7 Best Python Data Serialization Libraries

Here are the best open-source Python data serialization libraries for your applications. You can use these to convert data objects into a format that can be stored or transmitted easily while preserving their structure and meaning. This allows applications to transfer data across different platforms and/or between different programming languages.

Python has several built-in and third-party libraries that provide serialization capabilities. These libraries can serialize and deserialize Python objects, including custom classes and functions. They can also handle circular references and are widely used for saving and loading machine learning models. Most Python data serialization libraries offer a lightweight data-interchange format that is easy to read and write for humans and easy to parse and generate for machines. These libraries are supported by almost all programming languages and are commonly used for web-based applications.

Overall, Python data serialization libraries provide an efficient way to transfer data between applications or store them for later use. The choice of an apt library depends on the specific requirements of your project. We have handpicked the top and trending open-source Python data serialization libraries for your next application development project:

• PyYAML:
• Used for configuration files and data exchange between applications.
• Supports complex data structures and allows comments.
• Provides user-friendly interface for YAML data stream serialization.

MessagePack:

• Used in Utilities, Serialization applications, etc.
• Offers an efficient binary serialization format.
• Useful for real-time applications that require low latency and high throughput.

BSON:

• Used commonly in MongoDB applications.
• Offers a binary format similar to JSON.
• Supports additional data types, such as dates and binary data.

Protocol Buffers:

• Used in Utilities, Build Tool applications, etc.
• Offers a language-agnostic binary serialization format
• Allows for efficient data exchange between applications.

Avro:

• Used in Utilities, Serialization applications, etc.
• Offers a compact and efficient binary serialization format.
• Supports schema evolution.

HDF5/H5py:

• Used commonly in scientific computing.
• It’s a Pythonic interface to the HDF5 binary data format.
• Offers a data model and file format for storing and managing large and complex data collections.

Hickle:

• Used to work well with large scientific data sets.
• It’s a high-performance serialization library.
• Designed to be a "drop-in" replacement for pickle.