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How to Load Pickle Files Safely in Python
This code demonstrates how to load pickle files safely in Python by using a restricted unpickler that only allows specific, trusted classes, preventing arbitrary code execution from untrusted pickles.
import pickle
# Default pickle.load is unsafe: it executes arbitrary code when unpickling.
class Unsafe:
def __reduce__(self):
return (eval, ("open('/tmp/pickle_demo.txt', 'w').write('pwned')",))
# Create a malicious payload (simulating untrusted source)
malicious_data = pickle.dumps(Unsafe())
# Safe ap…How to Serialize a Python Object to Pickle Bytes in Memory
Serialize a Python object to pickle bytes in memory with pickle.dumps, then deserialize it back with pickle.loads and verify the roundtrip.
import pickle
class Person:
def __init__(self, name, age, skills):
self.name = name
self.age = age
self.skills = skills
def main():
person = Person("Alice", 30, ["Python", "SQL", "Docker"])
# Serialize to bytes in memory
pickle_bytes = pickle.dumps(person)
print(…How to Pickle a Python Dict and Load It Back
Save a dictionary to a binary file with pickle.dump() and reload it with pickle.load(), showing the round trip and type preservation.
import pickle
data = {"name": "Alice", "scores": [87, 92, 95], "active": True}
print("Original dict:", data)
with open("safe_demo.pkl", "wb") as f:
pickle.dump(data, f)
with open("safe_demo.pkl", "rb") as f:
loaded = pickle.load(f)
print("Loaded dict:", loaded)
print("Type:", type(loaded).__name__)
print(…How to Use __getstate__ and __setstate__ for Pickle in Python
Customize Python object serialization with the pickle __getstate__ and __setstate__ hooks to control exactly what data is stored and how it is restored.
import pickle
class Temperature:
def __init__(self, celsius):
self.celsius = celsius
def __getstate__(self):
"""Customize what gets pickled."""
state = self.__dict__.copy()
# Convert to Fahrenheit for storage (simulate transformation)
state['fahrenheit'] = (self.celsiu…How to Serialize Cache Values with JSON and Pickle in Python
Serialize cache values using JSON for simple types or pickle for arbitrary objects, with robust error handling for unsupported types like mocks.
import json
import pickle
from unittest.mock import Mock
def serialize(value, method="json"):
"""Serialize a cache value using JSON or pickle with type checking."""
if method == "json":
try:
return json.dumps(value).encode("utf-8")
except TypeError as e:
raise ValueErro…How to Save and Load a Mock Model with Pickle and joblib in Python
Serialize a custom machine learning model to a .joblib file with joblib.dump, reload it, and run a prediction with joblib.load.
import joblib
from pathlib import Path
class MockModel:
def __init__(self, weights):
self.weights = weights
def predict(self, features):
return sum(w * f for w, f in zip(self.weights, features))
def save_model_pickle(model, filepath):
with open(filepath, "wb") as f:
joblib.dump(…Browse by section
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