Converting a Python List of Bytes to JSON: 5 Effective Methods

Rate this post

πŸ’‘ Problem Formulation: Python developers often need to convert a list of bytes into a JSON string for data interchange or storage purposes. For instance, they may have an input like [b'{"name": "Alice"}', b'{"age": 30}]' and want the output to resemble a JSON string such as '[{"name": "Alice"}, {"age": 30}]'. This article presents various methods to achieve that transformation effectively.

Method 1: Using json and decode()

Decoding each bytes object in the list to a string and then converting the list to JSON is a straightforward approach. The Python json library’s loads() and dumps() functions are combined with list comprehension, which provides an effective and pythonic way to tackle the conversion.

Here’s an example:

import json

# List of bytes
list_of_bytes = [b'{"name": "Alice"}', b'{"age": 30}']

# Decoding bytes to  strings  and converting to JSON
decoded_list = [json.loads(item.decode("utf-8")) for item in list_of_bytes]
json_string = json.dumps(decoded_list)

print(json_string)

Output:

[{"name": "Alice"}, {"age": 30}]

This method first decodes each bytes object in the list into strings. Then, each string is converted into a Python dictionary using the json.loads() function. Finally, the list of dictionaries is converted back into a JSON string with json.dumps(). It’s a multi-step method but guarantees proper handling of JSON formatting.

Method 2: Using bytearray and json.loads()

For a list of bytes that collectively present a valid JSON structure when concatenated, you can use the bytearray() function to join the bytes before conversion. The resultant byte string is then loaded into a Python object with json.loads(). This method is simple and efficient for well-structured byte sequences.

Here’s an example:

import json

# List of bytes
list_of_bytes = [b'{"name": "Alice"}', b'{"age": 30}']

# Combine bytes and load as JSON
combined_bytes = bytearray().join(list_of_bytes)
json_data = json.loads(combined_bytes)

print(json_data)

Output:

[{"name": "Alice"}, {"age": 30}]

In this snippet, bytearray().join() is used to concatenate the bytes in the list, forming a byte string representing the complete JSON. Then json.loads() converts this byte string into a Python object (in this case, a list of dictionaries). This method is less granular and quick, but it requires the bytes to be in the correct order and format.

Method 3: Using ast.literal_eval() for Partially-Formed JSON Strings

If the list of bytes represents JSON objects that are not entirely well-formed when concatenated, ast.literal_eval() can be used to safely evaluate strings or concatenate bytes into strings and then into Python objects. This method is helpful when JSON objects in the list of bytes are not uniform or fully structured.

Here’s an example:

import json
import ast

# List of bytes with individually formed JSON objects
list_of_bytes = [b'{"name": "Alice"}', b'{"age": 30}']

# Convert bytes to string, evaluate as Python literals, and convert to JSON
decoded_list = [ast.literal_eval(item.decode("utf-8")) for item in list_of_bytes]
json_string = json.dumps(decoded_list)

print(json_string)

Output:

[{"name": "Alice"}, {"age": 30}]

This approach decodes each bytes object into a string and uses ast.literal_eval() to convert the string representation of a dictionary into an actual dictionary. ast.literal_eval() is safer than eval() as it only evaluates literals. After evaluating, json.dumps() is used to get the JSON representation of the list.

Method 4: Using the struct Library for Binary Data

When dealing with binary data that isn’t directly in JSON string format, the struct library allows unpacking binary data into Python objects which can then be serialized into JSON. This method is best suited for binary formats that align with structural patterns.

Here’s an example:

import json
import struct

# Binary data akin to records
binary_data = [b'\x01\x00\x00\x00Alice', b'\x02\x00\x00\x00\x1e']

# Unpack binary data and convert to JSON
decoded_list = [{'name': struct.unpack('5s', item)[0].decode("utf-8").strip('\x00')} if i == 0 else {'age':struct.unpack('I', item[1:])[0]} for i, item in enumerate(binary_data)]
json_string = json.dumps(decoded_list)

print(json_string)

Output:

[{"name": "Alice"}, {"age": 30}]

The example shows how to use struct.unpack() to extract structured data from bytes. Here, we assume a structure where the first byte indicates the type of record, followed by the actual content. The content is then processed accordingly, converted into dictionaries for each piece of data, and finally serialized into JSON with json.dumps(). This method requires precise knowledge of the binary structure.

Bonus One-Liner Method 5: Using Chain of Bytes and json.loads()

This one-liner uses a chain from the itertools module to concatenate bytes in the list without creating an intermediate combined bytes object. It’s a memory-efficient method for large lists of bytes.

Here’s an example:

import json
from itertools import chain

# List of bytes
list_of_bytes = [b'{"name": "Alice"}', b'{"age": 30}']

# Chain bytes and convert to JSON as a one-liner
json_data = json.loads(bytes(chain.from_iterable(list_of_bytes)))

print(json_data)

Output:

[{"name": "Alice"}, {"age": 30}]

This method avoids the creation of intermediate data structures and directly chains the list of bytes objects into a single bytes object, which is then loaded as JSON using json.loads(). It is concise and suitable for situations where minimizing memory usage is crucial.

Summary/Discussion

  • Method 1: Decoding and Loading. Strength: Works well for individual JSON objects in bytes. Weakness: Requires extra steps to decode then load.
  • Method 2: Bytearray Concatenation. Strength: Simple and effective for concatenated JSON structures. Weakness: Assumes proper ordering and format of bytes.
  • Method 3: AST Literal Evaluation. Strength: Good for irregular or partially formed JSON structures. Weakness: Can be relatively slower and less direct.
  • Method 4: Struct Unpacking for Binary Data. Strength: Ideal for structured binary data. Weakness: Requires understanding of the binary format.
  • Method 5: Efficient Chaining of Bytes. Strength: Memory-efficient one-liner. Weakness: Less readable and potentially perplexing for new coders.