count the number of occurrences of a certain value in a dictionary in python?

Question

If I have got something like this:

D = {'a': 97, 'c': 0 , 'b':0,'e': 94, 'r': 97 , 'g':0} 

If I want for example to count the number of occurrences for the "0" as a value without having to iterate the whole list, is that even possible and how?

Answer 1

As mentioned in THIS ANSWER using operator.countOf() is the way to go but you can also use a generator within sum() function as following:

sum(value == 0 for value in D.values()) # Or the following which is more optimized  sum(1 for v in D.values() if v == 0) 

Or as a slightly more optimized and functional approach you can use map function by passing the __eq__ method of the integer as the constructor function.

sum(map((0).__eq__, D.values())) 

Benchmark:

In [15]: D = dict(zip(range(1000), range(1000)))  In [16]: %timeit sum(map((0).__eq__, D.values())) 49.6 µs ± 770 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)  In [17]: %timeit sum(v==0 for v in D.values()) 60.9 µs ± 669 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)  In [18]: %timeit sum(1 for v in D.values() if v == 0) 30.2 µs ± 515 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)  In [19]: %timeit countOf(D.values(), 0) 16.8 µs ± 74.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) 

Note that although using map function in this case may be more optimized, but in order to have a more comprehensive and general idea about the two approaches you should run the benchmark for relatively large datasets as well. Then, you can use the most proper approach based on the structure and amount of data you have.