Highlighting operation in pandas DataFrame by specific way defined

Question

I have my script to highlight 2 max values for columns in my DataFrame as:

from termcolor import colored
import pandas as pd

data = {'Col1': [190, 88, 120, 170, 110, 150], 'Col2': [11, -6, 3, 16, -21, 8], 'Col3': [20, 25, 30, 35, 40, 45],'Col4': [-33, -8, 93, -18, 3, 23], 'Col5': [544, 19, 78, 270, 210, 369]}
df = pd.DataFrame(data)

def hightlight(series, excluded, spcl_col, spcl_val):
    if series.name == spcl_col:
        spcl_val = str(spcl_val)
        return series.astype(str).apply(lambda x: colored(x, None, 'on_green') if x == spcl_val else colored(x, 'white'))

    if series.name in excluded:
        return series.astype(str).apply(lambda x: colored(x, 'white'))
    colors = pd.Series([(None,'on_blue'), (None,'on_magenta')] + [('white',)]*(len(series)-2), index=series.sort_values(ascending=False).index).reindex(series.index)

    return [colored(str(x), *c) for x,c in zip(series, colors)]

col_to_exclude = ['Col3']
print(df.apply(hightlight, excluded=col_to_exclude, spcl_col='Col3', spcl_val=30)
.rename(columns=lambda x: colored(x, 'white', None)).to_string(index=False))

Now I want to add something in my above script so that all negative values are in red text and all positive values in green text for specific columns(let's say Col2 & Col4 here which can be changed) without using df.style method and text colour should not be changed for highlighted values already by above script.

Haw can I do it ??

Answer 1

I would recommend to reorganize your code to use subfunctions to format each series type. Having a modular code will make it easier to add extra functions. I also reworked a bit the logic to highlight the top two values:

from termcolor import colored
import pandas as pd
import numpy as np

data = {'Col1': [190, 88, 120, 170, 110, 150], 'Col2': [11, -6, 3, 16, -21, 8], 'Col3': [20, 25, 30, 35, 40, 45],'Col4': [-33, -8, 93, -18, 3, 23], 'Col5': [544, 19, 78, 270, 210, 369]}
df = pd.DataFrame(data)

def hightlight(series, excluded, spcl_col, spcl_val, extra_col=[]):

    def excl_formatter(series):
        return [colored(str(x), 'white') for x in series]
    
    def norm_formatter(series, extra=False):
        tmp = series.sort_values()
        if extra:
            tmp.iloc[:-2] = tmp.iloc[:-2].map(lambda x: colored(x,
                                              *[('white',),
                                                ('green',),
                                                ('red',),
                                               ][np.sign(x)])
                                             )
        else:
            tmp.iloc[:-2] = tmp.iloc[:-2].map(lambda x: colored(str(x), 'white'))
        tmp.iloc[-2] = colored(tmp.iloc[-2], None, 'on_magenta')
        tmp.iloc[-1] = colored(tmp.iloc[-1], None, 'on_blue')
        return tmp
        
    def spcl_formatter(series):
        return [colored(x, None, 'on_green') if x == spcl_val
                else colored(x, 'white')
                for x in series]
    
    if series.name == spcl_col:
        return spcl_formatter(series)
    if series.name in excluded:
        return excl_formatter(series)
    return norm_formatter(series, extra=series.name in extra_col)
                                                        
col_to_exclude = ['Col5']
print()
print(df.apply(hightlight, excluded=col_to_exclude, spcl_col='Col3',
               spcl_val=30, extra_col=['Col2', 'Col4'])
        .rename(columns=lambda x: colored(x, 'white', None)).to_string(index=False)
     )

Output: