I am required to use multiple hashtables, so in c++, I would normally use an std::unordered_map. So far I can understand that I can use a dictionary in Python, so let's assume the following code:
my_dict_1 = {}
my_dict_1['foo'] = 1
my_dict_2 = {}
my_dict_2['foo'] = 2
Will the two dictionaries be using different hash functions (notice that the key is the same), thus they can be considered two different hash tables (I mean that they will actually store the data differently)?
EDIT:
Yes the dictionaries are two different objects of course, but the question is about the technique that they will use to store the data!
A simple Python shell experiment to show that different dictionaries can use the same key:
>>> my_dict_1 = {'foo':1}
>>> my_dict_2 = {'foo':2}
>>> my_dict_1,my_dict_2
({'foo': 1}, {'foo': 2})
This is a good discussion of how it is implemented. The key point is that each dictionary is allocated its own portion of memory (which can of course grow as needed). The exact same hash function is used for both dictionaries, but is being used to probe different areas in memory.
id(...)
id(object) -> integer
Return the identity of an object. This is guaranteed to be unique among simultaneously existing objects. (Hint: it's the object's memory address.)
Above is the id
doc string, it says that object's identity is object's memory address, so we can use the id
function to see the variable's memory address:
In your program, I can see the address like this:
def main():
my_dict_1 = {}
my_dict_1['foo'] = 1
my_dict_2 = {}
my_dict_2['foo'] = 2
print(hex(id(my_dict_1['foo'])))
print(hex(id(my_dict_2['foo'])))
if __name__ == '__main__':
main()
This program output this:
0x9a33e0
0x9a3400
We can see that my_dict_1['foo']
and my_dict_2['foo']
have different memory address.
So I think the two dicts should use the same hash function, but the variable's memory address should be the sum of the hash value and a base value. In this way, the two variable will be stored in the different memory areas.
来源:https://stackoverflow.com/questions/37100390/towards-understanding-dictionaries