vaclavcadek

class Node:

    def __init__(self, data):
        self.data = data
        self.next = None

    def __next__(self):
        return self.next

class LinkedList:

vaclavcadek

import numpy as np
import multiprocessing

def do_calculation(i, **kwargs):
    np.random.seed()
    rand=np.random.randint(10)
    print(i, kwargs['foo'], kwargs['bar'], rand)

if __name__ == '__main__':
    jobs = []

vaclavcadek

import sched, time
from datetime import datetime

s = sched.scheduler(time.time, time.sleep)

def read_signal(sc):
    with open('/home/vasek/Downloads/test.csv', 'a') as log:
        log.write('{timestamp},{temperature}\n'.format(timestamp=datetime.now(), temperature=23.0))
    # do your stuff
    s.enter(1, 1, read_signal, (sc,))

vaclavcadek

%matplotlib notebook

import numpy as np
import matplotlib.pyplot as plt
import time

def pltsin(ax, colors=['b']):
    x = np.linspace(0,1,100)
    if ax.lines:
        for line in ax.lines:

vaclavcadek

import sys
import io

sys.stdin = io.StringIO("""
3 3
0 1 1 1
1 2 2 4
2 0 1 2
""".strip())

vaclavcadek

# List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

# Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

# Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(valuelist)]

vaclavcadek

for bits in product([0, 1], repeat=4):
  print("".join(str(b) for b in bits)

vaclavcadek

import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.cross_validation import StratifiedKFold
from sklearn.feature_selection import RFECV
from sklearn.datasets import make_classification

# Create the RFE object and compute a cross-validated score.
svc = SVC(kernel='linear')
# The "accuracy" scoring is proportional to the number of correct
# classifications

vaclavcadek

from sklearn.externals.six import StringIO
from sklearn import tree
from graphviz import Source
from IPython.display import SVG

def vizualize_tree(model, figsize=(14,8)):
    out = StringIO()
    tree.export_graphviz(model, out_file=out)
    graph = Source(out.getvalue())
    graph.format = 'svg'

vaclavcadek

import numpy as np

def plot_correlation(dataframe, title='', corr_type=''):
    lang_names = dataframe.columns.tolist()
    tick_indices = np.arange(0.5, len(lang_names) + 0.5)
    plt.figure(figsize=(12,7))
    plt.pcolor(dataframe.values, cmap='RdBu', vmin=-1, vmax=1)
    colorbar = plt.colorbar()
    colorbar.set_label(corr_type)
    plt.title(title)