What you are trying to do is not possible with dictionaries. In fact, it is contrary to the whole idea behind dictionaries.

Also, your Sets class won't help you, as it effectively gives each name a new (sort of random) hash code, making it difficult to retrieve items from the dictionary, other than checking all the items, which defeats the purpose of the dict. You can not do dict.get(Sets(some_name)), as this will create a new Sets object, having a different hash code than the one already in the dictionary!

What you can do instead is:

1. Just create a list of (name, value) pairs, or

   pairs = zip(names, values) # or list(zip(...)) in Python 3
   

2. create a dictionary mapping names to lists of values.

   dictionary = {}
   for n, v in zip(names, values):
       dictionary.setdefault(n, []).append(v)
   

The first approach, using lists of tuples, will have linear lookup time (you basically have to check all the entries), but the second one, a dict mapping to lists, is as close as you can get to "multi-key-dicts" and should serve your purposes well. To access the values per key, do this:

for key, values in dictionary.iteritems():
    for value in values:
        print key, value

I guess you mean this:

class Value:
    def __init__(self, v=None):
        self.v = v

v1 = Value(1)
v2 = Value(2)

d = {'a': v1, 'b': v1, 'c': v2, 'd': v2}
d['a'].v += 1

d['b'].v == 2 # True

• Python's strings and numbers are immutable objects,
• So, if you want d['a'] and d['b'] to point to the same value that "updates" as it changes, make the value refer to a mutable object (user-defined class like above, or a dict, list, set).
• Then, when you modify the object at d['a'], d['b'] changes at same time because they both point to same object.

One way is to put the value in the list for "Randy", as in dictionary we can't have multiple keys of same name. Here is the solution for the same,

class FileOwners:

    @staticmethod
    def group_by_owners(files):
        d={}
        for i in files:
           if files[i] in d:
            d[files[i]].append(i)
           else:
            d[files[i]]=[i]
        return d

files = {
    'Input.txt': 'Randy',
    'Code.py': 'Stan',
    'Output.txt': 'Randy'
}
print(FileOwners.group_by_owners(files))

You can iterate the over the lines of the file and use a defaultdict to add the value to the corresponding key.

Example:

from collections import defaultdict

res = defaultdict(list)

with open('file.txt') as f:
    for line in f:
        k, v = line.rstrip().split(',')
        res[k].append(int(v))

which leads to

defaultdict(<class 'list'>, {'Apple': [5, 1], 'Orange': [6, 2], 'Pear': [10]})

No, Dictionary must have unique key since it uses hashing algorithm

I want to build a dictionary that allows multiple keys for one value:

This is a very basic relational database.

So the first piece of feedback is that new programmers often start coding with poorly defined test-cases or non-normalised test-cases whereby they get lost developing their test portfolio. If you would have started with clarifying your case, I'm sure you would have cracked this problem.

Let's start defining our operations from examples

Sometimes we will have the database maintaining links:

• from 1 key to 1 value
• from 1 key to N values
• from N keys to 1 value
• from N keys to N values

A dictionary can hold 1 key to N values, but not N keys to 1 value.

Fortunately the reverse operation of N keys to 1 value is the reverse of the dictionary, so we can trick around this by creating a class:

class Database(object):
    """ A dictionary that allows multiple keys for one value """

    def __init__(self):
        self.keys = {}
        self.values = {}

Next we need figure out how to get data into the database. This is similar to the SQL INSERT STATEMENT, where we maintain both our keys and values in single transactions:

def __setitem__(self, key, value):

Lets first insert a new key, whilst keeping in mind that the value might already exist:

    if key not in self.keys:  
        if value not in self.values:  # it's a new value
            self.keys[key] = set()  # a new set
            self.keys[key].add(value)
            self.values[value] = set()  # a new set
            self.values[value].add(key)
        elif value in self.values:
            self.keys[key] = set()  # a new set
            self.keys[key].add(value)
            self.values[value].add(key) # (1)

(1) a new key to a known value only means an update to the values.

Next we worry about if it's a new relationship with a new value:

    elif key in self.keys:  # 
        self.keys[key].add(value)
        if value not in self.values:
            self.values[value] = set()
            self.values[value].add(key)
        elif value in self.values:
            self.values[value].add(key)

We have now implemented the INSERT STATEMENT in our database.

Now it's time to worry about how to delete records and relationships. For this we hack the __delitem__ function to both take a key and a value. Why? Because otherwise we won't know whether the user wants to delete a single relationship only, OR all entries associated with the key

I thereby choose that:

• if the value is None I delete every relationship associated with the key.
  • if the value is a valid relationship between the key and value, then I only delete that specific relationship.

