You can use the following basic syntax to compare the values in three columns in pandas:

df['all_matching'] = df.apply(lambda x: x.col1 == x.col2 == x.col3, axis = 1)

This syntax creates a new column called all_matching that returns a value of True if all of the columns have matching values, otherwise it returns False.

The following example shows how to use this syntax in practice.

Example: Compare Three Columns in Pandas

Suppose we have the following pandas DataFrame with three columns:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'A': [4, 0, 3, 3, 6, 8, 7, 9, 12],
                   'B': [4, 2, 3, 5, 6, 4, 7, 7, 12],
                   'C': [4, 0, 3, 5, 5, 10, 7, 9, 12]})

#view DataFrame
print(df)

    A   B   C
0   4   4   4
1   0   2   0
2   3   3   3
3   3   5   5
4   6   6   5
5   8   4  10
6   7   7   7
7   9   7   9
8  12  12  12

We can use the following code to create a new column called all_matching that returns True if all three columns match in a given row and False if they do not:

#create new column that displays whether or not all column values match
df['all_matching'] = df.apply(lambda x: x.A == x.B == x.C, axis = 1)

#view updated DataFrame
print(df)

    A   B   C  all_matching
0   4   4   4          True
1   0   2   0         False
2   3   3   3          True
3   3   5   5         False
4   6   6   5         False
5   8   4  10         False
6   7   7   7          True
7   9   7   9         False
8  12  12  12          True

The new column called all_matching shows whether or not the values in all three columns match in a given row.

For example:

• All three values match in the first row, so True is returned.
• Not every value matches in the second row, so False is returned.

And so on.

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