Python pandas difference of specific rows in groupby

I have a pandas dataframe

df = pd.DataFrame({'Firm': ['Firm1','Firm1','Firm1','Firm1','Firm1','Firm1','Firm2','Firm2','Firm2','Firm2','Firm2','Firm2'],'Location' : ['Country1', 'Country1', 'Country1', 'Country2', 'Country2', 'Country2','Country1', 'Country1', 'Country1', 'Country2', 'Country2', 'Country2'], 'Currency' : ['Curr1', 'Curr2', 'Curr3', 'Curr1', 'Curr2', 'Curr3','Curr1', 'Curr2', 'Curr3', 'Curr1', 'Curr2', 'Curr3'], 'Value' : [100, 105, 110, 100, 95, 120, 95, 110, 115, 105, 120, 90] })

which looks like this:

df:

     Firm  Location Currency  Value
0   Firm1  Country1    Curr1    100
1   Firm1  Country1    Curr2    105
2   Firm1  Country1    Curr3    110
3   Firm1  Country2    Curr1    100
4   Firm1  Country2    Curr2     95
5   Firm1  Country2    Curr3    120
6   Firm2  Country1    Curr1     95
7   Firm2  Country1    Curr2    110
8   Firm2  Country1    Curr3    115
9   Firm2  Country2    Curr1    105
10  Firm2  Country2    Curr2    120
11  Firm2  Country2    Curr3     90

Now I would like to calculate the difference between Curr3 and Curr2 (of column Value) for each Firm-Location group and change the value of Curr3 based on the outcome. The resulting df should then look like this:

     Firm  Location Currency  Value
0   Firm1  Country1    Curr1    100
1   Firm1  Country1    Curr2    105
2   Firm1  Country1    Curr3      5
3   Firm1  Country2    Curr1    100
4   Firm1  Country2    Curr2     95
5   Firm1  Country2    Curr3     25
6   Firm2  Country1    Curr1     95
7   Firm2  Country1    Curr2    110
8   Firm2  Country1    Curr3      5
9   Firm2  Country2    Curr1    105
10  Firm2  Country2    Curr2    120
11  Firm2  Country2    Curr3    -30

I have tried using .groupby and .apply which gives me the results, however I would like to do the transformation in the original dataframe.

df2 = df.groupby(['Firm','Location']).apply(lambda g: g[g.Currency == 'Curr3'].Value.values[0] - g[g.Currency == 'Curr2'].Value.values[0])

df2:

Firm    Location    0
Firm1   Country1    5
Firm1   Country2    25
Firm2   Country1    5
Firm2   Country2    -30

I cannot figure out how to do this inplace in the original df. I also tried the same this using .transform, however it creates an error:

df2 = df.groupby(['Firm','Location']).transform(lambda g: g[g.Currency == 'Curr3'].Value.values[0] - g[g.Currency == 'Curr2'].Value.values[0])

AttributeError: ("'Series' object has no attribute 'Currency'", 'occurred at index Currency')

---- Update based on Erfan's solution:

newvals = (
    df.where(df['Currency'].isin(['Curr2', 'Curr3']))
      .groupby(['Firm', 'Location'])['Value'].diff()
)
df['Value'] = newvals.fillna(df['Value'])

If df looks like this however (Currency not sorted), the solution no longer works (as diff() only calculates the difference to the previous value

    Firm    Location    Currency    Value
0   Firm1   Country1    Curr2   100
1   Firm1   Country1    Curr1   105
2   Firm1   Country1    Curr3   110
3   Firm1   Country2    Curr3   100
4   Firm1   Country2    Curr2   95
5   Firm1   Country2    Curr1   120
6   Firm2   Country1    Curr1   95
7   Firm2   Country1    Curr2   110
8   Firm2   Country1    Curr3   115
9   Firm2   Country2    Curr2   105
10  Firm2   Country2    Curr3   120
11  Firm2   Country2    Curr1   90

-> result:

    Firm    Location    Currency    Value
0   Firm1   Country1    Curr2   100.0
1   Firm1   Country1    Curr1   105.0
2   Firm1   Country1    Curr3   10.0
3   Firm1   Country2    Curr3   100.0
4   Firm1   Country2    Curr2   -5.0
5   Firm1   Country2    Curr1   120.0
6   Firm2   Country1    Curr1   95.0
7   Firm2   Country1    Curr2   110.0
8   Firm2   Country1    Curr3   5.0
9   Firm2   Country2    Curr2   105.0
10  Firm2   Country2    Curr3   15.0
11  Firm2   Country2    Curr1   90.0

Now, it is no longer the case that each time the difference between Curr3 and Curr 2 is calculated and replaces the value for Curr3.