Intersection of two list of dictionaries based on a key

You can also use pandas library for this:

In [102]: df1 = pd.DataFrame(list1)
In [104]: df2 = pd.DataFrame(list2)

In [106]: pd.merge(df2,df1, on='count', how='left').fillna('-')
Out[106]: 
     count att_value
0    359      nine
1    351       one
2    381         -

Assuming the dictionaries in list1 share the same keys and that you have Python 3.5 or newer, you can write the following list comprehension.

>>> count2dict = {d['count']:d for d in list1}                                                                                                                  
>>> dummies = dict.fromkeys(list1[0], '-')                                                                                                                      
>>> [{**count2dict.get(d['count'], dummies), **d} for d in list2]                                                                                              
[{'count': 359, 'att_value': 'nine', 'person_id': 4},
 {'count': 351, 'att_value': 'one', 'person_id': 12},
 {'count': 381, 'att_value': '-', 'person_id': 8}]

You can do this with a list comprehension. First, build a set of all counts from list2, and then filter out dictionaries based on constant time set membership check.

counts = {d2['count'] for d2 in list2}
list3 = [d for d in list1 if d['count'] in counts]

print(list3)
# [{'count': 351, 'att_value': 'one', 'person_id': 12}, 
#  {'count': 359, 'att_value': 'nine', 'person_id': 4}]

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(Re:Edit) To handle other keys (besides just "att_value") appropriately, giving a default value of '-' in the same way, you can use:

keys = list1[0].keys() - {'count'}
idx = {d['count'] : d for d in list1}
list3 = []
for d in list2:
    d2 = idx.get(d['count'], dict.fromkeys(keys, '-'))
    d2.update(d)
    list3.append(d2)

print(list3)
# [{'count': 359, 'att_value': 'nine', 'person_id': 4},
#  {'count': 351, 'att_value': 'one', 'person_id': 12},
#  {'person_id': 8, 'att_value': '-', 'count': 381}]