You can use the following basic syntax to replace values in a column of a pandas DataFrame based on a condition:

#replace values in 'column1' that are greater than 10 with 20
df.loc[df['column1'] > 10, 'column1'] = 20

The following examples show how to use this syntax in practice.

Example 1: Replace Values in Column Based on One Condition

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'position': ['G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'],
                   'points': [5, 7, 7, 9, 12, 13, 9, 14],
                   'assists': [3, 8, 2, 6, 6, 5, 9, 5]})

#view DataFrame
df

	team	position points	assists
0	A	G	 5	3
1	A	G	 7	8
2	A	F	 7	2
3	A	F	 9	6
4	B	G	 12	6
5	B	G	 13	5
6	B	F	 9	9
7	B	F	 14	5       

We can use the following code to replace every value in the ‘points’ column that is greater than 10 with a value of 20:

#replace any values in 'points' column greater than 10 with 20
df.loc[df['points'] > 10, 'points'] = 20

#view updated DataFrame
df

	team	position points	assists
0	A	G	 5	3
1	A	G	 7	8
2	A	F	 7	2
3	A	F	 9	6
4	B	G	 20	6
5	B	G	 20	5
6	B	F	 9	9
7	B	F	 20	5

Notice that each of the three values in the ‘points’ column that were greater than 10 got replaced with the value 20.

Example 2: Replace Values in Column Based on Multiple Conditions

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'position': ['G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'],
                   'points': [5, 7, 7, 9, 12, 13, 9, 14],
                   'assists': [3, 8, 2, 6, 6, 5, 9, 5]})

#view DataFrame
df

	team	position points	assists
0	A	G	 5	3
1	A	G	 7	8
2	A	F	 7	2
3	A	F	 9	6
4	B	G	 12	6
5	B	G	 13	5
6	B	F	 9	9
7	B	F	 14	5       

We can use the following code to replace every value in the ‘position’ column where points is less than 10 or where assists is less than 5 with the string ‘Bad’:

#replace string in 'position' column with 'bad' if points < 10 or assists < 5
df.loc[(df['points'] < 10) | (df['assists'] < 5), 'position'] = 'Bad'

#view updated DataFrame
df

        team	position points	assists
0	A	Bad	 5	3
1	A	Bad	 7	8
2	A	Bad	 7	2
3	A	Bad	 9	6
4	B	G	 20	6
5	B	G	 20	5
6	B	Bad	 9	9
7	B	F	 20	5

Similarly, we can use the following code to replace every value in the ‘position’ column where points is less than 10 and where assists is less than 5 with the string ‘Bad’:

#replace string in 'position' column with 'bad' if points < 10 and assists < 5
df.loc[(df['points'] < 10) & (df['assists'] < 5), 'position'] = 'Bad'

#view updated DataFrame
df

        team	position points	assists
0	A	Bad	 5	3
1	A	G	 7	8
2	A	Bad	 7	2
3	A	F	 9	6
4	B	G	 12	6
5	B	G	 13	5
6	B	F	 9	9
7	B	F	 14	5

Notice that the two rows where points was less than 10 and assists was less than 5 had their ‘position’ value replaced with the string ‘Bad’.

Additional Resources

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