Exploring Python Pickle: The Ultimate Resource for Object Serialization and Storage
In this article, we will briefly discuss how to use the Python pickle module with the help of examples. We will also discuss some of the best practices for using the Python pickle module and later also covered the advantages and disadvantages of the pickle module.
Python has numerous libraries and tools that allow developers to create various applications. In this article, we will discuss one such library Python Pickle Module. The Pickle module provides an efficient and straightforward way to serialize and deserialize Python objects.
This article will briefly discuss the Python pickle module’s functionalities, use cases, advantages, and disadvantages. Later in the article, we will share some best practices for using the pickle module.
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So, let’s explore the article.
Table of Content
- Serialization and Deserialization
- Pickling and Unpickling
- Pickle Installation
- Advantages and Disadvantages
- Best Practices
What are Serialization and Deserialization?
Serialization (or pickling) is converting an object into a byte stream. At the same time, Deserialization (or unpickling) is converting the byte stream back into a Python object, i.e. restructuring its original form.
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What are Pickling and Unpickling?
Python pickle is the concept of serialization and deserialization, commonly called “pickling” and “unpickling”.
As mentioned above, pickling (or serialization) converts Python objects into a byte stream, which can then be stored in a file, transmitted over a network, or passed between processes.
Unpickling (or deserialization) converts a byte stream into a Python object.
- Data Persistence: The primary use of the pickle module is to store Python objects persistently.
- Once the object is converted into byte streams, it can be easily written to a file, which can be read and reconstructed later.
- Inter-Process Communication: The Pickle module allows passing the Python objects between multiple processes.
- i.e., it allows for efficient serialization and deserialization of the Python objects, making inter-process communication more manageable.
- Object State Preservation: While using Pickle, the state of the Python objects being serialized is preserved, including attributes, class information, and the relationship between objects.
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How to install the pickle module in Python?
The pickle module is a Python built-in module, meaning you don’t need to install it separately.
To use the pickle module, you can import (similar to the import of other modules such as NumPy and Pandas) it at the beginning of your program.
import pickle
Here is an example of how to use the pickle module to serialize and de-serialize python objects.
import pickle
# Example Python object (a dictionary)data = {"key": "value"}
# Pickling (serializing) the objectpickled_data = pickle.dumps(data)
# Unpickling (deserializing) the objectunpickled_data = pickle.loads(pickled_data)
# The original data and unpickled_data are now the sameassert data == unpickled_data
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What are the Advantages and Disadvantages of Python Pickle?
The Python pickle module offers developers several advantages while working with the serialization and deserialization of Python objects. But it also has some limitations (or disadvantages) you should know.
The below table list some of the advantages and disadvantages of the pickle module.
| Advantage | Disadvantage |
| Easy to use | Compatibility Issues |
| Data Persistence | Security Risks |
| Inter-Process Communication | Inefficiency for some data types |
| Object-State Preservation | Not Human Readable |
| Built-in Libray | Limited Language Interoperability |
| Support for Custom Classes | Potential Performance Issues |
What are the best practices for using the Pickle module in Python?
Here are some of the best practices you must follow while using the pickle module in Python.
- Use the latest protocol version: While pickling, use the highest version of Python to benefit from the performance improvement and additional features.
import pickle
data = {"key": "value"}pickled_data = pickle.dumps(data, protocol=pickle.HIGHEST_PROTOCOL)
Use the above to specify the protocol version.
- Use the specialised library: Pickle module doesn’t work best for some data types (such as large NumPy array and Pandas DataFrame).
- Use dedicated libraries like JSON, MessagePack, or Parquet in such cases.
- Limited Pickling Scope: Try to break down the objects into smaller objects, i.e., break them into more manageable pieces.
- Avoid pickling large objects or the entire state of your application.
- Handle Exception: While serialization and de-serialization, always be prepared to handle exceptions such as “pickle.PickleError”.
- These errors are due to incompatible Python versions, unsupported objects, or file I/O errors.
- Trust Source: The rule of thumb while pickling and unpickling data is not to trust unknown sources.
- Always validate the source and content of pickled data before unpickling.
Conclusion
In this article, we have briefly discussed how to use the Python pickle module with the help of examples. We have also discussed the best practices for using the Python pickle module and later also covered the advantages and disadvantages of the pickle module.
Hope you will like the article.
Happy Learning!!
Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio
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