【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).

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().

# 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.

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.

# 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.

[
    {
        "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

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']}")

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?

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)

   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.

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