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You can use the following syntax to find the sum of rows in a pandas DataFrame that meet some criteria:

#find sum of each column, grouped by one columndf.groupby('group_column').sum()#find sum of one specific column, grouped by one columndf.groupby('group_column')['sum_column'].sum()

The following examples show how to use this syntax with the following data frame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['a', 'a', 'b', 'b', 'b', 'c', 'c'], 'points': [5, 8, 14, 18, 5, 7, 7], 'assists': [8, 8, 9, 3, 8, 7, 4], 'rebounds': [1, 2, 2, 1, 0, 4, 1]}) #view DataFrame df team points assists rebounds 0 a 5 8 1 1 a 8 8 2 2 b 14 9 2 3 b 18 3 1 4 b 5 8 0 5 c 7 7 4 6 c 7 4 1

**Example 1: Perform a SUMIF Function on One Column**

The following code shows how to find the sum of points for each team:

df.groupby('team')['points'].sum() team a 13 b 37 c 14

This tells us:

- Team â€˜aâ€™ scored a total of
**13**points - Team â€˜bâ€™ scored a total of
**37**points - Team â€˜câ€™ scored a total of
**14**points

**Example 2: Perform a SUMIF Function on Multiple Columns**

The following code shows how to find the sum of points and rebounds for each team:

df.groupby('team')[['points', 'rebounds']].sum() points rebounds team a 13 3 b 37 3 c 14 5

**Example 3: Perform a SUMIF Function on All Columns**

The following code shows how to find the sum of all columns in the data frame for each team:

df.groupby('team').sum() points assists rebounds team a 13 16 3 b 37 20 3 c 14 11 5

**Additional Resources**

How to Perform a COUNTIF Function in Pandas

How to Count Observations by Group in Pandas

How to Find the Max Value by Group in Pandas