JSON data is structured using key-value pairs. Keys are always strings, and values can be strings, numbers, booleans, arrays, objects, or null.

Objects are enclosed in {} and arrays in []. This structure allows nesting of data for complex use cases. Proper structure ensures easy parsing and validation.

In APIs, the client sends a request containing JSON data in the request body. The server processes the request and responds with JSON.

Headers specify Content-Type: application/json. JSON ensures structured communication between frontend and backend. This flow is the backbone of RESTful services.

Python converts JSON strings into dictionaries using json.loads().

Dictionaries are converted back to JSON strings using json.dumps().

This conversion allows easy data manipulation in Python. JSON files can also be read and written using json.load() and json.dump(). This process is commonly asked in Python interviews.

JSON is preferred in modern web and mobile applications.

Understanding this difference is very common in interviews.

Both are frequently used in Python projects.

These functions help serialize Python data.

No, JSON is not a programming language. It is a data format used for storing and exchanging data. It is often used with programming languages like Python, JavaScript, and Java. JSON only defines how data should be represented. This is a very common interview question.

JSON supports string, number, boolean, object, array, and null. It does not support functions or comments.

These basic types cover most data-exchange needs. Understanding JSON data types is essential for API development. Interviewers often ask this.

A JSON key is a string that identifies a value. Keys must always be enclosed in double quotes. Each key maps to exactly one value. Keys should be unique within an object. Proper key naming improves readability.

Yes, JSON supports nested objects and arrays. Nested structures allow representing complex relationships. APIs often return deeply nested JSON. Python can easily parse nested JSON into dictionaries. Handling nested JSON is a practical skill.

JSON files use the .json extension. These files store structured data in text format.

They are commonly used for configuration files. JSON files are platform-independent. They are easy to read and edit.

Serialization converts Python objects into JSON format. This is done using json.dumps() or json.dump().

Serialization is required before sending data over a network. It ensures data is transferable. This concept is frequently tested.

Deserialization converts JSON data back into Python objects. It is done using json.loads() or json.load().

This allows programs to use received data. Deserialization is essential for API responses. It is the reverse of serialization.

No, JSON does not support comments. Adding comments will cause parsing errors.

This keeps JSON simple and strict. Some developers confuse it with JavaScript objects. Interviewers often check this detail.

It is an HTTP header that indicates JSON data is being sent. It helps servers understand request format.

APIs rely on correct Content-Type headers. Without it, servers may reject requests. This is common in REST APIs.

JSON can be validated using schema definitions or online validators. Validation ensures correct structure and data types.

Python libraries like jsonschema help validate JSON. Validation prevents runtime errors. It improves API reliability.

JSON supports nested and structured data. CSV is flat and limited. JSON works well with objects and arrays. APIs need structured responses. This makes JSON a better choice.

JSON is the standard data format for APIs. Backend systems constantly send and receive JSON.

Python frameworks like Flask and Django use JSON heavily. Understanding JSON is mandatory for interviews. It connects frontend, backend, and databases.