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020			_a9781098121228 _qpaperback
020			_a1098121228 _qpaperback
035			_a(OCoLC)on1351696593
038			_aAZHAR
040			_aUKMGB _beng _erda _cUKMGB _dOCLCF _dIG$ _dUKMGB _dGPRCL _dOQX _dIWA _dYDX _dOCL _dDLC
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050	0	0	_aQA76.73.P98 _bV365 2022
082	0	4	_a006.312 _223 _bVAN
100	1		_aVanderplas, Jacob T., _eauthor. _9123570
245	1	0	_aPython data science handbook : _bessential tools for working with data / _cJake VanderPlas.
250			_aSecond edition.
260			_aSebastopol, CA : _bO'Reilly Media, _c2022.
264		4	_c©2023
300			_axxiv, 563 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
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.
563			_aPBK.
650		0	_aData mining _vHandbooks, manuals, etc. _9123571
650		0	_aPython (Computer program language) _vHandbooks, manuals, etc. _9123572
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aPython (Computer program language) _2fast _0(OCoLC)fst01084736
650		7	_aE-BOOKBANK.SEECSTEXTBOOK _9125472
655		7	_aHandbooks and manuals _2fast _0(OCoLC)fst01423877 _9280
655		7	_aHandbooks and manuals. _2lcgft _9280
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/41784
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