groupby a column and count items above 5 in another pandas

Question

So I have a df like this:

NAME    TRY SCORE  
Bob   1st   3  
Sue   1st   7  
Tom   1st   3  
Max   1st   8  
Jay   1st   4  
Mel   1st   7  
Bob   2nd   4  
Sue   2nd   2  
Tom   2nd   6  
Max   2nd   4  
Jay   2nd   7  
Mel   2nd   8  
Bob   3rd   3  
Sue   3rd   5  
Tom   3rd   6  
Max   3rd   3  
Jay   3rd   4  
Mel   3rd   6 

I want to count haw mant times each person scores more than 5?
into a new df2 that looks like this:

NAME    COUNT  
Bob     0  
Sue     1  
Tom     2  
Mary    1  
Jay     1  
Mel     3  

My attempts have been many - here is the latest

df2 = df.groupby('NAME')[['SCORE'] > 5].count().reset_index(name="count")

Answer 1

Just using groupby and sum

df.assign(SCORE=df.SCORE.gt(5)).groupby('NAME')['SCORE'].sum().astype(int).reset_index()
Out[524]: 
  NAME  SCORE
0  Bob      0
1  Jay      1
2  Max      1
3  Mel      3
4  Sue      1
5  Tom      2

Or we using set_index with sum

df.set_index('NAME').SCORE.gt(5).sum(level=0).astype(int)

Answer 2

First create boolean mask and then aggregate by sum- Trues values are processes like 1:

df2 = (df['SCORE'] > 5).groupby(df['NAME']).sum().astype(int).reset_index(name="count")
print (df2)
  NAME  count
0  Bob      0
1  Jay      1
2  Max      1
3  Mel      3
4  Sue      1
5  Tom      2

Detail:

print (df['SCORE'] > 5)

0     False
1      True
2     False
3      True
4     False
5      True
6     False
7     False
8      True
9     False
10     True
11     True
12    False
13    False
14     True
15    False
16    False
17     True
Name: SCORE, dtype: bool

Answer 3

One way to do this is to write a custom groupby function where you take the scores of each group and sum up those that are greater than 5 like this:

df.groupby('NAME')['SCORE'].agg(lambda x: (x > 5).sum())


NAME
Bob    0
Jay    1
Max    1
Mel    3
Sue    1
Tom    2
Name: SCORE, dtype: int64