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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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📈 Аналитический обзор Telegram-канала Python Interviews

Канал Python Interviews (@pythoninterviews) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 28 757 подписчиков, занимая 4 793 место в категории Технологии и приложения и 15 226 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 28 757 подписчиков.

Согласно последним данным от 04 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 95, а за последние 24 часа — 2, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 0.63%. В первые 24 часа после публикации контент обычно набирает 0.85% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 181 просмотров. В течение первых суток публикация набирает 243 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 1.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как |--, link:-, learning, sql, analytic.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
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

Благодаря высокой частоте обновлений (последние данные получены 05 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

28 757
Подписчики
+224 часа
+167 дней
+9530 день
Архив постов
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Top 10 Python interview questions with answers: 1. What are Python's key data types? Solution: Numeric types: int, float, complex Text type: str Sequence types: list, tuple Mapping type: dict Set types: set, frozenset Boolean type: bool 2. What is a list comprehension in Python? Solution: A concise way to create lists using a single line of code. Example: squares = [x**2 for x in range(10)] # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] 3. What is the difference between == and is in Python? Solution: == checks for value equality. is checks for object identity (whether two references point to the same object). a = [1, 2, 3] b = [1, 2, 3] print(a == b) # True, values are equal print(a is b) # False, different objects 4. How do you handle exceptions in Python? Solution: Using try, except, else, and finally blocks. Example: try: result = 10 / 0 except ZeroDivisionError: print("Cannot divide by zero!") else: print("No error occurred.") finally: print("This block runs regardless of an error.") 5. What are Python decorators and why are they used? Solution: Decorators are functions that modify the behavior of other functions or methods. They are used for adding functionality without changing the original function's code. Example: def my_decorator(func): def wrapper(): print("Something is happening before the function is called.") func() print("Something is happening after the function is called.") return wrapper @my_decorator def say_hello(): print("Hello!") say_hello() 6. What is a Python generator? Solution: A generator is a function that uses yield to return an iterator, which generates values on the fly without storing them in memory. Example: def my_generator(): yield 1 yield 2 yield 3 gen = my_generator() for value in gen: print(value) 7. How do you create a dictionary in Python? Solution: my_dict = {'name': 'John', 'age': 30, 'city': 'New York'} 8. What is the difference between append() and extend() in Python? Solution: append(): Adds a single element to the end of a list. extend(): Adds all elements from an iterable to the end of a list. my_list = [1, 2, 3] my_list.append([4, 5]) # [1, 2, 3, [4, 5]] my_list.extend([6, 7]) # [1, 2, 3, [4, 5], 6, 7] 9. What is a lambda function in Python? Solution: A lambda function is an anonymous function defined using the lambda keyword. It's often used for short, simple operations. Example: square = lambda x: x**2 print(square(5)) # 25 10. What is the Global Interpreter Lock (GIL)? Solution: The GIL is a mutex in CPython (the standard Python implementation) that prevents multiple native threads from executing Python bytecode at the same time. This can limit the performance of multithreaded Python programs in CPU-bound operations but not in I/O-bound operations. Here you can find essential Python Interview Resources👇 https://t.me/DataSimplifier Like this post for more resources like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Cheatsheet on Numpy and pandas for easy viewing 👀

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Top 40 commonly asked DSA questions : 𝗔𝗿𝗿𝗮𝘆𝘀 𝗮𝗻𝗱 𝗦𝘁𝗿𝗶𝗻𝗴𝘀: 1. Find the missing number in an array of integers. 2. Implement an algorithm to rotate an array. 3. Check if a string is a palindrome. 4. Find the first non-repeating character in a string. 5. Implement an algorithm to reverse a linked list. 6. Merge two sorted arrays. 7. Implement a stack using arrays/linked list. 8. Write a program to remove duplicates from a sorted array. 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁𝘀: 1. Detect a cycle in a linked list. 2. Find the intersection point of two linked lists. 3. Reverse a linked list in groups of k. 4. Implement a function to add two numbers represented by linked lists. 5. Clone a linked list with next and random pointer. 𝗧𝗿𝗲𝗲𝘀 𝗮𝗻𝗱 𝗕𝗶𝗻𝗮𝗿𝘆 𝗦𝗲𝗮𝗿𝗰𝗵 𝗧𝗿𝗲𝗲𝘀 (𝗕𝗦𝗧): 1. Find the height of a binary tree. 2. Check if a binary tree is balanced. 3. Find the lowest common ancestor in a binary tree. 4. Serialize and deserialize a binary tree. 5. Implement an algorithm for in-order traversal without recursion. 6. Convert a BST to a sorted doubly linked list. You can check these amazing resources for DSA Preparation All the best 👍👍

