Working with Pickled Data in Python

Introduction

Pickle is a Python module used for serializing and de-serializing Python object structures. Serialization is the process of converting a Python object (like a list, dictionary, or custom class instance) into a byte stream, which can be stored in a file or transmitted over a network. De-serialization is the reverse process – converting the byte stream back into a Python object. This tutorial explains how to read pickled data from a file in Python.

Understanding the Basics

The pickle module allows you to save the state of Python objects to a file and then restore them later. This is useful for tasks like caching, storing program state, or sending data between different processes.

Important Note: While pickle is convenient, it’s crucial to be cautious when unpickling data from untrusted sources. Unpickling malicious data can execute arbitrary code, posing a security risk.

Writing Pickled Data to a File

Before we discuss reading pickled data, let’s quickly review how to write it:

import pickle

data = {'name': 'Alice', 'age': 30, 'city': 'New York'}

filename = 'my_data.pkl'

with open(filename, 'wb') as file:
    pickle.dump(data, file)

In this code:

• import pickle imports the necessary module.
• data is the Python object we want to save.
• filename is the name of the file where the pickled data will be stored.
• open(filename, 'wb') opens the file in binary write mode ('wb'). It’s essential to open the file in binary mode when working with pickle.
• pickle.dump(data, file) serializes the data object and writes it to the opened file.

Reading Pickled Data from a File

Now, let’s focus on reading the pickled data back from the file. A common mistake is assuming pickle.load() will read all the data at once if multiple objects were pickled to the same file. pickle.load() reads only a single pickled object from the file. If you’ve appended multiple pickled objects to a file, you need to read them one by one until the end of the file is reached.

Here’s how to read a single pickled object:

import pickle

filename = 'my_data.pkl'

with open(filename, 'rb') as file:
    loaded_data = pickle.load(file)

print(loaded_data)

In this code:

• open(filename, 'rb') opens the file in binary read mode ('rb').
• pickle.load(file) de-serializes the pickled object from the file and assigns it to the loaded_data variable.

Reading Multiple Pickled Objects

If your file contains multiple pickled objects (created by repeatedly using pickle.dump() in append mode), you need to read them in a loop. A try-except block is the most robust way to handle this, catching the EOFError (End of File Error) that occurs when the end of the file is reached:

import pickle

filename = 'multiple_data.pkl'

objects = []
with open(filename, 'rb') as file:
    try:
        while True:
            obj = pickle.load(file)
            objects.append(obj)
    except EOFError:
        pass  # Reached the end of the file

print(objects)

In this code:

• We initialize an empty list objects to store the loaded objects.
• The while True loop continues reading objects from the file until an EOFError is raised.
• Inside the loop, pickle.load(file) reads a single pickled object, which is then appended to the objects list.
• The except EOFError block catches the EOFError and pass does nothing, effectively breaking the loop when the end of the file is reached.

Alternative Libraries

While pickle is a standard Python module, other libraries provide similar functionality.

• joblib: Designed for efficiently serializing NumPy arrays and scikit-learn models. It often offers performance improvements for these specific data types. However, under the hood it still leverages the standard pickle library.
• pandas: The pandas library provides read_pickle for loading pickled pandas DataFrames or Series. It is built upon pickle but adds features specific to pandas data structures.

Important Considerations

• Binary Mode: Always open pickle files in binary mode ('wb' for writing, 'rb' for reading).
• Security: Be cautious when unpickling data from untrusted sources.
• Compatibility: Pickle format can change between Python versions. Ensure compatibility if you are sharing pickled data between different versions of Python.
• File Structure: If you’re writing multiple objects to a file, you need to read them one by one, as pickle.load() reads only one object at a time.