Pandas for Everyone: Python Data Analysis

The book "Pandas for Everyone" provides a hands-on, example-rich introduction to Pandas data analysis in Python. It covers key concepts through practical examples, from combining datasets to fitting models, and demonstrates powerful data cleaning techniques. The book is designed for beginners and covers various advanced topics, such as scaling data manipulations, regularization, and clustering.

Format: Paperback / softback
Length: 416 pages
Publication date: 05 February 2018
Publisher: Pearson Education (US)

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python

Today, analysts face the challenge of managing data characterized by extraordinary variety, velocity, and volume. Fortunately, the open-source Pandas library empowers Python users to rapidly automate and execute virtually any data analysis task, regardless of its size or complexity. Pandas offers a multitude of features that enable users to ensure the accuracy of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

Pandas for Everyone is a comprehensive guide that brings together practical knowledge and insights for solving real problems with Pandas, even for those new to Python data analysis. Author Daniel Y. Chen introduces key concepts through simple but practical examples, gradually building on them to tackle more challenging, real-world problems.

Chen provides a solid foundation for using Pandas by guiding users through the process of working with DataFrames and Series, importing or exporting data, creating plots with matplotlib, seaborn, and Pandas, combining datasets and handling missing data, reshaping, tidying, and cleaning datasets to enhance their usability, converting data types and manipulating text strings, applying functions for scaling data manipulations, aggregating, transforming, and filtering large datasets with groupby, leveraging Pandas advanced date and time capabilities, and fitting linear models for prediction, clustering, inference, and exploration.

Throughout the book, Chen offers valuable tips on performance and scalability, introducing readers to the wider Python data analysis ecosystem. By the end of this hands-on guide, readers will have the skills and knowledge necessary to effectively leverage Pandas for data analysis and gain a competitive edge in their field.

Weight: 646g
Dimension: 179 x 232 x 25 (mm)
ISBN-13: 9780134546933