How to prettyprint a JSON file?

Question

How do I pretty-print a JSON file in Python?

Answer 1

After reading the data with the json standard library module, use the pprint standard library module to display the parsed data. Example:

import json
import pprint

json_data = None
with open('file_name.txt', 'r') as f:
    data = f.read()
    json_data = json.loads(data)

# print json to screen with human-friendly formatting
pprint.pprint(json_data, compact=True)

# write json to file with human-friendly formatting
pretty_json_str = pprint.pformat(json_data, compact=True).replace("'",'"')

with open('file_name.json', 'w') as f:
    f.write(pretty_json_str)

The default indent is 1, so you may want to specify your own.

By default, pprint will output lists like this:

'not compact': ['pprint',
                'will',
                'output',
                'lists',
                'like',
                'this'],

And that is no better than json.dump() or json.dumps() with an indent specified.

With compact=True, pprint will output lists something like this:

'compact': ['pprint', 'with compact=True', 'will', 'output',
            'lists', 'like', 'this'],

Note that you can specify where it will wrap with the width argument.

It is necessary to replace 'single quotes' with 'double quotes' in the string returned by pprint.pformat(), because single quotes are not valid json. When you look at the file in your text editor, it may be unable to highlight the json properly.

In any case, you may want to save it as valid json, so that you can as a human can simply read your json files comfortably, if it is not more important that they are formatted without spacing so they can be computer-processed with blinding speed.

Ultimately, the output will look like this:

{'address': {'city': 'New York',
             'postalCode': '10021-3100',
             'state': 'NY',
             'streetAddress': '21 2nd Street'},
 'age': 27,
 'children': [],
 'firstName': 'John',
 'isAlive': True,
 'lastName': 'Smith'}

Answer 2

A very simple way is using rich. with this method you can also highlight the json

This method reads data from a json file called config.json

from rich import print_json

setup_type = open('config.json')
data = json.load(setup_type)
print_json(data=data)

The Final Output will look like this.

Answer 3

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)

Answer 4

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.

Answer 5

Pygmentize is a powerful tool for coloring the output of terminal commands.

Here is an example of using it to add syntax highlighting to the json.tool output:

echo '{"foo": "bar"}' | python -m json.tool | pygmentize -l json

The result will look like:

In a previous Stack Overflow answer, I show in detail how to install and use pygmentize.

Answer 6

TL;DR: many ways, also consider print(yaml.dump(j, sort_keys=False))

For most uses, indent should do it:

print(json.dumps(parsed, indent=2))

A Json structure is basically tree structure. While trying to find something fancier, I came across this nice paper depicting other forms of nice trees that might be interesting: https://blog.ouseful.info/2021/07/13/exploring-the-hierarchical-structure-of-dataframes-and-csv-data/.

It has some interactive trees and even comes with some code including this collapsing tree from so:

Other samples include using plotly Here is the code example from plotly:

import plotly.express as px
fig = px.treemap(
    names = ["Eve","Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
    parents = ["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"]
)
fig.update_traces(root_color="lightgrey")
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()

And using treelib. On that note, This github also provides nice visualizations. Here is one example using treelib:

#%pip install treelib
from treelib import Tree

country_tree = Tree()
# Create a root node
country_tree.create_node("Country", "countries")

# Group by country
for country, regions in wards_df.head(5).groupby(["CTRY17NM", "CTRY17CD"]):
    # Generate a node for each country
    country_tree.create_node(country[0], country[1], parent="countries")
    # Group by region
    for region, las in regions.groupby(["GOR10NM", "GOR10CD"]):
        # Generate a node for each region
        country_tree.create_node(region[0], region[1], parent=country[1])
        # Group by local authority
        for la, wards in las.groupby(['LAD17NM', 'LAD17CD']):
            # Create a node for each local authority
            country_tree.create_node(la[0], la[1], parent=region[1])
            for ward, _ in wards.groupby(['WD17NM', 'WD17CD']):
                # Create a leaf node for each ward
                country_tree.create_node(ward[0], ward[1], parent=la[1])

