Multi tool use

groupby and filter pandas

df:

make country other_columns sale
honda tokyo data 1
honda hirosima data 0
toyota tokyo data 1
toyota hirosima data 0
suzuki tokyo data 0
suzuki hirosima data 0
ferrari tokyo data 1
ferrari hirosima data 0
nissan tokyo data 1
nissan hirosima data 0


Condition:
keep pairs that have 1,0

desired output

make country other_columns sale
honda tokyo data 1
honda hirosima data 0
toyota tokyo data 1
toyota hirosima data 0
ferrari tokyo data 1
ferrari hirosima data 0
nissan tokyo data 1
nissan hirosima data 0


What i tried (which is obviously not working )

outdf = df.groupby('sale').filter(lambda x: x > 0)


should i be grouping both the columns ('sale' and 'make')?

2 Answers
2

Here's a solution using groupby + set. This should be extendable and does not require ordering:

groupby

set

s = df.groupby('make')['sale'].apply(set)
res = df[df['make'].map(s) >= {0, 1}]

print(res)

make country other_columns sale
0 honda tokyo data 1
1 honda hirosima data 0
2 toyota tokyo data 1
3 toyota hirosima data 0
6 ferrari tokyo data 1
7 ferrari hirosima data 0
8 nissan tokyo data 1
9 nissan hirosima data 0


Indeed, you need groupby but on the column 'make' and you can use filter with a function creating a tuple from 'sale' column:

groupby

filter

tuple

outdf = df.groupby('make').filter(lambda x: tuple(x.sale)== (1,0))


and you get the expected output:

make country other_columns sale
0 honda tokyo data 1
1 honda hirosima data 0
2 toyota tokyo data 1
3 toyota hirosima data 0
6 ferrari tokyo data 1
7 ferrari hirosima data 0
8 nissan tokyo data 1
9 nissan hirosima data 0


if the order between 1 and 0 is not always satisfy for a same 'make', you can use a sort_values on 'sale' column first

sort_values

By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service, privacy policy and cookie policy, and that your continued use of the website is subject to these policies.