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Home> Data> Data Visualization> Matplotlib for Python Developers
Matplotlib for Python Developers
Matplotlib for Python Developers

Matplotlib for Python Developers: Effective techniques for data visualization with Python , Second Edition

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Profile Icon Aldrin YimProfile Icon Claire ChungProfile Icon Allen Yu
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€32.99
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Arrow left icon
Profile Icon Aldrin YimProfile Icon Claire ChungProfile Icon Allen Yu
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€32.99
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PaperbackApr 2018300 pages2nd Edition
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€8.98 €26.99
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Matplotlib for Python Developers

A complete example

To get further acquainted with Matplotlib functions, let us plot a multiline plot with axes, labels, title, and legend configured in one single snippet.

In this example, we take real-world data from the World Bank on agriculture. As the world population continues to grow, food security continues to be an important global issue. Let us have a look at the production data of a few major crops in the recent decade by plotting a multiline plot with the following code:

Datasource:https://data.oecd.org/agroutput/crop-production.htmOECD (2017), Crop production (indicator). doi: 10.1787/49a4e677-en (Accessed on 25 December 2017)
# Import relevant modulesimportpandasaspdimportmatplotlib.pyplotasplt# Import datasetcrop_prod =pd.read_csv('OECD-THND_TONNES.txt',delimiter='\t')years =crop_prod[crop_prod['Crop']=='SOYBEAN...
Download code iconDownload Code

Key benefits

  • Perform effective data visualization with Matplotlib and get actionable insights from your data
  • Design attractive graphs, charts, and 2D plots, and deploy them to the web
  • Get the most out of Matplotlib in this practical guide with updated code and examples

Description

Python is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you’ll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You’ll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you’ll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples.By the end of this book, you’ll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to build attractive, insightful, and powerful visualizations.

Who is this book for?

This book is essentially for anyone who wants to create intuitive data visualizations using the Matplotlib library. If you’re a data scientist or analyst and wish to create attractive visualizations using Python, you’ll find this book useful. Some knowledge of Python programming is all you need to get started.

What you will learn

  • Create 2D and 3D static plots such as bar charts, heat maps, and scatter plots
  • Get acquainted with GTK+3, Qt5, and wxWidgets to understand the UI backend of Matplotlib
  • Develop advanced static plots with third-party packages such as Pandas, GeoPandas, and Seaborn
  • Create interactive plots with real-time updates
  • Develop web-based, Matplotlib-powered graph visualizations with third-party packages such as Django
  • Write data visualization code that is readily expandable on the cloud platform
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Publication date :Apr 24, 2018
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Table of Contents

10 Chapters
Introduction to MatplotlibChevron down iconChevron up icon
Introduction to Matplotlib
What is Matplotlib?
Setting up Matplotlib
Summary
Getting Started with MatplotlibChevron down iconChevron up icon
Getting Started with Matplotlib
Loading data
Our first plots with Matplotlib
Adjusting axes, grids, labels, titles, and legends
A complete example
Saving plots to a file
Configuring Matplotlib
Summary
Decorating Graphs with Plot Styles and TypesChevron down iconChevron up icon
Decorating Graphs with Plot Styles and Types
Controlling the colors
Line and marker styles
More native Matplotlib plot types
Text and annotations
Using style sheets
Aesthetics and readability considerations in styling
Summary
Advanced MatplotlibChevron down iconChevron up icon
Advanced Matplotlib
Drawing Subplots
Adjusting axes and ticks
More on Pandas-Matplotlib integration
Expanding plot types with Seaborn 
Image plotting
Financial plotting
3D plots with Axes3D
Geographical plotting
Summary
Embedding Matplotlib in GTK+3Chevron down iconChevron up icon
Embedding Matplotlib in GTK+3
Installing and setting up GTK+3
Summary
Embedding Matplotlib in Qt 5Chevron down iconChevron up icon
Embedding Matplotlib in Qt 5
A brief introduction to Qt 5 and PyQt 5
Differences between Qt 4 and PyQt 4
Summary
Embedding Matplotlib in wxWidgets Using wxPythonChevron down iconChevron up icon
Embedding Matplotlib in wxWidgets Using wxPython
A brief introduction to wxWidgets and wxPython
Summary
Integrating Matplotlib with Web ApplicationsChevron down iconChevron up icon
Integrating Matplotlib with Web Applications
Installing Docker
More about Django
Showing Bitcoin prices using Django and Matplotlib
Summary
Matplotlib in the Real WorldChevron down iconChevron up icon
Matplotlib in the Real World
Typical API data formats
Importing and visualizing data from a JSON API
Scraping information from websites
Matplotlib graphical backends
Creating animated plot
Summary
Integrating Data Visualization into the WorkflowChevron down iconChevron up icon
Integrating Data Visualization into the Workflow
Getting started
Visualizing sample images from the dataset
Exploring the data nature by the t-SNE method
Creating a CNN to recognize digits
Evaluating prediction results with visualizations
Summary

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Customer reviews

Rating distribution
Full star iconFull star iconEmpty star iconEmpty star iconEmpty star icon2
(2 Ratings)
5 star0%
4 star0%
3 star50%
2 star0%
1 star50%
dAug 02, 2018
Full star iconFull star iconFull star iconEmpty star iconEmpty star icon3
This book is a good introduction to Matplotlib with a few advanced topics, however, almost half of the book is dedicated to embedding matplotlib in various GUIs. This could have been a book by itself and internal functionality of matplotlib, pandas, and seaborn could have been greatly expanded. The authors do a great job of introducing the reader to fundamental matplotlib concepts, but fail to delineate the core of Pyplot vs the use of Axes/Figure. They do not go into detail about how these two are different, how to access similar functions through the Axes object, or even what an Axes object is before using it in the advanced matplotlib section. The coverage of subplots is fairly good, but overall, the book could be improved further.
Amazon Verified reviewAmazon
bikiteronNov 06, 2019
Full star iconEmpty star iconEmpty star iconEmpty star iconEmpty star icon1
I thought this book will be colour printed but this book is not colour printed.
Amazon Verified reviewAmazon

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About the 3 authors

Left arrow icon
Profile icon Aldrin Yim
Aldrin Yim
Aldrin Yim is a PhD candidate and Markey Scholar in the Computation and System Biology program at Washington University, School of Medicine. His research focuses on applying big data analytics and machine learning approaches in studying neurological diseases and cancer. He is also the founding CEO of Codex Genetics Limited, which provides precision medicine solutions to patients and hospitals in Asia.
Read more
See other products by Aldrin Yim
Profile icon Claire Chung
Claire Chung
Claire Chung is pursuing her PhD degree as a Bioinformatician at the Chinese University of Hong Kong. She enjoys using Python daily for work and lifehack. While passionate in science, her challenge-loving character motivates her to go beyond data analytics. She has participated in web development projects, as well as developed skills in graphic design and multilingual translation. She led the Campus Network Support Team in college, and shared her experience in data visualization in PyCon HK 2017.
Read more
See other products by Claire Chung
Profile icon Allen Yu
Allen Yu
Allen Yu, PhD, is a Chevening Scholar, 2017-18, and an MSC student in computer science at the University of Oxford. He holds a PhD degree in Biochemistry from the Chinese University of Hong Kong, and he has used Python and Matplotlib extensively during his 10 years of bioinformatics experience. Apart from academic research, Allen is the co-founder of Codex Genetics Limited, which aims to provide a personalized medicine service in Asia through the use of the latest genomics technology.
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