# Output the hierarchical data
country_tree.show()

I have, based on this, created a function to convert json to a tree:

from treelib import Node, Tree, node

def create_node(tree, s, counter_byref, verbose, parent_id=None):
    node_id = counter_byref[0]
    if verbose:
        print(f"tree.create_node({s}, {node_id}, parent={parent_id})")
    tree.create_node(s, node_id, parent=parent_id)
    counter_byref[0] += 1
    return node_id

def to_compact_string(o):
    if type(o) == dict:
        if len(o)>1:
            raise Exception()
        k,v =next(iter(o.items()))
        return f'{k}:{to_compact_string(v)}'
    elif type(o) == list:
        if len(o)>1:
            raise Exception()
        return f'[{to_compact_string(next(iter(o)))}]'
    else:
        return str(o)

def to_compact(tree, o, counter_byref, verbose, parent_id):
    try:
        s = to_compact_string(o)
        if verbose:
            print(f"# to_compact({o}) ==> [{s}]")
        create_node(tree, s, counter_byref, verbose, parent_id=parent_id)
        return True
    except:
        return False

def json_2_tree(o , parent_id=None, tree=None, counter_byref=[0], verbose=False, compact_single_dict=False, listsNodeSymbol='+'):
    if tree is None:
        tree = Tree()
        parent_id = create_node(tree, '+', counter_byref, verbose)
    if compact_single_dict and to_compact(tree, o, counter_byref, verbose, parent_id):
        # no need to do more, inserted as a single node
        pass
    elif type(o) == dict:
        for k,v in o.items():
            if compact_single_dict and to_compact(tree, {k:v}, counter_byref, verbose, parent_id):
                # no need to do more, inserted as a single node
                continue
            key_nd_id = create_node(tree, str(k), counter_byref, verbose, parent_id=parent_id)
            if verbose:
                print(f"# json_2_tree({v})")
            json_2_tree(v , parent_id=key_nd_id, tree=tree, counter_byref=counter_byref, verbose=verbose, listsNodeSymbol=listsNodeSymbol, compact_single_dict=compact_single_dict)
    elif type(o) == list:
        if listsNodeSymbol is not None:
            parent_id = create_node(tree, listsNodeSymbol, counter_byref, verbose, parent_id=parent_id)
        for i in o:
            if compact_single_dict and to_compact(tree, i, counter_byref, verbose, parent_id):
                # no need to do more, inserted as a single node
                continue
            if verbose:
                print(f"# json_2_tree({i})")
            json_2_tree(i , parent_id=parent_id, tree=tree, counter_byref=counter_byref, verbose=verbose,listsNodeSymbol=listsNodeSymbol, compact_single_dict=compact_single_dict)
    else: #node
        create_node(tree, str(o), counter_byref, verbose, parent_id=parent_id)
    return tree

Then for example:

import json
j = json.loads('{"2": 3, "4": [5, 6], "7": {"8": 9}}')
json_2_tree(j ,verbose=False,listsNodeSymbol='+' ).show()  

gives:

+
├── 2
│   └── 3
├── 4
│   └── +
│       ├── 5
│       └── 6
└── 7
    └── 8
        └── 9

While

json_2_tree(j ,listsNodeSymbol=None, verbose=False ).show()  

+
├── 2
│   └── 3
├── 4
│   ├── 5
│   └── 6
└── 7
    └── 8
        └── 9

And

json_2_tree(j ,compact_single_dict=True,listsNodeSymbol=None).show() 

+
├── 2:3
├── 4
│   ├── 5
│   └── 6
└── 7:8:9

As you see, there are different trees one can make depending on how explicit vs. compact he wants to be. One of my favorites, and one of the most compact ones might be using yaml:

import yaml
j = json.loads('{"2": "3", "4": ["5", "6"], "7": {"8": "9"}}')
print(yaml.dump(j, sort_keys=False))

Gives the compact and unambiguous:

'2': '3'
'4':
- '5'
- '6'
'7':
  '8': '9'

Answer 7

I had a similar requirement to dump the contents of json file for logging, something quick and easy:

print(json.dumps(json.load(open(os.path.join('<myPath>', '<myjson>'), "r")), indent = 4 ))

if you use it often then put it in a function:

def pp_json_file(path, file):
    print(json.dumps(json.load(open(os.path.join(path, file), "r")), indent = 4))

Answer 8

It's far from perfect, but it does the job.

data = data.replace(',"',',\n"')

you can improve it, add indenting and so on, but if you just want to be able to read a cleaner json, this is the way to go.

