meganehouser

#coding:utf-8
import psutil
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

TIME_NUM = 90
FRAMES = 360

evanscottgray

docker ps | awk {' print $1 '} | tail -n+2 > tmp.txt; for line in $(cat tmp.txt); do docker kill $line; done; rm tmp.txt

ameyavilankar

import string
import nltk
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
import re

def preprocess(sentence):
	sentence = sentence.lower()
	tokenizer = RegexpTokenizer(r'\w+')
	tokens = tokenizer.tokenize(sentence)

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)

tfoldi

# CoffeeScript version of Google Spreadsheet Driver for Tableau Data Web Connector

init = ->
  if !tableau
    alert 'init- tableau NOT defined!'
    return
  tableau.scriptVersion = '1.0'
  tableau.log 'init'
  tableau.initCallback()

jesslilly

Cron Job Wrapper Wish List

I want a script that will give me:

1. Logging
2. Log purging!
3. Email errors!
4. Prevent duplicate processes! (flock)
5. Source an environment file!
6. Anything else?

bgusach

def multireplace(string, replacements, ignore_case=False):
    """
    Given a string and a replacement map, it returns the replaced string.

    :param str string: string to execute replacements on
    :param dict replacements: replacement dictionary {value to find: value to replace}
    :param bool ignore_case: whether the match should be case insensitive
    :rtype: str

    """

aneesha

from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF, LatentDirichletAllocation
import numpy as np

def display_topics(H, W, feature_names, documents, no_top_words, no_top_documents):
    for topic_idx, topic in enumerate(H):
        print "Topic %d:" % (topic_idx)
        print " ".join([feature_names[i]
                        for i in topic.argsort()[:-no_top_words - 1:-1]])
        top_doc_indices = np.argsort( W[:,topic_idx] )[::-1][0:no_top_documents]

benrules2

import random
import sys

def build_chain(text, chain = {}):
    words = text.split(' ')
    index = 1
    for word in words[index:]:
        key = words[index - 1]
        if key in chain:
            chain[key].append(word)

BrikerMan

from gensim.models import KeyedVectors

# Load gensim word2vec
w2v_path = '<Gensim File Path>'
w2v = KeyedVectors.load_word2vec_format(w2v_path)

import io

# Vector file, `\t` seperated the vectors and `\n` seperate the words
"""