Pandas style function to highlight specific columns

Question

I have been trying to write a function to use with pandas style. I want to highlight columns that I specify in the arguments. This is not very elegant, but, for example:

Answer 1

I think you can use Slicing in Styles for select columns B and C and then Styler.applymap for elementwise styles.

import pandas as pd
import numpy as np

data =  pd.DataFrame(np.random.randn(5, 3), columns=list('ABC'))
#print (data)

def highlight_cols(s):
    color = 'grey'
    return 'background-color: %s' % color

data.style.applymap(highlight_cols, subset=pd.IndexSlice[:, ['B', 'C']])

If you want more colors or be more flexible, use Styler.apply(func, axis=None), the function must return a DataFrame with the same index and column labels:

import pandas as pd
import numpy as np

data =  pd.DataFrame(np.random.randn(5, 3), columns=list('ABC'))
#print (data)

def highlight_cols(x):
    #copy df to new - original data are not changed
    df = x.copy()
    #select all values to default value - red color
    df.loc[:,:] = 'background-color: red'
    #overwrite values grey color
    df[['B','C']] = 'background-color: grey'
    #return color df
    return df    

data.style.apply(highlight_cols, axis=None)

Answer 2

You can do it bit more dynamically:

data =  pd.DataFrame(np.random.randn(5, 3), columns=list('ABC'))

# dictionary of column colors
coldict = {'A':'grey', 'C':'yellow'}

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

data.style.apply(highlight_cols, coldict=coldict)