Clean Messy Data in Python (Beginner Pandas Tutorial)
Clean messy data FAST using Python and Pandas.
In this beginner-friendly tutorial, you’ll learn:
✅ Importing a CSV into Jupyter Notebook
✅ Finding and removing duplicate rows
✅ Replacing missing/blank values
✅ Fixing data types and formatting
Perfect for data analysts, students, and anyone switching from Excel.
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Continue your learning :
If you’re learning Python for data analytics and want a structured guide, I put everything into a book with real examples.
📘 Python for Data Cleaning → 👉 https://a.co/d/iiMzQQH
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Timestamps:
00:00 Intro to Data Cleaning
02:04 Reviewing the dataset
03:07 Upload dataset (CSV) to Jupyter notebook
04:07 Create a new Workbook and rename the workbook (document)
04:45 Import libraries into your workbook to start coding
05:02 Python code to import the CSV file to Jupyter notebook & print out CSV dataset
06:26 Check information on the dataset
07:11 Review the variable names for misspellings, etc.
07:53 Drop a column by using python code
08:23 Create a new column naming it "Age Group" to group the ages in my dataset.
09:10 Checking for null values or N/A values
09:50 Dropping N/A (null) values
10:37 Checking for any duplicate values within the dataset
11:25 Dropping duplicates in the dataset using python code
11:54 Looking at the description of the dataset for "count, mean, std, min, max, etc."
12:27 Starting visualization
13:01 Pie chart visualization
16:19 Bar chart visualization
19:26 Pie chart visualization to compare another variable, "Season"
20:58 Bar chart visualization to review the variable "Age Group"
21:55 Bar chart visualization to review the variable "Age Group & Gender"
23:08 Thank you, and I hope you enjoyed the video. Please like and subscribe.
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** FREE code and dataset HERE**
The dataset used is "customer data on purchases" for a clothing store.
Get free access to the CODE and DATABASE. All I ask is to subscribe to my channel! Thanks for your support.
https://docs.google.com/spreadsheets/...
Get the code here: https://docs.google.com/document/d/1N...
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Disclaimer:
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