Python for Scientists

John M. Stewart, Michael Mommert

ISBN 10: 1009014803 / ISBN 13: 9781009014809

Editore: Cambridge University Press, GB, 2023

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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.

Codice articolo LU-9781009014809

Dati bibliografici

• Titolo: Python for Scientists
• Editore: Cambridge University Press, GB
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1009014803
• ISBN 13: 9781009014809
• Rilegatura: Paperback
• Edizione: 3rd.
• Condizione: New
• Dimensioni: 16,99 L x 1,73 A x 24,41 P centimetri 16,99 centimetri di larghezza per 1,73 centimetri di altezza per 24,41 centimetri di profondità
• Peso: 530 grammi

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Riassunto

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.