Create a new Column in pandas dataframe based on the filetered values in the row of other columns

Question

I have a dataframe which has the following columns:

	Date	Zip	Price
0	2019-01-01	90102	58.02
1	2019-01-01	90102	81.55
2	2019-01-01	90102	11.97
3	2019-01-01	90102	93.23
4	2019-01-01	90103	13.68

I want to create a 4th column which should have ratio of price based on the max price in that zip and on that date.

So I have used another df called df_max_price

df_max_price = df.groupby(['Date','Zip'], as_index=False)['Price'].max()

	Date	Zip	Price
0	2019-01-01	90102	93.23
1	2019-01-01	90103	13.68

Now I want to have a new column in the df which shall be the ratio of Price and maxprice for that date and zip code

	Date	Zip	Price	Ratio
0	2019-01-01	90102	58.02	0.622
1	2019-01-01	90102	81.55	0.875
2	2019-01-01	90102	11.97	0.128
3	2019-01-01	90102	93.23	1.000
4	2019-01-01	90103	13.68	1.000

The calculation is based on 58.02/93.23 and so on.

Can someone help me showing how it can be done using lambda function.

Answer 1

Use groupby and transform

df['ratio'] = df['Price'] / df.groupby(['Date','Zip'], as_index=False)['Price'].transform('max')['Price']

         Date    Zip  Price     ratio
0  2019-01-01  90102  58.02  0.622332
1  2019-01-01  90102  81.55  0.874718
2  2019-01-01  90102  11.97  0.128392
3  2019-01-01  90102  93.23  1.000000
4  2019-01-01  90103  13.68  1.000000