If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web.
Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server’s response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you’re likely to encounter.
Parse complicated HTML pages
Develop crawlers with the Scrapy framework
Learn methods to store data you scrape
Read and extract data from documents
Clean and normalize badly formatted data
Read and write natural languages
Crawl through forms and logins
Scrape JavaScript and crawl through APIs
Use and write image-to-text software
Avoid scraping traps and bot blockers
Use scrapers to test your website
2022-11-04 10:47:37
6.77MB
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A Multigrid Tutorial-William L. Briggs--second edition
2022-11-03 14:55:25
19.33MB
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Stephen L. Campbell, Jean-Philippe Chancelier
and Ramine N ikoukhah
Modeling and Simulation
in Scilab/Scicos with
ScicosLab 4 .4
Second Edition
2022-11-02 22:18:31
6.44MB
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Schaum's Outline of Probability and Statistics, Third Edition 2009.pdf
2022-11-01 22:40:22
8.99MB
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Numerical Recipes in C- The Art of Scientific Computing, 2nd Edition + source_code
2022-11-01 21:23:14
9.68MB
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The Official (ISC)2 CCSP CBK Reference Third Edition英文版
2022-11-01 18:00:12
1.48MB
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Introduction to Algorithms, Second Edition
by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein ISBN:0262032937
The MIT Press © 2001 (1180 pages)
A course in computer algorithms, suitable for use as a field reference for working software developers.
Table of Contents
Introduction to Algorithms, Second Edition
Preface
Part I - Foundations
Chapter 1 - The Role of Algorithms in Computing
Chapter 2 - Getting Started
Chapter 3 - Growth of Functions
Chapter 4 - Recurrences
Chapter 5 - Probabilistic Analysis and Randomized Algorithms
Part II - Sorting and Order Statistics
Chapter 6 - Heapsort
Chapter 7 - Quicksort
Chapter 8 - Sorting in Linear Time
Chapter 9 - Medians and Order Statistics
Part III - Data Structures
Chapter 10 - Elementary Data Structures
Chapter 11 - Hash Tables
Chapter 12 - Binary Search Trees
Chapter 13 - Red-Black Trees
Chapter 14 - Augmenting Data Structures
Part IV - Advanced Design and Analysis Techniques
Chapter 15 - Dynamic Programming
Chapter 16 - Greedy Algorithms
Chapter 17 - Amortized Analysis
Part V - Advanced Data Structures
Chapter 18 - B-Trees
Chapter 19 - Binomial Heaps
Chapter 20 - Fibonacci Heaps
Chapter 21 - Data Structures for Disjoint Sets
Part VI - Graph Algorithms
Chapter 22 - Elementary Graph Algorithms
Chapter 23 - Minimum Spanning Trees
Chapter 24 - Single-Source Shortest Paths
Chapter 25 - All-Pairs Shortest Paths
Chapter 26 - Maximum Flow
Part VII - Selected Topics
Chapter 27 - Sorting Networks
Chapter 28 - Matrix Operations
Chapter 29 - Linear Programming
Chapter 30 - Polynomials and the FFT
Chapter 31 - Number-Theoretic Algorithms
Chapter 32 - String Matching
Chapter 33 - Computational Geometry
Chapter 34 - NP-Completeness
Chapter 35 - Approximation Algorithms
Part VIII - Appendix: Mathematical Background
Appendix A - Summations
Appendix B - Sets, Etc.
Appendix C - Counting and Probability
Bibliography
Index
List of Figures
List of Corollaries
List of Problems
List of Exercises
2022-10-31 23:16:45
17.88MB
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