Basic Usage¶
Let’s return to the example from the Introduction:
>>> element_by_symbol = bidict(H='hydrogen')
As we saw, this behaves just like a dict,
but maintains a special
inverse attribute
giving access to inverse items:
>>> element_by_symbol.inverse['helium'] = 'He'
>>> del element_by_symbol.inverse['hydrogen']
>>> element_by_symbol
bidict({'He': 'helium'})
bidict.bidict supports the rest of the
collections.abc.MutableMapping interface
as well:
>>> 'C' in element_by_symbol
False
>>> element_by_symbol.get('C', 'carbon')
'carbon'
>>> element_by_symbol.pop('He')
'helium'
>>> element_by_symbol
bidict()
>>> element_by_symbol.update(Hg='mercury')
>>> element_by_symbol
bidict({'Hg': 'mercury'})
>>> 'mercury' in element_by_symbol.inverse
True
>>> element_by_symbol.inverse.pop('mercury')
'Hg'
Because inverse items are maintained alongside forward items, referencing a bidict’s inverse is always a constant-time operation.
Values Must Be Hashable¶
Because you must be able to look up keys by value as well as values by key, values must also be hashable.
Attempting to insert an unhashable value will result in an error:
>>> anagrams_by_alphagram = dict(opt=['opt', 'pot', 'top'])
>>> bidict(anagrams_by_alphagram)
Traceback (most recent call last):
...
TypeError: ...
So in this example, using a tuple or a frozenset instead of a list would do the trick:
>>> bidict(opt=('opt', 'pot', 'top'))
bidict({'opt': ('opt', 'pot', 'top')})
Values Must Be Unique¶
As we know, in a bidirectional map, not only must keys be unique, but values must be unique as well. This has immediate implications for bidict’s API.
Consider the following:
>>> b = bidict({'one': 1})
>>> b['two'] = 1
What should happen next?
If the bidict allowed this to succeed, because of the uniqueness-of-values constraint, it would silently clobber the existing item, resulting in:
>>> b
bidict({'two': 1})
This could result in surprises or problems down the line.
Instead, bidict raises a
ValueDuplicationError
so you have an opportunity to catch this early
and resolve the conflict before it causes problems later on:
>>> b['two'] = 1
Traceback (most recent call last):
...
ValueDuplicationError: 1
The purpose of this is to be more in line with the Zen of Python, which advises,
So if you really just want to clobber any existing items, all you have to do is say so:
>>> b.forceput('two', 1)
>>> b
bidict({'two': 1})
Similarly, initializations and update() calls
that would overwrite the key of an existing value
raise an exception too:
>>> bidict({'one': 1, 'uno': 1})
Traceback (most recent call last):
...
ValueDuplicationError: 1
>>> b = bidict({'one': 1})
>>> b.update([('two', 2), ('uno', 1)])
Traceback (most recent call last):
...
ValueDuplicationError: 1
If an update() call raises,
you can be sure that none of the supplied items were inserted:
>>> b
bidict({'one': 1})
Setting an existing key to a new value does not cause an error, and is considered an intentional overwrite of the value associated with the existing key, in keeping with dict’s behavior:
>>> b = bidict({'one': 1})
>>> b['one'] = 2 # succeeds
>>> b
bidict({'one': 2})
>>> b.update([('one', 3), ('one', 4), ('one', 5)])
>>> b
bidict({'one': 5})
>>> bidict([('one', 1), ('one', 2)])
bidict({'one': 2})
In summary, when attempting to insert an item whose key duplicates an existing item’s, bidict’s default behavior is to allow the insertion, overwriting the existing item with the new one. When attempting to insert an item whose value duplicates an existing item’s, bidict’s default behavior is to raise. This design naturally falls out of the behavior of Python’s built-in dict, and protects against unexpected data loss.
One set of alternatives to this behavior is provided by
forceput()
(mentioned above)
and forceupdate(),
which allow you to explicitly overwrite existing keys and values:
>>> b = bidict({'one': 1})
>>> b.forceput('two', 1)
>>> b
bidict({'two': 1})
>>> b.forceupdate([('three', 1), ('four', 1)])
>>> b
bidict({'four': 1})
For even more control,
you can use put()
and putall().
These variants allow you to pass
an OnDup instance
to explicitly specify custom OnDupActions
for each type of duplication that can occur.
