🧹 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.

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📂 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

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⚙️ Tools & Environment

• Python 3
• Google Colab
• Libraries: Pandas, NumPy, Matplotlib

python
from google.colab import files
uploaded = files.upload()

import pandas as pd
df = pd.read_csv('ecommerce_sales.csv')