Highlight element based on boolean pandas df

Question

I have 2 data frames with identical indices/columns:

df = pd.DataFrame({'A':[5.5, 3, 0, 3, 1],
                     'B':[2, 1, 0.2, 4, 5],
                     'C':[3, 1, 3.5, 6, 0]})

df_bool = pd.DataFrame({'A':[0, 1, 0, 0, 1],
                          'B':[0, 0, 1, 0, 0],
                          'C':[1, 1, 1, 0, 0]})

I want to apply a style function to df element-wise using df_bool as a mask.

This is the expected result:

Current failed function

def color_boolean(val):
  color =''
  if df_bool == 1:
    color = 'red'
  elif df_bool == 0:
    color = 'black'
  return f'color: {color}'

df.head().style.apply(color_boolean, axis=None)

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer 1

You can use a function that ignores the input and simply uses the other DataFrame:

def color_boolean(val):
    return f'color: {"red" if val else "black"}'

df.style.apply(lambda _: df_bool.applymap(color_boolean), axis=None)

or:

df.style.apply(lambda c: df_bool[c.name].apply(color_boolean))

output:

Answer 2

You can also use np.where to convert df_bool into a DataFrame of styles based on the locations of 1 values (df_bool.eq(1)).

By setting axis=None to Styler.apply we can effectively apply styles to the entire DataFrame.

true_css = 'color:red'
false_css = '' # No Styles (Change if needed)

df.style.apply(
    lambda _: np.where(df_bool.eq(1), true_css, false_css),
    axis=None
)

---

Optional format to round and match shown output:

true_css = 'color:red'
false_css = ''

df.style.apply(
    lambda _: np.where(df_bool.eq(1), true_css, false_css),
    axis=None
).format(formatter='{:1g}')

---

Imports (and versions):

import numpy as np  # 1.22.3
import pandas as pd  # 1.4.2