This Pull Request is for HacktoberFest 2020

Description of Change

Checklist

• Added description

I'm using mxnet to do some work, but there is nothing when I search the mxnet trial and example.

Tick-Tack-Toe game is not working for exception part

Description

Else part on line 247 takes you to infinite loop on getting any char value as input for position.

Expected Behavior

Is your feature request related to a problem? Please describe.
It might be useful to have a singular clean and performant way to check if all the columns of a dataframe are of the same dtype, such as a DataFrame property _is_homogeneous. This comes up in a lot of places, such as where we might want to dispatch to a cupy matrix implementation (Transpose, some row wise reductions I believe

https://igel.readthedocs.io/en/latest/_sources/readme.rst.txt includes a link to the assets/igel-help.gif, but that path is broken on readthedocs.

readme.rst is included as ../readme.rst in the sphinx build.
The gifs are in asses/igel-help.gif

The sphinx build needs to point to the asset directory, absolutely:

.. image:: /assets/igel-help.gif

I haven't made a patch, because I haven't

The marker no_bad_cuml_array_check was necessary in 0.16 to avoid asserting on bad uses of CumlArray in specific tests. As of 0.17, this is no longer necessary.

This marker should be removed from pytest.ini as well as any tests that used it. A quick search shows this is used in 2 tests in test_incrementa_pca.py

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually