Fastest way to convert a dict's keys & values from `unicode` to `str`?

Question

I'm receiving a dict from one "layer" of code upon which some calculations/modifications are performed before passing it onto another "layer". The original dict's keys & "string" values are unicode, but the layer they're being passed onto only accepts str.

This is going to be called often, so I'd like to know what would be the fastest way to convert something like:

{ u'spam': u'eggs', u'foo': True, u'bar': { u'baz': 97 } }

...to:

{ 'spam': 'eggs', 'foo': True, 'bar': { 'baz': 97 } }

...bearing in mind the non-"string" values need to stay as their original type.

Any thoughts?

Answer 1

def to_str(key, value):
    if isinstance(key, unicode):
        key = str(key)
    if isinstance(value, unicode):
        value = str(value)
    return key, value

pass key and value to it, and add recursion to your code to account for inner dictionary.

Answer 2

DATA = { u'spam': u'eggs', u'foo': frozenset([u'Gah!']), u'bar': { u'baz': 97 },
         u'list': [u'list', (True, u'Maybe'), set([u'and', u'a', u'set', 1])]}

def convert(data):
    if isinstance(data, unicode):
        return str(data)
    elif isinstance(data, dict):
        return dict(map(convert, data.iteritems()))
    elif isinstance(data, (list, tuple, set, frozenset)):
        return type(data)(map(convert, data))
    else:
        return data

print DATA
print convert(DATA)
# Prints:
# {u'list': [u'list', (True, u'Maybe'), set([u'and', u'a', u'set', 1])], u'foo': frozenset([u'Gah!']), u'bar': {u'baz': 97}, u'spam': u'eggs'}
# {'bar': {'baz': 97}, 'foo': frozenset(['Gah!']), 'list': ['list', (True, 'Maybe'), set(['and', 'a', 'set', 1])], 'spam': 'eggs'}

Assumptions:

• The only container types are dict, list, tuple, set and frozenset.
• You're happy to convert using the default encoding (use data.encode('utf-8') rather than str(data) if you need an explicit encoding).

If you need to support other container types, hopefully it's obvious how to follow the pattern and add cases for them.