Question

我有以下Pandas DataFrame df：

                                 Value
time                 Position
1493791210867023000  0.0           21156.0
                     1.0         1230225.0
                     2.0         1628088.0
                     3.0         2582359.0
                     4.0         3388164.0
1493791210880251000  0.0           21156.0
                     1.0         1230225.0
                     2.0         1628088.0
                     3.0         2582359.0
                     4.0         3388164.0
1493791210888418000  0.0           21156.0
                     1.0         1230225.0
...                  ...         ...

如何有效地总结指数＆＃34;位置＆＃34;？我试图实现的确切求和公式是：

                                 Value        Result
time                 Position
1493791210867023000  0.0           21156.0    Sum from 0.0 to 0.0
                     1.0         1230225.0    Sum from 0.0 to 1.0
                     2.0         1628088.0    Sum from 0.0 to 2.0
                     3.0         2582359.0    Sum from 0.0 to 3.0
                     4.0         3388164.0    Sum from 0.0 to 4.0
1493791210880251000  0.0           21156.0    Sum from 0.0 to 0.0
                     1.0         1230225.0    Sum from 0.0 to 1.0
                     2.0         1628088.0    Sum from 0.0 to 2.0
                     3.0         2582359.0    Sum from 0.0 to 3.0
...                  ...         ...          ...

我当前的解决方案需要太长时间（IndexSlice非常缓慢）而且我不太确定，我如何能够有效地将总和结果排序到（新创建的）＆＃34;结果＆＃34;列？

import pandas as pd
import numpy as np
idx = pd.IndexSlice
res = {}
for i in range(5):
    res[i] = df.loc[idx[:, :i]].groupby(level="time").sum()
df["Result"] = 0 #fill Result now with res[i] depending on position

Answer 1

尝试在cumsum

中使用groupby

df.assign(Result=df.groupby(level='time').Value.cumsum())
# suggested by @ScottBoston for pandas 0.20.1+
# df.assign(Result=df.groupby('time').Value.cumsum())

                                  Value     Result
time                Position                      
1493791210867023000 0.0         21156.0    21156.0
                    1.0       1230225.0  1251381.0
                    2.0       1628088.0  2879469.0
                    3.0       2582359.0  5461828.0
                    4.0       3388164.0  8849992.0
1493791210880251000 0.0         21156.0    21156.0
                    1.0       1230225.0  1251381.0
                    2.0       1628088.0  2879469.0
                    3.0       2582359.0  5461828.0
                    4.0       3388164.0  8849992.0
1493791210888418000 0.0         21156.0    21156.0
                    1.0       1230225.0  1251381.0

沿着Pandas Column求和MultiIndex值？

1 个答案: