Driscoll Michael. ReportLab: PDF Processing with Python (code)

Packt Publishing, 2015. — 404 p. — ISBN10: 1783984740, ISBN13: 978-1-78398-474-9. Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack. The SciPy stack is a collection of open-source libraries of the powerful scripting language Python, together with its interactive shells. This environment...

Wiley, 2015. — 360 p. — ISBN: 1118961749, 9781118961742 Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this...

Packt Publishing, 2016. — 272 p. — ISBN: 9781785282355 From Python programming good practices to the advanced use of analysis packages, this book teaches you how to write applications that will perform complex geoprocessing tasks that can be replicated and reused. Much more than simple scripts, you will write functions to import data, create Python classes that represent your...

O’Reilly Media, 2018. — 230 p. Expand your Python skills by working with data structures and algorithms in a refreshing context-through an eye-opening exploration of complexity science. Whether you're an intermediate-level Python programmer or a student of computational modeling, you'll delve into examples of complex systems through a series of worked examples, exercises, case...

Leanpub, 2018. — 428 p. — ISBN: 1983154547. ReportLab has been around since the year 2000 and has remained the primary package that Python developers use for creating reports in the PDF format. It is an extremely powerful package that works across all the major platforms. This book will also introduce the reader to other Python PDF packages.

Apress, 2017. — 169 p. — ISBN: 978-1-4842-2765-7. Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is...