The tell() function in Python 🐍

The tell() function returns the current position of the file pointer within the data stream. It is most often used when working with files. 📂

The function does not accept any arguments and returns an integer – the position in bytes from the beginning of the stream. 🔢

with open("file.txt", "rb") as f:
    print(f.tell())

#Python #Programming #Coding #FileHandling #DevTips #Tech

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

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

📌 Marking Deprecated Code in Python: deprecated()

In large projects, old functions are not always immediately removed. They are often left for compatibility, but it's important to warn developers that they should no longer be used.

Previously,

warnings.warn()

was often used for this purpose:

import warnings

def old_api():
    warnings.warn(
        "Use new_api()",
        DeprecationWarning
    )

In Python 3.13, a

deprecated()

decorator has been introduced:

from warnings import deprecated

@deprecated("Use new_api()")
def old_api():
    return "old"

Now, the information about deprecation is part of the API itself. It can be recognized not only during runtime, but also by editors and static analysis tools.

This also works for classes:

@deprecated("Use NewClient")
class OldClient:
    pass

This is convenient for libraries, SDKs, and large projects where the API is gradually changing.

#Python #Python313 #Deprecated #Coding #Programming #SoftwareDevelopment

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

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

👩‍💻 heapq.merge(): Combining sorted data!

If you have multiple sources of data that are already sorted, you don't need to collect them into a single collection and sort them again. heapq.merge() combines such sources into a single, ordered iterator.

In this guide:
• We will combine multiple sorted sequences;
• We will explore lazy processing of large data sources;
• We will configure comparison using the key argument;
• We will combine data sorted in reverse order.

This is especially useful when working with logs, files, and query results, where each source already provides data in the correct order.

#Python #heapq #DataProcessing #CodingTips #Programming #Algorithms

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

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

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.me/addlist/0f6vfFbEMdAwODBk

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

Python For Beginners Course In-Depth

Python: A Comprehensive Introduction to Fundamental Python Concepts and the Python Advanced Programming Concepts

🏷 Category: IT & Software
🌍 Language: English
👥 Students: 277,268 students
⭐️ Rating: 4.0/5.0
💰 Price: $39.99 ⟹ FREE
🆔 Coupon: •••••••••• (tap below to reveal)

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

💎 By: https://t.me/Udemy26
#Python #DataScience #Automation #FreeCourse #Udemy #OnlineLearning

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.me/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.me/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.me/addlist/0f6vfFbEMdAwODBk