Python Data Visualization Essentials Guide
Become a Data Visualization expert by building strong proficiency in Pandas, Matplotlib, Seaborn, Plotly, Numpy, and Bokeh
Kalilur Rahman
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Python Data Visualization Essentials Guide
Become a Data Visualization expert by building strong proficiency in Pandas, Matplotlib, Seaborn, Plotly, Numpy, and Bokeh
Kalilur Rahman
About This Book
Build your data science skills. Start data visualization Using Python. Right away. Become a good data analyst by creating quality data visualizations using Python.
Key Features
? Exciting coverage on loads of Python libraries, including Matplotlib, Seaborn, Pandas, and Plotly.
? Tons of examples, illustrations, and use-cases to demonstrate visual storytelling of varied datasets.
? Covers a strong fundamental understanding of exploratory data analysis (EDA), statistical modeling, and data mining.
Description
Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert.The book starts with a brief introduction to the world of data visualization and talks about why it is important, the history of visualization, and the capabilities it offers. You will learn how to do simple Python-based visualization with examples with progressive complexity of key features. The book starts with Matplotlib and explores the power of data visualization with over 50 examples. It then explores the power of data visualization using one of the popular exploratory data analysis-oriented libraries, Pandas.The book talks about statistically inclined data visualization libraries such as Seaborn. The book also teaches how we can leverage bokeh and Plotly for interactive data visualization. Each chapter is enriched and loaded with 30+ examples that will guide you in learning everything about data visualization and storytelling of mixed datasets.
What you will learn
? Learn to work with popular Python libraries and frameworks, including Seaborn, Bokeh, and Plotly.
? Practice your data visualization understanding across numerous datasets and real examples.
? Learn to visualize geospatial and time-series datasets.
? Perform correlation and EDA analysis using Pandas and Matplotlib.
Who this book is for
This book is for all data analytics professionals, data scientists, and data mining hobbyists who want to be strong data visualizers by learning all the popular Python data visualization libraries. Prior working knowledge of Python is assumed.
Table of Contents
1. Introduction to Data Visualization
2. Why Data Visualization
3. Various Data Visualization Elements and Tools
4. Using Matplotlib with Python
5. Using NumPy and Pandas for Plotting
6. Using Seaborn for Visualization
7. Using Bokeh with Python
8. Using Plotly, Folium, and Other Tools for Data Visualization
9. Hands-on Examples and Exercises, Case Studies, and Further Resources
About the Authors
Kallur Rahman is an IT industry leader with over 2 decades of experience in software development, testing, program/ project management, and management consultancy. He has been a developer, designer, technical architect, test program manager, delivery unit head, IT Services, and COE/Factory Services leader of various complexity spanning telecommunications, Life Sciences, Retail, and Healthcare Industries. He has a master's degree in Business Administration preceded by an Engineering degree in Computer Science. He has counseled CxO level executives in market-leading corporations for testing, business and technology transformation programs. As a thought-leader, he is a frequent invitee at several industry events spanning technical and domain-focused themes.
He is a believer in "Knowledge is Power" and is passionate about authoring and sharing his knowledge. He has published over 200 articles across LinkedIn, DevOps.Com, and other leading magazines.He is additionally an active quizzing aficionado who engages and contributes at corporate level quizzing. LinkedIn Bio: https://www.linkedin.com/in/kalilurrahman/
Frequently asked questions
Information
CHAPTER 1
Introduction to Data Visualization
Structure
- What is data visualization
- Key elements of data visualization
- Importance of data visualization
Objective
What is data visualization?
Brilliant use of data visualization in history
- Visualization of the cholera deaths by John Snow, known as the father of epidemiology, is a study of disease and patterns to identify measures to solve the issues. He visualized the cholera deaths for a London borough. While visualizing the outbreak in the city, he noted that the number of deaths at a particular street (Broad Street) near a water pump was high. This led to an insight that cholera was caused by germ-contaminated water than particles in the air. This changed the course of medicine and treatments for outbreaks.
- The brilliant use of wonderful data visualization by Florence Nightingale to record the causes of mortality during the Crimean war. Her fact-oriented visualizations proved that more soldiers died due to infections than that of actual fatality in the war. Her visualizations proved the power of inference of data. Data visualizations thrive on the power of insights and inference, and Nightingale's visualization brought the idea of a single picture being more powerful than thousand words to the fore. Florence Nightingale also produced other data visualization charts to prove a point to the government, healthcare professionals, and the public that sanitation is key for healthy lives. Florence Nightingale, also known as "The Lady with the Lamp," was a pioneering icon in statistics and data visualization.
- By helping the healthcare professionals to do the right type of diagnosis and analyzing to understand the patterns and outliers
- By focusing on statistically important aspects to build procedures, discover and develop medicines, and choose treatment
- By giving an insight into the trends, progress, and to make an informed decision for the betterment of business
Key elements of data visualization
Elements of data visualization
- Strategy: What is your data visualization strategy?
- Structure: How are you planning to structure your story?
- Data: What type of data are you planning to use? How many datasets are you planning to use?
- Style: A key element on your visualization style, choice of visualization elements such as graphs and charts, choice of colors and other visual elements, use of qualitative and quantitative information to convey a message
- User: The key to the success of the data visualization exercise. Who are your users? Why should they be using your product? What is the key takeaway for them?
- Story: Most important aspect of the exercise. What are you trying to convey, what would the key insights, messages, actions, inspirations they can take away to implement actions?
Strategy
- Having good data capturing, data extracting, data cleansing, data integration strategy is very important. This strategy is especially important for planning interactive, real-time, update-oriented dashboards and data visualization. There should be a data strategy for the visualization exercise.
- Another element to consider is the user experience and design thinking strategy to address the needs and wants of the users. Using a persona-based design of visual elements can help in designing better visualization elements.
- As one size fits all does not exist, a designer bias can be avoided by taking the user requirements, and user needs into consideration through the empathy-based user-centric design of elements.
- Design elements, style elements, visual themes, templates, messaging, colors, form factors, devices, and gesture-based themes and actions can all be thought in advance.
- Having a clear structure, simplicity, better visibility, and consistency in design could be thought through before the design is done.