Episode
Course 40 - Web Scraping with Python | Episode 5: From Environment Setup to Pandas DataFrames
- Podcast
- CyberCode Academy
- Published
- Jul 15, 2026
- Duration seconds
- 1410
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Summary
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 = BeautifulSoup(html, "html.parser") 🔹 Using CSS Selectorstable = soup.select("table.wikitable")[0] rows = table.select("tr") 👉 This allows precise targeting of elements4. Cleaning the Data🔹 Fix Column Names Remove whitespace Replace spaces with _ clean_header = header.text.strip().replace(" ", "_") 🔹 Remove Unwanted Patterns (Regex)Use Regular Expressionimport re clean_text = re.sub(r"\[.*?\]", "", raw_text) 👉 Removes things like: [1], [citation needed] 5. Structuring the Data🔹 Build a “List of Lists”data = [] for row in rows: cols = [col.text.strip() for col in row.select("td")] data.append(cols) 👉 Struc…