How to get string objects instead of Unicode from

问题:

I\'m using Python 2 to parse JSON from ASCII encoded text files.

When loading these files with either json or simplejson, all my string values are cast to Unicode objects instead of string objects. The problem is, I have to use the data with some libraries that only accept string objects. I can\'t change the libraries nor update them.

Is it possible to get string objects instead of Unicode ones?

Example

>>> import json
>>> original_list = [\'a\', \'b\']
>>> json_list = json.dumps(original_list)
>>> json_list
\'[\"a\", \"b\"]\'
>>> new_list = json.loads(json_list)
>>> new_list
[u\'a\', u\'b\']  # I want these to be of type `str`, not `unicode`

Update

This question was asked a long time ago, when I was stuck with Python 2. One easy and clean solution for today is to use a recent version of Python — i.e. Python 3 and forward.

回答1:

A solution with object_hook

import json

def json_load_byteified(file_handle):
    return _byteify(
        json.load(file_handle, object_hook=_byteify),
        ignore_dicts=True
    )

def json_loads_byteified(json_text):
    return _byteify(
        json.loads(json_text, object_hook=_byteify),
        ignore_dicts=True
    )

def _byteify(data, ignore_dicts = False):
    # if this is a unicode string, return its string representation
    if isinstance(data, unicode):
        return data.encode(\'utf-8\')
    # if this is a list of values, return list of byteified values
    if isinstance(data, list):
        return [ _byteify(item, ignore_dicts=True) for item in data ]
    # if this is a dictionary, return dictionary of byteified keys and values
    # but only if we haven\'t already byteified it
    if isinstance(data, dict) and not ignore_dicts:
        return {
            _byteify(key, ignore_dicts=True): _byteify(value, ignore_dicts=True)
            for key, value in data.iteritems()
        }
    # if it\'s anything else, return it in its original form
    return data

Example usage:

>>> json_loads_byteified(\'{\"Hello\": \"World\"}\')
{\'Hello\': \'World\'}
>>> json_loads_byteified(\'\"I am a top-level string\"\')
\'I am a top-level string\'
>>> json_loads_byteified(\'7\')
7
>>> json_loads_byteified(\'[\"I am inside a list\"]\')
[\'I am inside a list\']
>>> json_loads_byteified(\'[[[[[[[[\"I am inside a big nest of lists\"]]]]]]]]\')
[[[[[[[[\'I am inside a big nest of lists\']]]]]]]]
>>> json_loads_byteified(\'{\"foo\": \"bar\", \"things\": [7, {\"qux\": \"baz\", \"moo\": {\"cow\": [\"milk\"]}}]}\')
{\'things\': [7, {\'qux\': \'baz\', \'moo\': {\'cow\': [\'milk\']}}], \'foo\': \'bar\'}
>>> json_load_byteified(open(\'somefile.json\'))
{\'more json\': \'from a file\'}

How does this work and why would I use it?

Mark Amery\'s function is shorter and clearer than these ones, so what\'s the point of them? Why would you want to use them?

Purely for performance. Mark\'s answer decodes the JSON text fully first with unicode strings, then recurses through the entire decoded value to convert all strings to byte strings. This has a couple of undesirable effects:

• A copy of the entire decoded structure gets created in memory
• If your JSON object is really deeply nested (500 levels or more) then you\'ll hit Python\'s maximum recursion depth

This answer mitigates both of those performance issues by using the object_hook parameter of json.load and json.loads. From the docs:

object_hook is an optional function that will be called with the result of any object literal decoded (a dict). The return value of object_hook will be used instead of the dict. This feature can be used to implement custom decoders

Since dictionaries nested many levels deep in other dictionaries get passed to object_hook as they\'re decoded, we can byteify any strings or lists inside them at that point and avoid the need for deep recursion later.

Mark\'s answer isn\'t suitable for use as an object_hook as it stands, because it recurses into nested dictionaries. We prevent that recursion in this answer with the ignore_dicts parameter to _byteify, which gets passed to it at all times except when object_hook passes it a new dict to byteify. The ignore_dicts flag tells _byteify to ignore dicts since they already been byteified.

