Learn Python Coding

📂 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)

🔥 MappingProxyType allows you to provide a read-only view of the dictionary without copying and without the risk of mutation through the returned object. #Python #Immutable #DataProtection #MappingProxyType #ProgrammingTips #NoCopy ✨ 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

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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Safe rounding of numbers with math.fsum

import math

# Initial list with fractions
values = [0.1] * 10

# 1. Regular summation via sum()
print(f"Standard sum(): {sum(values)}") # 0.9999999999999999

# 2. Exact summation via math.fsum()
print(f"Exact math.fsum(): {math.fsum(values)}") # 1.0

Eliminating errors when calculating arrays We've already discussed why float in Python loses accuracy and how Decimal deals with this. But what if you need to add a million ordinary real numbers from a database or matrix, and it's not possible to convert everything to heavy Decimal objects due to a performance hit? The math.fsum() function comes to the rescue. — Eliminating accumulated error: When sequentially adding elements via the standard sum(), the microscopic errors of float are rounded at each step and "accumulate" in the loop. The math.fsum() function tracks intermediate accuracy losses and compensates for them during the calculations. — High performance: Since the math module is written in C, this function works several times faster than manually iterating through the array or using alternative data types. You get the speed of basic float calculations with near-perfect accuracy. — Stability in Data Science: This tool is indispensable when working with weights in neural networks, calculating averages of large samples, or processing financial transactions, where speed is important but it's critical not to lose valuable cents and fractions during mass operations. 🐍 #Python #DataScience #Coding #Programming #MathFsum #TechTips ✨ 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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