While using groupby, set column to value from some row within dataframe

Question

I have a df setup similar to the one below. Each Date and Ticker combo only has the Bool flip to 1 on one row.

Date	Ticker	High	Low	Bool
2023-02-20	AAPL	146	144	0
2023-02-20	AAPL	143	142	0
2023-02-20	AAPL	144	143	1
2023-02-20	MSFT	146	144	0
2023-02-20	MSFT	143	142	1
2023-02-20	MSFT	144	143	0
2023-02-21	AAPL	146	144	0
2023-02-21	AAPL	143	142	1
2023-02-21	AAPL	144	143	0

I want to create a new column where the entire column is equal to the High where the Bool turns to 1, when grouped by Date and Ticker.

So the output df would look like the following:

Date	Ticker	High	Low	Bool	New_Row
2023-02-20	AAPL	146	144	0	144
2023-02-20	AAPL	143	142	0	144
2023-02-20	AAPL	144	143	1	144
2023-02-20	MSFT	251	248	0	252
2023-02-20	MSFT	252	251	1	252
2023-02-20	MSFT	255	250	0	252
2023-02-21	AAPL	146	144	0	143
2023-02-21	AAPL	143	142	1	143
2023-02-21	AAPL	144	143	0	143

I am unsure of how to do this without just looping through everything manually and I’m thinking that it’s probably not the best way to go about it

Asked By: cam

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Answer 1

You may create a new column named High_times_Bool:

df['High_times_Bool']=df['High']*df['Bool']

Then you may group by Date, Ticker and select the max High_times_Bool for each sub group:

df.groupby(['Date', 'Ticker'])['High_time_Bool'].max()

Finally you may inject the output values back into the original dataframe, following the logic outlined here.

Answered By: Sheldon

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