This is how it works:

def __delitem__(self, key, value=None): 
    if value is None:
        # All the keys relations are to be deleted.
        try:
            value_set = self.keys[key]
            for value in value_set:
                self.values[value].remove(key)
                if not self.values[value]:
                    del self.values[value]
            del self.keys[key]  # then we delete the key.
        except KeyError:
            raise KeyError("key not found")
    else:  # then only a single relationships is being removed.
        try:
            if value in self.keys[key]:  # this is a set.
                self.keys[key].remove(value)
                self.values[value].remove(key)
            if not self.keys[key]:  # if the set is empty, we remove the key
                del self.keys[key]
            if not self.values[value]:  # if the set is empty, we remove the value
                del self.values[value]
        except KeyError:
            raise KeyError("key not found")

Now we can add and delete. But how about bulk updates? They constitute a special case because __setitem__ can't see that you want to propagate your update onto multiple values. So we need to tell it WHAT to update. Here is a proposal that uses the relationship between the key, the old value and the new value that should be accessible to all keys that other would have a relationship with the old value:

def update(self, key, old_value, new_value):
    if old_value in self.keys[key]:
        affected_keys = self.values[old_value]
        for key in affected_keys:
            self.__setitem__(key, new_value)
            self.keys[key].remove(old_value)
        del self.values[old_value]
    else:
        raise KeyError("key: {} does not have value: {}".format(key,old_value))

So far so good. A slightly annoying thing is that we can't really read what is in the database even though we can get things in and delete them. This calls for the SELECT STATEMENT - or python's __getitem__ method. But(!) we need to be careful, as our database stores data internally as a sets that are accessible from keys. So we need to unpack them onto something useful. As I like working with lists, I have chosen to provide lists, unless its a single value, whereby I only return the value itself:

def __getitem__(self, item):
    values = self.keys[item]
    if len(values) > 1:
        return sorted(list(values))
    elif len(values) == 1:
        return list(values)[0]

Now we just miss one thing to pass all your test: The bulk loading method where you map N keys to M values. This is a "cartesian" product, which is a fancy word all N's maps to all M's. In Python this is a walk in the park as we can iterate over both and reuse our __setitem__ method:

def iterload(self, key_list, value_list):
    for key in key_list:
        for value in value_list:
            self.__setitem__(key, value)

At this point we have implemented:

• A class for our database
• A "select" method that is compatible with python d[key]
• An "insert" method that is compatible with python d[key] = value
• An "update" method that maintains the relationship to multiple keys.
• A "delete" method that can both delete a relationship and all entries associated with a key.

Testing

Now I need to test this.

First we load a single key:value pair, where the key is a hashable structure. I chose a tuple with 3 integers:

def test01()
    # hashable key - a tuple
    d = Database()
    k, v = (1, 2, 3), 'magic'
    d[k] = v
    print(d)
    print(d[k])

That works fine as we can see our database object and the value

    # print(d)
    <__main__.Database object at 0x7fd9c3d17048>

    # print(d[k])
    magic

Next we can test our bulk-load method: iterload together with the "many keys - shared value" which is focal in your problem:

def test02():
    # non-hashable key - a list - becomes a many-key shared value
    d = Database()
    keys, value = [1, 2, 3], ['magic']
    d.iterload(keys, value)
    assert d[1] == d[2] == d[3]

This also works as our assertion in the last line doesn't complain: The value of d[1]...d[3] are the same: The text string magic

I add two more tests, which should look very familiar to you, though with minor exceptions:

def test03():
    d = Database()
    k, v = ['what', 'ever'], ['test']
    d.iterload(k,v)
    assert d['what'] == d['ever']
    d['what'] = 'testing'
    try:
        assert d['what'] == d['ever']
    except AssertionError:
        assert True

In test03 I need to pack the value into a list, because iterload needs two iterables. I could made my code much harder to read by accounting for many different cases, but I think the programmer should build a function for one thing and make it clear for future recall of what the function is supposed to do.

In test04 - below - I have added the usage of the update function.

def test04():
    d = Database()
    v = 'test'
    keys, values = ['what', 'ever'], [v]
    d.iterload(keys, values)
    d['whatever'] = 'test'
    assert v == d['whatever']
    assert v == d['what']
    assert d['whatever'] == d['what']
    d.update('whatever', 'test', 'new test')
    a, b = d['whatever'], d['what']
    assert a == b

Everything Together

Here you go:

