Python/ django
по всем вопросам @workakkk @itchannels_telegram - 🔥 все ит каналы @ai_machinelearning_big_data -ML @ArtificialIntelligencedl -AI @datascienceiot - 📚 @pythonlbooks РКН: clck.ru/3FmxmM
Show more📈 Analytical overview of Telegram channel Python/ django
Channel Python/ django (@pythonl) in the Russian language segment is an active participant. Currently, the community unites 58 385 subscribers, ranking 2 244 in the Technologies & Applications category and 10 292 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 58 385 subscribers.
According to the latest data from 03 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -242 over the last 30 days and by -32 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.00%. Within the first 24 hours after publication, content typically collects 3.06% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 501 views. Within the first day, a publication typically gains 1 784 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 18.
- Thematic interests: Content is focused on key topics such as github, claude, контекст, архитектура, api.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“по всем вопросам @workakkk
@itchannels_telegram - 🔥 все ит каналы
@ai_machinelearning_big_data -ML
@ArtificialIntelligencedl -AI
@datascienceiot - 📚
@pythonlbooks
РКН: clck.ru/3Fm...”
Thanks to the high frequency of updates (latest data received on 04 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
from abc import ABC, abstractmethod
class TextPrinter(ABC):
@abstractmethod
def print_text(self):
pass
class PlainTextPrinter(TextPrinter):
def __init__(self, text):
self.text = text
def print_text(self):
print(self.text)
class TextDecorator(TextPrinter):
def __init__(self, text_printer):
self.text_printer = text_printer
def print_text(self):
self.text_printer.print_text()
class BoldTextDecorator(TextDecorator):
def print_text(self):
print(f"<b>{self.text_printer.print_text()}</b>")
class ItalicTextDecorator(TextDecorator):
def print_text(self):
print(f"<i>{self.text_printer.print_text()}</i>")
# Client code
if __name__ == "__main__":
plain_printer = PlainTextPrinter("Hello, world!")
bold_printer = BoldTextDecorator(plain_printer)
italic_bold_printer = ItalicTextDecorator(bold_printer)
plain_printer.print_text()
bold_printer.print_text()
italic_bold_printer.print_text()
@pythonlgit clone https://github.com/alfredredbird/alfred
cd alfred && sudo pip install -r requirements.txt
python3 brib.py
@pythonl$ git clone https://github.com/N0rz3/Eyes.git
$ cd ./Eyes
$ pip3 install -r requirements.txt
▪Github
@pythonlpip3 install kot
▪Github
▪Docs
@pythonlimport cv2
import mediapipe as mp
# Initialize MediaPipe Hands module
mp_hands = mp.solutions.hands
hands = mp_hands.Hands()
# Initialize MediaPipe Drawing module for drawing landmarks
mp_drawing = mp.solutions.drawing_utils
# Open a video capture object (0 for the default camera)
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
continue
# Convert the frame to RGB format
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Process the frame to detect hands
results = hands.process(frame_rgb)
# Check if hands are detected
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
# Draw landmarks on the frame
mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
# Display the frame with hand landmarks
cv2.imshow('Hand Recognition', frame)
# Exit when 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the video capture object and close the OpenCV windows
cap.release()
cv2.destroyAllWindows()
@pythonl$ pip install wtfpython -U
▪Github
@pythonl