Finally, our implementations of json_load_byteified and json_loads_byteified call _byteify (with ignore_dicts=True) on the result returned from json.load or json.loads to handle the case where the JSON text being decoded doesn\'t have a dict at the top level.

回答2:

While there are some good answers here, I ended up using PyYAML to parse my JSON files, since it gives the keys and values as str type strings instead of unicode type. Because JSON is a subset of YAML it works nicely:

>>> import json
>>> import yaml
>>> list_org = [\'a\', \'b\']
>>> list_dump = json.dumps(list_org)
>>> list_dump
\'[\"a\", \"b\"]\'
>>> json.loads(list_dump)
[u\'a\', u\'b\']
>>> yaml.safe_load(list_dump)
[\'a\', \'b\']

Notes

Some things to note though:

• I get string objects because all my entries are ASCII encoded. If I would use unicode encoded entries, I would get them back as unicode objects — there is no conversion!

• You should (probably always) use PyYAML\'s safe_load function; if you use it to load JSON files, you don\'t need the \"additional power\" of the load function anyway.

• If you want a YAML parser that has more support for the 1.2 version of the spec (and correctly parses very low numbers) try Ruamel YAML: pip install ruamel.yaml and import ruamel.yaml as yaml was all I needed in my tests.

Conversion

As stated, there is no conversion! If you can\'t be sure to only deal with ASCII values (and you can\'t be sure most of the time), better use a conversion function:

I used the one from Mark Amery a couple of times now, it works great and is very easy to use. You can also use a similar function as an object_hook instead, as it might gain you a performance boost on big files. See the slightly more involved answer from Mirec Miskuf for that.

回答3:

There\'s no built-in option to make the json module functions return byte strings instead of unicode strings. However, this short and simple recursive function will convert any decoded JSON object from using unicode strings to UTF-8-encoded byte strings:

def byteify(input):
    if isinstance(input, dict):
        return {byteify(key): byteify(value)
                for key, value in input.iteritems()}
    elif isinstance(input, list):
        return [byteify(element) for element in input]
    elif isinstance(input, unicode):
        return input.encode(\'utf-8\')
    else:
        return input

Just call this on the output you get from a json.load or json.loads call.

A couple of notes:

• To support Python 2.6 or earlier, replace return {byteify(key): byteify(value) for key, value in input.iteritems()} with return dict([(byteify(key), byteify(value)) for key, value in input.iteritems()]), since dictionary comprehensions weren\'t supported until Python 2.7.
• Since this answer recurses through the entire decoded object, it has a couple of undesirable performance characteristics that can be avoided with very careful use of the object_hook or object_pairs_hook parameters. Mirec Miskuf\'s answer is so far the only one that manages to pull this off correctly, although as a consequence, it\'s significantly more complicated than my approach.

回答4:

You can use the object_hook parameter for json.loads to pass in a converter. You don\'t have to do the conversion after the fact. The json module will always pass the object_hook dicts only, and it will recursively pass in nested dicts, so you don\'t have to recurse into nested dicts yourself. I don\'t think I would convert unicode strings to numbers like Wells shows. If it\'s a unicode string, it was quoted as a string in the JSON file, so it is supposed to be a string (or the file is bad).

Also, I\'d try to avoid doing something like str(val) on a unicode object. You should use value.encode(encoding) with a valid encoding, depending on what your external lib expects.

So, for example:

def _decode_list(data):
    rv = []
    for item in data:
        if isinstance(item, unicode):
            item = item.encode(\'utf-8\')
        elif isinstance(item, list):
            item = _decode_list(item)
        elif isinstance(item, dict):
            item = _decode_dict(item)
        rv.append(item)
    return rv

def _decode_dict(data):
    rv = {}
    for key, value in data.iteritems():
        if isinstance(key, unicode):
            key = key.encode(\'utf-8\')
        if isinstance(value, unicode):
            value = value.encode(\'utf-8\')
        elif isinstance(value, list):
            value = _decode_list(value)
        elif isinstance(value, dict):
            value = _decode_dict(value)
        rv[key] = value
    return rv

obj = json.loads(s, object_hook=_decode_dict)

回答5:

That\'s because json has no difference between string objects and unicode objects. They\'re all strings in javascript.

