500 Essential Web Scraping Interview Questions
Start: https://hackmd.io/@husseinsheikho/WS-Interview
Start: https://hackmd.io/@husseinsheikho/WS-Interview
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Python Data Science Jobs & Interviews
500 Essential Web Scraping Interview Questions Start: https://hackmd.io/@husseinsheikho/WS-Interview ✉️ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
500 Essential Web Scraping Interview Questions with Answers - part 1 (1 to 238)
Link: https://hackmd.io/@husseinsheikho/WS-Ans238
500 Essential Web Scraping Interview Questions with Answers - part 2 (239 to 386)
Link: https://hackmd.io/@husseinsheikho/WS-Ans386
500 Essential Web Scraping Interview Questions with Answers - part 3 (387 to 500)
Link: https://hackmd.io/@husseinsheikho/WS-Ans500
https://t.iss.one/DataScienceQ✅
Link: https://hackmd.io/@husseinsheikho/WS-Ans238
500 Essential Web Scraping Interview Questions with Answers - part 2 (239 to 386)
Link: https://hackmd.io/@husseinsheikho/WS-Ans386
500 Essential Web Scraping Interview Questions with Answers - part 3 (387 to 500)
Link: https://hackmd.io/@husseinsheikho/WS-Ans500
https://t.iss.one/DataScienceQ
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Slugify module
A slug is a simplified version of a title or name where special characters are replaced with hyphens (-), and all letters are converted to lowercase. For example, the title
A slug is a friendly and readable string format commonly used in URLs to identify a resource.
🔸 The string is converted to lowercase.
🔸 Special characters and spaces are removed and replaced with hyphens.
🔸 The result is short and easy to read.
Library installation:
👉 @DataScience4
A slug is a simplified version of a title or name where special characters are replaced with hyphens (-), and all letters are converted to lowercase. For example, the title
"How to create a slug in Python!" becomes "how-to-create-a-slug-in-python"A slug is a friendly and readable string format commonly used in URLs to identify a resource.
from slugify import slugify
title = "Example post about creating slugs"
slug = slugify(title)
print(slug) # output: example-post-about-creating-slugs
Library installation:
pip install python-slugify
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Top 140 PyTorch Interview Questions and Answers
This comprehensive guide covers essential PyTorch interview questions across multiple categories, with detailed explanations for each.these 140 carefully curated questions represent the most important concepts you'll encounter in #PyTorch interviews.
🧠 Link: https://hackmd.io/@husseinsheikho/pytorch-interview
https://t.iss.one/CodeProgrammer
This comprehensive guide covers essential PyTorch interview questions across multiple categories, with detailed explanations for each.these 140 carefully curated questions represent the most important concepts you'll encounter in #PyTorch interviews.
https://t.iss.one/CodeProgrammer
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Master Python Interviews with These 150 Essential Questions.pdf
360.5 KB
Master Python Interviews with These 150 Essential Questions
Preparing for a Python-based role in data science, analytics, software development, or AI?
You need more than just coding skills — you need clarity on concepts, frameworks, and best practices.
This document contains 150 most commonly asked Python interview questions with clear, concise answers covering:
-Core Python – data types, control flow, OOP, memory management, iterators, decorators, and more
-Data Science Libraries – NumPy, Pandas, Matplotlib, Seaborn
-Frameworks – Flask, Django, Pyramid
-Data Handling – CSV reading, DataFrames, joins, merges, file handling
-Advanced Topics – GIL, multithreading, pickling, deep vs. shallow copy, generators
-Coding Challenges – from Fibonacci to palindrome checkers, sorting algorithms, and data structure problems
https://t.iss.one/DataScienceQ 🧠
Preparing for a Python-based role in data science, analytics, software development, or AI?
You need more than just coding skills — you need clarity on concepts, frameworks, and best practices.
This document contains 150 most commonly asked Python interview questions with clear, concise answers covering:
-Core Python – data types, control flow, OOP, memory management, iterators, decorators, and more
-Data Science Libraries – NumPy, Pandas, Matplotlib, Seaborn
-Frameworks – Flask, Django, Pyramid
-Data Handling – CSV reading, DataFrames, joins, merges, file handling
-Advanced Topics – GIL, multithreading, pickling, deep vs. shallow copy, generators
-Coding Challenges – from Fibonacci to palindrome checkers, sorting algorithms, and data structure problems
https://t.iss.one/DataScienceQ 🧠
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