Different approaches to add a new column based on existing columns in Pandas

Question

I have a table with more than 2000 entries represented in the following way:

 Rows: Id    doggo   floofer   pupper    puppo         
       3     None    floofer   None      None
       5     None    None      pupper    None    
       6     doggo   None      None      None

I want to add a new column called doggo_stage which stores the value of either of the 4 column if it's true:
Example:
floofer has entry floofer: doggo_stage-> floofer
pupper has entry pupper: doggo_stage -> pupper

 Rows: Id    doggo   floofer   pupper    puppo   doggo_stage       
       3     None    floofer   None      None    floofer
       5     None    None      pupper    None    pupper
       6     doggo   None      None      None    doggo

lambda? functions or loops.

EDIT: I should have added this before but I have 24 columns in total, the ones I added needs to be used for the new column that I require. Basically, I am having trouble with choosing these specific columns in the for loop.

Answer 1

dogo_stage = []
for column in df[['doggo','floofer','pupper','puppo']]:
    for i in range(len(df)):
        if df[column][i] is not None:
            dogo_stage.append(df[column][i])
df['dogo_stage'] = dogo_stage  

Answer 2

IIUC, you can use max with axis=1 :

df = df.set_index('Id')
df['dogo_stage'] = df.max(axis=1)
df.reset_index()

Output:

   Id  doggo  floofer  pupper puppo dogo_stage
0   3   None  floofer    None  None    floofer
1   5   None     None  pupper  None     pupper
2   6  doggo     None    None  None      doggo