Groupby based on a multiple logical conditions applied to a different columns DataFrame

Create mask by your conditions - here for greater by Series.gt with not equal by Series.ne chained by & for bitwise AND and then use GroupBy.transform for count Trues by sum:

mask = df['value'].gt(3) & df['type'].ne('medium')
df['count'] = mask.groupby(df['entity']).transform('sum')

Solution with helper column new:

mask = df['value'].gt(3) & df['type'].ne('medium')
df['count'] = df.assign(new = mask).groupby('entity')['new'].transform('sum')

---

print (df)
    value    type entity  count
0       1     big      R      3
1       2   small      R      3
2       3  medium      R      3
3       4     big      P      4
4       2     big      R      3
5      42     big      P      4
6      12     big      P      4
7      21  medium      P      4
8      21   small      R      3
9     424   small      R      3
10     34   small      P      4
11     12  medium      R      3
12     42   small      R      3

The solution in Pandas is superb. This is an alternative in a different package. The reason I am throwing this in here is because the original code was in data.table in R, and it might be useful for others, who probably want a similar solution within Python.

This is a solution in pydatatable, a library that aims to replicate data.table in python. Note that it is not as feature rich as Pandas; hopefully, with time, more features will be added.

Create the frame with datatable :

   from datatable import dt, f, by, update

    df = dt.Frame({'value':[1,2,3,4,2,42,12,21,21,424,34,12,42],
'type':['big','small','medium','big','big','big','big','medium','small','small','small','medium','small'],
'entity':['R','R','R','P','R','P','P','P','R','R','P','R','R']})

Create the condition - In datatable, the f symbol is a shortcut to refer to the dataframe:

condition = (f.type!="medium") & (f.value>3)

The syntax below should be familiar to users of data.table,

 DT[i, j, by] 

where i refers to anything that can occur in the rows, j refers to column operations, and by is for grouping operations. The update function is similar in function to the := function in data.table; it allows for creation of new columns or update of existing columns in place.

df[:, update(count=dt.sum(condition)), by('entity')]

df

 value  type    entity  count
0   1   big     R       3
1   2   small   R       3
2   3   medium  R       3
3   4   big     P       4
4   2   big     R       3
5   42  big     P       4
6   12  big     P       4
7   21  medium  P       4
8   21  small   R       3
9   424 small   R       3
10  34  small   P       4
11  12  medium  R       3
12  42  small   R       3