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Python for scientists / John M. Stewart, University of Cambridge, Michael Mommert, University of St Gallen, Switzerland.

By: Contributor(s): Material type: TextTextLanguage: English Cambridge, United Kingdom ;;New York, NY : Cambridge university Press, 2023Description: 1 online resource (x, 288 pages) : digital, PDF file(s)Content type:
  • text
Media type:
Carrier type:
  • online resource
ISBN:
  • 9781009029728 (ebook)
Subject(s): Additional physical formats: No titleDDC classification:
  • 005.13/3
LOC classification:
  • Q183.9 .S865 2023
Online resources: Summary: The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
List(s) this item appears in: Computer Science
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Item type Current library Call number URL Status Barcode
E-Book E-Book Ranganathan Library 5.133 (Browse shelf(Opens below)) Link to resource Available E01887

Title from publisher's bibliographic system (viewed on 01 Aug 2023).

The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.

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