print "Hello, Pylestras!"

?

import collections
collections.namedtuple?

collections.Counter??

*int*?

a = 1 + 2

print a

a

print In

print Out

%magic

%timeit range(100)

%%timeit
s = 0
for i in range(10):
    s += i ** 2

%%bash
echo "Meu path:"
pwd

%%ruby
6.times { puts 'Não faça isso em casa.' }

%%javascript
alert("Hello, Pylestras");

%lsmagic

!ls

files = !ls
for f in files:
    if f.endswith('.ipynb'):
        print f

python_variable = 42

!echo $python_variable

!echo {"{0:#b}".format(python_variable)}

%connect_info

%qtconsole

%matplotlib inline

"""
Demo of the fill function with a few features.

In addition to the basic fill plot, this demo shows a few optional features:

    * Multiple curves with a single command.
    * Setting the fill color.
    * Setting the opacity (alpha value).
"""
import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 100)
y1 = np.sin(x)
y2 = np.sin(3 * x)
plt.fill(x, y1, 'b', x, y2, 'r', alpha=0.3)
fig = plt.gcf()

%qtconsole

from IPython.display import Image

Image(url='http://python.org/images/python-logo.gif')

Image('http://ipython.org/_static/IPy_header.png')

from IPython.display import YouTubeVideo
YouTubeVideo('kkwiQmGWK4c')

from IPython.display import Audio

import numpy as np
import matplotlib.pyplot as plt
max_time = 3
f1 = 220.0
f2 = 224.0
rate = 44100.0
L = 3
times = np.linspace(0,L,rate*L)
signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)

Audio(data=signal, rate=rate)

audio_plt = plt.plot(times, signal)

from sympy import *
init_printing()

x = Symbol('x')

(pi + x)**2

(x+1)*(x+2)*(x+3)

expand((x+1)*(x+2)*(x+3))

from IPython.html.widgets import interact, interactive, fixed

def f(x):
    print x

interact(f, x=10);

interact(f, x=True);

interact(f, x='Hi there!');

@interact(x=True, y=1.0)
def g(x, y):
    print(x, y)

import skimage
from skimage import data, filter, io

from IPython.display import display

i = data.coffee()

lims = (0.0,1.0,0.01)

def edit_image(image, sigma=0.1, r=1.0, g=1.0, b=1.0):
    new_image = filter.gaussian_filter(image, sigma=sigma, multichannel=True)
    new_image[:,:,0] = r*new_image[:,:,0]
    new_image[:,:,1] = g*new_image[:,:,1]
    new_image[:,:,2] = b*new_image[:,:,2]
    new_image = io.Image(new_image)
    display(new_image)
    return new_image

w = interactive(edit_image, image=fixed(i), sigma=(0.0,10.0,0.1), r=lims, g=lims, b=lims)

display(w)

import pandas as pd

ts = pd.Series(np.random.randn(10), index=pd.date_range('1/1/2014', periods=10))

df = pd.DataFrame(np.random.randn(10, 4), index=ts.index,
                  columns=['A', 'B', 'C', 'D'])
 
df = df.cumsum()

df

plt.figure()
df.plot()
plt.legend(loc='best')

from IPython.display import IFrame

IFrame('http://nbviewer.ipython.org/', 800, 600)