Course 40 - Web Scraping with Python | Episode 5: From Environment Setup to Pandas DataFrames

Course 40 - Web Scraping with Python | Episode 5: From Environment Setup to Pandas DataFrames

From CyberCode Academy by CyberCode Academy

July 15, 2026 · 24 min

About this episode

This episode covers setting up a Python web scraping environment and extracting data from HTML using BeautifulSoup and pandas.

In this lesson, you’ll learn about: setting up a professional Python scraping environment, extracting web data step-by-step, and transforming raw HTML into structured datasets1. Setting Up Your Development Environment🔹 Python Version ManagementUse pyenv Install and switch between Python versions بسهولة Avoid compatibility issues across projects 🔹 Virtual Environments & DependenciesUse pipenv Create isolated environments Manage dependencies like: requests BeautifulSoup4 pandas 👉 Key Insight Clean environment = fewer bugs + reproducible projects🔹 Interactive DevelopmentUse JupyterLab Run code in cells step-by-step Inspect outputs instantly Explore files and HTML visually 2. Downloading & Inspecting Web Content🔹 Fetching HTML PagesUse Requestsimport requests url = "https://example.com" response = requests.get(url) html = response.text 🔹 Why Save Locally? Work offline Avoid repeated requests Debug faster 🔹 Inspecting the PageUse: JupyterLab HTML viewer Browser DevTools (Elements tab) 👉 Goal: Locate the exact HTML structure of your target data (e.g., tables, divs)3. Extracting Data with BeautifulSoup🔹 Parsing HTMLUse BeautifulSoupfrom bs4 import BeautifulSoup soup =…

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