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I want to create a new column in my dataframe with the value of a other row.
DataFrame

 TimeStamp Event Value 0 1603822620000 1 102.0 1 1603822680000 1 108.0 2 1603822740000 1 107.0 3 1603822800000 2 1 4 1603823040000 1 106.0 5 1603823100000 2 0 6 1603823160000 2 1 7 1603823220000 1 105.0

I would like to add a new column with the previous value where event = 1.

 TimeStamp Event Value PrevValue 0 1603822620000 1 102.0 NaN 1 1603822680000 1 108.0 102.0 2 1603822740000 1 107.0 108.0 3 1603822800000 2 1 107.0 4 1603823040000 1 106.0 107.0 5 1603823100000 2 0 106.0 6 1603823160000 2 1 106.0 7 1603823220000 1 105.0 106.0

So I can't simply use shift(1) and also not groupBy(event).shift(1).

Current solution

df["PrevValue"] =df.timestamp.apply(lambda ts: (df[(df.Event == 1) & (df.timestamp < ts)].iloc[-1].value))

But I guess, that's not the best solution.
Is there something like shiftUntilCondition(condition)?

Thanks a lot!

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1 Answer 1

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1

Try with

df['new'] = df['Value'].where(df['Event']==1).ffill().shift() Out[83]: 0 NaN 1 102.0 2 108.0 3 107.0 4 107.0 5 106.0 6 106.0 7 106.0 Name: Value, dtype: float64
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