Python Data Visualization Essentials Guide: Become a Data Visualization expert by building strong proficiency in Pandas, Matplotlib, Seaborn, Plotly, Numpy, and Bokeh

English | 2021 | ISBN: 978-9391030070 | 366 Pages | EPUB | 10 MB

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.

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.