python Dataframe: color cell of one column based on value

Question

I have a DataFrame with different columns. On some column, I have a function that given a value returns a boolean: True if the value is valid, False if not. I want to display in red cells with invalid values.

Here is a simple example:

df = pd.DataFrame([
      { 'name': 'mercury', 'celsius_temperature': -120, 'age': 1.8 * 10**9 },
      { 'name': 'sun', 'celsius_temperature': 5666, 'age': 3*10**9 },
      { 'name': 'XRP-189', 'celsius_temperature': -1000000, 'age': 4*10**9 },
      { 'name': 'XZT-11', 'celsius_temperature': 10**19, 'age': 12 },
])

def is_temp_valid(temp):
      return (temp > -273.15) and (temp < 10**10)

def is_age_valid(age):
      return (age > 10**8) and (age < 13*10**9)

On this example I would like to display something like this:

How to color cells in different columns based on different conditions on a DataFrame?

I tried to use style with barckround_gradient: https://pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.background_gradient.html But I need to use it with my functions that returns booleans (the example i gave is simplified, some function are much more complex than just intervals). And also, the gradient is not exactly what I want (I just want a red color but this is a minor problem).

I also looked at https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html But I did not find an example with custom functions.

Answer 1

Possible solution is following:

import pandas as pd

df = pd.DataFrame([
      {'name': 'mercury', 'celsius_temperature': -120, 'age': 1.8*10**9},
      {'name': 'sun', 'celsius_temperature': 5666, 'age': 3*10**9},
      {'name': 'XRP-189', 'celsius_temperature': -1000000, 'age': 4*10**9},
      {'name': 'XZT-11', 'celsius_temperature': 10**19, 'age': 12},
])


def is_temp_valid(temp):
    if -273.15 < temp < 10**10:
        return 'background-color: red'


def is_age_valid(age):
    if 10**8 < age < 13*10**9:
        return 'background-color: red'


s = df.style.applymap(is_temp_valid, subset=['celsius_temperature'])
s = s.applymap(is_age_valid, subset=['age'])

s

Returns