Pandas Style conditional formatting (highlight) on text

Question

I am trying to highlight the single whole cell in pandas based on text. For example, if Recommend is 'SELL', I want to highlight in red and green for 'BUY'. Appreciate if someone can guide me on this.

def color_negative_red(value):
    if value < 0:
        color = 'red'
    elif value > 0:
        color = 'green'
    else:
        color = 'black'
    return 'color: %s' % color

import pandas as pd

data = {'Stock': ['TSLA','GM','GOOG','MMM'],
        'Diff': [-200,-50,150,50],
        'Recommend' : ['SELL','SELL','BUY','BUY']
        }

df = pd.DataFrame(data, columns = ['Stock', 'Diff', 'Recommend'])

df.style.applymap(color_negative_red, subset=['Diff'])

### how to get a conditional highlight based on 'Recommend' ?????

Henry Ecker · Accepted Answer · 2021-06-20 15:07:36Z

5

Styles can be chained together. There are many ways to solve this problem, assuming 'BUY' and 'SELL' are the only options np.where + apply is a good choice:

def color_recommend(s):
    return np.where(s.eq('SELL'),
                    'background-color: red',
                    'background-color: green')

(
    df.style.applymap(color_negative_red, subset=['Diff'])
        .apply(color_recommend, subset=['Recommend'])
)

Alternatively in a similar way to color_negative_red:

def color_recommend(value):
    if value == 'SELL':
        color = 'red'
    elif value == 'BUY':
        color = 'green'
    else:
        return
    return f'background-color: {color}'


(
    df.style.applymap(color_negative_red, subset=['Diff'])
        .applymap(color_recommend, subset=['Recommend'])
)

answered Jun 20, 2021 at 15:07

Henry Ecker♦

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Comments

lytseeker · Accepted Answer · 2021-06-20 15:13:50Z

3

You are almost there!

def color_negative_red(value):
    if value < 0:
        color = 'pink'
    elif value > 0:
        color = 'lightgreen'
    else:
        color = 'white'
    return 'background-color: %s' % color

import pandas as pd

data = {'Stock': ['TSLA','GM','GOOG','MMM'],
        'Diff': [-200,-50,150,50],
        'Recommend' : ['SELL','SELL','BUY','BUY']
        }

df = pd.DataFrame(data, columns = ['Stock', 'Diff', 'Recommend'])

df.style.applymap(color_negative_red, subset=['Diff'])

Only change needed is color needs to become background-color: return 'background-color: %s' % color

If you wanted to highlight the entire row, try:

def color_negative_red(row):
    print(row)
    value = row.loc["Diff"]
    if value < 0:
        color = 'pink'
    elif value > 0:
        color = 'lightgreen'
    else:
        color = 'black'
    return ['background-color: %s' % color for r in row]

import pandas as pd

data = {'Stock': ['TSLA','GM','GOOG','MMM'],
        'Diff': [-200,-50,150,50],
        'Recommend' : ['SELL','SELL','BUY','BUY']
        }

df = pd.DataFrame(data, columns = ['Stock', 'Diff', 'Recommend'])

df.style.apply(color_negative_red, axis=1)

answered Jun 20, 2021 at 15:13

lytseeker

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lytseeker Over a year ago

A couple of years later and I am realising I did not answer exactly what was asked.

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