You can use the following methods to return the row of a pandas DataFrame that contains the max value in a particular column:

**Method 1: Return Row with Max Value**

df[df['my_column'] == df['my_column'].max()]

**Method 2: Return Index of Row with Max Value**

df['my_column'].idxmax()

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({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
'points': [18, 22, 19, 14, 14, 11, 28, 20],
'assists': [5, 7, 7, 9, 12, 9, 9, 4],
'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})
#view DataFrame
print(df)
team points assists rebounds
0 A 18 5 11
1 B 22 7 8
2 C 19 7 10
3 D 14 9 6
4 E 14 12 6
5 F 11 9 5
6 G 28 9 9
7 H 20 4 12**

**Example 1: Return Row with Max Value**

The following code shows how to return the row in the DataFrame with the max value in the **points** column:

**#return row with max value in points column
df[df['points'] == df['points'].max()]
team points assists rebounds
6 G 28 9 9
**

The max value in the points column was **28**, so the row that contained this value was returned.

**Example 2: Return Index of Row with Max Value**

The following code shows how to return only the index of the row with the max value in the **points** column:

**#return row that contains max value in points column
df['points'].idxmax()
6**

The row in index position **6** contained the max value in the **points** column, so a value of **6** was returned.

**Related:** How to Use idxmax() Function in Pandas (With Examples)

**Additional Resources**

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

How to Find the Max Value by Group in Pandas

How to Find the Max Value of Columns in Pandas