Understanding Pickling and Inheritance in Python

Introduction

In this article, we will explore the concept of pickling and inheritance in Python. We will delve into the details of why an object’s attribute that is inherited from another class may disappear during the pickling/unpickling process.

Background on Pickling

Pickling is a process used to serialize (convert into a byte stream) objects, allowing them to be stored or transmitted. The pickle module in Python provides functions to dump and load pickled objects.

import pickle

# Create an object
obj = MyObject()

# Serialize the object using pickle
with open("serialized_obj.pkl", "wb") as f:
    pickle.dump(obj, f)

Background on Inheritance

Inheritance is a fundamental concept in object-oriented programming (OOP). It allows one class to inherit properties and behavior from another class.

class ParentClass:
    def __init__(self):
        self.parent_attribute = "Parent attribute"

    def parent_method(self):
        return "Parent method"


class ChildClass(ParentClass):
    def __init__(self):
        super().__init__()
        self.child_attribute = "Child attribute"

    def child_method(self):
        return "Child method"

The Problem: Inherited Attributes Disappearing During Pickling/Unpickling

In the Stack Overflow post, a user attempts to pickle an object that inherits from pandas.DataFrame. However, the attribute added to the DataFrame disappears during the pickling/unpickling process.

import pandas as pd
import pickle

class Foo(pd.DataFrame):
    def __init__(self, tag, df):
        super().__init__(df)
        self._tag = tag

foo = Foo('mytag', pd.DataFrame({'a':[1,2,3],'b':[4,5,6]}))
print(foo)
print(foo._tag)

# Pickle the object
with open("foo.pkl", "wb") as pkl:
    pickle.dump(foo, pkl)

# Unpickle the object
with open("foo.pkl", "rb") as pkl:
    foo1 = pickle.load(pkl)

print(type(foo1))
print(foo1)
print(foo1._tag)

Analysis of the Issue

The error occurs because Foo inherits from pd.DataFrame, which overrides the __setattr__ method. This means that when we try to set an attribute on the Foo object, it is actually setting an attribute on the underlying DataFrame instead.

When we pickle and unpickle the Foo object, the attribute is lost because the pickling process serializes the attributes of the object as if it were a plain Python object. However, since pd.DataFrame overrides __setattr__, the serialization process does not include the additional attribute that was set on the Foo object.

Solution 1: Creating a Separate Class for Attributes

The most straightforward solution is to create a separate class that uses a DataFrame as an attribute, so that your own attributes are settable.

class Foo:
    def __init__(self, tag, df):
        self.df = df
        self._tag = tag

    # Override the __setattr__ method to include our custom attribute in serialization
    def __setattr__(self, name, value):
        if name == '_tag':
            super().__setattr__('df', value)
        else:
            super().__setattr__(name, value)

foo = Foo('mytag', pd.DataFrame({'a':[1,2,3],'b':[4,5,6]}))
print(foo)
print(foo._tag)

# Pickle the object
with open("foo.pkl", "wb") as pkl:
    pickle.dump(foo, pkl)

# Unpickle the object
with open("foo.pkl", "rb") as pkl:
    foo1 = pickle.load(pkl)

print(type(foo1))
print(foo1)
print(foo1._tag)

Solution 2: Using dill for Pickling

Another solution is to use the dill library, which allows us to pickle complex objects without issues.

import dill as pickle
import pandas as pd

class Foo:
    def __init__(self, tag, df):
        self.df = df
        self._tag = tag

foo = Foo('mytag', pd.DataFrame({'a':[1,2,3],'b':[4,5,6]}))
print(foo)
print(foo._tag)

# Pickle the object using dill
with open("foo.pkl", "wb") as f:
    pickle.dump(foo, f)

# Unpickle the object using dill
with open("foo.pkl", "rb") as f:
    foo1 = pickle.load(f)

print(type(foo1))
print(foo1)
print(foo1._tag)

Conclusion

In this article, we explored the issue of an inherited attribute disappearing during pickling/unpickling in Python. We analyzed the problem and provided two solutions: creating a separate class for attributes and using the dill library for pickling complex objects.

By understanding how pickling works and how inheritance affects it, we can develop strategies to handle these issues and ensure that our code behaves as expected when serializing and deserializing objects.

Last modified on 2024-02-10