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016	7		_a020807698 _2Uk
020			_a9781098121228
035			_a(OCoLC)on1351696593
040			_aUKMGB _beng _erda _cUKMGB _dOCLCF _dIG$ _dUKMGB _dGPRCL _dOQX _dIWA _dYDX _dOCL _dIIITD
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050	0	0	_aQA76.73.P98 _bV365 2022
082			_a006.312 _223 _bVAN-P
100			_aVanderPlas, Jake
245			_aPython data science handbook : _bessential tools for working with data _cby Jake VanderPlas.
250			_a2nd ed.
260			_aMumbai : _bShroff Publishers, _c©2023
300			_axxiv, 563 p. : _bill. ; _c24 cm.
504			_aThis book includes bibliographical references and index.
505			_tPart I: Jupyter: Beyond normal Python _t1. Getting started in in IPython and Jupyter -- 2. Enhanced interactive features -- 3. Debugging and profiling _tPart II: Introduction to NumPy _t4. 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 _tPart III: Data manipulation with Pandas _t13. 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 _tPart IV: Visualization with Matplotlib _t25. 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 _tPart V: Machine learning _t37. 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.
650			_aData mining _vHandbooks, manuals, etc.
650			_aPython (Computer program language) _vHandbooks, manuals, etc.
650			_aData mining. _2fast
650			_aPython (Computer program language) _2fast
655		7	_aHandbooks and manuals _2fast
655		7	_aHandbooks and manuals. _2lcgft
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK _05
999			_c172004 _d172004