I think JSON is right to return unicode objects. In fact, I wouldn\'t accept anything less, since javascript strings are in fact unicode objects (i.e. JSON (javascript) strings can store any kind of unicode character) so it makes sense to create unicode objects when translating strings from JSON. Plain strings just wouldn\'t fit since the library would have to guess the encoding you want.

It\'s better to use unicode string objects everywhere. So your best option is to update your libraries so they can deal with unicode objects.

But if you really want bytestrings, just encode the results to the encoding of your choice:

>>> nl = json.loads(js)
>>> nl
[u\'a\', u\'b\']
>>> nl = [s.encode(\'utf-8\') for s in nl]
>>> nl
[\'a\', \'b\']

回答6:

There exists an easy work-around.

TL;DR - Use ast.literal_eval() instead of json.loads(). Both ast and json are in the standard library.

While not a \'perfect\' answer, it gets one pretty far if your plan is to ignore Unicode altogether. In Python 2.7

import json, ast
d = { \'field\' : \'value\' }
print \"JSON Fail: \", json.loads(json.dumps(d))
print \"AST Win:\", ast.literal_eval(json.dumps(d))

gives:

JSON Fail:  {u\'field\': u\'value\'}
AST Win: {\'field\': \'value\'}

This gets more hairy when some objects are really Unicode strings. The full answer gets hairy quickly.

回答7:

I\'m afraid there\'s no way to achieve this automatically within the simplejson library.

The scanner and decoder in simplejson are designed to produce unicode text. To do this, the library uses a function called c_scanstring (if it\'s available, for speed), or py_scanstring if the C version is not available. The scanstring function is called several times by nearly every routine that simplejson has for decoding a structure that might contain text. You\'d have to either monkeypatch the scanstring value in simplejson.decoder, or subclass JSONDecoder and provide pretty much your own entire implementation of anything that might contain text.

The reason that simplejson outputs unicode, however, is that the json spec specifically mentions that \"A string is a collection of zero or more Unicode characters\"... support for unicode is assumed as part of the format itself. Simplejson\'s scanstring implementation goes so far as to scan and interpret unicode escapes (even error-checking for malformed multi-byte charset representations), so the only way it can reliably return the value to you is as unicode.

If you have an aged library that needs an str, I recommend you either laboriously search the nested data structure after parsing (which I acknowledge is what you explicitly said you wanted to avoid... sorry), or perhaps wrap your libraries in some sort of facade where you can massage the input parameters at a more granular level. The second approach might be more manageable than the first if your data structures are indeed deeply nested.

回答8:

Mike Brennan\'s answer is close, but there is no reason to re-traverse the entire structure. If you use the object_hook_pairs (Python 2.7+) parameter:

object_pairs_hook is an optional function that will be called with the result of any object literal decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict. This feature can be used to implement custom decoders that rely on the order that the key and value pairs are decoded (for example, collections.OrderedDict will remember the order of insertion). If object_hook is also defined, the object_pairs_hook takes priority.

With it, you get each JSON object handed to you, so you can do the decoding with no need for recursion:

def deunicodify_hook(pairs):
    new_pairs = []
    for key, value in pairs:
        if isinstance(value, unicode):
            value = value.encode(\'utf-8\')
        if isinstance(key, unicode):
            key = key.encode(\'utf-8\')
        new_pairs.append((key, value))
    return dict(new_pairs)

In [52]: open(\'test.json\').read()
Out[52]: \'{\"1\": \"hello\", \"abc\": [1, 2, 3], \"def\": {\"hi\": \"mom\"}, \"boo\": [1, \"hi\", \"moo\", {\"5\": \"some\"}]}\'                                        

In [53]: json.load(open(\'test.json\'))
Out[53]: 
{u\'1\': u\'hello\',
 u\'abc\': [1, 2, 3],
 u\'boo\': [1, u\'hi\', u\'moo\', {u\'5\': u\'some\'}],
 u\'def\': {u\'hi\': u\'mom\'}}

