Understanding Python Pickle with example

Python Pickle Example

Let's discuss a simple example of Python Pickle.

Code

• Python

Python

import pickle

# Sample data
data_to_pickle = {'name': 'Kartik', 'age': 20, 'city': 'Pune'}

# Serialize (pickle) the data
with open('data.pkl', 'wb') as file:
    pickle.dump(data_to_pickle, file)

# Deserialize (unpickle) the data
with open('data.pkl', 'rb') as file:
    loaded_data = pickle.load(file)

# Display result
print(loaded_data)

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Output

Explanation

The code begins by importing the pickle module, which provides functions for serializing and deserializing Python objects. The variable data_to_pickle is initialized with a Python dictionary containing sample data. Using the with statement and the 'wb' mode (binary write mode), the code opens a file named 'data.pkl' for writing. It then uses pickle.dump() to serialize (pickle) the data_to_pickle dictionary and save it to the file. Next, the code opens the 'data.pkl' file for reading using the 'rb' mode (binary read mode). It then uses pickle.load() to deserialize (unpickle) the data from the file and store it in the loaded_data variable. Finally, the code prints the loaded_data variable, which now contains the dictionary with the same data that was originally pickled.

Object Serialization

Object serialization is the process of converting an in-memory object (such as a dictionary, list, or custom class instance) into a format that can be easily saved to a file or transferred over a network. This format is typically a byte stream, JSON, or XML. Serialization allows us to store the structure and state of an object so that it can be reconstructed (deserialized) later, even after a program has terminated. In Python, serialization is often handled using modules like pickle (for byte streams) or json (for JSON format).

Why Do We Need Object Serialization?

Serialization is crucial for several reasons, especially in complex applications that require data to persist or be shared across different environments:

• Data Persistence: Serialization allows us to save complex data structures (like machine learning models, configurations, or user data) to files or databases, so they can be reloaded and used later without needing to recompute or reinitialize them.
• Data Transfer: In distributed systems or web applications, data often needs to be transmitted between different services or across networks. Serialization converts data into a format that can be easily transferred over the network and later deserialized on the receiving end.
• Caching: Serialization enables caching by saving objects in a serialized form, reducing the need to recreate or recompute data repeatedly. Cached data can be deserialized quickly when needed.
• Cross-Language Compatibility: Serialized data in a universal format (like JSON or XML) allows interoperability between applications written in different programming languages, making it possible to share data between diverse systems.

Python Pickle Dump

In Python, Pickle Dump is used to convert Python objects which are pickable into files or binary strings.

• Firstly, we import the Pickle module using the import pickle statement in Python.
   
• To pickle a Python object into a file, we use pickle.dump(object, file).
   
• To pickle a Python object into pickled bytes, we use pickle.dump(object).
   

Note: If we try to pickle an unpickable object, Python gives PicklingError.

Syntax

pickle.dump(object, file, protocol=None)

• object refers to the Python object that is to be pickled.
   
• file: This is the file object where we save the pickled data.
   
• protocol is an optional parameter used to specify the version of the Pickle protocol.
   

Example of Python Pickle Dump

Let us try to Pickle the Python dictionary using the Pickle dump function.

• Python

Python

import pickle
# Python Object
data = {"name": "Rohan", "age": 19}

# Open a file in binary mode to write the serialized data
with open("data.pickle", "wb") as f:
    # Serialize and save the Python object to the file
    pickle.dump(data, f)

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Explanation

• In this example, firstly, we created a Python dictionary, then opened a file named data.pickle in binary mode and then used the dump function to pickle the dictionary.
   
• “wb” is used to open the file in binary format for writing.

Python Pickle Load

In Python, Pickle Load is used to convert serialized or pickled data into a Python object.

Syntax 

pickle.load(file, *, fix_imports=True, encoding="ASCII")

• file is the object which is converted into a Python object previously serialized by the dump function. 
   
• The Fix_imports parameter is used to handle the backward compatibility issues.
   
• encoding is an optional parameter used to specify the encoding used for decoding byte strings.

Example of Python Pickle Load

Let us try to deserialize the previously pickled object using the Pickle load function.

• Python

Python

import pickle

# Open the file in binary mode to read the serialized data
with open("data.pickle", "rb") as f:
    # Deserialize and load the Python object from the file
    data = pickle.load(f)
print(data)

You can also try this code with Online Python Compiler

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Output

Explanation

• Here in this example, we opened data.pickle file in binary mode and used the load function to unpickle the file.
   
• “rb” is used while opening the file, and it specifies the file should be read in binary format.

Advantages of Pickle Module in Python

There are several advantages of using the Pickle module in Python.

• Python Pickle is easy to use and beginner friendly. We can easily serialize or deserialize Python objects.
   
• Python Pickle helps us to transfer data between different platforms.
   
• The pickle module can be used on most Python objects.
   
• Python Pickle also provides security while transferring data.

Disadvantages of Pickle Module in Python

While there are many advantages of using the Pickle module, there are some disadvantages we need to consider.

• The pickle module can only be used for Python objects. It cannot be used for other programming languages.
   
• The Pickled file can be larger, creating problems while storing and transferring data.
   

Also see, Convert String to List Python

Also see, How to Check Python Version in CMD

Frequently Asked Questions

Is pickle default Python?

Yes, pickle is a default module in Python. It's used for serializing and deserializing Python objects, allowing data to be saved to and loaded from files in a structured format.

Why is pickle called pickle in Python?

The term pickle in Python likely comes from the idea of preserving something, similar to pickling in food preservation. Pickling in Python refers to the process of preserving objects for later use or storage.

What is the use of the pickle model in Python?

To pickle a model in Python, you use the pickle module. It serializes (converts into a format that can be stored) the model object, allowing you to save it to a file. Later, you can deserialize (load) it from that file to use the model again. This is useful for storing trained machine learning models or other complex objects for future use without retraining or rebuilding.

How to load a pickle file in Python?

To load a pickle file in Python, use pickle.load() with an open file in binary read mode:

import pickle
with open('file.pkl', 'rb') as file:
   data = pickle.load(file)

What is python pickle used for?

Python Pickle is used to store and send Python data anywhere with ease.

What are some alternatives to Python Pickle?

Some alternatives to Python Pickle are JSON, YAML, and MessagePack.

Conclusion

This article discusses the topic of Python Pickle. In this blog, we have discussed what Python Pickle is, the main functions in Pickle module, along with its advantages and disadvantages. We hope this blog has helped you enhance your knowledge of Python Pickle. If you want to learn more, then check out our articles.

• Advanced functions in Pandas
• Python Introduction
• Getting started with Pandas
   

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