| for each in `ls . | grep -v "stackoverflow.com.7z.0"` ; do 7z e $each -o`echo $each | cut -d "." -f -2` ; done && 7z e stackoverflow.com.7z.001 -ostackoverflow |
| #!/bin/bash | |
| for f in `ls . | grep json.gz` | |
| do | |
| if [ `zcat $f | wc -l` == 0 ] ; then | |
| gunzip $f | |
| for unf in `ls *.json` | |
| do | |
| echo $unf | |
| sed -i 's/}{/}\n{/g' $unf |
On a Github profile, one can inform his location, such as "San Fransisco, CA" or "Paris, France". Some profiles use this feature to freely describe where they feel they are. Here's a list of funny locations found on github users profiles, ranked by the number of users claiming it.
Data was extracted from Github Archive. for 2013 only.
Their is numerous false positives.
by @mazieres.
| # coding: utf-8 | |
| """Generic usage of Principal Component Analysis""" | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from sklearn.decomposition import PCA | |
| import numpy as np | |
| def osetg(seq, idfun=None): | |
| # Ordered set generator | |
| # <http://www.peterbe.com/plog/uniqifiers-benchmark> | |
| if idfun is None: | |
| def idfun(x): return x | |
| seen = {} | |
| for item in seq: | |
| marker = idfun(item) | |
| if marker in seen: continue | |
| seen[marker] = 1 |
| def osetg(seq, idfun=None): | |
| # Ordered set generator | |
| # <http://www.peterbe.com/plog/uniqifiers-benchmark> | |
| if idfun is None: | |
| def idfun(x): return x | |
| seen = {} | |
| for item in seq: | |
| marker = idfun(item) | |
| if marker in seen: continue | |
| seen[marker] = 1 |
| def osetg(seq, idfun=None): | |
| # Ordered set generator | |
| # <http://www.peterbe.com/plog/uniqifiers-benchmark> | |
| if idfun is None: | |
| def idfun(x): return x | |
| seen = {} | |
| for item in seq: | |
| marker = idfun(item) | |
| if marker in seen: continue | |
| seen[marker] = 1 |
| #!/usr/bin/env python | |
| # by @mazieres for cortext.fr | |
| import sqlite3 | |
| import sys | |
| import os | |
| from collections import defaultdict | |
| # PATH to the DB downloaded from cortext |
| import pandas as pd | |
| import numpy as np | |
| def wannabe_projection(df): | |
| ''' | |
| https://stats.stackexchange.com/questions/142132/is-this-a-valid-method-for-unipartite-projection-of-a-bipartite-graph | |
| ''' | |
| n_samples = df.shape[0] | |
| res = np.zeros((n_samples, n_samples)) |
| import unittest | |
| class TestExtract(unittest.TestCase): | |
| def test_adjacency_matrix(self): | |
| X = np.array([ | |
| [1, 8, 3], | |
| [5, 0, 0], | |
| [0, 4, 2]]) | |
| tested = adjacency_matrix(X) |