>>> from bidict import OnDup, RAISE, DROP_OLD, DROP_NEW
>>> b = bidict({2: 4})
>>> b.put(2, 8, OnDup(key=RAISE, val=DROP_OLD))
Traceback (most recent call last):
...
KeyDuplicationError: 2
>>> b
bidict({2: 4})
>>> b.putall([(3, 9), (2, 8)], OnDup(key=RAISE))
Traceback (most recent call last):
...
KeyDuplicationError: 2
>>> # (2, 8) was the duplicate item triggering the error, but note
>>> # (3, 9) was not added either, i.e. updates fail clean.
>>> b
bidict({2: 4})
>>> b.putall([(3, 9), (1, 4)], OnDup(val=DROP_NEW))
>>> sorted(b.items()) # Note (1, 4) was dropped as requested:
[(2, 4), (3, 9)]
bidict provides the
ON_DUP_DEFAULT,
ON_DUP_RAISE, and
ON_DUP_DROP_OLD
OnDup instances
for convenience.
If no on_dup argument is passed,
put() and
putall()
will use ON_DUP_RAISE,
providing stricter-by-default alternatives to
__setitem__()
and
update().
(These defaults complement the looser alternatives
provided by forceput()
and forceupdate().)
Key and Value Duplication¶
Note that it’s possible for a given item to duplicate the key of one existing item, and the value of another existing item, as in:
>>> b.putall([(4, 16), (5, 25), (4, 25)])
Because the key and value deduplication actions that are in effect may differ,
OnDup’s kv argument
allows you to indicate how you want to handle this case
without ambiguity:
>>> on_dup = OnDup(key=DROP_OLD, val=DROP_NEW, kv=RAISE)
>>> b.putall([(4, 16), (5, 25), (4, 25)], on_dup)
Traceback (most recent call last):
...
KeyAndValueDuplicationError: (4, 25)
If not specified, kv defaults to whatever action for val is provided.
Note that if an entire (k, v) item is duplicated exactly, the duplicate item will just be ignored, no matter what on_dup is set to. The insertion of an entire duplicate item is construed as a no-op:
>>> sorted(b.items())
[(2, 4), (3, 9)]
>>> b.put(2, 4) # no-op, not a DuplicationError
>>> b.putall([(4, 16), (4, 16)]) # ditto
>>> sorted(b.items())
[(2, 4), (3, 9), (4, 16)]
See the YoloBidict Recipe for another way to customize this behavior.
Order Matters¶
Performing a bulk insert operation –
i.e. passing multiple items to
__init__(),
update(),
forceupdate(),
or putall() –
is like inserting each of those items individually in sequence.
1
Therefore, the order of the items provided to the bulk insert operation may affect the result:
>>> b = bidict({0: 0, 1: 2})
>>> b.forceupdate([(2, 0), (0, 1), (0, 0)])
>>> # 1. (2, 0) overwrites (0, 0) -> bidict({2: 0, 1: 2})
>>> # 2. (0, 1) is added -> bidict({2: 0, 1: 2, 0: 1})
>>> # 3. (0, 0) overwrites (0, 1) and (2, 0) -> bidict({0: 0, 1: 2})
>>> sorted(b.items())
[(0, 0), (1, 2)]
>>> b = bidict({0: 0, 1: 2}) # as before
>>> # Give the same items to forceupdate() but in a different order:
>>> b.forceupdate([(0, 1), (0, 0), (2, 0)])
>>> # 1. (0, 1) overwrites (0, 0) -> bidict({0: 1, 1: 2})
>>> # 2. (0, 0) overwrites (0, 1) -> bidict({0: 0, 1: 2})
>>> # 3. (2, 0) overwrites (0, 0) -> bidict({1: 2, 2: 0})
>>> sorted(b.items()) # different items!
[(1, 2), (2, 0)]
- 1
Albeit with an extremely important advantage: bulk insertion fails clean. i.e. If a bulk insertion fails, it will leave the bidict in the same state it was before, with none of the provided items inserted.
Interop¶
bidicts interoperate well with other types of mappings. For example, they support (efficient) polymorphic equality testing:
>>> bidict(a=1) == dict(a=1)
True
And converting back and forth works as expected (modulo any value duplication, as discussed above):
>>> dict(bidict(a=1))
{'a': 1}
>>> bidict(dict(a=1))
bidict({'a': 1})
See the Polymorphism section for more interoperability documentation.
Hopefully bidict feels right at home among the Python built-ins you already know. Proceed to Other bidict Types for documentation on the remaining bidict variants.
