messiest

find ~/Desktop -name "Screenshot*" -exec mv '{}' $SCREENSHOT_DIR \;

messiest

import time
from contextlib import ContextDecorator


class Timer(ContextDecorator):
    def __enter__(self):
        self.start = time.time()
        return self

    def __exit__(self, *exc):

messiest

def camel_case(s):
  s = s.split()
  leading = [x.lower() for x in s[:1]]
  trailing = [x.capitalize() for x in s[1:]]
  return ''.join(leading + trailing)

messiest

# Download installers
mkdir ~/Downloads/nvidia
cd ~/Downloads/nvidia
wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run
wget http://us.download.nvidia.com/XFree86/Linux-x86_64/384.59/NVIDIA-Linux-x86_64-384.59.run
sudo chmod +x NVIDIA-Linux-x86_64-384.59.run
sudo chmod +x cuda_8.0.61_375.26_linux-run
./cuda_8.0.61_375.26_linux-run -extract=~/Downloads/nvidia/
# Uninstall old stuff
sudo apt-get --purge remove nvidia-*

messiest

for c in df.columns:
    if np.issubdtype(df[c].dtype, np.number):
        # do stuff

messiest

def substring_index(strings, substring):
    for i, s in enumerate(strings):
        if substring in s:
            return i
    return False

messiest

#!/usr/bin/env python3
import ccxt


class Coin:
	price_dict = ccxt.kraken().fetchTickers()
	def __init__(self, abbr, name):
		self.name = name
		self.pair = "{}/USD".format(abbr)

messiest

from datetime import datetime

from pandas.tseries.offsets import DateOffset, WeekOfMonth
from pandas.tseries.holiday import sunday_to_monday, AbstractHolidayCalendar, \
    Holiday, USMemorialDay, USLaborDay, USColumbusDay, USThanksgivingDay


class BankCalendar(AbstractHolidayCalendar):
    rules = [
        Holiday("New Year's Day", month=1, day=1, observance=sunday_to_monday),

messiest

def list_splice(*args):
    out = [None] * (sum(len(i) for i in args))
    for i, l in enumerate(args):
        out[i::len(args)] = l

    return out

messiest

import gym


env = gym.make('SuperMarioBros-1-1-v0')
observation = env.reset()
done = False
t = 0
while not done:
    action = env.action_space.sample()  # choose random action
    observation, reward, done, info = env.step(action)  # feedback from environment