Pandas Vlookup 2 DF Columns Different Lengths & Perform Calculation - pandas

Question

I need to execute a vlookup-like calculation considering two df's of diff lengths with the same column name. Suppose i have a df called df1 such as:
Y M P D
2020 11 Red 10
2020 11 Blue 9
2020 11 Green 12
2020 11 Tan 7
2020 11 White 5
2020 11 Cyan 17
and a second df called df2 such as:
Y M P D
2020 11 Blue 4
2020 11 Red 12
2020 11 White 6
2020 11 Tan 7
2020 11 Green 20
2020 11 Violet 10
2020 11 Black 7
2020 11 BlackII 3
2020 11 Cyan 14
2020 11 Copper 6
I need a new df like df3['Res','P'] with 2 columns showing results from subtracting df1 from df2 such as:
Res P
Red -2
Blue 5
Green -8
Tan 0
White -1
Cyan 3
I have not been able to find anything with a lookup and then calculation on the web. I've tried merging df1 and df2 into one df but I do not see how to execute the calculation when the values in the "P" column match. I think that a merge of df1 and df2 is probably the first step though?

Answer 1

Based on the example, column 'Y' and 'M' do not matter for the merge. If these columns are relevant, then use a list with the on parameter (e.g. on=['Y', 'M', 'P']).
Currently, only columns [['P', 'D']] are being used from df1 and df2.
The following code, produces the desire output for the example, but it's difficult say what will happen with larger dataframes and if there are repeating values in 'P'.
import pandas as pd
# setup the dataframes
df1 = pd.DataFrame({'Y': [2020, 2020, 2020, 2020, 2020, 2020], 'M': [11, 11, 11, 11, 11, 11], 'P': ['Red', 'Blue', 'Green', 'Tan', 'White', 'Cyan'], 'D': [10, 9, 12, 7, 5, 17]})
df2 = pd.DataFrame({'Y': [2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020], 'M': [11, 11, 11, 11, 11, 11, 11, 11, 11, 11], 'P': ['Blue', 'Red', 'White', 'Tan', 'Green', 'Violet', 'Black', 'BlackII', 'Cyan', 'Copper'], 'D': [4, 12, 6, 7, 20, 10, 7, 3, 14, 6]})
# merge the dataframes
df = pd.merge(df1[['P', 'D']], df2[['P', 'D']], on='P', suffixes=('_1', '_2')).rename(columns={'P': 'Res'})
# subtract the values
df['P'] = (df.D_1 - df.D_2)
# drop the unneeded columns
df = df.drop(columns=['D_1', 'D_2'])
# display(df)
Res P
0 Red -2
1 Blue 5
2 Green -8
3 Tan 0
4 White -1
5 Cyan 3