Book Image

Jupyter Cookbook

By : Dan Toomey
Book Image

Jupyter Cookbook

By: Dan Toomey

Overview of this book

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Present a user-interactive graphic using Python

In this section, we use another Python library, bokeh, to display a chart where the user can adjust parameters of the graphic for different results.


The installation instructions for the bokeh library are very complex. Again, they're specific to the operating system and version of Python you are using in your installation.

We are presenting online voter information, with the data points showing, for each user ID, how many votes they received for some post they made.

How to do it...

We can use this script:

from import output_notebook, show
from bokeh.layouts import widgetbox
from bokeh.models.widgets import TextInput
from bokeh.models import WidgetBox
import numpy as np
import pandas as pd
from bokeh.plotting import figure, show
from bokeh.layouts import layout


# load the vote counts
from_counts = np.load("from_counts.npy")

# convert array to a dataframe (Histogram requires a dataframe)
df = pd.DataFrame({'Votes...