DataFrame Styling based on conditions for groups of columns

Question

I need to style a Dataframe:

df = DataFrame({\'A\':[\'Bob\',\'Rob\',\'Dob\'],\'B\':[\'Bob\', \'Rob\',\'Dob\'],\'C\':[\'Bob\',\'Dob\',\'Dob\'],\'D\':[\'Ben\',\'Te         


        

Answer 1

I believe need:

def highlight(x):
    c1 = 'background-color: red'
    c2 = '' 
    #define groups of columns for compare by first value of group ->
    #first with A, second with D
    cols = [['A','B','C'], ['D','E']]

    #join all masks together
    m = pd.concat([x[g].eq(x[g[0]], axis=0) for g in cols], axis=1)
    df1 = pd.DataFrame(c2, index=x.index, columns=x.columns)
    df1 = df1.where(m, c1)
    return df1

df.style.apply(highlight, axis=None)

EDIT: For multiple colors is possible create dictionary by colors with columns for compare:

def highlight(x):
    c = 'background-color: '
    cols = {'red': ['A','B','C'], 'blue':['D','E']}

    m = pd.concat([x[v].eq(x[v[0]], axis=0).applymap({False:c+k, True:''}.get) 
                   for k, v in cols.items()], axis=1)
    return m

EDIT1:

Alternative solution:

def highlight(x):
    c = 'background-color: '
    cols = {'red': ['A','B','C'], 'blue':['D','E']}

    df1 = pd.DataFrame(c, index=x.index, columns=x.columns)

    for k, v in cols.items():
        m = x[v].eq(x[v[0]], axis=0).reindex(columns=x.columns, fill_value=True)
        df1 = df1.where(m, c+k)
    return df1    

df.style.apply(highlight, axis=None)