Compare two columns in data frame and highlight value using pandas style

Question

I'm trying to highlight some values in some columns in data frame using pandas styles like:

import pandas as pd
import numpy as np

np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), 
columns=list('BCDE'))],axis=1)
df.iloc[0, 2] = np.nan

def highlight_greater(row):

    color=""
    if row['B'] > row['C']:
       color = 'red'
    elif row['D'] > row['E']:
        color = 'gray'

    background = ['background-color: {}'.format(color) for _ in row]
    return background

with open ('out.html','w') as out:
    print >> out, df.style.apply(highlight_greater, axis=1).render()

That's work fine, but not correspond to my objectif, i want only to highlight B and D columns. This script highlight all the columns in the row if match condition. Any Idea ? Thanks

jezrael · Accepted Answer · 2018-12-18 13:53:07Z

5

You can change custom function for DataFrame of styles:

def highlight_greater(x):
    r = 'red'
    g = 'gray'

    m1 = x['B'] > x['C']
    m2 = x['D'] > x['E']

    df1 = pd.DataFrame('background-color: ', index=x.index, columns=x.columns)
    #rewrite values by boolean masks
    df1['B'] = np.where(m1, 'background-color: {}'.format(r), df1['B'])
    df1['D'] = np.where(m2, 'background-color: {}'.format(g), df1['D'])
    return df1


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

answered Dec 18, 2018 at 13:53

jezrael

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