Python 熊猫分组和求和
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Pandas group-by and sum
提问by Trying_hard
I am using this data frame:
我正在使用这个数据框：
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.
我想按名称汇总，然后按水果汇总，以获得每个名称的水果总数。
Bob,Apples,16 ( for example )
I tried grouping by Name and Fruit but how do I get the total number of fruit.
我尝试按名称和水果分组，但如何获得水果的总数。
回答by Steven G
Use GroupBy.sum:
使用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
回答by Saurabh
Also you can use agg function,
您也可以使用 agg 功能，
df.groupby(['Name', 'Fruit'])['Number'].agg('sum')
回答by Gazala Muhamed
If you want to keep the original columns Fruitand Name, use reset_index(). Otherwise Fruitand Namewill become part of the index.
如果要保留原始列Fruitand Name，请使用reset_index(). 否则Fruit，Name将成为索引的一部分。
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:
正如其他答案中所见：
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
回答by Demetri Pananos
Both the other answers accomplish what you want.
其他两个答案都实现了您想要的。
You can use the pivotfunctionality to arrange the data in a nice table
您可以使用该pivot功能将数据排列在一个漂亮的表格中
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
回答by jared
df.groupby(['Fruit','Name'])['Number'].sum()
You can select different columns to sum numbers.
您可以选择不同的列来对数字求和。
回答by YOBEN_S
You can set the groupbycolumn to indexthen using sumwith level
您可以将groupby列设置为 index然后使用sumwithlevel
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
回答by xxyjoel
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.
.agg() 函数的变体；提供以下能力：(1) 保留类型 DataFrame，(2) 应用平均值、计数、求和等，以及 (3) 在保持易读性的同时在多列上启用 groupby。
df.groupby(['att1', 'att2']).agg({'att1': "count", 'att3': "sum",'att4': 'mean'})
using your values...
使用你的价值观...
df.groupby(['Name', 'Fruit']).agg({'Number': "sum"})
