drop certain columns from a dataframe while converting to list

Question

I have a sample dataframe:

    Id    Age  Name
0   0110  23   Max
1   2009  56   Stan
2   1167  25   Joy
3   8878  44   Lee

at present for the input dataframe I'm using

dfnew = df.drop(df.columns.difference(['Id']),1)

to drop unwanted column(i.e Id) in dataframe and proceeding ahead. Which is correct but there are two dataframes.

is there anyway we can optimise it?

while converting the dataframe to a list can we have only age and name in the list and ignore Id like lst = [[23,Max],[56,stan]....]

please assist on this. Thank you.

df[["Age", "Name"]].apply(list, axis=1).tolist() OR df.drop("Id", axis=1).apply(list, axis=1).tolist()? — d.b
– d.b, Commented Dec 22, 2021 at 14:25

score 1 · Accepted Answer · 2021-12-22 19:39:34Z

1

You can use drop the unwanted column, convert to a numpy array and convert to list. This is faster than apply(list, axis=1).

lst = df.drop(columns='Id').to_numpy().tolist()

Output:

[[23, 'Max'], [56, 'Stan'], [25, 'Joy'], [44, 'Lee']]

1 Answer 1