cwharland

conda list | tr -s ' ' | cut -d ' ' -f1 | uniq -c | awk '$1>1'

cwharland

import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
import skflow
import pytest

class TestLinearRegression:
    def setup_class(self):
        '''Simple linear model with a tiny amount of noise'''
        rng = np.random.RandomState(67)

cwharland

from bs4 import BeautifulSoup as bs
import urllib2
import re
import wget

# Where do you want to download the files?
path = '~/studs_terkel/%s'

# Page with all the mp3 links
url = 'http://conversations.studsterkel.org/htimes.php'

cwharland

import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix

labels = ['N', 'L', 'R', 'A', 'P', 'V']
df = pd.DataFrame([
    [1971, 19, 1, 8, 0, 1],
    [16, 1940, 2, 23, 9, 10],
    [8, 3, 181, 87, 0, 11],
    [2, 25, 159, 1786, 16, 12],

cwharland

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
pd.Series(np.random.normal(1,10, size=1000)).hist()
plt.title('Sweet')

cwharland

# Flatten a nested array or list
[item for sublist in l for item in sublist]

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custom_style = {'axes.labelcolor': 'white',
                'axes.linewidth': 2,
                'xtick.color': 'white',
                'ytick.color': 'white',
                'lines.color': 'white',
                'patch.edgecolor': 'white',
                'text.color': 'white',
                'axes.facecolor': '#1d1f21',
                'axes.edgecolor': 'white',
                'axes.labelcolor': 'white',

cwharland

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console