Pandas groupby 意味着 - 进入数据帧? [英] Pandas groupby mean - into a dataframe?
问题描述
假设我的数据如下所示:
Say my data looks like this:
date,name,id,dept,sale1,sale2,sale3,total_sale
1/1/17,John,50,Sales,50.0,60.0,70.0,180.0
1/1/17,Mike,21,Engg,43.0,55.0,2.0,100.0
1/1/17,Jane,99,Tech,90.0,80.0,70.0,240.0
1/2/17,John,50,Sales,60.0,70.0,80.0,210.0
1/2/17,Mike,21,Engg,53.0,65.0,12.0,130.0
1/2/17,Jane,99,Tech,100.0,90.0,80.0,270.0
1/3/17,John,50,Sales,40.0,50.0,60.0,150.0
1/3/17,Mike,21,Engg,53.0,55.0,12.0,120.0
1/3/17,Jane,99,Tech,80.0,70.0,60.0,210.0
我想要一个新列average，它是每个name,id,dept元组的total_sale的平均值
I want a new column average, which is the average of total_sale for each name,id,dept tuple
我试过了
df.groupby(['name', 'id', 'dept'])['total_sale'].mean()
这确实返回了一个均值系列:
And this does return a series with the mean:
name id dept
Jane 99 Tech 240.000000
John 50 Sales 180.000000
Mike 21 Engg 116.666667
Name: total_sale, dtype: float64
但是我将如何引用数据?该系列是形状 (3,) 的一维系列.理想情况下，我希望将其放回具有适当列的数据框中，以便我可以通过 name/id/dept 正确引用.
but how would I reference the data? The series is a one dimensional one of shape (3,). Ideally I would like this put back into a dataframe with proper columns so I can reference properly by name/id/dept.
推荐答案
如果你在你拥有的系列上调用 .reset_index()，它会给你一个你想要的数据框(每个级别的索引将转换为列):
If you call .reset_index() on the series that you have, it will get you a dataframe like you want (each level of the index will be converted into a column):
df.groupby(['name', 'id', 'dept'])['total_sale'].mean().reset_index()
为了回应 OP 的评论，将此列添加回原始数据框有点棘手.您的行数与原始数据框中的行数不同，因此您还不能将其分配为新列.但是，如果您将索引设置为相同，pandas 很聪明，会为您正确填写值.试试这个:
to respond to the OP's comment, adding this column back to your original dataframe is a little trickier. You don't have the same number of rows as in the original dataframe, so you can't assign it as a new column yet. However, if you set the index the same, pandas is smart and will fill in the values properly for you. Try this:
cols = ['date','name','id','dept','sale1','sale2','sale3','total_sale']
data = [
['1/1/17', 'John', 50, 'Sales', 50.0, 60.0, 70.0, 180.0],
['1/1/17', 'Mike', 21, 'Engg', 43.0, 55.0, 2.0, 100.0],
['1/1/17', 'Jane', 99, 'Tech', 90.0, 80.0, 70.0, 240.0],
['1/2/17', 'John', 50, 'Sales', 60.0, 70.0, 80.0, 210.0],
['1/2/17', 'Mike', 21, 'Engg', 53.0, 65.0, 12.0, 130.0],
['1/2/17', 'Jane', 99, 'Tech', 100.0, 90.0, 80.0, 270.0],
['1/3/17', 'John', 50, 'Sales', 40.0, 50.0, 60.0, 150.0],
['1/3/17', 'Mike', 21, 'Engg', 53.0, 55.0, 12.0, 120.0],
['1/3/17', 'Jane', 99, 'Tech', 80.0, 70.0, 60.0, 210.0]
]
df = pd.DataFrame(data, columns=cols)
mean_col = df.groupby(['name', 'id', 'dept'])['total_sale'].mean() # don't reset the index!
df = df.set_index(['name', 'id', 'dept']) # make the same index here
df['mean_col'] = mean_col
df = df.reset_index() # to take the hierarchical index off again
这篇关于Pandas groupby 意味着 - 进入数据帧?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
