How to use Pandas stylers for coloring an entire row based on a given column?

Question

I've been trying to print out a Pandas dataframe to html and have specific entire rows highlighted if the value of one specific column's value for that row is over a threshold. I've looked through the Pandas Styler Slicing and tried to vary the highlight_max function for such a use, but seem to be failing miserably; if I try, say, to replace the is_max with a check for whether a given row's value is above said threshold (e.g., something like

is_x = df['column_name'] >= threshold

), it isn't apparent how to properly pass such a thing or what to return.

I've also tried to simply define it elsewhere using df.loc, but that hasn't worked too well either.

Another concern also came up: If I drop that column (currently the criterion) afterwards, will the styling still hold? I am wondering if a df.loc would prevent such a thing from being a problem.

Answer 1

Assume you have the following dataframe and you want to highlight the rows where id is greater than 3 to red

   id char       date
0   0    s 2022-01-01
1   1    t 2022-02-01
2   2    y 2022-03-01
3   3    l 2022-04-01
4   4    e 2022-05-01
5   5    r 2022-06-01

---

You can try Styler.set_properties with pandas.IndexSlice

# Subset your original dataframe with condition
df_ = df[df['id'].gt(3)]

# Pass the subset dataframe index and column to pd.IndexSlice
slice_ = pd.IndexSlice[df_.index, df_.columns]

s = df.style.set_properties(**{'background-color': 'red'}, subset=slice_)
s.to_html('test.html')

---

You can also try Styler.apply with axis=None which passes the whole dataframe.

def styler(df):
    color = 'background-color: {}'.format
    mask = pd.concat([df['id'].gt(3)] * df.shape[1], axis=1)
    style = np.where(mask, color('red'), color('green'))
    return style

s = df.style.apply(styler, axis=None)

Answer 2

Here is a simpler approach:

1. Assume you have a 100 x 10 dataframe, df. Also assume you want to highlight all the rows corresponding to a column, say "duration", greater than 5.

2. You first need to define a function that highlights the cells. The real trick is that you need to return a row, not a single cell. For example:

   def highlight(s):
       if s.duration > 5:
           return ['background-color: yellow'] * len(s)
       else:
           return ['background-color: white'] * len(s)
   

**Note that the return part should be a list of 10 (corresponding to the number of columns). This is the key part.

3. Now you can apply this to the dataframe style as:

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

Answer 3

This solution allows for you to pass a column label or a list of column labels to highlight the entire row if that value in the column(s) exceeds the threshold.

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_greaterthan(s, threshold, column):
    is_max = pd.Series(data=False, index=s.index)
    is_max[column] = s.loc[column] >= threshold
    return ['background-color: yellow' if is_max.any() else '' for v in is_max]


df.style.apply(highlight_greaterthan, threshold=1.0, column=['C', 'B'], axis=1)

Output:

Or for one column

df.style.apply(highlight_greaterthan, threshold=1.0, column='E', axis=1)