Web Scraping + Data Analysis + Visualization (BeautifulSoup + Pandas + Matplotlib) for Beginners скачать в хорошем качестве

Web Scraping + Data Analysis + Visualization (BeautifulSoup + Pandas + Matplotlib) for Beginners

Welcome to this step-by-step Python project tutorial! 🚀 In this video, we’ll go from Web Scraping → Data Analysis → Data Visualization — all in one session! You’ll learn to scrape HTML data using BeautifulSoup (bs4), analyze it with Pandas, and visualize insights using Matplotlib. Perfect for beginners looking to build a small but complete Data Science mini project. 🧩 Topics Covered & Timestamps 0:00 – Introduction & What We’ll Learn 0:06 – Overview: Web Scraping + Pandas + Matplotlib 0:17 – Understanding the HTML file structure 0:28 – Identifying HTML tags and attributes to scrape (title, instructor, rating, etc.) 0:47 – Installing required libraries (beautifulsoup4, pandas, matplotlib) 1:04 – Importing libraries and setting up environment 1:48 – Opening and reading the HTML file safely with encoding 3:12 – Printing and previewing HTML content 3:39 – Parsing HTML with BeautifulSoup 4:21 – Creating a BeautifulSoup object using html.parser 4:28 – Finding all course cards with .find_all() 5:12 – Extracting multiple course blocks from HTML 6:03 – Looping through all cards and preparing to extract details 6:15 – Extracting course title using .find() and .text.strip() 7:39 – Extracting instructor name using nested .find() and .span 9:59 – Extracting course rating (float conversion) 11:42 – Extracting number of students (integer conversion + text cleaning) 13:56 – Extracting price and cleaning ₹ symbol 14:48 – Printing all extracted values (title, instructor, rating, students, price) 15:02 – Appending all rows into a list of lists for DataFrame 15:40 – Creating Pandas DataFrame with proper column names 16:42 – Viewing the final structured table 17:00 – Getting DataFrame info using .info() 17:50 – Summarizing dataset using .describe() 18:25 – Sorting & finding Top 3 Courses by Rating 20:00 – Finding Top 3 Courses by Students Enrolled 20:48 – Finding Cheapest & Most Expensive Courses (idxmin / idxmax) 22:56 – Creating a derived column: Estimated Revenue = price × students 23:59 – Finding Highest Revenue Course 24:28 – Starting Data Visualization with Matplotlib 24:39 – Line Chart: Course vs Rating 26:33 – Rotating X-axis labels for better readability 27:53 – Adding markers and adjusting line width 28:38 – Bar Chart: Students Enrolled per Course 30:34 – Customizing Bar Colors, Edges & Gridlines 31:28 – Pie Chart: Price Share per Course 33:48 – Adding start angle & percentage display 34:07 – Final Recap and Project Summary 34:27 – Outro: What we learned + next steps 🧠 What You’ll Learn Basics of Web Scraping using BeautifulSoup Cleaning & organizing data with Pandas Performing Data Analysis with .info(), .describe(), .sort_values() Creating Visualizations (Line, Bar, Pie charts) with Matplotlib Building a complete Python Data Project from scratch 🧰 Libraries Used beautifulsoup4 pandas matplotlib 💡 Project Idea Mini Project: Course Analytics Dashboard Scrape course data from an HTML page Analyze top-rated and most enrolled courses Visualize insights to make data-driven conclusions 🔗 Learn More : https://chatgpt.com/share/6910f14e-c1... #Python #WebScraping #DataAnalysis #Matplotlib #BeautifulSoup #Pandas #PythonProjects #DataScience #MiniProject #CodingForBeginners