Using pickletools.dis() to analyze serialized data.

🐍 pickle doesn't just store a snapshot of an object; it stores a sequence of instructions for its subsequent reconstruction.

Normally, we only see the result of serialization:

import pickle

payload = pickle.dumps(
    {"name": "Alex", "roles": ["admin", "user"]}
)

print(len(payload))

The standard library includes pickletools.dis(), which disassembles the pickle stream and shows its instructions in a readable format.

import pickletools

pickletools.dis(payload)

The output shows the creation of a dictionary, strings, a list, and the operations used to assemble the final object.

EMPTY_DICT
SHORT_BINUNICODE 'name'
SHORT_BINUNICODE 'Alex'
SHORT_BINUNICODE 'roles'
EMPTY_LIST

This is useful when debugging your own classes: you can check which global objects and reconstruction mechanisms are included in the serialization.

class User:
    def init(self, name):
        self.name = name

payload = pickle.dumps(User("Alex"))

pickletools.dis(payload)

For further analysis, there's pickletools.optimize(): it removes some unused operations from the pickle stream without changing the object being reconstructed.

optimized = pickletools.optimize(payload)

assert pickle.loads(optimized).name == "Alex"

⚠️ pickletools is designed for analyzing pickle streams, not for safely reading untrusted data. You should still not pass unknown pickle data to pickle.loads().

🔥 pickletools.dis() allows you to peek inside the serialization and see the instructions from which pickle reconstructs the object.

#Python #Pickle #DataScience #Debugging #Coding #DevTools

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Hands On Python Data Science - Data Science Bootcamp

Master Python for Data Science with Real-World Applications: Dive Deep into Data Analysis, Machine Learning

🏷 Category: Development
🌍 Language: English
👥 Students: 31,524 students
⭐️ Rating: 4.3/5.0
💰 Price: $14.99 ⟹ FREE
🆔 Coupon: •••••••••• (tap below to reveal)

🔓 Tap "Get Coupon" below — the code unlocks inside the app after a short rewarded ad.

💎 By: https://t.iss.one/Udemy26
#Programming #Coding #Development #Tech #Python #DataScience

📱 270 tools for Python developers

The repository contains:
🫡 Linters and formatters;
🫡 Type checkers;
🫡 Testing tools;
🫡 Debugging and profiling tools;
🫡 Package managers;
🫡 Logging tools;
🫡 Security tools;
🫡 Documentation and package building tools.

Save this to your favorites, as the list is updated regularly.

⛓️ Link to the repository

#Python #DevTools #Programming #Coding #SoftwareDevelopment #GitHub

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Python Lists: A Quick Reference 🐍

*   Definition: Lists are ordered, mutable collections that can contain various data types and duplicates.
*   Creating Lists: Creating empty, numerical, mixed, nested lists, and lists with duplicates.
*   Accessing List Elements: Accessing elements using positive and negative indexing, as well as slicing with

start:end:step

.
*   Common Operations: Adding, inserting, concatenating, deleting, clearing, searching, sorting, and reversing lists.
*   List Methods:

append()

,

insert()

,

extend()

,

remove()

,

pop()

,

clear()

,

index()

,

count()

,

sort()

, and

reverse()

.
*   List Comprehension: Quickly creating lists using expressions and conditions.
*   List vs. Tuple: Differences between mutable lists and immutable tuples.
*   Important Points: Specifics of indexing, slicing, data types, and working with lists.

#Python #Coding #Programming #DataStructures #PythonTips #LearnPython

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

📚 Download 10 Python books for FREE:

1. Think Python — O’Reilly
https://t.co/X7y3pX68IW

2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/

3. Data Structures
https://opendatastructures.org/ods-python.pdf

4. Data Science Handbook
https://t.co/9aOLdiAbim

5. Data Analysis
https://wesmckinney.com/book/

6. Data Science
https://t.co/TusyBWGfrx

7. Machine Learning
https://t.co/7veOwESe8q

8. Statistics
https://t.co/waWDQylQpY

9. Think Bayes
https://allendowney.github.io/ThinkBayes2/

10. Clean Code in Python — Packt
https://t.co/OHhVgF1L65

#Python #FreeBooks #Programming #DataScience #MachineLearning #Coding

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A