In this advanced Python tutorial, you’ll learn how to clean and transform real-world data using Pandas in Jupyter Notebook! *What You'll Learn* : - Drop unnecessary variables - Handle missing values like NaNs and None - Rename confusing columns for clarity - Convert string dates to datetime format - Calculate number of days between two dates using `. ` - Categorize records based on time difference (e.g. "below 30 Days" vs "30+ Days") ✅ Tools Used: - Python - Pandas - Jupyter Notebook 📥 Download the Dataset: 👉 *Please help Support my channel* : 🔔 *Don’t forget to LIKE & SUBSCRIBE* for more Python & Data Analysis tutorials! 💎 *Want to Buy Me A Coffee* : ______________________________________________ *Download Anaconda to use Jupyter Notebook for Python coding:* ===== *Continue your learning* ====== Python *Get free resources to continue learning: * Excel == *Great Books For Mastering Data Science and Data Cleaning* == Python For Data Analysis: Python Data Science Handbook: Hands-On Machine Learning with Scikit-Learn & TensorFlow: Python Machine Learning by Sebastian Raschka: Modern Python Cookbook: updated: _______________________________________ ⏳ *Timestamps* ⏳ 00:00 Introduction 01:26 Upload the Dataset into Jupyter Notebook 02:40 Step 1: Import Libraries 03:05 Step 2: Load the CSV File & review the data head 04:41 Step 3: Drop Unused Columns 06:23 Step 4: Handle Missing Values 11:03 Step 5: Rename Columns (Variables) 14:10 Step 6: Analyze Time Between Dates using . 23:12 Step 7: Summarize the Results 24:27 Step 8: Summary of Purchase Window 25:06 Save the Cleaned Dataset 26:12 Create a quick visualization graph of results using matplotlib and seaborn #Python #DataCleaning #JupyterNotebook #Pandas #DataAnalysis #pythonforbeginners Disclaimer: This content is for educational purposes only. Affiliate links may be included, and I may earn a small commission at no extra cost to you. Thank you for supporting the channel!