使用这个函数，不用担心，你必须再次记住你的JSON是str还是dict-看看漂亮的打印：

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)

TL；DR：在很多方面，也可以考虑打印（yaml.dump（j，sort_keys=False））

对于大多数用途，缩进应该做到：

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

Json结构基本上是树结构。当我试图找到一些更奇特的东西时，我偶然发现了这张漂亮的纸，上面描绘了其他形式的漂亮树木，可能很有趣：https://blog.ouseful.info/2021/07/13/exploring-the-hierarchical-structure-of-dataframes-and-csv-data/.

它有一些交互树，甚至还附带了一些代码，包括以下折叠树：

其他示例包括使用plotly以下是plotly的代码示例：

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()

并使用treelib。注意，这个github还提供了很好的可视化效果。下面是一个使用treelib的示例：

#%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()

基于此，我创建了一个将json转换为树的函数：

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

例如：

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

给予：

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

虽然

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

正如你所看到的，有不同的树，这取决于他想要的明确与紧凑程度。我最喜欢的一个，也是最紧凑的一个可能是使用yaml：

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

简洁明了：

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

这远不是完美的，但它确实起到了作用。

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

你可以改进它，添加缩进等等，但是如果你只是想读一个更干净的json，这就是方法。

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

import pprint
pprint.pprint(json)

print（）与pprint.pprint（）比较

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'}

我有一个类似的要求来转储json文件的内容以进行日志记录，这是一种快速而简单的方法：

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

如果您经常使用它，请将其放在函数中：

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

使用json.dump（）或json.dumps（）的indent关键字参数指定要使用的缩进量：

>>> 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
        ]
    }
]

要解析文件，请使用json.load（）：

with open('filename.txt', 'r') as handle:
    parsed = json.load(handle)