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082	0	4	_a006.312 _223 _bV224p
100			_aVanderPlas, Jake _97113
245	1	0	_aPython data science handbook : _bessential tools for working with data / _cJake VanderPlas.
250			_aSecond edition.
260			_aIndia: _bShroff Publishers and Distributors Pvt.Ltd., _c2023
300			_axxiv, 563 pages : _billustrations ; _c24 cm
500			_aPrevious edition: 2016.
504			_aIncludes bibliographical references and index.
505	0		_aPart 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.
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.
526			_aCSE _bps _lREF
541			_aRisaam
650		0	_aData mining _vHandbooks, manuals, etc.
650		0	_aPython (Computer program language) _vHandbooks, manuals, etc.
650		7	_aData mining. _2fast
650		7	_aPython (Computer program language) _2fast
655		7	_aHandbooks and manuals _2fast
655		7	_aHandbooks and manuals. _2lcgft
942			_2ddc _cBK
999			_c57696 _d57870