What does "hashable" mean in Python?

From the Python glossary:

An object is hashable if it has a hash value which never changes during its lifetime (it needs a __hash__() method), and can be compared to other objects (it needs an __eq__() or __cmp__() method). Hashable objects which compare equal must have the same hash value.

Hashability makes an object usable as a dictionary key and a set member, because these data structures use the hash value internally.

All of Python’s immutable built-in objects are hashable, while no mutable containers (such as lists or dictionaries) are. Objects which are instances of user-defined classes are hashable by default; they all compare unequal, and their hash value is their id().

All the answers here have good working explanation of hashable objects in python, but I believe one needs to understand the term Hashing first.

Hashing is a concept in computer science which is used to create high performance, pseudo random access data structures where large amount of data is to be stored and accessed quickly.

For example, if you have 10,000 phone numbers, and you want to store them in an array (which is a sequential data structure that stores data in contiguous memory locations, and provides random access), but you might not have the required amount of contiguous memory locations.

So, you can instead use an array of size 100, and use a hash function to map a set of values to same indices, and these values can be stored in a linked list. This provides a performance similar to an array.

Now, a hash function can be as simple as dividing the number with the size of the array and taking the remainder as the index.

For more detail refer to https://en.wikipedia.org/wiki/Hash_function

Here is another good reference: http://interactivepython.org/runestone/static/pythonds/SortSearch/Hashing.html

Anything that is not mutable (mutable means, likely to change) can be hashed. Besides the hash function to look for, if a class has it, by eg. dir(tuple) and looking for the __hash__ method, here are some examples

#x = hash(set([1,2])) #set unhashable
x = hash(frozenset([1,2])) #hashable
#x = hash(([1,2], [2,3])) #tuple of mutable objects, unhashable
x = hash((1,2,3)) #tuple of immutable objects, hashable
#x = hash()
#x = hash({1,2}) #list of mutable objects, unhashable
#x = hash([1,2,3]) #list of immutable objects, unhashable

List of immutable types:

int, float, decimal, complex, bool, string, tuple, range, frozenset, bytes

List of mutable types:

list, dict, set, bytearray, user-defined classes

In my understanding according to Python glossary, when you create an instance of objects that are hashable, an unchangeable value is also calculated according to the members or values of the instance. For example, that value could then be used as a key in a dictionary as below:

>>> tuple_a = (1, 2, 3)
>>> tuple_a.__hash__()
2528502973977326415
>>> tuple_b = (2, 3, 4)
>>> tuple_b.__hash__()
3789705017596477050
>>> tuple_c = (1, 2, 3)
>>> tuple_c.__hash__()
2528502973977326415
>>> id(a) == id(c)  # a and c same object?
False
>>> a.__hash__() == c.__hash__()  # a and c same value?
True
>>> dict_a = {}
>>> dict_a[tuple_a] = 'hiahia'
>>> dict_a[tuple_c]
'hiahia'

We can find that the hash value of tuple_a and tuple_c are the same since they have the same members. When we use tuple_a as the key in dict_a, we can find that the value for dict_a[tuple_c] is the same, which means that, when they are used as the key in a dictionary, they return the same value because the hash values are the same. For those objects that are not hashable, the method __hash__ is defined as None:

>>> type(dict.__hash__) 
<class 'NoneType'>

I guess this hash value is calculated upon the initialization of the instance, not in a dynamic way, that's why only immutable objects are hashable. Hope this helps.