list comprehension in pandas

Question

I\'m giving a toy example but it will help me understand what\'s going on for something else I\'m trying to do. Let\'s say I want a new column in a dataframe \'optimal_fruit

Answer 1

If you do not want to repeat df2 for each column:

[row[0][0]*row[0][1]-row[0][2] for row in zip(df2[['apples', 'oranges', 'bananas']].to_numpy())]

or

def func(row):
    print(row[0]*row[1]-row[2])

[func(*row) for row in zip(df2[['apples', 'oranges', 'bananas']].to_numpy())]

See also:

• Memory efficient way for list comprehension of pandas dataframe using multiple columns
• Dataframe list comprehension "zip(...)": loop through chosen df columns efficiently with just a list of column name strings

EDIT:

Please use df.iloc and df.loc instead of df[[...]], see Selecting multiple columns in a pandas dataframe