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04193aam a22003498i 4500 001 3EF927ACD37711ED83DD52344BECA4DB 003 SILO 005 20230405010043 008 221104t20222023caua bf 001 0 eng d 020 $a 1098121228 020 $a 9781098121228 035 $a (OCoLC)1351696593 040 $a UKMGB $b eng $e rda $c UKMGB $d OCLCF $d IG$ $d UKMGB $d GPRCL $d OQX $d IWA $d SILO 050 4 $a QA76.73.P98 V365 2022 100 1 $a Vanderplas, Jacob T., $e author. $1 https://isni.org/isni/0000000418491725 245 10 $a Python data science handbook : $b essential tools for working with data / $c Jake VanderPlas. 250 $a Second edition. 260 $a [S.l.] : $b O'REILLY MEDIA, $c 2023. 300 $a xxiv, 563 pages : $b illustrations ; $c 24 cm 520 $a "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. 505 0 $a Part I: Jupyter: Beyond normal Python -- 1. Geting startein 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. 504 $a Includes bibliographical references and index. 500 $a Previous edition: 2016. 650 0 $a Data mining $v Handbooks, manuals, etc. 650 0 $a Python (Computer program language) $v Handbooks, manuals, etc. 650 7 $a Data mining. $2 fast $0 (OCoLC)fst00887946 650 7 $a Python (Computer program language) $2 fast $0 (OCoLC)fst01084736 655 7 $a Handbooks and manuals. $2 lcgft 941 $a 1 952 $l USUX851 $d 20230503011913.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=3EF927ACD37711ED83DD52344BECA4DB 994 $a C0 $b IWAInitiate Another SILO Locator Search