𝟳 𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍 Want to land a ca
𝟳 𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍 Want to land a career in data analytics? 📊💥 It’s not about stacking degrees anymore—it’s about mastering in-demand skills that make you stand out in a competitive job market🧑‍💻📌 𝐋𝐢𝐧𝐤👇:- http://pdlink.in/3Uxh5TR Start small, practice every day, and add these skills to your portfolio✅️

Mastering LLM & Generative AI ✅
+9
Mastering LLM & Generative AI ✅

𝟒 𝐁𝐞𝐬𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 𝐭𝐨 𝐒𝐤𝐲𝐫𝐨𝐜𝐤𝐞𝐭 𝐘𝐨𝐮𝐫 𝐂𝐚𝐫𝐞𝐞𝐫😍 In today’s data-driv
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10 Steps to Landing a High Paying Job in Data Analytics 1. Learn SQL - joins & windowing functions is most important 2. Learn Excel- pivoting, lookup, vba, macros is must 3. Learn Dashboarding on POWER BI/ Tableau 4. ⁠Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries 5. ⁠Know basics of descriptive statistics 6. ⁠With AI/ copilot integrated in every tool, know how to use it and add to your projects 7. ⁠Have hands on any 1 cloud platform- AZURE/AWS/GCP 8. ⁠WORK on atleast 2 end to end projects and create a portfolio of it 9. ⁠Prepare an ATS friendly resume & start applying 10. ⁠Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those. Give more interview to boost your chances through consistent practice & feedback 😄👍

𝟯 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍 Want to break into Data Science
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Machine Learning Algorithms every data scientist should know: 📌 Supervised Learning: 🔹 Regression ∟ Linear Regression ∟ Ridge & Lasso Regression ∟ Polynomial Regression 🔹 Classification ∟ Logistic Regression ∟ K-Nearest Neighbors (KNN) ∟ Decision Tree ∟ Random Forest ∟ Support Vector Machine (SVM) ∟ Naive Bayes ∟ Gradient Boosting (XGBoost, LightGBM, CatBoost) 📌 Unsupervised Learning: 🔹 Clustering ∟ K-Means ∟ Hierarchical Clustering ∟ DBSCAN 🔹 Dimensionality Reduction ∟ PCA (Principal Component Analysis) ∟ t-SNE ∟ LDA (Linear Discriminant Analysis) 📌 Reinforcement Learning (Basics): ∟ Q-Learning ∟ Deep Q Network (DQN) 📌 Ensemble Techniques: ∟ Bagging (Random Forest) ∟ Boosting (XGBoost, AdaBoost, Gradient Boosting) ∟ Stacking Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc. Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D React ❤️ for more free resources

𝐄𝐚𝐫𝐧 𝐅𝐑𝐄𝐄 𝐎𝐫𝐚𝐜𝐥𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 — 𝐂𝐥𝐨𝐮𝐝, 𝐀𝐈 & 𝐃𝐚𝐭𝐚!😍 Oracle’s Race to C
𝐄𝐚𝐫𝐧 𝐅𝐑𝐄𝐄 𝐎𝐫𝐚𝐜𝐥𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 — 𝐂𝐥𝐨𝐮𝐝, 𝐀𝐈 & 𝐃𝐚𝐭𝐚!😍 Oracle’s Race to Certification is here — your chance to earn globally recognized certifications for FREE!💥 💡 Choose from in-demand certifications in: ☁️ Cloud 🤖 AI 📊 Data …and more! 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4lx2tin ⚡But hurry — spots are limited, and the clock is ticking!✅️