1.7. Getting help and finding documentation

Author: Emmanuelle Gouillart

Rather than knowing all functions in Numpy and Scipy, it is important to find rapidly information throughout the documentation and the available help. Here are some ways to get information:

  • In Ipython, help function opens the docstring of the function. Only type the beginning of the function’s name and use tab completion to display the matching functions.

    In [204]: help np.v

    np.vander     np.vdot       np.version    np.void0      np.vstack

    np.var        np.vectorize  np.void       np.vsplit

    

    In [204]: help np.vander

In Ipython it is not possible to open a separated window for help and documentation; however one can always open a second Ipython shell just to display help and docstrings…

../../_images/scipy_doc.png

  • Numpy’s and Scipy’s documentations can be browsed online on http://docs.scipy.org/doc. The search button is quite useful inside the reference documentation of the two packages (http://numpy.org/doc/stable/reference/ and http://docs.scipy.org/doc/scipy/reference/).

    Tutorials on various topics as well as the complete API with all docstrings are found on this website.

    ../../_images/docwiki.png

  • Numpy’s and Scipy’s documentation is enriched and updated on a regular basis by users on a wiki http://numpy.org/doc/stable/. As a result, some docstrings are clearer or more detailed on the wiki, and you may want to read directly the documentation on the wiki instead of the official documentation website. Note that anyone can create an account on the wiki and write better documentation; this is an easy way to contribute to an open-source project and improve the tools you are using!

  • The SciPy Cookbook https://scipy-cookbook.readthedocs.io gives recipes on many common problems frequently encountered, such as fitting data points, solving ODE, etc.

    ../../_images/matplotlib.png

  • Matplotlib’s website http://matplotlib.org/ features a very nice gallery with a large number of plots, each of them shows both the source code and the resulting plot. This is very useful for learning by example. More standard documentation is also available.

Finally, two more “technical” possibilities are useful as well:

  • In Ipython, the magical function %psearch search for objects matching patterns. This is useful if, for example, one does not know the exact name of a function.

    In [3]: import numpy as np

    In [4]: %psearch np.diag*

    np.diag

    np.diagflat

    np.diagonal

  • numpy.lookfor looks for keywords inside the docstrings of specified modules.

    In [45]: numpy.lookfor('convolution')

    Search results for 'convolution'

    --------------------------------

    numpy.convolve

        Returns the discrete, linear convolution of two one-dimensional

    sequences.

    numpy.bartlett

        Return the Bartlett window.

    numpy.correlate

        Discrete, linear correlation of two 1-dimensional sequences.

    In [46]: numpy.lookfor('remove', module='os')

    Search results for 'remove'

    ---------------------------

    os.remove

        remove(path)

    os.removedirs

        removedirs(path)

    os.rmdir

        rmdir(path)

    os.unlink

        unlink(path)

    os.walk

        Directory tree generator.

  • If everything listed above fails (and Google doesn’t have the answer)… don’t despair! Write to the mailing-list suited to your problem: you should have a quick answer if you describe your problem well. Experts on scientific python often give very enlightening explanations on the mailing-list.