.2-jdk-7-onbuild');
    
    stage 'Mirror'
    // First make sure the slave has this image.
    // (If you could set your registry below to mirror Docker Hub,
    // this would be unnecessary as maven32.inside would pull the image.)
    maven32.pull()
    // We are pushing to a private secure docker registry in this demo.

amatellanes

/* Useful celery config.

app = Celery('tasks',
             broker='redis://localhost:6379',
             backend='redis://localhost:6379')

app.conf.update(
    CELERY_TASK_RESULT_EXPIRES=3600,
    CELERY_QUEUES=(
        Queue('default', routing_key='tasks.#'),

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

DmZ

#!/bin/sh
 
if git rev-parse --verify HEAD >/dev/null 2>&1
then
    against=HEAD
else
    # Initial commit: diff against an empty tree object
    against=4b825dc642cb6eb9a060e54bf8d69288fbee4904
fi
 

mspan

pauloconnor

# Get rid of color codes
mutate {
  gsub => ["message", "\x1B\[([0-9]{1,2}(;[0-9]{1,2})?)?[m|K]", ""]
}

oubiwann

Listing queues ...
hipri   5
lopri   0
midpri  0
...done.