【Python 学习笔记】json

How does python work with json?

1. Loading JSON Data

If you have JSON data as a string or in a file, you can load it into a Python dictionary (or list, if the JSON data is an array) using json.loads() (for strings) or json.load() (for files).

python 复制代码
import json

# From a JSON string
json_data = '{"name": "Alice", "age": 25, "city": "New York"}'
data = json.loads(json_data)
print(data)  # Output: {'name': 'Alice', 'age': 25, 'city': 'New York'}

# From a JSON file
with open('data.json') as file:
    data = json.load(file)
    print(data)

To convert a Python dictionary (or other JSON-serializable Python objects) into a JSON string, you can use json.dumps(). For writing to a file, use json.dump().

python 复制代码
# Convert Python dict to JSON string
python_data = {"name": "Alice", "age": 25, "city": "New York"}
json_string = json.dumps(python_data)
print(json_string)  # Output: {"name": "Alice", "age": 25, "city": "New York"}

# Write to a JSON file
with open('output.json', 'w') as file:
    json.dump(python_data, file)

3. Handling Nested JSON

If the JSON data has nested objects, you can access them by chaining dictionary keys or list indices.

python 复制代码
nested_json = '{"person": {"name": "Alice", "age": 25}, "city": "New York"}'
data = json.loads(nested_json)
print(data['person']['name'])  # Output: Alice

4. Formatting Options

json.dumps() has optional parameters for pretty-printing, sorting keys, and customizing output format.

python 复制代码
# Pretty-print JSON
formatted_json = json.dumps(python_data, indent=4, sort_keys=True)
print(formatted_json)

Why Use sort_keys?

  • Readability: It makes it easier to locate keys in large JSON outputs.
  • Consistency: Sorted keys provide consistent output, which is useful when comparing JSON strings.
  • Debugging: Having a predictable order can simplify debugging by avoiding unnecessary differences due to key ordering.

example for JSON data is an array?

Certainly! JSON data can be structured as an array, where the top-level element is a list (array) of JSON objects, rather than a single JSON object. This is common when representing collections of data, like a list of users, products, or other similar records.

python 复制代码
[
    {
        "id": 1,
        "name": "Alice",
        "age": 25,
        "city": "New York"
    },
    {
        "id": 2,
        "name": "Bob",
        "age": 30,
        "city": "Chicago"
    },
    {
        "id": 3,
        "name": "Charlie",
        "age": 35,
        "city": "San Francisco"
    }
]

How to Work with JSON Arrays in Python

You can load this JSON array into Python and iterate over the list of dictionaries

python 复制代码
import json

# JSON array as a string
json_array = '''
[
    {"id": 1, "name": "Alice", "age": 25, "city": "New York"},
    {"id": 2, "name": "Bob", "age": 30, "city": "Chicago"},
    {"id": 3, "name": "Charlie", "age": 35, "city": "San Francisco"}
]
'''

# Parse JSON array into Python list
data = json.loads(json_array)

# Accessing elements
for person in data:
    print(f"Name: {person['name']}, Age: {person['age']}, City: {person['city']}")
python 复制代码
Name: Alice, Age: 25, City: New York
Name: Bob, Age: 30, City: Chicago
Name: Charlie, Age: 35, City: San Francisco

Key Points

  • JSON arrays are loaded as Python lists.
  • Each item in the array is typically a JSON object (dictionary in Python).
  • You can iterate through the list and access each dictionary by its keys.

how do you load this json_array to a table?

python 复制代码
import json
import pandas as pd

# JSON array as a string
json_array = '''
[
    {"id": 1, "name": "Alice", "age": 25, "city": "New York"},
    {"id": 2, "name": "Bob", "age": 30, "city": "Chicago"},
    {"id": 3, "name": "Charlie", "age": 35, "city": "San Francisco"}
]
'''

# Load JSON data from file
with open('data.json') as file:
    data = json.load(file)

# Parse JSON array into a Python list
data = json.loads(json_array)
python 复制代码
   id     name  age           city
0   1    Alice   25       New York
1   2      Bob   30        Chicago
2   3  Charlie   35  San Francisco

Summary

  • Use json.loads() to parse a JSON array from a string or json.load() from a file.
  • Convert the parsed JSON data to a DataFrame using pd.DataFrame(data).
  • Each JSON object in the array becomes a row in the DataFrame.

"Answer Generated by OpenAI's ChatGPT"

相关推荐
-一杯为品-26 分钟前
【51单片机】程序实验5&6.独立按键-矩阵按键
c语言·笔记·学习·51单片机·硬件工程
风尚云网1 小时前
风尚云网前端学习:一个简易前端新手友好的HTML5页面布局与样式设计
前端·css·学习·html·html5·风尚云网
熙曦Sakura2 小时前
完全竞争市场
笔记
EterNity_TiMe_3 小时前
【论文复现】(CLIP)文本也能和图像配对
python·学习·算法·性能优化·数据分析·clip
sanguine__3 小时前
java学习-集合
学习
lxlyhwl3 小时前
【STK学习】part2-星座-目标可见性与覆盖性分析
学习
nbsaas-boot3 小时前
如何利用ChatGPT加速开发与学习:以BPMN编辑器为例
学习·chatgpt·编辑器
dr李四维3 小时前
iOS构建版本以及Hbuilder打iOS的ipa包全流程
前端·笔记·ios·产品运营·产品经理·xcode
CV学术叫叫兽4 小时前
一站式学习:害虫识别与分类图像分割
学习·分类·数据挖掘
我们的五年4 小时前
【Linux课程学习】:进程程序替换,execl,execv,execlp,execvp,execve,execle,execvpe函数
linux·c++·学习