Comparing dataframe columns with acceptable deviation in Python

How can I have the comparison between two pandas dataframe rows considering an acceptable deviation instead of a 100% match?

For example:

Acceptable deviation: 10

column1 | column2 | Result
100     | 110     | True
0       | 20      | False
0       | 9       | True

I wasn’t able to find any references to built-in functions or similar scenarios.

>Solution :

Use:

df['Result'] = abs(df['column1'] - df['column2']) <= 10
print(df)

# Output:
   column1  column2  Result
0      100      110    True
1        0       20   False
2        0        9    True

Alternative, chained methods:

df['Result'] = df['column1'].sub(df['column2']).abs().le(10)