david_eads

> I recently started a project that I got to write from the ground up by myself. I was happy with the processing side of things. I was very sad with the data I was getting in and putting out. There's some impedance mismatch that doesn't need to exist.

Impedance mismatch is a great way to put it. For me, if I can deal with that mismatch so that newbies/journalism colleagues don't have to, I'll do it.

> Sadly in my field matplotlib is the professional tool (hah!). The end goal is the matplotlib plots. I'd be all fine for tweaking things in a designing program and putting it up by I'd be upset with myself.

I used to work in science and have found journalism to have better solved many of these issues (at the expense, of course, of specialization and depth -- even a yearlong project isn't quite the same as decades of experience working in a single area). The solutions aren't pure or pretty -- they're more about workflow and held together with duct tape and baling wire. But the competitive pressure to deliver data that has a good user experience on deadline is very powerful and has led to some effective practices.

I'm actually so with you on a lot of this. The inability to use Pythonisms with Pandas is insane and I had to do a data analysis where I really, genuinely needed to do some looping and some simple map/reduce and it almost drove me insane.

You might like [Agate](http://agate.readthedocs.io/) better.

I haven't done a ton of Jupyter in the newsroom yet, but what I've found myself doing is abstracting out the stuff I want to do in normal Python into one or more utility modules and having those return dataframes into my notebook. That way I can mostly write normal Python but have access to some of the nicer pandas features and get to do more exploratory work.

I don't mind that matplotlib is kind of awful -- that data viz would never go in a published piece in any event. I just want some hints as to what I or more likely a teammate would build in D3 around the specifics of the data.

It's also really valuable for sharing. At NPR, I did an analysis of Trump's tweets that was used in a digital post and Morning Edition piece. The notebook was easy to share with the reporter, editor, and readers and accessible enough for them to understand (https://github.com/nprapps/trump-tweet-analysis/blob/master/...).

Exactly this. I'm the author of the post and was a programmer by trade for a long time before I became a journalist. I _don't_ actually find this more intuitive than more explicit and fundamental programming techniques. But my students grokked it immediately, whereas even simple structures like loops seem to be harder to get for them to get their heads around.

Given I had ten weeks to cram a lot of material in but did want to show them some amount of programming, this worked pretty nicely.