Python data science handbook : (Record no. 172004)

MARC details
000 -LEADER
fixed length control field 04176nam a22003857a 4500
003 - CONTROL NUMBER IDENTIFIER
control field IIITD
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240510020004.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231213b xxu||||| |||| 00| 0 eng d
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2022438305
015 ## - NATIONAL BIBLIOGRAPHY NUMBER
National bibliography number GBC2K4887
Source bnb
016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER
Record control number 020807698
Source Uk
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781098121228
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)on1351696593
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
-- IIITD
042 ## - AUTHENTICATION CODE
Authentication code lccopycat
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.73.P98
Item number V365 2022
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.312
Edition number 23
Item number VAN-P
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name VanderPlas, Jake
245 ## - TITLE STATEMENT
Title Python data science handbook :
Remainder of title essential tools for working with data
Statement of responsibility, etc by Jake VanderPlas.
250 ## - EDITION STATEMENT
Edition statement 2nd ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Mumbai :
Name of publisher, distributor, etc Shroff Publishers,
Date of publication, distribution, etc ©2023
300 ## - PHYSICAL DESCRIPTION
Extent xxiv, 563 p. :
Other physical details ill. ;
Dimensions 24 cm.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc This book includes bibliographical references and index.
505 ## - FORMATTED CONTENTS NOTE
Title 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining
Form subdivision Handbooks, manuals, etc.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Python (Computer program language)
Form subdivision Handbooks, manuals, etc.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
Source of heading or term fast
650 ## - 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
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b cbc
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Koha issues (borrowed), all copies 5
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Bill No. Bill Date Cost, normal purchase price PO No. PO Date Total Checkouts Total Renewals Full call number Barcode Due Date Date last seen Date checked out Cost, replacement price Price effective from Vendor/Supplier Koha item type
    Dewey Decimal Classification     Computer Science and Engineering IIITD IIITD General Stacks 13/12/2023 1163345 2023-12-07 1225.00 IIITD/LIC/BS/2021/04/55 2023-11-16 4 4 006.312 VAN-P 012494 05/06/2024 09/05/2024 09/05/2024 1750.00 13/12/2023 Atlantic Publishers & Distributors (P) Ltd. Books
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