Python Serialization


For a data scientist, sets of data are always important and widely used as dictionaries, data frames, etc. While they work upon a program writing, it becomes a crucial need for them to save data. By saving data, it becomes helpful to store and use them while writing a program or while sending them to a person, who is on the other end. Understanding this peculiar need for data storage, Python provides an eminent feature for its users to serialize data.

Serialization in Python

Serialization refers to the process of converting the object or the data structures into a format where they can be stored or obtained later. Since the data is transformed and stored in another format, it provides the feature of restoring and deserializing the original data from the serialized format. In addition to the data conversion, serialization even provides the opportunity to reduce the data size so that it can fit into the required disk space or bandwidth.

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What is Pickling in Python?

With the effective methods of serialization and deserialization, which are commonly known as pickling and unpickling; Python provides an easier interface of data conversion. 

How can Pickling be beneficial and where can it be applied?

If an application requires a nominal amount of data persistency, then picking is the better option to be used. Pickling helps in saving the data on the disk, and thus you can re-work it whenever needed. Pickling is the best choice while working on algorithms related to machine learning; there will be great time management since there will not be any rewrites or instructions to the model.

Pickling can be performed for certain data types such as Booleans, integers, floats, complex numbers, lists, tuples, strings, dictionaries with pickable objects, and sets. However, to pickle these data types, it may be necessary to pickle the functions and classes.

In Python, pickle is protocol specific, hence data cannot be used across various programming languages; in other words, no cross-language compatibility. Similarly, it works the same for different Python versions. You cannot unpickle a file in the XYZ version, which was picked in a Python ABC version; doing so may unnecessarily execute malicious code.

Module Interface for Pickling and Unpickling

In the Pickle module, the data format is Python-specific. Therefore, it is essential to write the required code while performing the serializing or deserializing. The Python function which is used for serializing an object hierarchy is dumps(), and the function which is used for de-serializing is loads().

What are the Pickle Protocols?

Protocols act as the conventions for deconstructing and constructing the objects in Python. There are about 5 protocols used in pickling. When a higher protocol version is used, you will need the recent version of Python for obtaining the highly compatible and readable pickle.

  • Protocol version 0: It is the “human-readable” original protocol; it is compatible with the use of interfaces and data from the before versions of Python.
  • Protocol version 1: It is an old binary format. Similar to protocol version 0, it is even compatible with the older versions of Python.
  • Protocol version 2: It is the version that has come into effect during the release of Python 2.3. The version helps in providing more efficient new-style classes in pickling.
  • Protocol version 3: It was discovered during Python 3.0. The version is the best in supporting the byte objects; however, it has a drawback to get unpickled by Python 2.x.
  • Protocol version 4: The version came into effect during Python 3.4. Within this version, large objects can be supported; different objects can be pickled, and data formats can be optimized.

However, in order to serialize the data which is completely designed with the fundamentals objects of Python in the fastest way, it is recommended for preferring the marshal module. The module helps with the function to read and write a Python value into a binary format. 

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What is Internal Python Object Serialization or Marshal?

Marshal module provides the feature of object serialization which is much similar to that of the pickle module. Though the method doesn’t provide help for data maintenance and transmission of Python objects, it helps the interpreter to do read and write operations to the compiled versions of Python modules. The marshal module is popularly known as internal object serialization due to its varying data format usage. Since the data format keeps on changing, it fails in proving compatible across the Python versions. This reason makes the marshal module to be known as Internal Python Object Serialization. 

The Marshal module also defines load() and dump() functions to read and write the marshaled objects.

1. dump(): It supports objects with standard data types; and by marshaling these Python objects, it returns a similar byte object. 

2. loads(): By using the function, once can convert a byte object to a corresponding Python object. If the conversion fails to provide the required Python object, then it raises the TypeError or ValueError.  

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What is Python Object Persistence or Shelve?

The shelf is another module type from Python’s standard library. The module type is simpler and known as a powerful tool to maintain data storage when there isn’t any need to obtain a relational database solution. The files that are stored in Shelve replicate the DBM database and accept the string-type objects, and values that are of pickable objects.

Effectively, the Python object persistence or the modules of shelve have three classes, namely:

  • Shelf

  • BsdDbShelf

  • DbfilenameShelf

Below you can find the information about the three classes:

ShelfIt is a base class, initialized with the dict-like object and used for implementing shelf.
BsdDbShelfIt is the subclass of the Shelf class. The dict-like object should support first(), previous(), next(), set location(), and last() methods while passing through its constructor.
DbfilenameShelfIt is another subclass of Shelf. It doesn’t accept dict object as a parameter, rather it accepts the filename for its constructor.

However, defining the open() function in the Shelve module will effectively return a DbfilenameShelf. Also, it is easiest for obtaining a Shelf object.

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What is JSON?

JSON or the JavaScript Object Notation is a popular serialization and deserialization format. It is much similar to pickle and known as a lightweight for data interchanging format. When comparing Python and JSON, it is evident that Pickle is a serialization over Python-specific; while, JSON format is an implementation by many languages. However, there is a similarity, i.e. the JSON module in Python’s standard library describes the function for object serialization and it has similar functions like dumps() and loads() to serialize a Python object into an encoded string. With the functions used, i.e. with load() and dump(), it is easier to serialize a Python object from/to a file so that we can read or write.

Below find the functions of dumps() and loads().

1. Dumps(): The function helps in converting a Python object into JSON format.

2. Loads(): The function helps in the conversion of JSON string back into a Python object.

JSON Module is further classified into two different classes, i.e. JSONEncoder and JSONDecoder.

JSONEncoder class:

The object of JSONEncoder is an encoder for Python data structures. The below table illustrates the conversion of Python data type to their corresponding JSON type.

Python Data type
Corresponding JSON type
list, tuple
int, float, int- & float-derived Enums

JSONDEcoder class:

The object of JSONDEcoder helps to decode a JSON string back to its Python data structure; find the below table as reference:

number (int)int
number (real)float


Serialization is the practice that simplifies the data storage methods of a data scientist. Python Serialization is one of the best features which eases the interface of data conversion. Pickling and unpickling, which are popularly known for serialization and deserialization are effective ways to transform and store data in another format or vice-versa.

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Last updated: 03 Apr 2023
About Author

Anjaneyulu Naini is working as a Content contributor for Mindmajix. He has a great understanding of today’s technology and statistical analysis environment, which includes key aspects such as analysis of variance and software,. He is well aware of various technologies such as Python, Artificial Intelligence, Oracle, Business Intelligence, Altrex, etc. Connect with him on LinkedIn and Twitter.

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