Use a table to categorise the various "basics" demos (#1608)
Use a table to categorise the various "basics" demos (#1608)

A table is a little more structured, and makes it easy to include more info in
an easily digestible way, like supported formats and a hint about performance.

I chose the subset of formats to list by searching for the relevant reading
function on GitHub and including the ones that are most common, using this as a
proxy for the formats that are encountered the most, in the wild. For NetworkX,
I didn't include the edge list format (even though it was common), because
that's better done via Pandas.

Example searches:

- Pandas <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>:
  `"pd.read_csv"` https://github.com/search?q=%22pd.read_csv%22&type=Code
- NetworkX
  <https://networkx.github.io/documentation/stable/reference/readwrite/index.html>:
  `"nx.read_gml"` https://github.com/search?q=%22nx.read_gml%22&type=Code

Caveats:

- these searches are fairly noisy but it should still be correlated to the
  frequency of use. For example, it includes results from the repo itself and
  any duplicates of the source code, and will miss calls with different
  phrasings like `pandas.read_csv(...)` or `from networkx import read_gml;
  ... read_gml(...)`.
- these searches aren't limited to just people doing graph work but I'm not sure
  we can do much better. For example, it might be that people working with
  graphs use SQL much more than HDF5.

See: #1270

stellargraph/stellargraph,stellargraph/stellargraph