Abstract
This book is about using Python for numerical computing. Python is a high-level, general-purpose interpreted programming language that is widely used in scientific computing and engineering. As a general-purpose language, Python was not specifically designed for numerical computing, but many of its characteristics make it well suited for this task. First and foremost, Python is well known for its clean and easy-to-read code syntax. Good code readability improves maintainability, which in general results in fewer bugs and better applications overall, but it also enables rapid code development. This readability and expressiveness are essential in exploratory and interactive computing, which requires fast turnaround for testing various ideas and models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For example, MKL, the Math Kernel Library from Intel, https://software.intel.com/en-us/intel-mkl ; openBLAS, https://www.openblas.net ; or ATLAS, the Automatically Tuned Linear Algebra Software, available at http://math-atlas.sourceforge.net
- 2.
The Python language and the default Python interpreter are managed and maintained by the Python Software Foundation: http://www.python.org .
- 3.
See the IPython project web page, http://ipython.org , for more information and its official documentation.
- 4.
When %automagic is activated (type %automagic at the IPython prompt to toggle this feature), the % sign that precedes the IPython commands can be omitted, unless there is a name conflict with a Python variable or function. However, for clarity, the % signs are explicitly shown here.
- 5.
The Python function dir provides a similar feature.
- 6.
Which can, for example, be used with the standard Python interpreter to profile scripts by running python -m cProfile script.py
- 7.
For more information about Jupyter, see http://jupyter.org .
- 8.
This web application is by default only accessible locally from the system where the notebook application was launched.
- 9.
The path/filename is relative to the notebook directory.
- 10.
For more information about how to create interactive applications using Jupyter and IPython widgets, see the documentation for the ipywidgets library https://ipywidgets.readthedocs.io/en/latest .
- 11.
The IPython nbconvert application uses the jinja2 template engine. See http://jinja.pocoo.org for more information and documentation of its syntax.
- 12.
- 13.
- 14.
- 15.
- 16.
- 17.
- 18.
- 19.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Robert Johansson
About this chapter
Cite this chapter
Johansson, R. (2019). Introduction to Computing with Python. In: Numerical Python . Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4246-9_1
Download citation
DOI: https://doi.org/10.1007/978-1-4842-4246-9_1
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-4245-2
Online ISBN: 978-1-4842-4246-9
eBook Packages: Professional and Applied ComputingApress Access BooksProfessional and Applied Computing (R0)