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du -h <dir> | grep '[0-9\.]\+G' | |
# e.g.: | |
# du -h /home/peterparker | grep '[0-9\.]\+G' |
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from multiprocessing import Pool | |
def job(x): | |
return x ** 2 | |
# End of job(...). | |
if __name__ == "__main__": | |
p = Pool(processes=50) | |
data_1 = p.map(job, range(10)) | |
data_2 = p.map(job, [99, 111, 7236]) |
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import networkx as nx | |
from networkx.algorithms import bipartite | |
B = nx.Graph() | |
B.add_nodes_from([1,2,3,4], bipartite=0) # Add the node attribute "bipartite" | |
B.add_nodes_from(['a', 'b', 'c', 'd'], bipartite=1) | |
B.add_edges_from([(1,'a'), (1,'b'), (2,'b'), (2,'c'), (3,'c'), (4,'a')]) | |
print 'Is connected? %s' % nx.is_connected(B) |
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import re | |
# Returns all the emoji in this string. 'text' is a unicode string. | |
def get_emoji(text): | |
text = unicode(text, 'ignore') | |
try: | |
ranges = re.compile(u'([\U00002600-\U000027BF])|([\U0001f300-\U0001f64F])|([\U0001f680-\U0001f6FF])') | |
except re.error: | |
ranges = re.compile(u'([\u2600-\u27BF])|([\uD83C][\uDF00-\uDFFF])|([\uD83D][\uDC00-\uDE4F])|([\uD83D][\uDE80-\uDEFF])') | |
emojis = (ranges.findall(text)) |
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import argparse | |
import sys | |
def main(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--x', type=float, default=1.0, | |
help='What is the first number?') | |
parser.add_argument('--y', type=float, default=1.0, | |
help='What is the second number?') | |
parser.add_argument('--operation', type=str, default='add', |
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# -*- coding: utf-8 -*- | |
from afinn import Afinn | |
import spacy | |
import re | |
class TargetedSentimentAnalysis(object): | |
def __init__(self): | |
self.afinn = Afinn(emoticons=True) |
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from sklearn.linear_model import SGDRegressor | |
# https://adventuresindatascience.wordpress.com/2014/12/30/minibatch-learning-for-large-scale-data-using-scikit-learn/ | |
def iter_minibatches(chunksize, numtrainingpoints): | |
# Provide chunks one by one | |
chunkstartmarker = 0 | |
while chunkstartmarker < numtrainingpoints: | |
chunkrows = range(chunkstartmarker,chunkstartmarker+chunksize) | |
X_chunk, y_chunk = getrows(chunkrows) |
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def foo(s): | |
if len(s) <= 0: | |
return None | |
else: | |
output, curr_char, curr_count = '', '', 0 | |
for idx in range(0, len(s)): | |
if s[idx] == curr_char: | |
curr_count += 1 | |
else: | |
output += curr_char + str(curr_count) if curr_count > 0 else curr_char |
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from functools import lru_cache | |
@lru_cache(maxsize=100) | |
def fibonacci(n): | |
# Check that the input is a positive integer | |
if type(n) != int: | |
raise TypeError("n must be a positive int") | |
if n < 1: | |
raise ValueError("n must be a positive int") | |
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# =================================================================================== | |
# Many thanks to: | |
# https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/20/conda/ | |
# | |
# More info: | |
# https://www.continuum.io/blog/developer-blog/python-packages-and-environments-conda | |
# https://conda-forge.github.io/#about | |
# =================================================================================== | |
# conda info --env |