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Python Interviews

Python Interviews

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Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free For collaborations: @coderfun

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📈 Аналітичний огляд Telegram-каналу Python Interviews

Канал Python Interviews (@pythoninterviews) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 28 836 підписників, посідаючи 4 615 місце в категорії Технології та додатки та 14 432 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 28 836 підписників.

За останніми даними від 26 липня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 78, а за останні 24 години на -1, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 2.43%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.57% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 701 переглядів. Протягом першої доби публікація в середньому набирає 163 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 2.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як |--, link:-, learning, sql, analytic.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free For collaborations: @coderfun

Завдяки високій частоті оновлень (останні дані отримано 27 липня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.

28 836
Підписники
-124 години
+417 днів
+7830 день
Архів дописів
To be GOOD in Data Science you need to learn: - Python - SQL - PowerBI To be GREAT in Data Science you need to add: - Business Understanding - Knowledge of Cloud - Many-many projects But to LAND a job in Data Science you need to prove you can: - Learn new things - Communicate clearly - Solve problems #datascience

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Quick Python Cheat Sheet for Beginners 🐍✍️ Python is widely used for data analysis, automation, and AI—perfect for beginners starting their coding journey. Aggregation Functions 📊 • sum(list) → Adds all values 👉 sum([1,2,3]) = 6 • len(list) → Counts total elements 👉 len([1,2,3]) = 3 • max(list) → Highest value 👉 max([4,7,2]) = 7 • min(list) → Lowest value 👉 min([4,7,2]) = 2 • sum(list)/len(list) → Average 👉 sum([10,20])/2 = 15 Lookup / Searching 🔍 • in → Check existence 👉 5 in [1,2,5] = True • list.index(value) → Position of value 👉 [10,20,30].index(20) = 1 • Dictionary lookup 👉 data = {"name": "John", "age": 25} data["name"] # John Logical Operations 🧠 • if condition: → Decision making 👉 if x > 10: print("High") else: print("Low") • and → All conditions true • or → Any condition true • not → Reverse condition Text (String) Functions 🔤 • len(text) → Length 👉 len("hello") = 5 • text.lower() → Lowercase • text.upper() → Uppercase • text.strip() → Remove spaces 👉 " hi ".strip() = "hi" • text.replace(old, new) 👉 "hi".replace("h","H") = "Hi" • String concatenation 👉 "Hello " + "World" Date Time Functions 📅 • from datetime import datetime • datetime.now() → Current date time • Extract values: now = datetime.now() now.year now.month now.day Math Functions ➗ • import math • math.sqrt(x) → Square root • math.ceil(x) → Round up • math.floor(x) → Round down • abs(x) → Absolute value Conditional Aggregation (Like Excel SUMIF) ⚡ • Using list comprehension nums = [10, 20, 30, 40] sum(x for x in nums if x > 20) # 70 • Count condition len([x for x in nums if x > 20]) # 2 Pro Tip for Data Analysts 💡 👉 For real-world work, use libraries: pandas & numpy Example: import pandas as pd df["salary"].mean() Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L Double Tap ♥️ For More

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🔰 Python Set Methods
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🔰 Python Set Methods

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Top linked list questions to practice: 1. 🔄 Reverse a Linked List 2. 🔁 Detect a Cycle in a Linked List 3. 🤝 Find the Merge Point of Two Linked Lists 4. 🚫 Remove N-th Node From End of List 5. 🔗 Merge Two Sorted Linked Lists 6. 🖼️ Check if a Linked List is a Palindrome 7. 🚨 Remove Duplicates from a Sorted List 8. 🎯 Find the Middle of a Linked List 9. 🔄 Rotate a Linked List 10. 📑 Implement a Doubly Linked List 11. 📊 Implement a Circular Linked List 12. 🛠️ Add Two Numbers Represented by Linked Lists 13. 🧹 Remove Linked List Elements 14. 🧩 Partition List around a value 15. 🔄 Reverse Nodes in k-Group

🔰 Take Screenshots using Python
🔰 Take Screenshots using Python

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