In [54]: json.load(open(\'test.json\'), object_pairs_hook=deunicodify_hook)
Out[54]: 
{\'1\': \'hello\',
 \'abc\': [1, 2, 3],
 \'boo\': [1, \'hi\', \'moo\', {\'5\': \'some\'}],
 \'def\': {\'hi\': \'mom\'}}

Notice that I never have to call the hook recursively since every object will get handed to the hook when you use the object_pairs_hook. You do have to care about lists, but as you can see, an object within a list will be properly converted, and you don\'t have to recurse to make it happen.

EDIT: A coworker pointed out that Python2.6 doesn\'t have object_hook_pairs. You can still use this will Python2.6 by making a very small change. In the hook above, change:

for key, value in pairs:

to

for key, value in pairs.iteritems():

Then use object_hook instead of object_pairs_hook:

In [66]: json.load(open(\'test.json\'), object_hook=deunicodify_hook)
Out[66]: 
{\'1\': \'hello\',
 \'abc\': [1, 2, 3],
 \'boo\': [1, \'hi\', \'moo\', {\'5\': \'some\'}],
 \'def\': {\'hi\': \'mom\'}}

Using object_pairs_hook results in one less dictionary being instantiated for each object in the JSON object, which, if you were parsing a huge document, might be worth while.

回答9:

As Mark (Amery) correctly notes: Using PyYaml\'s deserializer on a json dump works only if you have ASCII only. At least out of the box.

Two quick comments on the PyYaml approach:

1. NEVER use yaml.load on data from the field. Its a feature(!) of yaml to execute arbitrary code hidden within the structure.

2. You can make it work also for non ASCII via this:

   def to_utf8(loader, node):
       return loader.construct_scalar(node).encode(\'utf-8\')
   yaml.add_constructor(u\'tag:yaml.org,2002:str\', to_utf8)
   

But performance wise its of no comparison to Mark Amery\'s answer:

Throwing some deeply nested sample dicts onto the two methods, I get this (with dt[j] = time delta of json.loads(json.dumps(m))):

     dt[yaml.safe_load(json.dumps(m))] =~ 100 * dt[j]
     dt[byteify recursion(Mark Amery)] =~   5 * dt[j]

So deserialization including fully walking the tree and encoding, well within the order of magnitude of json\'s C based implementation. I find this remarkably fast and its also more robust than the yaml load at deeply nested structures. And less security error prone, looking at yaml.load.

=> While I would appreciate a pointer to a C only based converter the byteify function should be the default answer.

This holds especially true if your json structure is from the field, containing user input. Because then you probably need to walk anyway over your structure - independent on your desired internal data structures (\'unicode sandwich\' or byte strings only).

Why?

Unicode normalisation. For the unaware: Take a painkiller and read this.

So using the byteify recursion you kill two birds with one stone:

1. get your bytestrings from nested json dumps
2. get user input values normalised, so that you find the stuff in your storage.

In my tests it turned out that replacing the input.encode(\'utf-8\') with a unicodedata.normalize(\'NFC\', input).encode(\'utf-8\') was even faster than w/o NFC - but thats heavily dependent on the sample data I guess.

回答10:

The gotcha is that simplejson and json are two different modules, at least in the manner they deal with unicode. You have json in py 2.6+, and this gives you unicode values, whereas simplejson returns string objects. Just try easy_install-ing simplejson in your environment and see if that works. It did for me.

回答11:

So, I\'ve run into the same problem. Guess what was the first Google result.

Because I need to pass all data to PyGTK, unicode strings aren\'t very useful to me either. So I have another recursive conversion method. It\'s actually also needed for typesafe JSON conversion - json.dump() would bail on any non-literals, like Python objects. Doesn\'t convert dict indexes though.

