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.

Asked by Trying_hard

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

Answered by Steven G

Solution #2

You can also use the agg function.

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

Answered by Saurabh

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

Answered by Gazala Muhamed

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

Answered by Demetri Pananos

Solution #5

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

To sum numbers, you can choose from a variety of columns.

Answered by jared

Post is based on https://stackoverflow.com/questions/39922986/how-do-i-pandas-group-by-to-get-sum