Python loop over values in dataframe and change to binary values

Question

I have a series y in Python with values Accepted and Rejected. I want to create a new dataframe with value 1 for Ac

Answer 1

you can use:

df['dummy'] = df.y.apply(lambda x:  1 if  x == 'Accepted' else 0)

if you want to use a for loop:

new_dummy_data = []

for value in df.y.values:
    if value == 'Accepted':
        new_dummy_data.append(1)
    else:
        new_dummy_data.append(0)

df['dummy'] = new_dummy_data

Answer 2

Here loop is not necessary, because slow. Better is convert boolean mask to True/False to 0,1 by converting to integers or use numpy.where:

df['dummy'] =  (df['y']=='Approved').astype(int)

---

df['dummy'] =  np.where(df['y']=='Approved', 1, 0)

---

Your solution should be changed (loopy slow solution):

print (df)

0  Accepted
1  Rejected
2  Accepted
3  Accepted
4  Accepted

out = []
for i in range(0,len(df)):
    if df.loc[i, 'y']=='Accepted': 
        out.append(1)
    else: 
        out.append(0)

print (out)
[1, 0, 1, 1, 1]

df['dummy'] = out
print (df)
          y  dummy
0  Accepted      1
1  Rejected      0
2  Accepted      1
3  Accepted      1
4  Accepted      1