How to get string objects instead of Unicode from JSON?


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?


>>> 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'


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.

Answer rating: 187

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"]


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 ‚Äî 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.


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.

Answer rating: 145

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")
        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.

Answer rating: 114

A solution with object_hook

[edit]: Updated for Python 2.7 and 3.x compatibility.

import json

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

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

def _byteify(data, ignore_dicts = False):
    if isinstance(data, str):
        return data

    # 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.items() # changed to .items() for python 2.7/3

    # python 3 compatible duck-typing
    # if this is a unicode string, return its string representation
    if str(type(data)) == "<type "unicode">":
        return data.encode("utf-8")

    # 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")
>>> 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.

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