# removes any objects, turns unicode back into str
def filter_data(obj):
        if type(obj) in (int, float, str, bool):
                return obj
        elif type(obj) == unicode:
                return str(obj)
        elif type(obj) in (list, tuple, set):
                obj = list(obj)
                for i,v in enumerate(obj):
                        obj[i] = filter_data(v)
        elif type(obj) == dict:
                for i,v in obj.iteritems():
                        obj[i] = filter_data(v)
        else:
                print \"invalid object in data, converting to string\"
                obj = str(obj) 
        return obj

回答12:

Just use pickle instead of json for dump and load, like so:

    import json
    import pickle

    d = { \'field1\': \'value1\', \'field2\': 2, }

    json.dump(d,open(\"testjson.txt\",\"w\"))

    print json.load(open(\"testjson.txt\",\"r\"))

    pickle.dump(d,open(\"testpickle.txt\",\"w\"))

    print pickle.load(open(\"testpickle.txt\",\"r\"))

The output it produces is (strings and integers are handled correctly):

    {u\'field2\': 2, u\'field1\': u\'value1\'}
    {\'field2\': 2, \'field1\': \'value1\'}

回答13:

Support Python2&3 using hook (from https://stackoverflow.com/a/33571117/558397)

import requests
import six
from six import iteritems

requests.packages.urllib3.disable_warnings()  # @UndefinedVariable
r = requests.get(\"http://echo.jsontest.com/key/value/one/two/three\", verify=False)

def _byteify(data):
    # if this is a unicode string, return its string representation
    if isinstance(data, six.string_types):
        return str(data.encode(\'utf-8\').decode())

    # if this is a list of values, return list of byteified values
    if isinstance(data, list):
        return [ _byteify(item) for item in data ]

    # if this is a dictionary, return dictionary of byteified keys and values
    # but only if we haven\'t already byteified it
    if isinstance(data, dict):
        return {
            _byteify(key): _byteify(value) for key, value in iteritems(data)
        }
    # if it\'s anything else, return it in its original form
    return data

w = r.json(object_hook=_byteify)
print(w)

Returns:

 {\'three\': \'\', \'key\': \'value\', \'one\': \'two\'}

回答14:

This is late to the game, but I built this recursive caster. It works for my needs and I think it\'s relatively complete. It may help you.

def _parseJSON(self, obj):
    newobj = {}

    for key, value in obj.iteritems():
        key = str(key)

        if isinstance(value, dict):
            newobj[key] = self._parseJSON(value)
        elif isinstance(value, list):
            if key not in newobj:
                newobj[key] = []
                for i in value:
                    newobj[key].append(self._parseJSON(i))
        elif isinstance(value, unicode):
            val = str(value)
            if val.isdigit():
                val = int(val)
            else:
                try:
                    val = float(val)
                except ValueError:
                    val = str(val)
            newobj[key] = val

    return newobj

Just pass it a JSON object like so:

obj = json.loads(content, parse_float=float, parse_int=int)
obj = _parseJSON(obj)

I have it as a private member of a class, but you can repurpose the method as you see fit.

回答15:

I rewrote Wells\'s _parse_json() to handle cases where the json object itself is an array (my use case).

def _parseJSON(self, obj):
    if isinstance(obj, dict):
        newobj = {}
        for key, value in obj.iteritems():
            key = str(key)
            newobj[key] = self._parseJSON(value)
    elif isinstance(obj, list):
        newobj = []
        for value in obj:
            newobj.append(self._parseJSON(value))
    elif isinstance(obj, unicode):
        newobj = str(obj)
    else:
        newobj = obj
    return newobj

回答16:

here is a recursive encoder written in C: https://github.com/axiros/nested_encode

Performance overhead for \"average\" structures around 10% compared to json.loads.

