# Dataframe column: to find (cumulative) local maxima

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 cumulative 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 gives for every vector ( = subset corresponding to ’Portfolio =1 ’) contained in column “CumRetperTrade” the cumulative maximum value of (all its previous) values. The numeric example is below. Thanks in advance!
PS In other words, I guess that we need to use cummax() but to apply it only to the consequent (where ‘Portfolio’ = 1) subsets of ‘CumRetperTrade’

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

0   1           2               2
1   1           3               3
2   1           2               3
3   1           1               3
4   0           0               0
5   0           0               0
6   0           0               0
7   1           4               4
8   1           2               4
9   1           1               4```

PPS I already asked a similar question previously (Dataframe column: to find local maxima) and received a correct answer to my question, however in my question I did not explicitly mention the requirement of cumulative local maxima

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### Method 1

You only need a small modification to the previous answer:

``````df1["PeakCumRet"] = (
df1.groupby(df1["Portfolio"].diff().ne(0).cumsum())
.droplevel(0)
)
``````

`expanding().max()` is what produces the local maxima.

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