python 实现分组求和与分组累加求和代码
我就废话不多说了,大家还是直接看代码吧!
# -*- encoding=utf-8 -*- import pandas as pd data=['abc','abc','abc','asc','ase','ase','ase'] num=[1,2,2,1,2,1,2] df1=pd.DataFrame({'name':data,'num':num}) print(df1) df1['mmm']=df1['num'] df2=df1.groupby(['name', 'num'], as_index=False).count() print(df2) df2.sort_values(['name', 'num'], ascending=[1, 1], inplace=True) print(df2) df2['sum']=df2.groupby(['name'])['mmm'].cumsum() print(df2) kk=df2.groupby(['name'],as_index=False)['num'].sum() print(kk) df3 = pd.merge(df2, kk, on='name', how='left',) print(df3) df3['ratio']=df3['sum']/df3['num_y'] df3.columns = ['name', 'num', 'mmm', 'sum','numsum','ratio'] print(df3) df4=df3.groupby(['mmm'],as_index=False)['ratio'].mean() print(df4)
运行:
name num 0 abc 1 1 abc 2 2 abc 2 3 asc 1 4 ase 2 5 ase 1 6 ase 2 name num mmm 0 abc 1 1 1 abc 2 2 2 asc 1 1 3 ase 1 1 4 ase 2 2 name num mmm 0 abc 1 1 1 abc 2 2 2 asc 1 1 3 ase 1 1 4 ase 2 2 name num mmm sum 0 abc 1 1 1 1 abc 2 2 3 2 asc 1 1 1 3 ase 1 1 1 4 ase 2 2 3 name num 0 abc 3 1 asc 1 2 ase 3 name num_x mmm sum num_y 0 abc 1 1 1 3 1 abc 2 2 3 3 2 asc 1 1 1 1 3 ase 1 1 1 3 4 ase 2 2 3 3 name num mmm sum numsum ratio 0 abc 1 1 1 3 0.333333 1 abc 2 2 3 3 1.000000 2 asc 1 1 1 1 1.000000 3 ase 1 1 1 3 0.333333 4 ase 2 2 3 3 1.000000 mmm ratio 0 1 0.555556 1 2 1.000000 Process finished with exit code 0
补充知识:python项目篇-对符合条件的某个字段进行求和,聚合函数annotate(),aggregate()函数
对符合条件的某个字段求和
需求是,计算每日的收入和
1、
new_dayincome = request.POST.get("dayincome_time", None) # total_income = models.bathAccount.objects.filter(dayBath=new_dayincome).aggregate(nums=Sum('priceBath')) total_income = models.bathAccount.objects.values('priceBath').annotate(nums=Sum('priceBath')).filter(dayBath=new_dayincome) print("total_income",total_income[0]['nums'])
输出结果:total_income 132
2、
from django.db.models import Sum,Count new_dayincome = request.POST.get("dayincome_time", None) total_income = models.bathAccount.objects.filter(dayBath=new_dayincome).aggregate(nums=Sum('priceBath')) print("total_income",total_income['nums'])
输出结果:total_income 572
第二种输出的是正确的数字
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