How to subtract rows between two different dataframes and replace original value?

Question

I have two dataframes shown as below. How can I replace Bank1 data by subtracting 10 by 3, and 55 by 2?

import pandas as pd

data = [['Bank1', 10, 55], ['Bank2', 15,65], ['Bank3', 14,54]]
df1 = pd.DataFrame(data, columns = ['BankName', 'Value1','Value2'])

df2 = pd.DataFrame([[3, 2]], columns = ['Value1','Value2'])

Desired Output(Only replace values in Bank1):

BankName	Value1	Value2
Bank1	7	53
Bank2	15	65
Bank3	14	54

Answer 1

First solution is create index in df22 by Bankname for align by df1 for correct row subracting:

df.set_index('BankName').sub(df2.set_index([['Bank1']]), fill_value=0)

df.set_index('BankName').sub(df2.set_index([['Bank2']]), fill_value=0)

You need create new column to df2 with BankName, convert BankName to index in both DataFrames, so possible subtract by this row:

df22 = df2.assign(BankName = 'Bank1').set_index('BankName')
df = df1.set_index('BankName').sub(df22, fill_value=0).reset_index()
print (df)
  BankName  Value1  Value2
0    Bank1     7.0    53.0
1    Bank2    15.0    65.0
2    Bank3    14.0    54.0

Subtract by Bank2:

df22 = df2.assign(BankName = 'Bank2').set_index('BankName')
df = df1.set_index('BankName').sub(df22, fill_value=0).reset_index()
print (df)

  BankName  Value1  Value2
0    Bank1    10.0    55.0
1    Bank2    12.0    63.0
2    Bank3    14.0    54.0

Another solution with filter by BankName:

m = df1['BankName']=='Bank1'
df1.loc[m, df2.columns] = df1.loc[m, df2.columns].sub(df2.iloc[0])
print (df1)
  BankName  Value1  Value2
0    Bank1       7      53
1    Bank2      15      65
2    Bank3      14      54

m = df1['BankName']=='Bank2'
df1.loc[m, df2.columns] = df1.loc[m, df2.columns].sub(df2.iloc[0])