**Problem :**

I’m trying to understand how the `any()`

and `all()`

Python built-in functions work.

I’m trying to compare the tuples so that if any value is different then it will return `True`

and if they are all the same it will return `False`

. How are they working in this case to return [False, False, False]?

`d`

is a `defaultdict(list)`

.

```
print d['Drd2']
# [[1, 5, 0], [1, 6, 0]]
print list(zip(*d['Drd2']))
# [(1, 1), (5, 6), (0, 0)]
print [any(x) and not all(x) for x in zip(*d['Drd2'])]
# [False, False, False]
```

To my knowledge, this should output

```
# [False, True, False]
```

since (1,1) are the same, (5,6) are different, and (0,0) are the same.

**Why is it evaluating to False for all tuples?**

**Solution :**

You can roughly think of `any`

and `all`

as series of logical `or`

and `and`

operators, respectively.

**any**

`any`

will return `True`

when **at least one of the elements** is Truthy. Read about Truth Value Testing.

**all**

`all`

will return `True`

only when **all the elements** are Truthy.

**Truth table**

```
+-----------------------------------------+---------+---------+
| | any | all |
+-----------------------------------------+---------+---------+
| All Truthy values | True | True |
+-----------------------------------------+---------+---------+
| All Falsy values | False | False |
+-----------------------------------------+---------+---------+
| One Truthy value (all others are Falsy) | True | False |
+-----------------------------------------+---------+---------+
| One Falsy value (all others are Truthy) | True | False |
+-----------------------------------------+---------+---------+
| Empty Iterable | False | True |
+-----------------------------------------+---------+---------+
```

**Note 1:** The empty iterable case is explained in the official documentation, like this

Return

`True`

if any element of the iterable is true.If the iterable is empty, return`False`

Since none of the elements are true, it returns `False`

in this case.

Return

`True`

if all elements of the iterable are true (or if the iterable is empty).

Since none of the elements are false, it returns `True`

in this case.

**Note 2:**

Another important thing to know about `any`

and `all`

is, it will short-circuit the execution, the moment they know the result. The advantage is, entire iterable need not be consumed. For example,

```
>>> multiples_of_6 = (not (i % 6) for i in range(1, 10))
>>> any(multiples_of_6)
True
>>> list(multiples_of_6)
[False, False, False]
```

Here, `(not (i % 6) for i in range(1, 10))`

is a generator expression which returns `True`

if the current number within 1 and 9 is a multiple of 6. `any`

iterates the `multiples_of_6`

and when it meets `6`

, it finds a Truthy value, so it immediately returns `True`

, and rest of the `multiples_of_6`

is not iterated. That is what we see when we print `list(multiples_of_6)`

, the result of `7`

, `8`

and `9`

.

This excellent thing is used very cleverly in this answer.

With this basic understanding, if we look at your code, you do

```
any(x) and not all(x)
```

which makes sure that, atleast one of the values is Truthy but not all of them. That is why it is returning `[False, False, False]`

. If you really wanted to check if both the numbers are not the same,

```
print [x[0] != x[1] for x in zip(*d['Drd2'])]
```

## How do Python’s

`any`

and`all`

functions work?

`any`

and `all`

take iterables and return `True`

if any and all (respectively) of the elements are `True`

.

```
>>> any([0, 0.0, False, (), '0']), all([1, 0.0001, True, (False,)])
(True, True) # ^^^-- truthy non-empty string
>>> any([0, 0.0, False, (), '']), all([1, 0.0001, True, (False,), {}])
(False, False) # ^^-- falsey
```

If the iterables are empty, `any`

returns `False`

, and `all`

returns `True`

.

```
>>> any([]), all([])
(False, True)
```

I was demonstrating `all`

and `any`

for students in class today. They were mostly confused about the return values for empty iterables. Explaining it this way caused a lot of lightbulbs to turn on.

## Shortcutting behavior

They, `any`

and `all`

, both look for a condition that allows them to stop evaluating. The first examples I gave required them to evaluate the boolean for each element in the entire list.

(Note that list literal *is not itself* lazily evaluated – you could get that with an *Iterator* – but this is just for illustrative purposes.)

Here’s a Python implementation of any and all:

```
def any(iterable):
for i in iterable:
if i:
return True
return False # for an empty iterable, any returns False!
def all(iterable):
for i in iterable:
if not i:
return False
return True # for an empty iterable, all returns True!
```

Of course, the real implementations are written in C and are much more performant, but you could substitute the above and get the same results for the code in this (or any other) answer.

`all`

`all`

checks for elements to be `False`

(so it can return `False`

), then it returns `True`

if none of them were `False`

.

```
>>> all([1, 2, 3, 4]) # has to test to the end!
True
>>> all([0, 1, 2, 3, 4]) # 0 is False in a boolean context!
False # ^--stops here!
>>> all([])
True # gets to end, so True!
```

`any`

The way `any`

works is that it checks for elements to be `True`

(so it can return `True), then it returns`

False`if none of them were`

True`.

```
>>> any([0, 0.0, '', (), [], {}]) # has to test to the end!
False
>>> any([1, 0, 0.0, '', (), [], {}]) # 1 is True in a boolean context!
True # ^--stops here!
>>> any([])
False # gets to end, so False!
```

I think if you keep in mind the short-cutting behavior, you will intuitively understand how they work without having to reference a Truth Table.

## Evidence of `all`

and `any`

shortcutting:

First, create a noisy_iterator:

```
def noisy_iterator(iterable):
for i in iterable:
print('yielding ' + repr(i))
yield i
```

and now let’s just iterate over the lists noisily, using our examples:

```
>>> all(noisy_iterator([1, 2, 3, 4]))
yielding 1
yielding 2
yielding 3
yielding 4
True
>>> all(noisy_iterator([0, 1, 2, 3, 4]))
yielding 0
False
```

We can see `all`

stops on the first False boolean check.

And `any`

stops on the first True boolean check:

```
>>> any(noisy_iterator([0, 0.0, '', (), [], {}]))
yielding 0
yielding 0.0
yielding ''
yielding ()
yielding []
yielding {}
False
>>> any(noisy_iterator([1, 0, 0.0, '', (), [], {}]))
yielding 1
True
```

### The source

Let’s look at the source to confirm the above.

Here’s the source for `any`

:

```
static PyObject *
builtin_any(PyObject *module, PyObject *iterable)
{
PyObject *it, *item;
PyObject *(*iternext)(PyObject *);
int cmp;
it = PyObject_GetIter(iterable);
if (it == NULL)
return NULL;
iternext = *Py_TYPE(it)->tp_iternext;
for (;;) {
item = iternext(it);
if (item == NULL)
break;
cmp = PyObject_IsTrue(item);
Py_DECREF(item);
if (cmp < 0) {
Py_DECREF(it);
return NULL;
}
if (cmp > 0) {
Py_DECREF(it);
Py_RETURN_TRUE;
}
}
Py_DECREF(it);
if (PyErr_Occurred()) {
if (PyErr_ExceptionMatches(PyExc_StopIteration))
PyErr_Clear();
else
return NULL;
}
Py_RETURN_FALSE;
}
```

And here’s the source for `all`

:

```
static PyObject *
builtin_all(PyObject *module, PyObject *iterable)
{
PyObject *it, *item;
PyObject *(*iternext)(PyObject *);
int cmp;
it = PyObject_GetIter(iterable);
if (it == NULL)
return NULL;
iternext = *Py_TYPE(it)->tp_iternext;
for (;;) {
item = iternext(it);
if (item == NULL)
break;
cmp = PyObject_IsTrue(item);
Py_DECREF(item);
if (cmp < 0) {
Py_DECREF(it);
return NULL;
}
if (cmp == 0) {
Py_DECREF(it);
Py_RETURN_FALSE;
}
}
Py_DECREF(it);
if (PyErr_Occurred()) {
if (PyErr_ExceptionMatches(PyExc_StopIteration))
PyErr_Clear();
else
return NULL;
}
Py_RETURN_TRUE;
}
```

I know this is old, but I thought it might be helpful to show what these functions look like in code. This really illustrates the logic, better than text or a table IMO. In reality they are implemented in C rather than pure Python, but these are equivalent.

```
def any(iterable):
for item in iterable:
if item:
return True
return False
def all(iterable):
for item in iterable:
if not item:
return False
return True
```

In particular, you can see that the result for empty iterables is just the natural result, not a special case. You can also see the short-circuiting behaviour; it would actually be more work for there *not* to be short-circuiting.

