| { | |
| "regiones": [ | |
| { | |
| "region": "Arica y Parinacota", | |
| "comunas": ["Arica", "Camarones", "Putre", "General Lagos"] | |
| }, | |
| { | |
| "region": "Tarapacá", | |
| "comunas": ["Iquique", "Alto Hospicio", "Pozo Almonte", "Camiña", "Colchane", "Huara", "Pica"] | |
| }, |
| class BaseHandler(tornado.web.RequestHandler): | |
| def __init__(self, application, request, **kwargs): | |
| super(BaseHandler, self).__init__(application, request, **kwargs) | |
| @property | |
| def db(self): | |
| return self.application.db | |
| @staticmethod | |
| def current_timestamp(): |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # Copyright (c) 2013 Asumi Kamikaze Inc. | |
| # Copyright (c) 2013 The Octopus Apps Inc. | |
| # Licensed under the Apache License, Version 2.0 (the "License") | |
| # Author: Alejandro M. Bernardis | |
| # Email: alejandro.bernardis at gmail.com | |
| # Created: 04/Oct/2013 09:37 | |
| import urllib |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # | |
| # Copyright (c) 2012 The Octopus Apps Inc. | |
| # Licensed under the Apache License, Version 2.0 (the "License") | |
| # | |
| # Author: Alejandro M. Bernardis | |
| # Email: alejandro.m.bernardis at gmail.com | |
| # Created: Feb 6, 2012, 2:44:12 PM | |
| # |
(Task for GCI 2015-16) Year: 2015-16 This guide will show how to create a simple login page with Flask (a python microframework) and a sqlite3 database.
As we are creating a simple user login app we need to store 2 basic values in the database: the username and the password. In order to build the database we need to define a schema:
| # Reindexing after chard and replicates changed | |
| input { | |
| elasticsearch { | |
| hosts => [ "HOSTNAME_HERE" ] | |
| port => "9200" | |
| index => "INDEXNAME_HERE" | |
| size => 1000 | |
| scroll => "5m" | |
| docinfo => true |
| --------------------------------------------------------------------------------------------- | |
| #Installing in silent mode | |
| wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh | |
| bash ~/miniconda.sh -b -p $HOME/miniconda | |
| --------------------------------------------------------------------------------------------- | |
| #anaconda cloud | |
| >anaconda login | |
| git clone https://github.com/Anaconda-Platform/anaconda-client | |
| cd anaconda-client/example-packages/conda/ |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <meta http-equiv="X-UA-Compatible" content="IE=edge"> | |
| <title>CutOut text effect using blending mode</title> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <link rel="stylesheet" type="text/css" media="screen" href="/css/style.css" /> | |
| </head> |
To see what shell you are using, you can:
echo $SHELL
and it will tell you.
Use the below only if you are using bash.
Run the following to create ~/.git-completion.bash:
curl https://raw.github.com/git/git/master/contrib/completion/git-completion.bash > ~/.git-completion.bash
| R to python useful 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 |