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Welcome to Scientific Python and its community. If you're a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You'll learn how to write elegant code that's clear, concise, and efficient at executing the task at hand.
Throughout the book, you'll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you'll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries.
- Explore the NumPy array, the data structure that underlies numerical scientific computation
- Use quantile normalization to ensure that measurements fit a specific distribution
- Represent separate regions in an image with a Region Adjacency Graph
- Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform
- Solve sparse matrix problems, including image segmentations, with SciPy's sparse module
- Perform linear algebra by using SciPy packages
- Explore image alignment (registration) with SciPy's optimize module
- Process large datasets with Python data streaming primitives and the Toolz library
About the author
Juan Nunez-Iglesias is a freelance consultant and a Research Scientist at the University of Melbourne, Australia. Prior positions include Research Associate at HHMI Janelia Farm (where he worked with Mitya Chklovskii) and Research Assistant/PhD student at the University of Southern California (where he studied computational biology supervised by Xianghong Jasmine Zhou). His principal research interests are neuroscience and image analysis. He is also interested in graph methods in bioinformatics and in biostatistics.
Stéfan van der Walt is an assistant researcher at the Berkeley Institute for Data Science at the University of California, Berkeley and a senior lecturer in applied mathematics at Stellenbosch University, South Africa. He has been involved in the development of scientific open-source software for more than a decade, and enjoys teaching Python at workshops and conferences. Stéfan is the founder of scikit-image and a contributor to numpy, scipy, and dipy.
Harriet Dashnow is a bioinformatician and has worked at the Murdoch Childrens Research Institute, the Department of Biochemistry at the University of Melbourne and the Victorian Life Sciences Computation Initiative (VLSCI). Harriet obtained a BA (Psychology), a BS (Genetics and Biochemistry), and a MS (Bioinformatics) from the University of Melbourne. She is currently working towards a PhD. She organises and teaches computational skills workshops in such areas as genomics, Software Carpentry, Python, R, Unix, and Git version control.
Summary
Welcome to Scientific Python and its community. If you’re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice.
