Kjell Wooding

If the output of the notebook is generally the same, running clear and run all (then checking in) might be the best approach. Obviously, we don't need to run nbval...

I've been running with a variant of this idea for a couple of months. I added a `{{ cookiecutter.module_name }}` as there have been cases where the project name is...

On the `make_features.py` and so on, I think these should be invoked as modules. e.g. My makefile does things like: ``` fetch: $(PYTHON_INTERPRETER) -m {{ cookiecutter.module_name }}.data.make_dataset fetch ``` that...

I've made these changes (both adding `module_name` and using `-m` when invoking things like `make_dataset.py` in PR #148

You can have a look at https://github.com/hackalog/bus_number/ which was from a tutorial we just gave at PyData NYC. There's a sizable framework in src/, but you should be able to...

You could check out the work we are doing with https://github.com/hackalog/cookiecutter-easydata which we maintain quite actively. It started as our experimental fork of this project. Also see the tutorial at...

Yes, I think there should likely be a `make fetch_data` or similar target that populates the `raw` directory, perhaps by calling a `fetch_dataset.py`

I took some of this thread to heart when writing PR #124 . (e.g. allowing the user to specify conda/virtualenv explicitly). I'd happily extend that PR to include pipenv support....

This is all true. It ended up here a long time ago as part of a corner case where ipykernel was failing to link to the local conda environments when...

Ah, It wasn’t an issue with ipykernel. It must have been even older than that (before i was using it). I was thinking of this issue: https://github.com/Anaconda-Platform/nb_conda_kernels/issues/158 Will look at...