在 Pandas DataFrame 中按字典分组
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Group by a Dictionary in Pandas DataFrame
提问by tonyibm
I read data from an excel file, it is like this:
我从一个excel文件中读取数据，是这样的：
and I want to get the sum of Q1 and Q2 for each employee, so I create a dictionary, and want to group by based on that,
我想得到每个员工的 Q1 和 Q2 的总和，所以我创建了一个字典，并想根据它进行分组，
import pandas as pd
mb2 = pd.read_excel('C:\Users\IBM_ADMIN\Desktop\ml-1m\工作簿1.xlsx', sheetname='Sheet3')
mapping = {'Jan':'Q1','Feb':'Q1','Mar':'Q1','Apr':'Q2','May':'Q2','Jun':'Q2'}
mb2.groupby(by=mapping,axis=1).sum()
How to show also the field Name? I try to do like this, but it didn't work:
如何同时显示字段名称？我尝试这样做，但没有奏效：
mb2.groupby(by=(mapping,'Name'),axis=1).sum()
采纳答案by Blackecho
A quick solution is to save the resulting DataFramein a new variable (grouped_datain the following code) and then assign the Namecolumn to it using the original DataFrame:
一个快速的解决方案是将结果保存DataFrame在一个新变量中（grouped_data在以下代码中），然后Name使用原始变量将列分配给它DataFrame：
grouped_data = mb2.groupby(by=mapping,axis=1).sum()
grouped_data['Name'] = mb2['Name']
print(grouped_data)
EDIT:Another way of achieving the same result is to modify the mappingdictionary, adding a "Name": "Name"pair to it, and calling mb2.groupby(by=mapping,axis=1).sum()as before.
编辑：实现相同结果的另一种方法是修改mapping字典，向其中添加一"Name": "Name"对，然后mb2.groupby(by=mapping,axis=1).sum()像以前一样调用。
回答by jezrael
You can use set_indexfirst, then groupbyand last reset_index:
您可以使用set_indexfirst ， thengroupby和 last reset_index：
df = mb2.set_index('Name').groupby(by=mapping,axis=1).sum().reset_index()
print (df)
Name Q1 Q2
0 Tony 1367 1835
1 Rafal 1405 2574
2 John 1550 1409
3 Katrina 1555 1509
4 Kathy 1583 1491
5 Tom 1344 1874
