rbonvall

# encoding=utf-8

# Obtener el dígito verificador del RUT en Python.
#
# La función recibe el RUT como un entero,
# y entrega el dígito verificador como un entero.
# Si el resultado es 10, el RUT es "raya k".

from itertools import cycle

douglasmiranda

# This is a really old post, in the comments (and stackoverflow too) you'll find better solutions.

def find(key, dictionary):
    for k, v in dictionary.iteritems():
        if k == key:
            yield v
        elif isinstance(v, dict):
            for result in find(key, v):
                yield result
        elif isinstance(v, list):

karmi

st4lk

# Logging settings for django projects, works with django 1.5+
# If DEBUG=True, all logs (including django logs) will be
# written to console and to debug_file.
# If DEBUG=False, logs with level INFO or higher will be
# saved to production_file.
# Logging usage:

# import logging
# logger = logging.getLogger(__name__)
# logger.info("Log this message")

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

ondrej-kvasnovsky

# install missing libraries (if any)
cd ~
sudo yum update
yum install java-1.7.0-openjdk.x86_64
yum install unzip
yum install mc
yum install wget
yum install curl

# get and unpack elasticsearch zip file

stevehanson

import os
import sys


# constants, configure to match your environment
HOST = 'http://localhost:9200'
INDEX = 'test'
TYPE = 'attachment'
TMP_FILE_NAME = 'tmp.json'

stevehanson

import os
import sys

# constants, configure to match your environment
HOST = 'http://localhost:9200'
INDEX = 'test'
TYPE = 'attachment'
TMP_FILE_NAME = 'tmp.json'
# for supported formats, see apache tika - http://tika.apache.org/1.4/formats.html
INDEX_FILE_TYPES = ['html','pdf', 'doc', 'docx', 'xls', 'xlsx', 'xml']

aronwc

""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






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