Python Pandas style highlight specific cells for each column with different condition

I’m trying to highlight specific cells for each column with different condition which their value matches the condition for each row.

Below image is what I want to achieve: The table I attempt to achieve

I searched google and stackoverflow but none of these can meet my requirement. Can anyone who’s familiar with Pandas Style could assist?

Below are the codes I tried and failed:

Ex1

import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))

def highlight(s):
    return ['background-color: yellow' if (v>2) else 'background-color: white' for v in s]
df.style.apply(highlight, axis=0)

Ex2

import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))

Column_limit = (df['A'] > 6) | (df['B'] > 2) | (df['C'] < 3)
df[Column_limit].style.applymap(lambda x: 'background-color: yellow', subset=pd.IndexSlice[:, ['A', 'C']])

Ex3

import pandas as pd
df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))

subsets = pd.IndexSlice[:, 'A']
df.style.applymap(lambda x: 'background-color: yellow', subset = subsets)

Answer

If there is same number of conditions like some number of columns use:

df = pd.DataFrame([[10,3,1], [3,7,2], [2,4,4]], columns=list("ABC"))

def highlight(x):
    c1 = 'background-color: yellow'

    # condition
    m = pd.concat([(x['A'] > 6), (x['B'] > 2), (x['C'] < 3)], axis=1)
    #print (m)
    #empty DataFrame of styles
    df1 = pd.DataFrame('', index=x.index, columns=x.columns)
    #set new columns by condition
    return df1.mask(m, c1)


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

If there is a lot of columns and need processing only some of them:

def highlight(x):
    c1 = 'background-color: yellow'

    #empty DataFrame of styles
    df1 = pd.DataFrame('', index=x.index, columns=x.columns)
    #set new columns by condition
    
    df1.loc[(x['A'] > 6), 'A'] = c1
    df1.loc[(x['B'] > 2), 'B'] = c1
    df1.loc[(x['C'] < 3), 'C'] = c1
    
    return df1

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

EDIT:

If need specified all masks but in last step filter only some columns use:

def highlight(x):
    c1 = 'background-color: yellow'

    #empty DataFrame of styles
    df1 = pd.DataFrame('', index=x.index, columns=x.columns)
    #set new columns by condition
    
    df1.loc[(x['A'] > 6), 'A'] = c1
    df1.loc[(x['B'] > 2), 'B'] = c1
    df1.loc[(x['C'] < 3), 'C'] = c1
    
    need = ['A','C']
    df1 = df1[need].reindex(x.columns, fill_value='', axis=1)
    return df1

Or remove masks which not necessary:

def highlight(x):
    c1 = 'background-color: yellow'

    #empty DataFrame of styles
    df1 = pd.DataFrame('', index=x.index, columns=x.columns)
    #set new columns by condition
    
    df1.loc[(x['A'] > 6), 'A'] = c1
    df1.loc[(x['C'] < 3), 'C'] = c1
    
    return df1

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