Pandas find most bought item given ClientID ItemID ItemQuantity

df.groupby(["ClientID", "FoodID"])['Quantity'].sum().reset_index().sort_values(
    ["ClientID", 'Quantity'], ascending=False).drop_duplicates(
    ["ClientID"]).sort_values('ClientID')

First get a df with contains the total Quantity for each ClientID, FoodID combination. Then sort the df on ClientID, Quantity so that highest Quantity per client appears on the top and finally drop the duplicates per client which will drop all the clients records except the top which happens to be max quantity.

Test case:

np.random.seed(0)

df = pd.DataFrame({
    'ClientID' : np.random.randint(1,10, 1000),
    'FoodID' : np.random.randint(1,10, 1000),
    'Quantity' : np.random.randint(1,10, 1000),
})

df.groupby(["ClientID", "FoodID"])['Quantity'].sum().reset_index().sort_values(
    ["ClientID", 'Quantity'], ascending=False).drop_duplicates(
    ["ClientID"]).sort_values('ClientID')

Output:

    ClientID    FoodID  Quantity
3   1           4       97
16  2           8       82
26  3           9       100
35  4           9       98
44  5           9       85
47  6           3       107
54  7           1       94
69  8           7       107
73  9           2       109