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020 _a9781098121228
020 _a1098121228
040 _cAKCL
042 _alccopycat
082 0 4 _a006.312
_bVAN-P2
100 1 _aVanderPlas, Jake
100 1 _eauthor.
245 1 0 _aPython Data Science Handbook :
_bEssential Tools for Working with Data /
_cJake VanderPlas.
250 _aSecond edition.
260 _aCalifornia:
_bO'Reilly Media, Inc.,
_c2023
264 1 _aSebastopol, CA :
_bO'Reilly Media,
_c2022.
264 4 _c©2023
300 _axxiv, 563 pages :
300 _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.
650 0 _aComputer Science
650 0 _aArtificial Intelligence
650 0 _aAI
650 0 _aMachine Learning
650 0 _aData mining
650 0 _aPython (Computer program language)
650 0 _vHandbooks, manuals, etc.
650 0 _vHandbooks, manuals, etc.
650 7 _2fast
_0(OCoLC)fst00887946
650 7 _2fast
_0(OCoLC)fst01084736
655 7 _aHandbooks and manuals
655 7 _aHandbooks and manuals.
655 7 _2fast
_0(OCoLC)fst01423877
655 7 _2lcgft
906 _a7
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