How to replace values in a pandas dataframe based on conditions?

Question

I attempt to apply a different formula to a new column "result" based on the column 'C' containing the condition. If C is 'add' then I would like to add X and Y. When C is 'mult' the result should be X * Y.

df = pd.DataFrame({'X': [0, 1, 2, 3, 4],
                   'Y': [5, 6, 7, 8, 9],
                   'C': ['add', 'add', 'mult', 'mult', 'mult']})
df['result'] = df['X'] * df['Y']

df.loc[df.C =='add', 'result'] = df.loc[df['C'] =='add', 'X'] \
                                 + df.loc[df['C'] =='add', 'Y']
df

The result I get is:

      C  X  Y  result
0   add  0  5       5
1   add  1  6       5
2  mult  2  7      14
3  mult  3  8      24
4  mult  4  9      36

What I need is 'result' in row 1 being 7

      C  X  Y  result
0   add  0  5       5
1   add  1  6       7
2  mult  2  7      14
3  mult  3  8      24
4  mult  4  9      36

Answer 1

your code gives right results, but if you want a direct way

df['result'] = df.apply(lambda x :  x['X']+x['Y'] if x['C'] == 'add' else x['X']*x['Y'], axis=1 ) 

output :

   X  Y     C  result
0  0  5   add       5
1  1  6   add       7
2  2  7  mult      14
3  3  8  mult      24
4  4  9  mult      36

Answer 2

Your solution working nice, also is posible use this alternative with numpy.where :

mask = df.C =='add'
df['result'] = np.where(mask, df['X'] + df['Y'], df['X'] * df['Y'])        
print (df)

   X  Y     C  result
0  0  5   add       5
1  1  6   add       7
2  2  7  mult      14
3  3  8  mult      24
4  4  9  mult      36

If more conditions is possible use numpy.select :

m1 = df.C =='add'
m2 = df.C =='mult'
m3 = df.C =='div'
v1 = df['X'] + df['Y']
v2 = df['X'] * df['Y']
v3 = df['X'] / df['Y']

df['result'] = np.select([m1, m2, m3], [v1, v2, v3])        
print (df)

   X  Y     C     result
0  0  5   add   5.000000
1  1  6   add   7.000000
2  2  7  mult  14.000000
3  3  8  mult  24.000000
4  4  9   div   0.444444