You can use the following methods to select rows in a pandas DataFrame where two columns are (or are not) equal:

Method 1: Select Rows where Two Columns Are Equal

df.query('column1 == column2')

Method 2: Select Rows where Two Columns Are Not Equal

df.query('column1 != column2') 

The following examples show how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'painting': ['A', 'B', 'C', 'D', 'E', 'F'],
                   'rater1': ['Good', 'Good', 'Bad', 'Bad', 'Good', 'Good'],
                   'rater2': ['Good', 'Bad', 'Bad', 'Good', 'Good', 'Good']})

#view DataFrame
print(df)

  painting rater1 rater2
0        A   Good   Good
1        B   Good    Bad
2        C    Bad    Bad
3        D    Bad   Good
4        E   Good   Good
5        F   Good   Good

Example 1: Select Rows where Two Columns Are Equal

We can use the following syntax to select only the rows in the DataFrame where the values in the rater1 and rater2 column are equal:

#select rows where rater1 is equal to rater2
df.query('rater1 == rater2')

 painting  rater1  rater2
0	A    Good    Good
2	C     Bad     Bad
4	E    Good    Good
5	F    Good    Good

Notice that only the rows where rater1 and rater2 are equal are selected.

We could also use the len() function if we simply want to count how many rows have equal values in the rater1 and rater2 columns:

#count the number of rows where rater1 is equal to rater2
len(df.query('rater1 == rater2'))

4

This tells us that there are 4 rows where the values in the rater1 and rater2 column are equal.

Example 2: Select Rows where Two Columns Are Not Equal

We can use the following syntax to select only the rows in the DataFrame where the values in the rater1 and rater2 column are not equal:

#select rows where rater1 is not equal to rater2
df.query('rater1 != rater2')

 painting  rater1  rater2
1	B    Good     Bad
3	D     Bad    Good

The following tutorials explain how to perform other common tasks in pandas: