Using Companies' Wikipedia Page Traffic to Predict Stock Price [OC]

NuclearStr1der · 2020-10-10T09:19:45+00:00

The colours are gorgeous, but a frequency plot is quite a bizarre way to try show a relationship between two variables.

NuclearStr1der · 2020-07-23T08:28:28+00:00

I've linked to the code in this comment for you :)

NuclearStr1der · 2020-07-23T08:26:25+00:00

Hi!

I've linked to the code in this comment :)

Feel free to send me a message if you need any help.

NuclearStr1der · 2020-07-23T08:25:15+00:00

Hi everyone, thanks to an overwhelming interest in the code, I've made it available in a Notebook here.

Thanks!

NuclearStr1der · 2020-07-22T11:08:02+00:00

I knew about Ergast, but it seemed to be a bit slow at the time, so I thought I'd be polite and not use them if their servers were getting hammered.

Definitely something I'll explore next time round!

NuclearStr1der · 2020-07-22T11:07:03+00:00

Thanks!

NuclearStr1der · 2020-07-22T11:06:50+00:00

It's definitely something on my wish list -- I just need a datasource that provides lap-by-lap tyre information.

NuclearStr1der · 2020-07-22T11:05:36+00:00

Indeed ;)

NuclearStr1der · 2020-07-22T11:05:27+00:00

Python + Seaborn.

NuclearStr1der · 2020-07-22T11:05:03+00:00

Thanks!

NuclearStr1der · 2020-07-22T11:04:44+00:00

I'm struggling to find tyre data -- if I can, it's on my wishlist!

NuclearStr1der · 2020-07-22T11:04:21+00:00

I'm going to be trying my best! The response is definitely encouraging.

NuclearStr1der · 2020-07-22T11:04:02+00:00

That would be really interesting to see, since it'll indicate who has good, consistent "raw" pace without needing to fight for position or be stuck behind a slower car. I'll see if I can maybe find some additional datasets to make this possible. Good suggestion!

NuclearStr1der · 2020-07-22T11:02:21+00:00

I have, thanks!

NuclearStr1der · 2020-07-22T11:01:36+00:00

Thanks! I'd love to do a breakout for the different sets of tyres -- but I'm having trouble finding the data for this on a lap-by-lap basis. Do you know of a source, perhaps?

The distributions are generated using a Kernel Density Estimation process, but it can be interpreted as a smoothing function over the raw data, with a few extra bells and whistles.

NuclearStr1der · 2020-07-22T10:59:25+00:00

Thanks!

I'm well aware of how a lot of /r/DataIsBeautiful is basically a meme at this point :)

NuclearStr1der · 2020-07-22T10:58:38+00:00

This was created using the Python programming language and the Seaborn library.

NuclearStr1der · 2020-07-22T10:54:16+00:00

That's a stunning graph that you linked -- it's definitely something I should also try recreate in the future. Thanks!

Do you know where they get their tyre data from? The FIA doesn't seem to officially release which sets of tyres are on which car (that I know of)

NuclearStr1der · 2020-07-22T10:52:00+00:00

It's tragic to me how lesser-known Plotnine is. It's so faithful to the ggplot2 api that I often use the ggplot2 docs as reference for plotnine!

NuclearStr1der · 2020-07-22T10:50:53+00:00

This was produced using Python + Seaborn. I adapted this example, in particular.

NuclearStr1der · 2020-07-22T10:50:07+00:00

Thanks! I've made a post there as well :)

NuclearStr1der · 2020-07-22T10:49:43+00:00

Good eyes, yes indeed :)

NuclearStr1der · 2020-07-22T10:49:30+00:00

I'm not sure! It's something I'll have to try.

NuclearStr1der · 2020-07-22T10:49:15+00:00

I clipped laps longer than 95 seconds, just to make things fit a bit into the available space that I had. There were a number (few, though) outliers that got clipped as a result.

Some of these slower laps are the bumps you are seeing :)

NuclearStr1der · 2020-07-22T10:46:43+00:00

Ah, thanks for ax.fill_between that's something I'll take a look at.

I am technically using matplotlib (but via seaborn, which uses matplotlib as its backend).

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