一次性完成任意数量的不同groupby级别

Question

有没有一种方法可以使用一些预先构建的函数一次性计算任意数量的不同groupby级别？ 下面是一个包含两列的简单示例

import pandas as pd

df1 = pd.DataFrame( { 
    "name" : ["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"], 
    "city" : ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"],  
    "dollars":[1, 1, 1, 1, 1, 1] })

group1 = df1.groupby("city").dollars.sum().reset_index()
group1['name']='All'

group2 = df1.groupby("name").dollars.sum().reset_index()
group2['city']='All'

group3 = df1.groupby(["name", "city"]).dollars.sum().reset_index()

total = df1.dollars.sum()
total_df=pd.DataFrame({ 
    "name" : ["All"], 
    "city" : ["All"],  
    "dollars": [total] })

all_groups = group3.append([group1, group2, total_df], sort=False)


    name    city    dollars
0   Alice   Seattle     1
1   Bob     Seattle     2
2   Mallory Portland    2
3   Mallory Seattle     1
0   All     Portland    2
1   All     Seattle     4
0   Alice   All         1
1   Bob     All         2
2   Mallory All         3
0   All     All         6

所以我带了本。T示例，并将其从sum（）重建为agg（）。对我来说，下一步是构建一个选项来传递groupby组合的特定列表，以防不需要所有组合

from itertools import combinations
import pandas as pd

df1 = pd.DataFrame( { 
    "name" : ["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"], 
    "city" : ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"],  
    "dollars":[1, 2, 6, 5, 3, 4],
    "qty":[2, 3, 4, 1, 5, 6] ,
    "id":[1, 1, 2, 2, 3, 3] 
})

col_gr = ['name', 'city']
agg_func={'dollars': ['sum', 'max', 'count'], 'qty': ['sum'], "id":['nunique']}

def multi_groupby(in_df, col_gr, agg_func, all_value="ALL"):
    tmp1 = pd.DataFrame({**{col: all_value for col in col_gr}}, index=[0])
    tmp2 = in_df.agg(agg_func)\
                .unstack()\
                .to_frame()\
                .transpose()\
                .dropna(axis=1)
    tmp2.columns = ['_'.join(col).strip() for col in tmp2.columns.values]
    total = tmp1.join(tmp2)

    for r in range(len(col_gr), 0, -1):
        for cols in combinations(col_gr, r):
            tmp_grp = in_df.groupby(by=list(cols))\
                .agg(agg_func)\
                .reset_index()\
                .assign(**{col: all_value for col in col_gr if col not in cols})
            tmp_grp.columns = ['_'.join(col).rstrip('_') for col in tmp_grp.columns.values]
            total = pd.concat([total]+[tmp_grp], axis=0, ignore_index=True)
    return total

multi_groupby(df1, col_gr, agg_func)

Answer 1

这也是我一直在寻找的东西。下面是其他人写的两种方法的链接，它们帮助我解决了这个问题。当然也会对其他拍摄感兴趣

Link 1 Link 2