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Now I want to compute at how many of the L positions in the binary representation there is a 1 as well as a zero 0. In my example the result would be return=2 since there is always a 1 in the third (last) position for these entries. I want to compute this inside a function with a numba decorator. Currently my code is:

Comparing the performance:

def count_mixed_bits_timeit():
    lst = [random.randint(0, 500) for i in range(10000)]
    for _ in range(300):

        xor = reduce(and_, lst) ^ reduce(or_, lst)
        retu = bin(xor).count("1")
    return retu

@nb.njit
def count_mixed_bits_v2_timeit():
    lst = [random.randint(0, 500) for i in range(10000)]
    for _ in range(300):

        andnumber = ornumber = lst[0]
        for value in lst:
            andnumber &= value
            ornumber |= value
        xornumber = andnumber ^ ornumber
        result = 0
        while xornumber > 0:
            result += xornumber & 1
            xornumber >>= 1
    return result


@nb.njit
def count_mixed_bits_v3_timeit():
    lst = [random.randint(0, 500) for i in range(10000)]
    for _ in range(300):

        and_, or_ = ~0, 0
        for x in lst:
            and_ &= x
            or_ |= x
        xor_ = and_ ^ or_
        result = 0
        while xor_ > 0:
           result += 1
           xor_ &= xor_ - 1
    return result


print(timeit(lambda: count_mixed_bits_timeit(), number=10))
print(timeit(lambda: count_mixed_bits_v2_timeit(), number=10))
print(timeit(lambda: count_mixed_bits_v3_timeit(), number=10))

>>>2.4878
>>>0.1198
>>>0.1279

Now I want to compute at how many of the L positions in the binary representation there is a 1 as well as a zero 0. In my example the result would be return=2 since there is always a 1 in the third (last) position for these entries. I want to compute this inside a function with a numba decorator. Currently my code is:

def count_mixed_bits_timeit():
    for _ in range(300):
        lst = [random.randint(0, 500) for i in range(10000)]
        xor = reduce(and_, lst) ^ reduce(or_, lst)
    return bin(xor).count("1")


@nb.njit
def count_mixed_bits_v2_timeit():
    for _ in range(300):
        lst = [random.randint(0, 500) for i in range(10000)]
        andnumber = ornumber = lst[0]
        for value in lst:
            andnumber &= value
            ornumber |= value
        xornumber = andnumber ^ ornumber
        result = 0
        while xornumber > 0:
           result += xornumber & 1
           xornumber >>= 1
    return result


@nb.njit
def count_mixed_bits_v3_timeit():
    for _ in range(300):
        lst = [random.randint(0, 500) for i in range(10000)]

        and_, or_ = ~0, 0
        for x in lst:
            and_ &= x
            or_ |= x
        xor_ = and_ ^ or_
        result = 0
        while xor_ > 0:
           result += 1
           xor_ &= xor_ - 1
    return result


print(timeit(lambda: count_mixed_bits_timeit(), number=100))
print(timeit(lambda: count_mixed_bits_v2_timeit(), number=100))
print(timeit(lambda: count_mixed_bits_v3_timeit(), number=100))

>>>0.084050
>>>1.318490
>>>0.068723

def count_mixed_bits_timeit():
    lst = [random.randint(0, 500) for i in range(10000)]
    for _ in range(300):

        xor = reduce(and_, lst) ^ reduce(or_, lst)
        retu = bin(xor).count("1")
    return retu

@nb.njit
def count_mixed_bits_v2_timeit():
    lst = [random.randint(0, 500) for i in range(10000)]
    for _ in range(300):

        andnumber = ornumber = lst[0]
        for value in lst:
            andnumber &= value
            ornumber |= value
        xornumber = andnumber ^ ornumber
        result = 0
        while xornumber > 0:
            result += xornumber & 1
            xornumber >>= 1
    return result


@nb.njit
def count_mixed_bits_v3_timeit():
    lst = [random.randint(0, 500) for i in range(10000)]
    for _ in range(300):

        and_, or_ = ~0, 0
        for x in lst:
            and_ &= x
            or_ |= x
        xor_ = and_ ^ or_
        result = 0
        while xor_ > 0:
           result += 1
           xor_ &= xor_ - 1
    return result


print(timeit(lambda: count_mixed_bits_timeit(), number=10))
print(timeit(lambda: count_mixed_bits_v2_timeit(), number=10))
print(timeit(lambda: count_mixed_bits_v3_timeit(), number=10))

>>>2.4878
>>>0.1198
>>>0.1279

Comparing the performance it seems that count_mixed_bits is much faster.

from timeit import timeit
count = 100
import random
randomlist = [random.randint(0,100) for i in range(10000)]
print(timeit(lambda:count_mixed_bits(randomlist), number=count))
print(timeit(lambda:count_mixed_bits_v2(randomlist), number=count))

>>>0.07977239999999997
>>>1.4027323

Comparing the performance:

def count_mixed_bits_timeit():
    for _ in range(300):
        lst = [random.randint(0, 500) for i in range(10000)]
        xor = reduce(and_, lst) ^ reduce(or_, lst)
    return bin(xor).count("1")


@nb.njit
def count_mixed_bits_v2_timeit():
    for _ in range(300):
        lst = [random.randint(0, 500) for i in range(10000)]
        andnumber = ornumber = lst[0]
        for value in lst:
            andnumber &= value
            ornumber |= value
        xornumber = andnumber ^ ornumber
        result = 0
        while xornumber > 0:
           result += xornumber & 1
           xornumber >>= 1
    return result


@nb.njit
def count_mixed_bits_v3_timeit():
    for _ in range(300):
        lst = [random.randint(0, 500) for i in range(10000)] 

        and_, or_ = ~0, 0
        for x in lst:
            and_ &= x
            or_ |= x
        xor_ = and_ ^ or_
        result = 0
        while xor_ > 0:
           result += 1
           xor_ &= xor_ - 1
    return result


print(timeit(lambda: count_mixed_bits_timeit(), number=100))
print(timeit(lambda: count_mixed_bits_v2_timeit(), number=100))
print(timeit(lambda: count_mixed_bits_v3_timeit(), number=100))

>>>0.084050
>>>1.318490
>>>0.068723