Clean Messy Data in Python (Step-by-Step for Beginners) | Pandas Tutorial 2025
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: https://docs.google.com/spreadsheets/...
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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
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