We Keep Coding

Navigating through Github repos

I am currently trying to find end-to-end speech recognition solutions to implement in python (I am a data science student btw). I have searched for projects on github and find it very hard to comprehend how these repositories work and how I can use them for my own project.
I am mainly confused with the following:
how do repositories usually get used by other developers and how can I use them best for my specific issue?
How do I know if the proposed solution is working in python?
What is the usual process in installing the project from the repo?
Sorry for the newbie question but I am fairly new to this.
Thank you

how do repositories usually get used by other developers and how can I use them best for my specific issue?
The best way to get started with GitHub is to review the documentation.
How do I know if the proposed solution is working in python?
Every repository includes a monochrome / colorful line between the caption and the code section. On clicking this line, you can see the languages of the current project (here is the example for Tensorflow)
What is the usual process in installing the project from the repo?
Usually the project installation and usage steps are described in README.md file that should be attached to GitHub repository. This assumes installed git and basic knowledge of its commands.

You can read the documentation(README.md) there you can have all the information you need.
You can install the project from a repo by cloning or by downloading zip.

Where to place example data in python package?

I'm setting up my first Python package, and I want to install it with some example data so that users can run the code straight off. In case it's relevant my package is on github and I'm using pip.
At the moment my example data is being installed with the rest of the package into site_packages/, by setting include_package_data=True in setup.py, and referencing the files I want to include in MANIFEST.in. However, while this makes sense to me for files used by the code as part of its processing, it doesn't seem especially appropriate for example data.
What is best/standard practice for deploying example data with a python package?

You can put your example data in the repository in examples folder next to your project sources and exclude it from package with prune examples in your manifest file.
There is actually no universal and standard advice for that. Do whatever suits your needs.

How to use scipy's local sphinx documentation?

On my Linux computer, I have built scipy from source via the command
python setup.py build_sphinx
what has generated a bunch of rst files in the scipy/doc/source/generated folder.
But how do I access this documentation? Do I have to generate html files before?

reStructuredText is intended to be (mostly) readable as source, but it is probably best rendered as HTML for human eyes using docutils.

How does Python's website generate its online documentation?

Created using Sphinx 0.6.5.
I know Python's documentation uses reStructuredText, but it has different sections like http://docs.python.org/whatsnew/2.6.html and http://docs.python.org/tutorial/index.html.
How do you do this in reStructuredText? Do you run rst2html in a bunch of directories, keeping its structure?
I know that it uses Sphinx (not rst2html directly as said by Thomas Wouters in an answer), but how you should organize your rst structure with source code files in a repository so you have a full blown automated doc website?

The Python documentation uses reST for its markup, but it generates the HTML using sphinx, not rst2html directly. The sphinx tool is what converts all the individual .rst files into multiple .html files. To see how the Python docs are generated, see http://svn.python.org/projects/python/trunk/Doc/Makefile

I found the sampledoc tutorial on the matplotlib site quite useful to get started with Sphinx.

Here is how to build Python documentation exactly.

What is the relationship between docutils and Sphinx?

There seems to be a plethora of documentation tools for Python. Another one that I've run across is epydoc. It seems like Sphinx is the de facto standard, because it's used to generate the official Python docs. Can someone please sort out the current state of Python's documentation tools for me?

epydoc and Sphinx are different types of tools.
They are the same in that they:
Both use ReST via docutils.
Both are very Pythonic in their focus
Both can generate HTML and PDF documentation
They are different in their focus.
Epydoc is focused on API (Developer) documentation. You get more results quicker with Epydoc. You point it at your source code and you get something. This is what I want from an API documentation tool.
Sphinx is focused on general (User) documentation. While Sphinx does pull a lot of information from your source code you need to put a little more into it. Sphinx can generate API documentation but, it's not as straight forward, and IMO doesn't do as good of a job as Epydoc. I know some will disagree because Sphinx is chosen for Python. BUT, it depends on how much work you want to put into it. I do love Sphinx for general documentation.
So it depends on how much work you want to put into it and what you are documenting.

I recently changed from Python2 to Python3 and found that there was no Epydoc package for Python3. So it seems with Python3 there is a clear focus on using Sphinx as API documentation.

It seems like Epydoc is, if not dead, at least pining for the fjords.
Sphynx with the sphinx-apidoc command and the autodoc extension looks to be the most popular option for API docs at this time.
Good examples are:
requests (src)
boto (src)
See: Should we use Epydoc, Sphinx or something else for documenting Python APIs? (SO5579198)
Sphinx encourages a more narrative style than tools in the javadoc mold, but maybe this is a good thing?