When Guido van Rossum (the creator of Python) first proposed adding `any()`

and `all()`

, he explained them by just posting exactly the above snippets of code.

The code in question you’re asking about comes from my answer given here. It was intended to solve the problem of comparing multiple bit arrays – i.e. collections of `1`

and `0`

.

`any`

and `all`

are useful when you can rely on the “truthiness” of values – i.e. their value in a boolean context. 1 is `True`

and 0 is `False`

, a convenience which that answer leveraged. 5 happens to also be `True`

, so when you mix that into your possible inputs… well. Doesn’t work.

You could instead do something like this:

```
[len(set(x)) > 1 for x in zip(*d['Drd2'])]
```

It lacks the aesthetics of the previous answer (I *really* liked the look of `any(x) and not all(x)`

), but it gets the job done.

```
>>> any([False, False, False])
False
>>> any([False, True, False])
True
>>> all([False, True, True])
False
>>> all([True, True, True])
True
```

```
s = "eFdss"
s = list(s)
all(i.islower() for i in s ) # FALSE
any(i.islower() for i in s ) # TRUE
```

The concept is simple:

```
M =[(1, 1), (5, 6), (0, 0)]
1) print([any(x) for x in M])
[True, True, False] #only the last tuple does not have any true element
2) print([all(x) for x in M])
[True, True, False] #all elements of the last tuple are not true
3) print([not all(x) for x in M])
[False, False, True] #NOT operator applied to 2)
4) print([any(x) and not all(x) for x in M])
[False, False, False] #AND operator applied to 1) and 3)
# if we had M =[(1, 1), (5, 6), (1, 0)], we could get [False, False, True] in 4)
# because the last tuple satisfies both conditions: any of its elements is TRUE
#and not all elements are TRUE
```

The all() function is used to check every member of a collection as being truthy or not. For example, the all() function can be used to more succinctly conditionalize statements of the following form:

```
if all entre's are vegan this is a vegan restaurant
```

In code:

```
restaurant_is_vegan = all(x is vegan for x in menu)
```

If every item (x) on the menu (iterator) evaluates to True for the conditional (is vegan; x == vegan) the all statement will evaluate to True.

More examples here: https://www.alpharithms.com/python-all-function-223809/

```
list = [1,1,1,0]
print(any(list)) # will return True because there is 1 or True exists
print(all(list)) # will return False because there is a 0 or False exists
return all(a % i for i in range(3, int(a ** 0.5) + 1)) # when number is divisible it will return False else return True but the whole statement is False .
```

I think there is something strange in the logic how `any()`

evaluates the conditions. The Python documentation (as also reported here) says that at least one condition should evaluate to True, but it does not tell that ALL conditions are evaluated!

For instance, I was struggling with the below code, because I was thinking that `any()`

does not evaluate all conditions:

```
def compare(list_a, list_b):
if any([list_a is None, list_b is None, len(list_a) == 0, len(list_b) == 0]):
return 'no comparison'
else:
return 'need comparison'
print(compare(list_a=None, list_b=[1, 2, 3]))
```

The above code raises an exception because any still evaluates `len(list_a) == 0`

.

In this case, the logic used by `any()`

is VERY dangerous, because I would have expected that only the first condition is evaluated.

Below code must be used in this case:

```
def compare(list_a, list_b):
if list_a is None or list_b is None or len(list_a) == 0 or len(list_b) == 0:
return 'no comparison'
else:
return 'need comparison'
print(compare(list_a=None, list_b=[1, 2, 3]))
```