How to make operations on pandas' Dataframe column based on condition in other column

Question

for example:

   A  B
1  3  0
2  4  0
3  6  1
4  8  0

I'd like to know if there is a simple way to add for example 10 to the value of column A whene in B there is 1.

I'd like to have a df output like below.

   A  B
1  3  0
2  4  0
3  16 1
4  8  0

I've found something like the line of code below but this one replace the 6 with 1, I'd like to use that 6 to make some operations on it.

PS. pardón for my english

df.loc[df['B'] == 1, 'A'] = 1

Answer 1

You are close, use 10 with += :

#short version
df.loc[df['B'] == 1, 'A'] += 10
#long version
#df.loc[df['B'] == 1, 'A'] = 10 + df.loc[df['B'] == 1, 'A']
print (df)
    A  B
1   3  0
2   4  0
3  16  1
4   8  0

Answer 2

using np.where

df['A'] = np.where(df['B'] == 1,df['A'] + 10, df['A'])

Output

    A  B
1   3  0
2   4  0
3  16  1
4   8  0

Answer 3

One possible solution is to define a function with two parameters:

def cond_func(a,b):
    if b = 1:
        return a + 10
    else:
        return a

Then apply to each row in the dataframe

df['A'] = df.apply(lambda x: cond_func(x['A'], x['B'], axis=1)