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 Portfolio CumRetperTrade PeakCumRet 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

## Answers:

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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())
["CumRetperTrade"].expanding().max()
.droplevel(0)
)
```

`expanding().max()`

is what produces the local maxima.

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