熊猫分组和

I am using this data frame:

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

I want to aggregate this by name and then by fruit to get a total number of fruit per name.

Bob,Apples,16 ( for example )

I tried grouping by Name and Fruit but how do I get the total number of fruit.

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

Also you can use agg function,

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

If you want to keep the original columns Fruit and Name, use reset_index(). Otherwise Fruit and Name will become part of 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 seen in the other answers:

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

Both the other answers accomplish what you want.

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()

You can select different columns to sum numbers.

You can set the groupby column to index then using sum with level

df.set_index(['Fruit','Name']).sum(level=[0,1])
Out[175]: 
               Number
Fruit   Name         
Apples  Bob        16
        Mike        9
        Steve      10
Oranges Bob        67
        Tom        15
        Mike       57
        Tony        1
Grapes  Bob        35
        Tom        87
        Tony       15

A variation on the .agg() function; provides the ability to (1) persist type DataFrame, (2) apply averages, counts, summations, etc. and (3) enables groupby on multiple columns while maintaining legibility.

df.groupby(['att1', 'att2']).agg({'att1': "count", 'att3': "sum",'att4': 'mean'})

using your values…

df.groupby(['Name', 'Fruit']).agg({'Number': "sum"})