如何根据总数的百分比拆分字段值

Question

我有按date_month 、 device和channel分组的交易总和，如下所示

date_month   device            channel  transactions
2017-01-01  desktop         AFFILIATES           413
2017-01-01   mobile         AFFILIATES           501
2017-01-01    other         AFFILIATES            22
2017-01-01   tablet         AFFILIATES           250
2017-01-01  desktop             DIRECT         13979
etc...       etc...             etc...        etc...

date_month 范围是从2017-01-01到当前日期

我正在尝试做的是将device的other领域拆分为mobile 、 desktop或tablet

示例流程：

• Pivot 设备'other' ，其价值transactions作为额外列 ( other_transactions )
• 获取按date_month和channel ( total_transactions ) 分区/分组的transactions总数
• 然后将transactions除以total_transactions以获得总百分比（ percent_total ）
• 将other_transactions和other_split相乘得到percent_total
• 将other_split添加到transactions以获取更新的 transactions 字段

获取总数并应用简单的数学运算应该不是问题。 我会按照df['total_transactions']=df.groupby(['date_month', 'channel'])['transactions'].transform('sum')的方式做一些事情来获得total_transactions但我遇到的问题拥有正在将other交易放入单独的列中，就像这样

date_month   device            channel  transactions  other_trans
2017-01-01  desktop         AFFILIATES           413           22
2017-01-01   mobile         AFFILIATES           501           22
2017-01-01   tablet         AFFILIATES           250           22
2017-01-01  desktop             DIRECT         13979          etc
etc...       etc...             etc...        etc...

最后，我希望有一个数据框，它从device列中删除other设备，并使用其交易来根据他们在该date_month和channel的交易份额来增加剩余的设备交易

Answer 1

IIUC，您可以先使用groupby创建另一个 dataframe ，将行与others行一起删除，然后执行merge ：

import pandas as pd

df = pd.DataFrame({'date_month': {0: '2017-01-01', 1: '2017-01-01', 2: '2017-01-01', 3: '2017-01-01', 4: '2017-01-01', 5:"2017-01-01"},
                   'device': {0: 'desktop', 1: 'mobile', 2: 'other', 3: 'tablet', 4: 'desktop', 5:"other"},
                   'channel': {0: 'AFFILIATES', 1: 'AFFILIATES', 2: 'AFFILIATES', 3: 'AFFILIATES', 4: 'DIRECT', 5: 'DIRECT'},
                   'transactions': {0: 413, 1: 501, 2: 22, 3: 250, 4: 13979, 5: 234}})

other = df.groupby("device").get_group("other")[["date_month","channel","transactions"]]

df = df.drop(df[df["device"].str.contains("other")].index)

df = df.merge(other, on=["date_month","channel"], how="left", suffixes=("","_other"))

print (df)

结果：

   date_month   device     channel  transactions  transactions_other
0  2017-01-01  desktop  AFFILIATES           413                  22
1  2017-01-01   mobile  AFFILIATES           501                  22
2  2017-01-01   tablet  AFFILIATES           250                  22
3  2017-01-01  desktop      DIRECT         13979                 234