python speed.py                                                                                            
  json loads            [0.16sec]: {u\'a\': [{u\'b\': [[1, 2, [u\'\\xd6ster..
  json loads + encoding [0.18sec]: {\'a\': [{\'b\': [[1, 2, [\'\\xc3\\x96ster.
  time overhead in percent: 9%

using this teststructure:

import json, nested_encode, time

s = \"\"\"
{
  \"firstName\": \"Jos\\\\u0301\",
  \"lastName\": \"Smith\",
  \"isAlive\": true,
  \"age\": 25,
  \"address\": {
    \"streetAddress\": \"21 2nd Street\",
    \"city\": \"\\\\u00d6sterreich\",
    \"state\": \"NY\",
    \"postalCode\": \"10021-3100\"
  },
  \"phoneNumbers\": [
    {
      \"type\": \"home\",
      \"number\": \"212 555-1234\"
    },
    {
      \"type\": \"office\",
      \"number\": \"646 555-4567\"
    }
  ],
  \"children\": [],
  \"spouse\": null,
  \"a\": [{\"b\": [[1, 2, [\"\\\\u00d6sterreich\"]]]}]
}
\"\"\"


t1 = time.time()
for i in xrange(10000):
    u = json.loads(s)
dt_json = time.time() - t1

t1 = time.time()
for i in xrange(10000):
    b = nested_encode.encode_nested(json.loads(s))
dt_json_enc = time.time() - t1

print \"json loads            [%.2fsec]: %s...\" % (dt_json, str(u)[:20])
print \"json loads + encoding [%.2fsec]: %s...\" % (dt_json_enc, str(b)[:20])

print \"time overhead in percent: %i%%\"  % (100 * (dt_json_enc - dt_json)/dt_json)

回答17:

I had a JSON dict as a string. The keys and values were unicode objects like in the following example:

myStringDict = \"{u\'key\':u\'value\'}\"

I could use the byteify function suggested above by converting the string to a dict object using ast.literal_eval(myStringDict).

回答18:

Check out this answer to a similar question like this which states that

The u- prefix just means that you have a Unicode string. When you really use the string, it won\'t appear in your data. Don\'t be thrown by the printed output.

For example, try this:

print mail_accounts[0][\"i\"]

You won\'t see a u.

回答19:

With Python 3.6, sometimes I still run into this problem. For example, when getting response from a REST API and loading the response text to JSON, I still get the unicode strings. Found a simple solution using json.dumps().

response_message = json.loads(json.dumps(response.text))
print(response_message)

回答20:

I ran into this problem too, and having to deal with JSON, I came up with a small loop that converts the unicode keys to strings. (simplejson on GAE does not return string keys.)

obj is the object decoded from JSON:

if NAME_CLASS_MAP.has_key(cls):
    kwargs = {}
    for i in obj.keys():
        kwargs[str(i)] = obj[i]
    o = NAME_CLASS_MAP[cls](**kwargs)
    o.save()

kwargs is what I pass to the constructor of the GAE application (which does not like unicode keys in **kwargs)

Not as robust as the solution from Wells, but much smaller.

回答21:

I\'ve adapted the code from the answer of Mark Amery, particularly in order to get rid of isinstance for the pros of duck-typing.

The encoding is done manually and ensure_ascii is disabled. The python docs for json.dump says that

If ensure_ascii is True (the default), all non-ASCII characters in the output are escaped with \\uXXXX sequences

Disclaimer: in the doctest I used the Hungarian language. Some notable Hungarian-related character encodings are: cp852 the IBM/OEM encoding used eg. in DOS (sometimes referred as ascii, incorrectly I think, it is dependent on the codepage setting), cp1250 used eg. in Windows (sometimes referred as ansi, dependent on the locale settings), and iso-8859-2, sometimes used on http servers. The test text Tüskéshátú kígyóbűvölő is attributed to Koltai László (native personal name form) and is from wikipedia.

