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# Pandas: How to Sum Columns Based on a Condition

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```