Color Specific columns based on condition of another column

Question

I'm trying to colour columns "Col2", "Col3", "Col4", "Col5", "Col6" based on Col1 and return other columns without any colour.

Sample Data

from random import randint

x = [randint(0, 1) for p in range(0, 10)]

sample_dict = {"Col1": [randint(0, 1) for p in range(0, 10)],
               "Col2": [randint(0, 1) for p in range(0, 10)],
               "Col3": [randint(0, 1) for p in range(0, 10)],
               "Col4": [randint(0, 1) for p in range(0, 10)],
               "Col5": [randint(0, 1) for p in range(0, 10)],
               "Col6": [randint(0, 1) for p in range(0, 10)]}
abcd = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))
sample = pd.DataFrame(sample_dict)

data = pd.concat([sample, abcd], axis=1)


Col1    Col2    Col3    Col4    Col5    Col6    A   B   C   D
0   0   1   1   1   0   1   -1.358769   -0.310314   -1.056405   -0.567577
1   0   1   0   1   1   0   -0.539893   -0.139629   0.270759    -0.430564
2   1   1   1   0   1   0   -0.009886   0.023482    0.966884    0.612875
3   0   1   1   1   0   1   0.962987    0.191210    -0.228937   -0.338486
4   1   1   1   0   0   1   -0.867326   -0.461046   0.832390    0.956392
5   0   0   0   0   1   1   0.348276    0.711152    -1.016820   0.394526
6   1   1   1   1   0   0   0.622317    0.519261    -2.022494   -1.170836
7   0   1   0   0   1   0   0.033249    0.491181    -0.065532   0.936868
8   1   0   0   1   1   1   1.064310    -0.257726   -0.197229   0.348314
9   0   1   0   1   1   0   0.017713    -0.624656   -0.341611   -1.433317

So far, I'm able to colour it by

data["Col1", "Col2", "Col3", "Col4", "Col5", "Col6"].style.apply(lambda x: ["background-color: orange" if v != x.iloc[0] else "background_color: none" for v in x], axis=1)

but unable to return the other columns.

How to add colour to columns "Col2", "Col3", "Col4", "Col5", "Col6" based on Col1? Iss it possible to apply different color for each column in "Col2", "Col3", "Col4", "Col5", "Col6"?

mozway · Accepted Answer · 2022-01-26 14:06:48Z

2

You could iterate per column and use a dictionary of colors:

colors = {'Col2': 'orange',
          'Col3': 'lightblue',
          'Col4': 'lightgreen',
          'Col5': 'lightpink',
          'Col6': 'yellow'
         }

import numpy as np
(data
 .style.apply(lambda c: np.where(sample['Col1'].eq(c),
                                 'background-color: none',
                                f'background-color: {colors.get(c.name, "none")}')
             )
)

output:

edited Jan 26, 2022 at 14:06

answered Jan 26, 2022 at 14:04

mozway

267k13 gold badges56 silver badges106 bronze badges

Sign up to request clarification or add additional context in comments.

5 Comments

mozway Over a year ago

@Pluviophile I already updated the answer ;) (but this was already demonstrating the effect with Col1)

mozway Over a year ago

Yes it maps the colors from the dictionary for the defined columns and uses no color instead

Ailurophile Over a year ago

I have large data, Just wanted to check the code only looks for keys/columns that are defined in the dictionary right?

mozway Over a year ago

No, it will currently evaluate all columns and set None, but if you want open a new question to not pollute this one (I thought the main topic was the multiple colors) and I can provide an alternative

Ailurophile Over a year ago

Could you please provide an alternate here?

Collectives™ on Stack Overflow

Color Specific columns based on condition of another column

1 Answer 1

5 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

5 Comments

Your Answer

Sign up or log in

Post as a guest

Related