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

df.loc[df['col1'] == some_value, 'col2'].sum()

This tutorial provides several examples of how to use this syntax in practice using the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'C'], 'conference': ['East', 'East', 'East', 'West', 'West', 'East'], 'points': [11, 8, 10, 6, 6, 5], 'rebounds': [7, 7, 6, 9, 12, 8]}) #view DataFrame df team conference points rebounds 0 A East 11 7 1 A East 8 7 2 A East 10 6 3 B West 6 9 4 B West 6 12 5 C East 5 8

**Example 1: Sum One Column Based on One Condition**

The following code shows how to find the sum of the points for the rows where team is equal to â€˜Aâ€™:

df.loc[df['team'] == 'A', 'points'].sum() 29

**Example 2: Sum One Column Based on Multiple ConditionsÂ **

The following code shows how to find the sum of the points for the rows where team is equal to â€˜Aâ€™ *and* where conference is equal to â€˜Eastâ€™:

df.loc[(df['team'] == 'A') & (df['conference'] == 'East'), 'points'].sum() 29

**Example 3: Sum One Column Based on One of Several Conditions**

The following code shows how to find the sum of the points for the rows where team is equal to â€˜Aâ€™ *or *â€˜Bâ€™:

df.loc[df['team'].isin(['A', 'B']), 'points'].sum() 41

You can find more pandas tutorials on this page.