Jupyter Interactive widgets

The notebook comes alive with the interactive widgets:

Part of the Jupyter project
BSD Licensed

Installation for the legacy notebook:

conda install -c conda-forge ipywidgets

Speeding up the bottleneck in the REPL



In [1]:

    
12 * 12









    Out[1]:





144



In [16]:

    
def f(x, y):
    print(x * y)



In [17]:

    
from ipywidgets import *
from traitlets import dlink



In [18]:

    
interact(f, x=(0, 100), y=['a',  'b']);

Interactive Jupyter widgets



In [19]:

    
slider = FloatSlider(
    value=7.5,
    min=5.0,
    max=10.0,
    step=0.1,
    description='Input:',
)

slider



In [20]:

    
slider



In [21]:

    
slider.value = 8



In [22]:

    
slider.keys









    Out[22]:





['_dom_classes',
 '_model_module',
 '_model_module_version',
 '_model_name',
 '_view_count',
 '_view_module',
 '_view_module_version',
 '_view_name',
 'continuous_update',
 'description',
 'description_tooltip',
 'disabled',
 'layout',
 'max',
 'min',
 'orientation',
 'readout',
 'readout_format',
 'step',
 'style',
 'value']



In [23]:

    
slider.readout_format = '.4f'



In [24]:

    
text = FloatText(description='Value')
dlink((slider, 'value'), (text, 'value'))
text



In [25]:

    
slider

Widgets are represented in the back-end by a single object. Each time a widget is displayed, a new representation of that same object is created in the front-end. These representations are called views.



In [26]:

    
slider



In [27]:

    
ft = FloatText()



In [28]:

    
def foo(change):
    ft.value = change.new

slider.observe(foo, names=['value'])



In [29]:

    
ft



In [30]:

    
from ipywidgets import HBox



In [31]:

    
HBox([slider, slider, ft])



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