# coding: utf-8
\"\"\"
This file should be encoded correctly with utf-8.
\"\"\"
import json

def encode_items(input, encoding=\'utf-8\'):
    u\"\"\"original from: https://stackoverflow.com/a/13101776/611007
    adapted by SO/u/611007 (20150623)
    >>> 
    >>> ## run this with `python -m doctest <this file>.py` from command line
    >>> 
    >>> txt = u\"Tüskéshátú kígyóbűvölő\"
    >>> txt2 = u\"T\\\\u00fcsk\\\\u00e9sh\\\\u00e1t\\\\u00fa k\\\\u00edgy\\\\u00f3b\\\\u0171v\\\\u00f6l\\\\u0151\"
    >>> txt3 = u\"uúuutifu\"
    >>> txt4 = b\'u\\\\xfauutifu\'
    >>> # txt4 shouldn\'t be \'u\\\\xc3\\\\xbauutifu\', string content needs double backslash for doctest:
    >>> assert u\'\\\\u0102\' not in b\'u\\\\xfauutifu\'.decode(\'cp1250\')
    >>> txt4u = txt4.decode(\'cp1250\')
    >>> assert txt4u == u\'u\\\\xfauutifu\', repr(txt4u)
    >>> txt5 = b\"u\\\\xc3\\\\xbauutifu\"
    >>> txt5u = txt5.decode(\'utf-8\')
    >>> txt6 = u\"u\\\\u251c\\\\u2551uutifu\"
    >>> there_and_back_again = lambda t: encode_items(t, encoding=\'utf-8\').decode(\'utf-8\')
    >>> assert txt == there_and_back_again(txt)
    >>> assert txt == there_and_back_again(txt2)
    >>> assert txt3 == there_and_back_again(txt3)
    >>> assert txt3.encode(\'cp852\') == there_and_back_again(txt4u).encode(\'cp852\')
    >>> assert txt3 == txt4u,(txt3,txt4u)
    >>> assert txt3 == there_and_back_again(txt5)
    >>> assert txt3 == there_and_back_again(txt5u)
    >>> assert txt3 == there_and_back_again(txt4u)
    >>> assert txt3.encode(\'cp1250\') == encode_items(txt4, encoding=\'utf-8\')
    >>> assert txt3.encode(\'utf-8\') == encode_items(txt5, encoding=\'utf-8\')
    >>> assert txt2.encode(\'utf-8\') == encode_items(txt, encoding=\'utf-8\')
    >>> assert {\'a\':txt2.encode(\'utf-8\')} == encode_items({\'a\':txt}, encoding=\'utf-8\')
    >>> assert [txt2.encode(\'utf-8\')] == encode_items([txt], encoding=\'utf-8\')
    >>> assert [[txt2.encode(\'utf-8\')]] == encode_items([[txt]], encoding=\'utf-8\')
    >>> assert [{\'a\':txt2.encode(\'utf-8\')}] == encode_items([{\'a\':txt}], encoding=\'utf-8\')
    >>> assert {\'b\':{\'a\':txt2.encode(\'utf-8\')}} == encode_items({\'b\':{\'a\':txt}}, encoding=\'utf-8\')
    \"\"\"
    try:
        input.iteritems
        return {encode_items(k): encode_items(v) for (k,v) in input.iteritems()}
    except AttributeError:
        if isinstance(input, unicode):
            return input.encode(encoding)
        elif isinstance(input, str):
            return input
        try:
            iter(input)
            return [encode_items(e) for e in input]
        except TypeError:
            return input

def alt_dumps(obj, **kwargs):
    \"\"\"
    >>> alt_dumps({\'a\': u\"T\\\\u00fcsk\\\\u00e9sh\\\\u00e1t\\\\u00fa k\\\\u00edgy\\\\u00f3b\\\\u0171v\\\\u00f6l\\\\u0151\"})
    \'{\"a\": \"T\\\\xc3\\\\xbcsk\\\\xc3\\\\xa9sh\\\\xc3\\\\xa1t\\\\xc3\\\\xba k\\\\xc3\\\\xadgy\\\\xc3\\\\xb3b\\\\xc5\\\\xb1v\\\\xc3\\\\xb6l\\\\xc5\\\\x91\"}\'
    \"\"\"
    if \'ensure_ascii\' in kwargs:
        del kwargs[\'ensure_ascii\']
    return json.dumps(encode_items(obj), ensure_ascii=False, **kwargs)

I\'d also like to highlight the answer of Jarret Hardie which references the JSON spec, quoting:

A string is a collection of zero or more Unicode characters

In my use-case I had files with json. They are utf-8 encoded files. ensure_ascii results in properly escaped but not very readable json files, that is why I\'ve adapted Mark Amery\'s answer to fit my needs.

The doctest is not particularly thoughtful but I share the code in the hope that it will useful for someone.