Dataframe column: to find local maxima

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In the below dataframe the column "CumRetperTrade" is a column which consists of a few vertical vectors (=sequences of numbers) separated by zeros. (= these vectors correspond to non-zero elements of column "Portfolio"). I would like to find the local maxima of every non-zero vector contained in column "CumRetperTrade"
To be precise, I would like to transform (using vectorization - or other - methods) column "CumRetperTrade" to the column "PeakCumRet" (desired result) which provides maxima for every vector contained in column "CumRetperTrade" its local max value. The numeric example is below. Thanks in advance.

import numpy as np
import pandas as pd
df1 = pd.DataFrame({"Portfolio": [1, 1, 1, 0 , 0, 0, 1, 1, 1],
"CumRetperTrade": [3, 2, 1, 0 , 0, 0, 4, 2, 1],
"PeakCumRet": [3, 3, 3, 0 , 0, 0, 4, 4, 4]})
df1

Portfolio   CumRetperTrade  PeakCumRet
1           3               3
1           2               3
1           1               3
0           0               0
0           0               0
0           0               0
1           4               4
1           2               4
1           1               4
pandas dataframe max numpy python
1 Answers

You can use :

df1['PeakCumRet'] = (df1.groupby(df1['Portfolio'].ne(df1['Portfolio'].shift()).cumsum())
                     ['CumRetperTrade'].transform('max')
                     )

Output:

   Portfolio  CumRetperTrade  PeakCumRet
0          1               3           3
1          1               2           3
2          1               1           3
3          0               0           0
4          0               0           0
5          0               0           0
6          1               4           4
7          1               2           4
8          1               1           4
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