Book Image

Jupyter for Data Science

By : Dan Toomey
Book Image

Jupyter for Data Science

By: Dan Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Interactive visualization


There is a Python package, Bokeh, that can be used to generate a figure in your notebook where the user can interact and change the figure.

In this example, I am using the same data from the histogram example later in this chapter (also included in the file set for this chapter) to display an interactive Bokeh histogram.

The coding is as follows:

from bokeh.io import show, output_notebook
from bokeh.charts import Histogram
import numpy as np
import pandas as pd
# this step is necessary to have display inline in a notebook
output_notebook()
# load the counts from other histogram example
from_counts = np.load("from_counts.npy")
# convert array to a dataframe for Histogram
df = pd.DataFrame({'Votes':from_counts})
# make sure dataframe is working correctly
print(df.head())
   Votes
0     23
1     29
2     23
3    302
4     24
# display the Bokeh histogram
hist = Histogram(from_counts, \
title="How Many Votes Made By Users", \
bins=12)
show(hist) 

We can see the histogram...