README

These are the files for the book Learn to Code with Fantasy Football.

If you're not familiar with Git or GitHub, no problem. Just click the Source code link under the latest release to download the files. This will download a file called ltcwff-files-vX.X.X.zip, where X.X.X is the latest version.

When you unzip these (note in the book I've dropped the version number and renamed the directory just ltcwff-files, which you can do too) you'll see four sub-directories: code, data, anki, solutions-to-excercises.

You don't have to do anything with these right now except know where you put them. For example, on my mac, I have them in my home directory:

/Users/nathanbraun/ltcwff-files

If I were using Windows, it might look like this:

C:\Users\nathanbraun\ltcwff-files

Set these aside for now and we'll pick them up in chapter 2.

Changelog

v0.12.0 (2024-12-06)

Updated instructions for installing Python (now reccomend Spyder directly vs via Anaconda). Also cover a few other options.

Minor typos and fixes.

v0.11.0 (2024-06-18)

Update visualization code to get rid of a few seaborn warnings.

v0.10.10 (2024-02-07)

Fix Pandas mean example. More: Pandas changed their defaults to throw an error if you try to call this on string columns. Fixed example to explicitely only call it on numeric data.

v0.10.9 (2024-02-07)

Misc typos.

v0.10.8 (2024-01-14)

Fixed issue #2 (updated exercise 5.1.3 to reflect updated FFC data format). Thanks Damon!

v0.10.4-7 (2023)

Misc typos.

v0.10.3 (2023-02-07)

Added a missing ignore_index in the visualization chapter (thanks Chad!)

v0.10.2 (2022-12-01)

Cleaned up a few minor typos.

v0.10.1 (2021-09-19)

Fixed the solution for exercise 5.1 so it's no longer cut off (thanks Graydon!)

v0.10.0 (2021-09-19)

Misc edits and Anki card updates.

v0.9.1 (2021-06-16)

Added a note explaning granularity in the main text, before asking any end of chapter exercises on it (thanks Jay!)

v0.9.0 (2021-06-16)

Updated visualization section + associated homework problems to use Seaborn 0.11.x (September 2020), which added a new displot function. This means making our distribution plots change from, say:

g = (sns.FacetGrid(df)
     .map(sns.kdeplot, 'std', shade=True))

To:

g = sns.displot(df, x='std', kind='kde', fill=True)

It also opens up some new possibilities (e.g. with plotting empirical CDFs) that I might discuss in a future update.

v0.8.0

Add this changelog, bundle files in an github release vs including with SendOwl.