Issue Assigning Values With Iterrows

Good morning, I was having issues trying to assign values. Here’s the df:

data = {
    "col1": ['a', 'b', 'c', 'd', 'e'],
    "col2": [1, 1, 2, 2, 3],
    "col3": [20, 3, 5, 4, 1],
    "col4": [np.nan, np.nan, np.nan, np.nan, np.nan]
}
df = pd.DataFrame(data)

df

So what I want to do is if col2 is a 1, multiply col3’s value by 3 and assign that value to the same row on col4. If col2 is 2, multiply by 2 and do the same thing. If col2 is 1, multiply by 1.

Here’s what I tried to do:

for i, row in df.iterrows():
    if row['col2'] == 1:
        row['col4'] = row['col3'] * 3
    if row['col2'] == 2:
        row['col4'] = row['col3'] * 2
    if row['col2'] == 3:
        row['col4'] = row['col3'] * 1
df

However, my code didn’t seem to change the value.

I feel like my code was missing something small. I appreciate your help.

>Solution :

A possible solution, which uses a list to record the successive values of col4:

l = []

for _, row in df.iterrows():
    if row['col2'] == 1:
        l.append(row['col3'] * 3)
    if row['col2'] == 2:
        l.append(row['col3'] * 2)
    if row['col2'] == 3:
        l.append(row['col3'] * 1)
        
df['col4'] = l

Alternatively,

df['col4'] = (np.where(df['col2'] == 1, df['col3'] * 3,
              np.where(df['col2'] == 2, df['col3'] * 2, df['col3'] * 1)))

Output:

  col1  col2  col3  col4
0    a     1    20    60
1    b     1     3     9
2    c     2     5    10
3    d     2     4     8
4    e     3     1     1