Check if all values in list are greater than a cer

问题:

my_list1 = [30,34,56]
my_list2 = [29,500,43]

How to I check if all values in list are >= 30? my_list1 should work and my_list2 should not.

The only thing I could think of doing was:

boolean = 0
def func(ls):
    for k in ls:
        if k >= 30:
            boolean = boolean + 1
        else:
            boolean = 0
    if boolean > 0:
        print 'Continue'
    elif boolean = 0:
        pass

Update 2016:

In hindsight, after dealing with bigger datasets where speed actually matters and utilizing numpy...I would do this:

>>> my_list1 = [30,34,56]
>>> my_list2 = [29,500,43]

>>> import numpy as np
>>> A_1 = np.array(my_list1)
>>> A_2 = np.array(my_list2)

>>> A_1 >= 30
array([ True,  True,  True], dtype=bool)
>>> A_2 >= 30
array([False,  True,  True], dtype=bool)

>>> ((A_1 >= 30).sum() == A_1.size).astype(np.int)
1
>>> ((A_2 >= 30).sum() == A_2.size).astype(np.int)
0

You could also do something like:

len([*filter(lambda x: x >= 30, my_list1)]) > 0

回答1:

Use the all() function with a generator expression:

>>> my_list1 = [30, 34, 56]
>>> my_list2 = [29, 500, 43]
>>> all(i >= 30 for i in my_list1)
True
>>> all(i >= 30 for i in my_list2)
False

Note that this tests for greater than or equal to 30, otherwise my_list1 would not pass the test either.

If you wanted to do this in a function, you'd use:

def all_30_or_up(ls):
    for i in ls:
        if i < 30:
            return False
    return True

e.g. as soon as you find a value that proves that there is a value below 30, you return False, and return True if you found no evidence to the contrary.

Similarly, you can use the any() function to test if at least 1 value matches the condition.

回答2:

...any reason why you can't use min()?

def above(my_list, minimum):
    if min(my_list) >= minimum:
        print "All values are equal or above", minimum
    else:
        print "Not all values are equal or above", minimum

I don't know if this is exactly what you want, but technically, this is what you asked for...

回答3:

There is a builtin function all:

all (x > limit for x in my_list)

Being limit the value greater than which all numbers must be.

回答4:

You can use all():

my_list1 = [30,34,56]
my_list2 = [29,500,43]
if all(i >= 30 for i in my_list1):
    print 'yes'
if all(i >= 30 for i in my_list2):
    print 'no'

Note that this includes all numbers equal to 30 or higher, not strictly above 30.

回答5:

You could do the following:

def Lists():

    my_list1 = [30,34,56]
    my_list2 = [29,500,43]

    for element in my_list1:
        print(element >= 30)

    for element in my_list2:
        print(element >= 30)

Lists()

This will return the values that are greater than 30 as True, and the values that are smaller as false.

回答6:

The overall winner between using the np.sum, np.min, and all seems to be np.min in terms of speed for large arrays:

N = 1000000
def func_sum(x):
    my_list = np.random.randn(N)
    return np.sum(my_list < x )==0

def func_min(x):
    my_list = np.random.randn(N)
    return np.min(my_list) >= x

def func_all(x):
    my_list = np.random.randn(N)
    return all(i >= x for i in my_list)

(i need to put the np.array definition inside the function, otherwise the np.min function remembers the value and does not do the computation again when testing for speed with timeit)

The performance of "all" depends very much on when the first element that does not satisfy the criteria is found, the np.sum needs to do a bit of operations, the np.min is the lightest in terms of computations in the general case.

When the criteria is almost immediately met and the all loop exits fast, the all function is winning just slightly over np.min:

>>> %timeit func_sum(10)
10 loops, best of 3: 36.1 ms per loop

>>> %timeit func_min(10)
10 loops, best of 3: 35.1 ms per loop

>>> %timeit func_all(10)
10 loops, best of 3: 35 ms per loop

But when "all" needs to go through all the points, it is definitely much worse, and the np.min wins:

>>> %timeit func_sum(-10)
10 loops, best of 3: 36.2 ms per loop

>>> %timeit func_min(-10)
10 loops, best of 3: 35.2 ms per loop

>>> %timeit func_all(-10)
10 loops, best of 3: 230 ms per loop

But using

np.sum(my_list<x)

can be very useful is one wants to know how many values are below x.

回答7:

I write this function

def larger(x, than=0):
    if not x or min(x) > than:
        return True
    return False

Then

print larger([5, 6, 7], than=5)  # False
print larger([6, 7, 8], than=5)  # True
print larger([], than=5)  # True
print larger([6, 7, 8, None], than=5)  # False

Empty list on min() will raise ValueError. So I added if not x in condition.