python - Drop pandas dataframe rows based on groupby() condition -

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there pandas dataframe on input: 

store_id item_id  items_sold        date 1          1          0        2015-12-28 1          2          1        2015-12-28 1          1          0        2015-12-28 2          2          0        2015-12-28 2          1          1        2015-12-29 2          2          1        2015-12-29 2          1          0        2015-12-29 3          1          0        2015-12-30 3          1          0        2015-12-30

i need drop rows items have never been sold in particular store: pairs (1,1), (3,1) of (store_id, item_id) in dataframe

the output expect following:

store_id item_id  items_sold        date 1          2          1        2015-12-28 2          2          0        2015-12-28 2          1          1        2015-12-29 2          2          1        2015-12-29 2          1          0        2015-12-29

i've figured out how find required pairs of (store_id, item_id) using pd.groupby()[].sum(), stuck dropping them initial dataframe

is want?

in [30]: df[df.groupby(['store_id', 'item_id'])['items_sold'].transform('sum') > 0] out[30]:    store_id  item_id  items_sold        date 1         1        2           1  2015-12-28 3         2        2           0  2015-12-28 4         2        1           1  2015-12-29 5         2        2           1  2015-12-29 6         2        1           0  2015-12-29

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