04462cam a22005417i 4500 OSt 20260629080515.0 230831t20222023caua bf 001 0 eng d 9781098121228 1098121228 AKCL lccopycat 006.312 VAN-P2 VanderPlas, Jake author. Python Data Science Handbook : Essential Tools for Working with Data / Jake VanderPlas. Second edition. California: O'Reilly Media, Inc., 2023 Sebastopol, CA : O'Reilly Media, 2022. ©2023 xxiv, 563 pages : 24 cm text txt rdacontent unmediated n rdamedia volume nc rdacarrier Previous edition: 2016. Includes bibliographical references and index. Part I: Jupyter: Beyond normal Python -- 1. Getting started in in IPython and Jupyter -- 2. Enhanced interactive features -- 3. Debugging and profiling -- Part II: Introduction to NumPy -- 4. Understanding data types in Python -- 5. The basics of NumPy arrays -- 6. Computation on NumPy arrays: Universal functions -- 7. Aggregations: min, max, and everything in between -- 8. Computation on arrays: broadcasting -- 9. Comparisons, masks, and boolean logic -- 10. Fancy indexing -- 11. Sorting arrays -- 12. Structured data: NumPy's structured arrays -- Part III: Data manipulation with Pandas -- 13. Introducing Pandas objects -- 14. Data indexing and selection -- 15. Operating on data in Pandas -- 16. Handling missing data -- 17. Hierarchial indexing -- 18. Combining datasets: concat and append -- 19. Combining datasets: merge and join -- 20. Aggregation and grouping -- 21. Pivot tables -- 22. Vectorized string operations -- 23. Working with time series -- 24. High-performace Pandas: eval and query -- Part IV: Visualization with Matplotlib -- 25. General Matplotlib tips -- 26. Simple line plots -- 27. Simple scatter plots -- 28. Density and contour plots -- 29. Customizing plot legends -- 30. Customizing colorbars -- 31. Multiple subplots -- 32. Text and annitatuin -- 33. Customizing ticks -- 34. Customizing Matplotlib: Configurations and stylesheets -- 35. Three-dimensional plottin in Matplotlib -- 36. Visualization with Seaborn -- Part V: Machine learning -- 37. What is machine learning? -- 38. Introducing Scitit-Learn -- 39. Hyperparameters and model validation -- 40. Feature engineering -- 41. In depth: Naive beyes classification -- 42. In depth: Linear regression -- 43> In depth: Support vector machines -- 44. In depth: Decision trees and random forests -- 45> In depth: Principal component analysis -- 46> In depth: Manifold learning -- 47. In depth: k-means clustering -- 48. In depth: Gaussian mixture models -- 49. In depth: Kernel density estimation -- 50. Application: a face detection pipeline. "Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python."--Publisher marketing. Computer Science Artificial Intelligence AI Machine Learning Data mining Python (Computer program language) Handbooks, manuals, etc. Handbooks, manuals, etc. fast (OCoLC)fst00887946 fast (OCoLC)fst01084736 Handbooks and manuals Handbooks and manuals. fast (OCoLC)fst01423877 lcgft 7 cbc copycat 2 ncip 20 y-gencatlg BK ddc 0 44952 44952 0 0 ddc 0 0 AKCL AKCL 2026-06-29 1750.00 0 006.312 VAN-P2 69307 2026-06-29 08:05:55 1750.00 2026-06-29 BK