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Learn Python Coding
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Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho
Show moreπ Analytical overview of Telegram channel Learn Python Coding
Channel Learn Python Coding (@pythonre) in the English language segment is an active participant. Currently, the community unites 39 140 subscribers, ranking 3 511 in the Technologies & Applications category and 10 551 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 39 140 subscribers.
According to the latest data from 07 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 433 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.62%. Within the first 24 hours after publication, content typically collects 1.01% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 026 views. Within the first day, a publication typically gains 395 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
- Thematic interests: Content is focused on key topics such as math, harvard, oxford, supervision, waybienad.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
Thanks to the high frequency of updates (latest data received on 08 June, 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.
39 140
Subscribers
+1024 hours
+887 days
+43330 days
Posts Archive
39 142
π The bitcoin is falling, boss!
We will teach Python to monitor the cryptocurrency rate and notify if the rate is above or below the threshold.
We will connect the
requests library and import time:
import requests
import timedef get_btc_price():
url = "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd"
r = requests.get(url)
return r.json()["bitcoin"]["usd"]threshold = 65000 # specify your goal
while True:
price = get_btc_price()
print(f"BTC: ${price}")
if price > threshold:
print("π Time to sell!")
break
time.sleep(60)
39 142
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
4οΈβ£ Artificial Intelligence
5οΈβ£ Data Analysis
6οΈβ£ Statistics
7οΈβ£ Deep Learning
8οΈβ£ programming Languages
β
https://t.me/addlist/8_rRW2scgfRhOTc0
β
https://t.me/Codeprogrammer
39 142
β¨ poetry-core | Python Tools β¨
π A lightweight build backend for Python.
π·οΈ #Python
39 142
Repost from Machine Learning with Python
I rarely say this, but this is the best repository for mastering Python.
The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley).
In this PythonMastery.pdf, all the information is structured
πΎ Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf
In the Exercises folder, all the exercises are located
πΎ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises
In the Solutions folder β the solutions
πΎ Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions
π @codeprogrammer
39 142
β¨ isort | Python Tools β¨
π A command-line utility and library for sorting and organizing Python imports.
π·οΈ #Python
39 142
β¨ mypy | Python Tools β¨
π A static type checker for Python.
π·οΈ #Python
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Tip: Efficiently Slice Iterators and Large Sequences with
itertools.islice
Explanation:
Traditional list slicing (my_list[start:end]) creates a new list in memory containing the sliced elements. While convenient for small lists, this becomes memory-inefficient for very large lists and is impossible for pure iterators (like generators or file objects) that don't support direct indexing.
itertools.islice provides a memory-optimized solution by returning an iterator that yields elements from a source iterable (list, generator, file, etc.) between specified start, stop (exclusive), and step indices, without first materializing the entire slice into a new collection. This "lazy" consumption of the source iterable is crucial for processing massive datasets, infinite sequences, or streams where only a portion is needed, preventing excessive memory usage and improving performance. It behaves syntactically similar to standard slicing but operates at the iterator level.
Example:
import itertools
import sys
# A generator for a very large sequence
def generate_large_sequence(count):
for i in range(count):
yield f"Data_Item_{i}"
# Imagine needing to process only a small segment of 10 million items
total_items = 10**7
data_stream = generate_large_sequence(total_items)
# Get items from index 500 to 509 (inclusive)
# Using islice:
print("--- Using itertools.islice ---")
# islice(iterable, [start], stop, [step])
# Here, start=500, stop=510 (exclusive)
for item in itertools.islice(data_stream, 500, 510):
print(item)
# Compare memory usage (conceptual, as actual list materialization would be massive)
# If you tried:
# large_list = list(generate_large_sequence(total_items)) # <-- HUGE memory consumption here!
# for item in large_list[500:510]:
# print(item)
# islice consumes minimal memory, only holding iterator state.
# The `data_stream` generator itself only holds its current state, not the whole sequence.
print("\n`itertools.islice` memory footprint is negligible compared to creating a full list slice.")39 142
β¨ Quiz: Writing DataFrame-Agnostic Python Code With Narwhals β¨
π If you're a Python library developer wondering how to write DataFrame-agnostic code, the Narwhals library is the solution you're looking for.
π·οΈ #advanced #data-science #python
39 142
β¨ Pylint | Python Tools β¨
π A static code checker for Python.
π·οΈ #Python
39 142
β¨ pytest | Python Tools β¨
π A test runner and framework for Python.
π·οΈ #Python
39 142
Repost from Machine Learning with Python
Real Python - Pocket Reference (Important)
#python #py #PythonTips #programming
https://t.me/CodeProgrammer π©΅
39 142
β¨ flake8 | Python Tools β¨
π A command-line Python linter.
π·οΈ #Python
39 142
β¨ Git | Python Tools β¨
π A distributed version control system.
π·οΈ #Python
