With Python 2.6+ or 3 you can use the json.tool module:
echo '{"foo": "lorem", "bar": "ipsum"}' | python -m json.tool
or, if the JSON is in a file, you can do:
python -m json.tool my_json.json
if the JSON is from an internet source such as an API, you can use
curl http://my_url/ | python -m json.tool
For convenience in all of these cases you can make an alias:
alias prettyjson='python -m json.tool'
For even more convenience with a bit more typing to get it ready:
prettyjson_s() {
echo "$1" | python -m json.tool
}
prettyjson_f() {
python -m json.tool "$1"
}
prettyjson_w() {
curl "$1" | python -m json.tool
}
for all the above cases. You can put this in .bashrc and it will be available every time in shell. Invoke it like prettyjson_s '{"foo": "lorem", "bar": "ipsum"}'.
Note that as @pnd pointed out in the comments below, in Python 3.5+ the JSON object is no longer sorted by default. To sort, add the --sort-keys flag to the end. I.e. ... | python -m json.tool --sort-keys.
Another useful option might be --no-ensure-ascii which disables escaping of non-ASCII characters (new in version 3.9).
With Python 2.6+ or 3 you can use the json.tool module:
echo '{"foo": "lorem", "bar": "ipsum"}' | python -m json.tool
or, if the JSON is in a file, you can do:
python -m json.tool my_json.json
if the JSON is from an internet source such as an API, you can use
curl http://my_url/ | python -m json.tool
For convenience in all of these cases you can make an alias:
alias prettyjson='python -m json.tool'
For even more convenience with a bit more typing to get it ready:
prettyjson_s() {
echo "$1" | python -m json.tool
}
prettyjson_f() {
python -m json.tool "$1"
}
prettyjson_w() {
curl "$1" | python -m json.tool
}
for all the above cases. You can put this in .bashrc and it will be available every time in shell. Invoke it like prettyjson_s '{"foo": "lorem", "bar": "ipsum"}'.
Note that as @pnd pointed out in the comments below, in Python 3.5+ the JSON object is no longer sorted by default. To sort, add the --sort-keys flag to the end. I.e. ... | python -m json.tool --sort-keys.
Another useful option might be --no-ensure-ascii which disables escaping of non-ASCII characters (new in version 3.9).
You can use: jq
It's very simple to use and it works great! It can handle very large JSON structures, including streams. You can find their tutorials here.
Usage examples:
$ jq --color-output . file1.json file1.json | less -R
$ command_with_json_output | jq .
$ jq # stdin/"interactive" mode, just enter some JSON
$ jq <<< '{ "foo": "lorem", "bar": "ipsum" }'
{
"bar": "ipsum",
"foo": "lorem"
}
Or use jq with identity filter:
$ jq '.foo' <<< '{ "foo": "lorem", "bar": "ipsum" }'
"lorem"
Videos
Use the indent= parameter of json.dump() or json.dumps() to specify how many spaces to indent by:
>>> import json
>>> your_json = '["foo", {"bar": ["baz", null, 1.0, 2]}]'
>>> parsed = json.loads(your_json)
>>> print(json.dumps(parsed, indent=4))
[
"foo",
{
"bar": [
"baz",
null,
1.0,
2
]
}
]
To parse a file, use json.load():
with open('filename.txt', 'r') as handle:
parsed = json.load(handle)
You can do this on the command line:
python3 -m json.tool some.json
(as already mentioned in the commentaries to the question, thanks to @Kai Petzke for the python3 suggestion).
Actually python is not my favourite tool as far as json processing on the command line is concerned. For simple pretty printing is ok, but if you want to manipulate the json it can become overcomplicated. You'd soon need to write a separate script-file, you could end up with maps whose keys are u"some-key" (python unicode), which makes selecting fields more difficult and doesn't really go in the direction of pretty-printing.
You can also use jq:
jq . some.json
and you get colors as a bonus (and way easier extendability).
Addendum: There is some confusion in the comments about using jq to process large JSON files on the one hand, and having a very large jq program on the other. For pretty-printing a file consisting of a single large JSON entity, the practical limitation is RAM. For pretty-printing a 2GB file consisting of a single array of real-world data, the "maximum resident set size" required for pretty-printing was 5GB (whether using jq 1.5 or 1.6). Note also that jq can be used from within python after pip install jq.