This content originally appeared on DEV Community and was authored by Sujitha Selvaraj
🧹 Data Cleaning Challenge with Pandas (Google Colab)
Data cleaning is one of the most crucial steps in any data science or analytics project. In this challenge, I worked on a real-world dataset from Kaggle with over 100,000 rows, performing various Pandas operations to clean, preprocess, and prepare it for further analysis.
📂 Dataset Details
For this challenge, I selected the E-commerce Sales Dataset from Kaggle containing around 120,000 rows and 12 columns.
It includes data such as:
🧾 Order ID
👤 Customer Name
🛒 Product & Quantity
💰 Sales & Discount
🌍 Region
📅 Order Date
Before Cleaning:
Rows → 120,000
Columns → 12
File format → .csv
⚙️ Tools & Environment
Python 3
Google Colab
Libraries: Pandas, NumPy, Matplotlib
This content originally appeared on DEV Community and was authored by Sujitha Selvaraj
Sujitha Selvaraj | Sciencx (2025-11-09T13:03:47+00:00) 🧹 Data Cleaning Challenge with Pandas (Google Colab). Retrieved from https://www.scien.cx/2025/11/09/%f0%9f%a7%b9-data-cleaning-challenge-with-pandas-google-colab/
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