Python DataVisualization Cookbook: Python is a popular programming language data scientists, engineers, and developers use to analyze, manipulate and visualize data. Data visualization is an essential part of data analysis that helps in understanding complex data sets and presenting them meaningfully. The Python Data Visualization Cookbook is an excellent resource for those looking to learn about data visualization with Python. The Python Data Visualization Cookbook is a comprehensive guide that covers various techniques for visualizing data in Python. The cookbook is authored by Igor Milovanović, Aleksandar Erkalović, and Dimitry Foures-Angelov. The book is divided into three parts, each focusing on a particular aspect of data visualization.

Part 1: Getting Started with Python Data Visualization
The first part of the book covers the basics of data visualization and introduces the libraries used in Python for data visualization, including Matplotlib, Seaborn, and Plotly. The authors explain how to create basic plots such as scatter plots, line charts, and bar charts using Matplotlib. They also demonstrate how to use Seaborn, a library built on top of Matplotlib, to create more complex visualizations such as heatmaps, violin plots, and box plots. The authors also introduce Plotly, a web-based tool for creating interactive plots.
Part 2: Advanced Data Visualization Techniques
The second part of the book covers advanced data visualization techniques such as 3D plots, geospatial data visualization, and network visualization. The authors introduce the Mayavi library, used for 3D visualization in Python. They also cover the basics of geospatial data visualization using the Basemap library and demonstrate how to create interactive maps using Folium. The authors also introduce NetworkX, a library used for network visualization, and demonstrate how to create network visualizations.
Part 3: Best Practices for Data Visualization
The final part of the book covers best practices for data visualization, including designing effective visualizations, choosing appropriate color schemes, and presenting data in a meaningful way. The authors also cover data visualization tools used in the industry, including Tableau and Power BI.
Overall, the Python Data Visualization Cookbook is an excellent resource for anyone looking to learn about data visualization with Python. The book is well-structured, and the authors provide clear explanations of each topic covered. The cookbook is also full of practical examples, making it easy for readers to apply the techniques learned in the book to their own data sets.