Are you tired of messy datasets that ruin your analysis? In this beginner-friendly tutorial, you’ll learn how to clean and prepare data step-by-step using Python and Pandas—just like a real data analyst! We’ll cover: ✅ Handling missing and incorrect values ✅ Cleaning text, currency, and numeric data ✅ Fixing inconsistent categories ✅ Creating simple, powerful visualizations to validate your cleaning process Whether you’re a beginner in Python or just starting your data analytics journey, this video will help you understand the why and how behind every step—explained in simple, clear language. 🧠 What You’ll Learn: - How to detect and handle missing values - How to convert messy data into clean, usable formats - How to standardize categories for accurate visuals - How to use Matplotlib & Pandas for quick data checks *Get the Dataset* 📂 Download the dataset used in this video: *More Tutorials You’ll Love* 📘 *How to Download and Install Anaconda Navigator for Jupyter Notebook* 👉 *FREE Python Starter Course* 👉 📊 *Master SQL Data Analysis* 👉 🧼 *Step-by-Step Data Cleaning in Python with Pandas* 👉 📈 *Dashboard Visualization—Seaborn Tutorial* 👉 *Support My Channel* 🔔 Don’t forget to LIKE & SUBSCRIBE for more Python & data analysis tutorials! ☕ Buy Me A Coffee: 💎 Donate via PayPal: Timestamp: 00:00 Intro 00:49 Overview of the messy dataset (excel or csv file) 02:04 Open Anaconda Navigator to work in Jupyter Notebook 02:52 Upload the messy dataset .csv file into Jupyter Notebook 03:32 Overview of each steps for this video tutorial 05:04 Why it is important to clean data for data analytics 05:32 Step 1: Import libraries 06:21 Step 2: Import the dataset and how to view the first 5 or 15 rows of the dataset using pandas 08:58 Step 3: Check for missing value and info 09:56 Step 3.5: Handling missing values (NaN) 11:16 Ensure numeric columns are numeric before filling the NaNs in the dataset 13:00 Filling the missing numeric values with the column mean 15:07 Step 4: Clean column names (remove leading/trailing spaces) 15:55 Step 5: Fix date formats (turn inconsistent text into real date format). 15:55 Step 6: 17:21 Standardize text columns (make names consistent and neat) 18:00 Step 7: Convert Units Sold column to numeric and turned non-numeric (text like "twenty" into NaNs) 18:40 Step 8: Clean currency column (Unit Price & Total Sales) 19:42 Step 9: Fix profit margin 21:06 Step 10: Drop rows and missing key data (clean final dataset) 21:51 Step 11: Check cleaned results 23:02 Step 12: Total sales by region with a bar chart. 25:33 Step 13: Sales trend over time with a line chart. 26:57 Step 14: Category share pie chart 28:38 Outro & how we discovered more cleaning #python #datacleaning #pandas #dataanalysis #jupyternotebook #beginnertutorials #datascience #machinelearning #analytics #matplotlib #pythontutorial #learnpython #powerbi #sql #excel