How to color individual cells of a pandas data frame based on dictionary values

Question

I have the following pandas df

      Player    Team     EPA
0   L.Jackson   BAL     0.33
1   P.Mahomes   KC      0.25
2   D.Brees     NO      0.24
3   M.Stafford  DET     0.21
4   D.Prescott  DAL     0.19
5   R.Tannehill TEN     0.18

That I want to style using the colors by applying the following dictionary values, where the keys match the Team field. I would also like to make the corresponding values of the Player field the same color.

COLORS = {'BAL':'#241773','DAL':'#B0B7BC','DET':'#046EB4',
          'KC':'#CA2430','NO':'#A08A58','TEN':'#4095D1'}

I tried looking at this question and had no success with the following code

def highlight_cols(s, coldict):
    if qbs.Team in COLORS.keys():
        return ['background-color: {}'.format(COLORS[qbs.Team])] * len(s)
    return [''] * len(s)

qbs.style.apply(highlight_cols, coldict=COLORS)

I have been reading through the DataFrame.style documentation and have been trying to figure out a way where I can color each Team value corresponding with the hexcode in the dictionary. In cells 5 and 6 of the notebook in the documentation, it shows how you can write a function and use df.style.applymap() to apply a function, but I'm not entirely sure how to go about this specific function. I tried using a .loc to no avail. Any help is appreciated. Thanks!

Answer 1

Not sure if you just want the EPA column colored, if you want the entire row remove the subset argument from the last line.

def highlight_cols(s, coldict):
    return ['background-color: {}'.format(COLORS[v]) if v else '' for v in qbs.Team.isin(COLORS.keys())*qbs.Team.values]
    

qbs.style.apply(highlight_cols, coldict=COLORS, subset='EPA')