Solving problem is about exposing yourself to as many situations as possible like Pandas group-by and sum and practice these strategies over and over. With time, it becomes second nature and a natural way you approach any problems in general. Big or small, always start with a plan, use other strategies mentioned here till you are confident and ready to code the solution.
In this post, my aim is to share an overview the topic about Pandas group-by and sum, which can be followed any time. Take easy to follow this discuss.
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.
Answer #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
Answer #2:
Also you can use agg function,
df.groupby(['Name', 'Fruit'])['Number'].agg('sum')
Answer #3:
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
Answer #4:
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
Answer #5:
df.groupby(['Fruit','Name'])['Number'].sum()
You can select different columns to sum numbers.
Answer #6:
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
Answer #7:
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"})
