Pandas arthmetic between two different sized dataframes given common columns

Question

DF 1

| ColA     | Colb           | Stock    | Date       |
| -------- | -------------- | -------- | ---------- |
| A        | 1              | 3        | 2022-26-12 |
| B        | 2              | 3        | 2022-26-12 |
| C        | 3              | 3        | 2022-26-12 |

DF 2

| ColA     | Colb           | Sales    | Date       |
| -------- | -------------- | -------- | ---------- |
| A        | 1              | 1        | 2022-26-12 |
| B        | 2              | 1        | 2022-26-12 |
| C        | 3              | 1        | 2022-26-12 |

Given any number of columns to join on, how do you do Dataframe arithmetic in pandas, for instance if I wanted to subtract the above two Dataframes to get something like this

STOCK AT END OF THE DAY

| ColA     | Colb           | Stock    | Date       |
| -------- | -------------- | -------- | ---------- |
| A        | 1              | 2        | 2022-26-12 |
| B        | 2              | 2        | 2022-26-12 |
| C        | 3              | 2        | 2022-26-12 |

So stock - sales given all the common columns, in this case

Edit: (Since I was told off) The SQL equivalent of what I want in this case would like this if my SQL is correct:

SELECT
    DF1.ColA,
    DF1.Colb,
    DF1.Date,
    DF1.Stock - coalesce(DF2.Sales, 0)
FROM
    DF1
    LEFT JOIN DF2
        on
            DF1.ColA = DF2.ColA and
            DF1.Colb = DF2.Colb and
            DF1.Date = DF2.Date

Answer 1

If they have the same number of rows and columns then do something like that:

df3 = df1[['ColA', 'Colb','Date']]
df3['Stock'] = df1.Stock - df2.Sales

However, if they are different merge them then do what you want:

df3= pd.merge(df1, df2, on='ColA', how='inner')
df3['Stock'] = df3.Stock - df3.Sales