Harriet dashnow (9 Ergebnisse)

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Zustand: good. Good: The book has been read but is in good condition. It has very minimal damage to the cover, including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. The binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, and no writing in the margins. There are no missing pages. See the seller's listing for full details and a description of any imperfections.…

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: New. In English.

Paperback. Zustand: Brand New. 251 pages. 9.00x6.75x0.75 inches. In Stock.

Kartoniert / Broschiert. Zustand: New. 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.

Taschenbuch. Zustand: Neu. Neuware - 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.…