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OverviewManage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set. New features to the second edition include: Extended coverage of plotting and the seaborn data visualization library Expanded examples and resources Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries Online bonus material on geopandas, Dask, and creating interactive graphics with Altair Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine data sets and handle missing data Reshape, tidy, and clean data sets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large data sets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” one Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning Full Product DetailsAuthor: Daniel ChenPublisher: Pearson Education (US) Imprint: Addison Wesley Edition: 2nd edition Dimensions: Width: 17.80cm , Height: 2.80cm , Length: 23.20cm Weight: 0.801kg ISBN: 9780137891153ISBN 10: 0137891156 Pages: 512 Publication Date: 17 February 2023 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In stock  We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of ContentsForeword by Anne Brown Foreword by Jared Lander Preface Acknowledgments About the Author Part I. Introduction Chapter 1. Pandas DataFrame Basics Chapter 2. Pandas Data Structures Basics Chapter 3. Plotting Basics Chapter 4. Tidy Data Chapter 5. Apply Functions Part II. Data Processing Chapter 6. Data Assembly Chapter 7. Data Normalization Chapter 8. Groupby Operations: Split-Apply-Combine Part III. Data Types Chapter 9. Missing Data Chapter 10. Data Types Chapter 11. Strings and Text Data Chapter 12. Dates and Times Part IV. Data Modeling Chapter 13.Linear Regression (Continuous Outcome Variable) Chapter 14. Generalized Linear Models Chapter 15. Survival Analysis Chapter 16. Model Diagnostics Chapter 17. Regularization Chapter 18. Clustering Part V. Conclusion Chapter 19. Life Outside of Pandas Chapter 20. It’s Dangerous to Go Alone! AppendicesReviewsAuthor InformationDaniel Chen is a graduate student in the Interdisciplinary PhD program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry as an instructor, Mentoring Committee Member, and currently serves as the Assessment Committee Chair. He completed his Masters in Public Health at Columbia University Mailman School of Public Health in Epidemiology with a certificate in Advanced Epidemiology and currently extending his Master's thesis work in the Social and Decision Analytics Laboratory under the Virginia Bioinformatics Institute on attitude diffusion in social networks. Tab Content 6Author Website:Countries AvailableAll regions |
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