Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Practical Business Intelligence

You're reading from   Practical Business Intelligence Optimize Business Intelligence for Efficient Data Analysis

Arrow left icon
Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781785885433
Length 352 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Ahmed Sherif Ahmed Sherif
Author Profile Icon Ahmed Sherif
Ahmed Sherif
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Practical Business Intelligence
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Practical Business Intelligence FREE CHAPTER 2. Web Scraping 3. Analysis with Excel and Creating Interactive Maps and Charts with Power BI 4. Creating Bar Charts with D3.js 5. Forecasting with R 6. Creating Histograms and Normal Distribution Plots with Python 7. Creating a Sales Dashboard with Tableau 8. Creating an Inventory Dashboard with QlikSense 9. Data Analysis with Microsoft SQL Server

Alternative plotting libraries with Python


matplotlib is not the only game in town for plotting with Python. There are several other visualization libraries that are very powerful. One of them is seaborn. seaborn is actually based on matplotlib, so it contains similar functionality but makes more visually appealing plots with minimal coding.

Note

To learn more about seaborn, visit the following website:  http://seaborn.pydata.org

Before we can get started with seaborn, we will need to install it using either PyCharm or manually through the command line:

pip install seaborn

Once it is installed we can call the module into our Jupyter Notebook using the following command:

import seaborn as sb 

We can plot a histogram using VacationHours with the following script:

sb.distplot(VacationHours, kde = False, rug=True) 

When the script is executed, we can see the following histogram built with seaborn:

The rug parameter is set to True. This feature allows for the short rug-like bars at the bottom...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image