％time，％timeit和%% time

import numpy as np

from numpy.random import randint



# A function to simulate one million dice throws.

def one_million_dice():

    return randint(low=1, high=7, size=1000000)



# Let's try %time first

%time throws = one_million_dice()

%time mean = np.mean(throws)



# Outputs:

# Wall time: 20.6 ms

# Wall time: 3.01 ms



# Let's do the same with %timeit

%timeit throws = one_million_dice()

%timeit mean = np.mean(throws)



# Outputs:

# 10 loops, best of 3: 22.2 ms per loop

# 100 loops, best of 3: 2.86 ms per loop



# And finally %%time

%%time

throws = one_million_dice()

mean = np.mean(throws)



# Outputs:

# Wall time: 36.6 ms

％matplotlib

from numpy.random import randint

import matplotlib.pyplot as plt



# Sample 1000 random values to create a scatterplot

x = randint(low=1, high=1000, size=100)

y = randint(low=1, high=1000, size=100)



# This will show nothing in a Jupyter Notebook

plt.scatter(x, y)

plt.show()



# Let the magic happen!

%matplotlib inline

plt.scatter(x, y)

plt.show()

％load_ext autoreload

# load the autoreload extension

%load_ext autoreload

# Set extension to reload modules every time before executing code

%autoreload 2



from helperfunctions import complicated_function_to_return_a_number



complicated_function_to_return_a_number()

# Output: 123



# Go to helperfunctions.py and change something withing the function



complicated_function_to_return_a_number()

# Output: 321

%system

# Easy to read version

%system date



# Shorthand with "!!" instead of "%system" works equally well

!!date

％who_ls

# Outputs a list of all interactive variables in your environment

%who_ls



# Reduces the output to interactive variables of type "function"

%who_ls function