Replace value in column in pandas dataframe based on another column value in same row?

I have a dataframe with two columns. Column A contains values 0 and 1 and column B contains values 0 and 99. Like so:

    df = pd.DataFrame({'A': [0,0,0,1,1,0,0,1,1,0], 
                   'B': [99,1,0,99,99,1,1,99,99,99]})

   A   B
0  0  99
1  0   1
2  0   0
3  1  99
4  1  99
5  0   1
6  0   1
7  1  99
8  1  99
9  0  99

I need to replace all values of 99 with 0s within column B when the corresponding value of column A is 1, and I tried this:

    df = df[df['A']==1].replace({'B': {99: 0}})

   A  B
3  1  0
4  1  0
7  1  0
8  1  0

but when trying this, I lose the portion of the dataframe where A is 0. How can I perform this without losing that portion?

>Solution :

here is one way to do it

using Loc
df.loc[(df['A']== 1) & (df['B']==99), 'B'] = 0
df

OR

# using mask
df['B']= df['B'].mask((df['A']== 1) & (df['B']==99), 0)
df

    A   B
0   0   0
1   0   1
2   0   0
3   1   0
4   1   0
5   0   1
6   0   1
7   1   0
8   1   0
9   0   0