# How do I get the sum of Pandas by grouping them together?

## Problem

This data frame is what I’m working with:

``````Fruit   Date      Name  Number
Apples  10/6/2016 Bob    7
Apples  10/6/2016 Bob    8
Apples  10/6/2016 Mike   9
Apples  10/7/2016 Steve 10
Apples  10/7/2016 Bob    1
Oranges 10/7/2016 Bob    2
Oranges 10/6/2016 Tom   15
Oranges 10/6/2016 Mike  57
Oranges 10/6/2016 Bob   65
Oranges 10/7/2016 Tony   1
Grapes  10/7/2016 Bob    1
Grapes  10/7/2016 Tom   87
Grapes  10/7/2016 Bob   22
Grapes  10/7/2016 Bob   12
Grapes  10/7/2016 Tony  15
``````

To acquire a total number of Fruit per Name, I’d like to aggregate this by Name and then by fruit. Consider the following scenario:

``````Bob,Apples,16
``````

I tried grouping by Name and Fruit, but I can’t seem to figure out how to get the overall amount of fruits.

## Solution #1

Use GroupBy.sum:

``````df.groupby(['Fruit','Name']).sum()

Out[31]:
Number
Fruit   Name
Apples  Bob        16
Mike        9
Steve      10
Grapes  Bob        35
Tom        87
Tony       15
Oranges Bob        67
Mike       57
Tom        15
Tony        1
``````

## Solution #2

You can also use the agg function.

``````df.groupby(['Name', 'Fruit'])['Number'].agg('sum')
``````

## Solution #3

If you want to keep the original columns Fruit and Name, use reset_index(). If not, Fruit and Name will be included in the index.

``````df.groupby(['Fruit','Name'])['Number'].sum().reset_index()

Fruit   Name       Number
Apples  Bob        16
Apples  Mike        9
Apples  Steve      10
Grapes  Bob        35
Grapes  Tom        87
Grapes  Tony       15
Oranges Bob        67
Oranges Mike       57
Oranges Tom        15
Oranges Tony        1
``````

As may be seen in the other responses:

``````df.groupby(['Fruit','Name'])['Number'].sum()

Number
Fruit   Name
Apples  Bob        16
Mike        9
Steve      10
Grapes  Bob        35
Tom        87
Tony       15
Oranges Bob        67
Mike       57
Tom        15
Tony        1
``````

## Solution #4

Both of the other options achieve your goal.

You can use the pivot functionality to arrange the data in a nice table

``````df.groupby(['Fruit','Name'],as_index = False).sum().pivot('Fruit','Name').fillna(0)

Name    Bob     Mike    Steve   Tom    Tony
Fruit
Apples  16.0    9.0     10.0    0.0     0.0
Grapes  35.0    0.0     0.0     87.0    15.0
Oranges 67.0    57.0    0.0     15.0    1.0
``````

``````df.groupby(['Fruit','Name'])['Number'].sum()