Practical Data Analysis and Visualization with Python

Practical Data Analysis and Visualization with Python provides a hands-on guide to modern data workflows, taking readers from raw data to actionable insights through practical, integrated techniques. It provides a structured, hands-on approach to modern data workflows, guiding readers from raw data to meaningful insights. Instead of focusing on isolated tools or abstract theory, the book integrates data cleaning, exploratory analysis, visualization, and scalable processing into a coherent and practical pipeline.

Through real-world examples, readers learn how to work efficiently with modern Python tools such as Pandas, Polars, and PySpark, create effective visualizations, and build interactive dashboards. The focus is on developing practical skills and sound judgment, enabling readers to design reproducible workflows and handle data at scale.

In this book, you will learn how to:

• Clean, transform, and prepare real-world datasets
• Perform effective exploratory data analysis (EDA)
• Create compelling visualizations with Matplotlib and Seaborn
• Build interactive visualizations using hvPlot and Lets-Plot
• Visualize categorical data with PyWaffle and develop interactive dashboards
• Work with Pandas, Polars, and PySpark for scalable data processing
• Efficiently handle large datasets with Parquet, columnar storage, and Apache Arrow
• Implement partitioned workflows and leverage DuckDB for analytical queries
• Deploy interactive dashboards with Streamlit
• Apply best practices for performance, clarity, and usability

After finishing this book, readers will be able to design and implement complete data analysis workflows, from cleaning and exploring raw data to creating clear visualizations and interactive dashboards. They will gain practical experience with modern Python tools such as Pandas, Polars, PySpark, Matplotlib, Seaborn, hvplot, Lers-plots, Streamlit, DuckDB, etc., and develop the ability to work efficiently with datasets of varying size and complexity. More importantly, readers will build the judgment needed to choose the right tools and approaches for real-world data problems.

$64.99 – Buy eBook (PDF) Checkout Added to cart

📖 Read Sample

Buy Print Copy

@book{Wei2026dataanalysis,
  author    = {Wei, Shouke},
  title     = {Practical Data Analysis and Visualization with {Python}: Data Exploration, Visualization, and Scalable Data Processing},
  edition   = {1st},
  publisher = {Deepsim Press},
  address   = {Abbotsford, BC},
  year      = {2026},
  doi       = {10.5281/zenodo.19388650},
  url       = {https://press.deepsim.ca},
  isbn      = {978-1-0675592-0-5},
  note      = {Also available in hardcover (978-1-0675592-1-2) and paperback (978-1-0675592-2-9) editions.}
}

Publication Details

• Author: Shouke Wei
• Publisher: Deepsim Press
• Series: Practical Data Science with Python
• Format: PDF (Digital)
• Edition: First edition
• Print length: 544 pages
• Dimensions: 7.24 x 1.35 x 10.24 inches
• Language: English
• ISBN: 978-1-0675592-0-5 (eBook) | 978-1-0675592-1-2 (Hardcover) | 978-1-0675592-2-9 (Paperback)
• DOI: 10.5281/zenodo.19388650
• Publication date: 03/04/2026
• Book 1 of 3: Practical Data Science with Python

About the Author

Shouke Wei

Shouke Wei, PhD, is a researcher, scientist, and entrepreneur specializing in intelligent IoT systems, robotics, big data analytics, modeling and forecasting, early-warning systems, and edge computing. With academic and industry experience across Europe, North America, and Asia, Dr. Wei is recognized for bridging advanced theory with real-world, production-ready systems.

Dr. Wei earned his Ph.D. in Environmental and Resource Management from the Department of Environmental Informatics at Brandenburg University of Technology Cottbus–Senftenberg (Germany). He conducted postdoctoral research at the Swiss Federal Institute of Aquatic Science and Technology (Eawag), where he also served as a doctoral supervisor, and held research positions at the University of British Columbia (Canada). He has held distinguished and adjunct professorships at multiple institutions, including Yantai University, Ludong University, and Jining University. He has served as a graduate supervisor and distinguished professor in computer science, control engineering, and applied mathematics.

Dr. Wei’s work focuses on making advanced computational methods—particularly wavelet-based signal processing—accessible, practical, and impactful for researchers and practitioners worldwide. [More About the Author]