I have the following 2 data frames:

```
df1 = pd.DataFrame({
    'dates': ['02-Jan','03-Jan','30-Jan'],
    'currency': ['aud','gbp','eur'],
    'amount': [100,330,500]
})

df2 = pd.DataFrame({
    'dates': ['01-Jan','02-Jan','03-Jan','30-Jan'],
    'aud': [0.72,0.73,0.74,0.71],
    'gbp': [1.29,1.30,1.4,1.26],
    'eur': [1.15,1.16,1.17,1.18]
})

```

I want to obtain the intersection of `df1.dates` & `df1.currency`. For eg: Looking up the prevalent `'aud'` exchange rate on '02-Jan'

It can be solved using the `Index + Match` functionality of excel. What shall be the best way to replicate it in Pandas.

Desired Output: add a new column `'price'`

```
dates  currency amount price
02-Jan aud      100    0.73
03-Jan gbp      330    1.4
30-Jan eur      500    1.18

```