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Math 480: Open Source Mathematical Software

2016-05-16

William Stein

Lectures 22: Numpy, Matplotlib, and Scipy (part 1 of 3)

  1. Homework DUE 2016-05-20 is now available.

  2. Reminder: peer grading of homework DUE-2016-05-06 is due tonight at 6pm.

  3. Peer grading guidelines for homework DUE-2016-05-13 are available.

  4. Start screencast

  5. 10min -- Very brief first intro to numpy, matplotlib and scipy (more Wed and Friday)

  6. 15min+ -- Talk about the 3 homework problems

  7. Get started on homework or start working through some of the tutorials on your own in a worksheet.

10 minute intro to numpy, matplotlib and scipy.

  • Numpy, Matplotlib, and Scipy are (along with Cython) the foundation of the Scientific Python Stack.

  • Pretty much everything in the world of Python "data science" depends on them.

  • The community is unified around them as the foundation on which to build.

  • All are very polished and heavily used at this point.

  • Written in mostly C, C++ and some Fortran (scipy). Very highly optimized for speed. Lots of "vectorized operations".

  • When you hear about running Python in your web browser, or from Java, or on top of a JIT, probably none of Numpy, Matplotlib, and Scipy are supported in that context, so people doing numerical python work will not be excited.

### [Numpy:](http://www.numpy.org/) provides an nn-dimensional array object and lots of functionality with it. > "NumPy is the fundamental package for scientific computing with Python."

You should spend 2 hours and go through the Numpy Tutorial.

### [Matplotlib:](http://matplotlib.org/) provides graphics much like matlab (and much more). > "matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms."

You should spend an hour and go through this Matplotlib tutorial.

### [Scipy:](https://www.scipy.org/scipylib/index.html) provides many numerical algorithms beyonds just matrices... > "Scipy is the fundamental library for scientific computing. It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization."

You should spend several hours on the Scipy tutorial.