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

import sys
import io

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

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 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

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

class Node:

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

    def __next__(self):
        return self.next

class LinkedList:

vaclavcadek

from concurrent import futures
import time

def long_running_task(result):
    print('Task is running!')
    time.sleep(1)
    return result

start = time.perf_counter()

vaclavcadek

from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(5, 8))


def update(i):
    im_normed = np.random.random((64, 64))
    ax.imshow(im_normed)

vaclavcadek

def logger(func):
    def wrapped(*args, **kwargs):
        print('Calling {} with args = {} and kwargs = {}'.format(func, args, kwargs))
        return func(*args, **kwargs)
    return wrapped

@logger
def factorial(n):
    if n == 1:
        return 1

vaclavcadek

    permutation = list(np.random.permutation(m))
    shuffled_X = X[:, permutation]
    shuffled_Y = Y[:, permutation].reshape((1,m))