How to read JSON string with Python?

JSON (JavaScript Object Notation) is a lightweight data exchange format that uses a text structure to store key-value pairs or list data. It is widely used in areas such as API interfaces and configuration files. As a common requirement for data processing, Python provides a variety of efficient ways to parse JSON strings, and IP2world's proxy IP service provides stable support for developers who need to access external data frequently.

1. Python core method for parsing JSON strings

Python's built-in json module is a standard tool for processing JSON data. Its main functions include:

json.loads(): Convert a JSON string to a Python dictionary or list

json.dumps(): Serialize a Python object to a JSON string

json.load(): Read JSON data from a file object

json.dump(): Write JSON data to a file

For example, when parsing the JSON string returned by the API through json.loads(), the required fields can be directly extracted. If the data comes from an interface that requires authentication, combining it with IP2world's static ISP proxy can ensure the stability and security of the request.

2. Advanced techniques for handling complex JSON structures

When faced with JSON that is nested in multiple layers or contains special data types, pay attention to the following issues:

Type mapping: JSON null corresponds to Python None, true/false corresponds to True/False

Date handling: custom parsers to handle non-standard date formats

Big data optimization: Use ijson library to stream and parse very large JSON files

Encoding issue: Specify ensure_ascii=False to support Chinese characters

3. Exception handling and data verification

Common errors when parsing JSON include format errors and data type mismatches, which can be avoided in the following ways:

Use try-except to catch json.JSONDecodeError exception

Use the jsonschema library to verify whether the data structure meets expectations

Set default values for key fields to avoid program interruptions caused by missing keys

4. Performance optimization and extended application

Strategies to improve JSON processing efficiency:

Use orjson or ujson third-party libraries instead of standard libraries (speed increased by 2-5 times)

Avoid repeated parsing: cache parsed JSON objects

Combine asynchronous IO (such as aiohttp) to achieve high concurrent data requests

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