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

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10 GitHub Repositories for Web Development in Python 🐍 Explore the best Python web development repositories for building APIs, full-stack web apps, dashboards, machine learning demos, internal tools, and interactive Python-based user interfaces. 🔥 https://www.kdnuggets.com/10-github-repositories-for-web-development-in-python #Python #WebDevelopment #GitHub #Coding #API #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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📂 Reminder on Python set — set methods! For example, add() adds an element to the set, update() combines several elements, and intersection() helps quickly find common values between data sets. In the picture — the main methods and operations for working with set: addition, removal, union, intersection, difference, and set checks. Save it to not lose it! #Python #SetMethods #Coding #DataScience #Programming #HelloEncyclo ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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Python can make a dictionary immutable without copying data! Usually, to protect configurations and the overall state, a copy of the dictionary is made, which creates unnecessary memory allocations.

safe = dict(config)

MappingProxyType creates a read-only proxy over a dictionary — writing through it becomes impossible, but the data is not copied.

readonly["debug"] = True  # TypeError

At the same time, the proxy remains alive: if the original dictionary changes, the changes will automatically be reflected in the read-only view.

config["debug"] = True

This is especially useful for configurations, internal APIs, overall state, and data protection within libraries.

def get_settings():
    return MappingProxyType(settings)

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Smart counting of elements using collections.Counter 📊

from collections import Counter

# Initial list with duplicate elements
logs = ["error", "info", "error", "warning", "error", "info"]

# 1. Instantly count the number of occurrences
count_dict = Counter(logs)
print(count_dict)  # Counter({'error': 3, 'info': 2, 'warning': 1})

# 2. Get the most frequent elements (Top-2)
print(count_dict.most_common(2))  # [('error', 3), ('info', 2)]

# 3. Set math for counters
clicks_day1 = Counter(item=4, banner=2)
clicks_day2 = Counter(item=1, banner=5)
# Combine the results of two days in a single operation
print(clicks_day1 + clicks_day2)  # Counter({'banner': 7, 'item': 5})

Forget about manual loops and dictionaries 🚫🔄 When you need to count the frequency of words in a text, the distribution of log types, or popular products in a store, developers usually create an empty dictionary and write a loop with a check if key not in dict: dict[key] = 1. The Counter class takes all this dirty work on itself and makes it as efficient as possible. — Automatic initialization: You no longer need to check if a key exists in the dictionary. If the element is not there, Counter will not throw a KeyError, but simply return 0. 🛡️ — Finding leaders without sorting: The most_common(k) method returns a list of the k most frequently occurring elements. Under the hood, Python uses optimized heap algorithms, which work much faster than a full dictionary sort via sorted(). 🏆 — Mathematical operations: You can add, subtract, intersect, and merge Counter objects. This turns them into a powerful tool for aggregating metrics and analytics from different data sources in a few lines of code. ➕➖ #Python #DataScience #Coding #Programming #Automation #DevOps ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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