How to append a new row in a dataframe by searching for an existing column value without iterating?

Question

I'm trying to find the best way to create new rows for every 1 row when a certain value is contained in a column.

Example Dataframe

Index	Person	Drink_Order
1	Sam	Jack and Coke
2	John	Coke
3	Steve	Dr. Pepper

I'd like to search the DataFrame for Jack and Coke, remove it and add 2 new records as Jack and Coke are 2 different drink sources.

Index	Person	Drink_Order
2	John	Coke
3	Steve	Dr. Pepper
4	Sam	Jack Daniels
5	Sam	Coke

Example Code that I want to replace as my understanding is you should never modify rows you are iterating

for index, row in df.loc[df['Drink_Order'].str.contains('Jack and Coke')].iterrows():
    df.loc[len(df)]=[row['Person'],'Jack Daniels']
    df.loc[len(df)]=[row['Person'],'Coke']

df = df[df['Drink_Order']!= 'Jack and Coke']

Answer 1

Split using and. That will result in a list. Explode list to get each element in a list appear as an individual row. Then conditionally rename Jack to Jack Daniels

 df= df.assign(Drink_Order=df['Drink_Order'].str.split('and')).explode('Drink_Order')
df['Drink_Order']=np.where(df['Drink_Order'].str.contains('Jack'),'Jack Daniels',df['Drink_Order'])

    Index Person   Drink_Order
0      1    Sam  Jack Daniels
0      1    Sam          Coke
1      2   John          Coke
2      3  Steve    Dr. Pepper