MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04084nam\a2200337\a\4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
57696 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260208152706.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
230831t20222023caua bf 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| ISBN |
9789355422552 |
| Qualifying information |
paperback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UKMGB |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
UKMGB |
| Modifying agency |
OCLCF |
| -- |
IG$ |
| -- |
UKMGB |
| -- |
GPRCL |
| -- |
OQX |
| -- |
IWA |
| -- |
YDX |
| -- |
OCL |
| -- |
BD-DhIUB |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.312 |
| Edition number |
23 |
| Item number |
V224p |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
VanderPlas, Jake |
| 9 (RLIN) |
7113 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Python data science handbook : |
| Remainder of title |
essential tools for working with data / |
| Statement of responsibility, etc |
Jake VanderPlas. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
Second edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
India: |
| Name of publisher, distributor, etc |
Shroff Publishers and Distributors Pvt.Ltd., |
| Date of publication, distribution, etc |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxiv, 563 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Previous edition: 2016. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Part 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 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"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 ## - STUDY PROGRAM INFORMATION NOTE |
|---|
| School name |
School of engineering, Technology &Sciences |
| Department |
Physical Science |
| Shelving Location |
Reference Stacks |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Source of acquisition |
Risaam |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| Form subdivision |
Handbooks, manuals, etc. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| Form subdivision |
Handbooks, manuals, etc. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining. |
| Source of heading or term |
fast |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| Source of heading or term |
fast |
| 655 #7 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Handbooks and manuals |
| Source of term |
fast |
| 655 #7 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Handbooks and manuals. |
| Source of term |
lcgft |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |