Mike Driscoll recently tweeted about making colored out with pandas DataFrames and I just had to try it for myself

Use Case

First though… why? My biggest use case is a monitoring pipeline of mine… The details aside, the output of my pipeline is a dataframe where each row has information about a failed pipeline that I need to go look into. I dump that result to a simle html file that’s hosted on an internal site and the file is updated every couple of hours. Adding some colored indicators automatically to the rows to help me assess severity of each record would be a handy way to quickly get an understanding the state of our pipelines.

How?

The docs for the applymap method state simply:

Apply a CSS-styling function elementwise.

Updates the HTML representation with the result.

So we can write a function that returns color: {color} based on the dataframe values and when we drop that dataframe to html we’ll have some simple css styling applied automagically!

By default the function will be applied to all columns of the dataframe, but that’s not useful if the columns are different types which is usually the case. Luckily there is a subset keyword to only apply to the columns you need!

Consider my example

sandbox   main via 3.8.11(sandbox) ipython
❯ df = pd.read_csv("cars.csv")

sandbox   main via 3.8.11(sandbox) ipython
❯ def mpg_color(val: float):
...:     color = "red" if val < 21 else "green"
...:     return f"color: {color}"

sandbox   main via 3.8.11(sandbox) ipython
❯ df.style.applymap(mpg_color, subset="mpg").to_html("color.html")

I want to quickly see if the mpg is any good for the cars in the cars dataset and I’ll define “good” as better than 21 mpg (not great I know but just for the sake of discussion…)

The function returns an appropriate css string and after I style.applymap on just the mpg column we get this!