Answer 9

def saveJson(date,fileToSave):
    with open(fileToSave, 'w+') as fileToSave:
        json.dump(date, fileToSave, ensure_ascii=True, indent=4, sort_keys=True)

It works to display or save it to a file.

Answer 10

You could try pprintjson.

---

Installation

$ pip3 install pprintjson

Usage

Pretty print JSON from a file using the pprintjson CLI.

$ pprintjson "./path/to/file.json"

Pretty print JSON from a stdin using the pprintjson CLI.

$ echo '{ "a": 1, "b": "string", "c": true }' | pprintjson

Pretty print JSON from a string using the pprintjson CLI.

$ pprintjson -c '{ "a": 1, "b": "string", "c": true }'

Pretty print JSON from a string with an indent of 1.

$ pprintjson -c '{ "a": 1, "b": "string", "c": true }' -i 1

Pretty print JSON from a string and save output to a file output.json.

$ pprintjson -c '{ "a": 1, "b": "string", "c": true }' -o ./output.json

Output

Answer 11

Use pprint: https://docs.python.org/3.6/library/pprint.html

import pprint
pprint.pprint(json)

print() compared to pprint.pprint()

print(json)
{'feed': {'title': 'W3Schools Home Page', 'title_detail': {'type': 'text/plain', 'language': None, 'base': '', 'value': 'W3Schools Home Page'}, 'links': [{'rel': 'alternate', 'type': 'text/html', 'href': 'https://www.w3schools.com'}], 'link': 'https://www.w3schools.com', 'subtitle': 'Free web building tutorials', 'subtitle_detail': {'type': 'text/html', 'language': None, 'base': '', 'value': 'Free web building tutorials'}}, 'entries': [], 'bozo': 0, 'encoding': 'utf-8', 'version': 'rss20', 'namespaces': {}}

pprint.pprint(json)
{'bozo': 0,
 'encoding': 'utf-8',
 'entries': [],
 'feed': {'link': 'https://www.w3schools.com',
          'links': [{'href': 'https://www.w3schools.com',
                     'rel': 'alternate',
                     'type': 'text/html'}],
          'subtitle': 'Free web building tutorials',
          'subtitle_detail': {'base': '',
                              'language': None,
                              'type': 'text/html',
                              'value': 'Free web building tutorials'},
          'title': 'W3Schools Home Page',
          'title_detail': {'base': '',
                           'language': None,
                           'type': 'text/plain',
                           'value': 'W3Schools Home Page'}},
 'namespaces': {},
 'version': 'rss20'}

Answer 12

I think that's better to parse the json before, to avoid errors:

def format_response(response):
    try:
        parsed = json.loads(response.text)
    except JSONDecodeError:
        return response.text
    return json.dumps(parsed, ensure_ascii=True, indent=4)

Answer 13

Here's a simple example of pretty printing JSON to the console in a nice way in Python, without requiring the JSON to be on your computer as a local file:

import pprint
import json 
from urllib.request import urlopen # (Only used to get this example)

# Getting a JSON example for this example 
r = urlopen("https://mdn.github.io/fetch-examples/fetch-json/products.json")
text = r.read() 

# To print it
pprint.pprint(json.loads(text))

Answer 14

To be able to pretty print from the command line and be able to have control over the indentation etc. you can set up an alias similar to this:

alias jsonpp="python -c 'import sys, json; print json.dumps(json.load(sys.stdin), sort_keys=True, indent=2)'"

And then use the alias in one of these ways:

cat myfile.json | jsonpp
jsonpp < myfile.json

Answer 15

Use this function and don't sweat having to remember if your JSON is a str or dict again - just look at the pretty print:

import json

def pp_json(json_thing, sort=True, indents=4):
    if type(json_thing) is str:
        print(json.dumps(json.loads(json_thing), sort_keys=sort, indent=indents))
    else:
        print(json.dumps(json_thing, sort_keys=sort, indent=indents))
    return None

pp_json(your_json_string_or_dict)