A Primer on Scientific Programming with Python
- Hans Petter Langtangen
ISBN: 3642024742, Springer, 693 pages (July 2009)
An example- and problem-oriented introduction to computer programming of scientific applications.
NumPy 1.5 Beginner's Guide
- Ivan Idris
ISBN: 1849515301, Packt Publishing, 234 pages (November 2011)
An action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples.
Participatory Geospatial Development Using Python
- Ravish Bapna
ISBN: 1479316474, CreateSpace, 146 pages (September 2012)
The book contains discussion on raster and vector data processing using Python binding of GDAL/OGR library. Also, different approaches of representing spatial reference system are enumerated. There is a discussion on LIDAR data processing using Python binding of libLAS library. Apart from processing geospatial data, the book also covers plotting of geospatial data. The last chapter deals with freely available geospatial data, such as ASTER GDEM, SRTM data etc.
- Willi Richert and Luis Pedro Coelho
ISBN: 1782161406, PACKT Publishing, 350 pages (September 2013)
- A practical, scenario-based tutorial to get into the right mind set of a machine learner (data exploration)
- Master the diverse ML Python libraries and start building your Python-based ML systems
- Wide and practical coverage of ML areas to immediately implement in your projects - Classification, Regression, Recommender Systems, Computer Vision, and much more
Bioinformatics Programming Using Python
- Mitchell L Model
ISBN: 059615450X, O'Reilly Media, 528 pages (December 2009)
- Become familiar with Python's fundamentals, including ways to develop simple applications
- Learn how to use Python modules for pattern matching, structured text processing, online data retrieval, and database access
- Discover generalized patterns that cover a large proportion of how Python code is used in bioinformatics
- Learn how to apply the principles and techniques of object-oriented programming
- Yuxing Yan
ISBN: 1783284374, PACKT Publishing, 408 pages (April 2014)
- Estimate market risk, form various portfolios, and estimate their variance-covariance matrixes using real-world data
- Explains many financial concepts and trading strategies with the help of graphs
- A step-by-step tutorial with many Python programs that will help you learn how to apply Python to finance
- Clinton Sheppard
ISBN: 1540324001, CreateSpace, 532 pages (April 2016)
This book provides a hands-on introduction to machine learning with genetic algorithms using Python. It features working, step-by-step code examples, that give you experience solving problems with genetic algorithms. Topics covered include handling multiple fitness goals, phenotype vs genotype, gene constraints, memetic algorithms, local minimums and maximums, simulated annealing, branch and bound, variable length chromosomes, using one genetic algorithm to tune another, and genetic programming. Also available in Kindle and PDF formats.