Pandas groupby 意味着 - 进入数据帧？
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Pandas groupby mean - into a dataframe?
提问by Craig
Say my data looks like this:
假设我的数据如下所示：
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
I want a new column average, which is the average of total_salefor each name,id,depttuple
我想要一个新列average，它是total_sale每个name,id,dept元组的平均值
I tried
我试过
df.groupby(['name', 'id', 'dept'])['total_sale'].mean()
And this does return a series with the mean:
这确实返回了一个具有平均值的系列：
name id dept
Jane 99 Tech 240.000000
John 50 Sales 180.000000
Mike 21 Engg 116.666667
Name: total_sale, dtype: float64
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.
但我将如何引用数据？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.
回答by Nathan
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):
如果您调用.reset_index()您拥有的系列，它将为您提供您想要的数据框（索引的每个级别都将转换为一列）：
df.groupby(['name', 'id', 'dept'])['total_sale'].mean().reset_index()
EDIT: 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, pandasis smart and will fill in the values properly for you. Try this:
编辑：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则很聪明，并且会为您正确填写值。尝试这个：
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
回答by A.Kot
You are very close. You simply need to add a set of brackets around [['total_sale']]to tell python to select as a dataframe and not a series:
你很亲近。您只需要在周围添加一组括号[['total_sale']]来告诉 python 选择作为数据框而不是系列：
df.groupby(['name', 'id', 'dept'])[['total_sale']].mean()
If you want all columns:
如果您想要所有列：
df.groupby(['name', 'id', 'dept'], as_index=False).mean()[['name', 'id', 'dept', 'total_sale']]
回答by YOBEN_S
Adding to_frame
添加 to_frame
df.groupby(['name', 'id', 'dept'])['total_sale'].mean().to_frame()
回答by Tahir Ahmad
The answer is in two lines of code:
答案在两行代码中：
The first line creates the hierarchical frame.
第一行创建分层框架。
df_mean = df.groupby(['name', 'id', 'dept'])[['total_sale']].mean()
The second line converts it to a dataframe with four columns('name', 'id', 'dept', 'total_sale')
第二行将其转换为具有四列的数据框（'name', 'id', 'dept', 'total_sale'）
df_mean = df_mean.reset_index()
