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Numerical Python pp 1–24Cite as

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Introduction to Computing with Python

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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 less bugs and better applications overall, but it also encourages rapid code development. This readability and expressiveness is essential in exploratory and interactive computing, which requires fast turnaround for testing various ideas and models.

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Notes

  1. 1.

    For example, MKL, the Math Kernel Library from Intel, https://software.intel.com/en-us/intel-mkl , or ATLAS, the Automatically Tuned Linear Algebra Software, available at http://math-atlas.sourceforge.net .

  2. 2.

    The Python language and the default Python interpreter are managed and maintained by the Python Software Foundation: http://www.python.org .

  3. 3.

    See the IPython project web page, http://ipython.org , for more information and its official documentation.

  4. 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. 5.

    The Python function dir provides a similar feature.

  6. 6.

    Which can, for example, be used with the standard Python interpreter to profile scripts by running python -m cProfile script.py.

  7. 7.

    Currently the IPython notebook project is in the process of restructuring the application into a Python agnostic tool, and the project is being renamed to Jupyter. To follow this development, see http://jupyter.org .

  8. 8.

    This web application is by default only accessible locally from the system where the notebook application was launched.

  9. 9.

    The path/filename is relative to the notebook directory.

  10. 10.

    The IPython nbconvert application uses the jinja2 template engine. See http://jinja.pocoo.org for more information and documentation its the syntax.

  11. 11.

    http://code.google.com/p/spyderlib .

  12. 12.

    http://www.eclipse.org .

  13. 13.

    http://pydev.org .

  14. 14.

    http://www.jetbrains.com/pycharm .

  15. 15.

    http://www.pylint.org .

  16. 16.

    http://github.com/pyflakes/pyflakes .

  17. 17.

    http://pep8.readthedocs.org .

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Authors and Affiliations

  1. Chiba, Chiba, Japan

    Robert Johansson

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  1. Robert Johansson
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© 2015 Robert Johansson

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Johansson, R. (2015). Introduction to Computing with Python. In: Numerical Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-0553-2_1

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