__author__ = 'root-11'


class Database(object):
    """ A dictionary that allows multiple keys for one value """

    def __init__(self):
        self.keys = {}
        self.values = {}

    def __getitem__(self, item):  # <---SQL SELECT statement
        values = self.keys[item]
        if len(values) > 1:
            return sorted(list(values))
        elif len(values) == 1:
            return list(values)[0]

    def __setitem__(self, key, value):
        if key not in self.keys:  # it's a new key <---SQL INSERT statement
            if value not in self.values:  # it's a new value
                self.keys[key] = set()  # a new set
                self.keys[key].add(value)
                self.values[value] = set()  # a new set
                self.values[value].add(key)
            elif value in self.values:
                self.keys[key] = set()  # a new set
                self.keys[key].add(value) # a new key
                self.values[value].add(key)  # but just an update to the values
        elif key in self.keys:  # it's a new relationships
            self.keys[key].add(value)
            if value not in self.values:
                self.values[value] = set()
                self.values[value].add(key)
            elif value in self.values:
                self.values[value].add(key)

    def update(self, key, old_value, new_value):
        """update is a special case because __setitem__ can't see that
        you want to propagate your update onto multiple values. """
        if old_value in self.keys[key]:
            affected_keys = self.values[old_value]
            for key in affected_keys:
                self.__setitem__(key, new_value)
                self.keys[key].remove(old_value)
            del self.values[old_value]
        else:
            raise KeyError("key: {} does not have value: {}".format(key,old_value))

    def __delitem__(self, key, value=None):  # <---SQL DELETE statement
        if value is None:
            # All the keys relations are to be deleted.
            try:
                value_set = self.keys[key]
                for value in value_set:
                    self.values[value].remove(key)
                    if not self.values[value]:
                        del self.values[value]
                del self.keys[key]  # then we delete the key.
            except KeyError:
                raise KeyError("key not found")
        else:  # then only a single relationships is being removed.
            try:
                if value in self.keys[key]:  # this is a set.
                    self.keys[key].remove(value)
                    self.values[value].remove(key)
                if not self.keys[key]:  # if the set is empty, we remove the key
                    del self.keys[key]
                if not self.values[value]:  # if the set is empty, we remove the value
                    del self.values[value]
            except KeyError:
                raise KeyError("key not found")

    def iterload(self, key_list, value_list):
        for key in key_list:
            for value in value_list:
                self.__setitem__(key, value)

def test01():
    # hashable key - a tuple
    d = Database()
    k, v = (1, 2, 3), 'magic'
    d[k] = v
    print(d)
    print(d[k])


def test02():
    # non-hashable key - a list - becomes a many-key shared value
    d = Database()
    keys, value = [1, 2, 3], ['magic']
    d.iterload(keys, value)
    assert d[1] == d[2] == d[3]


def test03():
    d = Database()
    k, v = ['what', 'ever'], ['test']
    d.iterload(k,v)
    assert d['what'] == d['ever']
    d['what'] = 'testing'
    try:
        assert d['what'] == d['ever']
    except AssertionError:
        assert True


def test04():
    d = Database()
    v = 'test'
    keys, values = ['what', 'ever'], [v]
    d.iterload(keys, values)
    d['whatever'] = 'test'
    assert v == d['whatever']
    assert v == d['what']
    assert d['whatever'] == d['what']
    d.update('whatever', 'test', 'new test')
    a, b = d['whatever'], d['what']
    assert a == b


def do_all():
    for k, v in sorted(globals().items()):
        if k.startswith("test") and callable(v):
            v()

if __name__ == "__main__":
    do_all()

For consistency, you should have the dictionary map keys to lists (or sets) of values, of which some can be empty. There is a nice idiom for this:

from collections import defaultdict
d = defaultdict(set)

d["key"].add(...)

(A defaultdict is like a normal dictionary, but if a key is missing it will call the argument you passed in when you instantiated it and use the result as the default value. So this will automatically create an empty set of values if you ask for a key which isn't already present.)

If you need the object to look more like a dictionary (i.e. to set a value by d["key"] = ...) you can do the following. But this is probably a bad idea, because it goes against the normal Python syntax, and is likely to come back and bite you later. Especially if someone else has to maintain your code.

class Multidict(defaultdict):
    def __init__(self):
        super(Multidict, self).__init__(set)

    def __setitem__(self, key, value):
        if isinstance(value, (self.default_factory)): # self.default_factory is `set`
            super().__setitem__(key, value)
        else:
            self[key].append(value)

I haven't tested this.

You could do something like:

import itertools as it

unique_dict = {}
value_key=lambda x: x[1]
sorted_items = sorted(your_current_dict.items(), key=value_key)
for value, group in it.groupby(sorted_items, key=value_key):
    for key in group:
        unique_dict[key] = value

This transforms your dictionary into a dictionary where equal values of any kind(but comparable) are unique. If your values are not comparable(but are hashable) you could use a temporary dict:

from collections import defaultdict
unique_dict = {}
tmp_dict = defaultdict(list)

for key, value in your_current_dict.items():
    tmp_dict[value].append(key)

for value, keys in tmp_dict.items():
    unique_dict.update(zip(keys, [value] * len(keys)))