Hands-on Data Cleaning Using Pandas in Google Colab

jayanth anbu — Sat, 08 Nov 2025 06:22:17 +0000

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

Exploring NoSQL Data Analysis: A Practical Study Using a Kaggle Dataset

jayanth anbu — Sat, 08 Nov 2025 06:04:03 +0000

🗂️ Step 1: Setting up MongoDB Atlas
Go to MongoDB Atlas.
Create a free cluster (use the Shared Tier option).
Under Network Access, add your IP:
Click Network Access → Add IP Address → Allow access from anywhere (0.0.0.0/0).
Create a database user and remember the credentials. Example:
Username: 22cs056 Password: JAYANTH

Once your cluster is ready, click “Connect → Connect using MongoDB Shell” and copy the connection string.

💻 Step 2: Connect from Mongo Shell
Open PowerShell or Command Prompt, then run:

bash mongosh "mongodb+srv://m0.wpjmxqh.mongodb.net/" --apiVersion 1 --username 22cs098_db_user

Then enter your password when prompted:

Enter password: JAYANTH

If connection succeeds, you’ll see:

Atlas atlas-xxxx-shard-0 [primary]>

📥 Step 3: Create a Database and Insert Records
Switch to a database (it will auto-create):

javascript use businessDB

Insert 10 sample business review records:

javascript db.reviews.insertMany([ { "business_id": "B001", "name": "Cafe Aroma", "rating": 4.6, "review": "Good food and fast service!", "date": "2025-11-07" }, { "business_id": "B002", "name": "Pizza Palace", "rating": 4.8, "review": "Amazing crust and cheese quality!", "date": "2025-11-07" }, { "business_id": "B003", "name": "Tea Time", "rating": 4.2, "review": "Nice ambience and friendly staff.", "date": "2025-11-07" }, { "business_id": "B004", "name": "Sweet Treats", "rating": 3.9, "review": "Desserts were good but service was slow.", "date": "2025-11-07" }, { "business_id": "B005", "name": "Veggie Delight", "rating": 4.1, "review": "Healthy food with good taste.", "date": "2025-11-07" }, { "business_id": "B006", "name": "Burger Hub", "rating": 4.9, "review": "Best burgers ever!", "date": "2025-11-07" }, { "business_id": "B007", "name": "Ocean Dine", "rating": 4.7, "review": "Fresh seafood and great view.", "date": "2025-11-07" }, { "business_id": "B008", "name": "Spice Route", "rating": 3.8, "review": "Food was okay, but spicy.", "date": "2025-11-07" }, { "business_id": "B009", "name": "Bakers Street", "rating": 4.5, "review": "Good pastries and coffee.", "date": "2025-11-07" }, { "business_id": "B010", "name": "Quick Bite", "rating": 4.0, "review": "Good service and clean place.", "date": "2025-11-07" } ])

🔍 Step 4: Queries
🏆 4.1 Top 5 Businesses by Rating
javascript db.reviews.find().sort({ rating: -1 }).limit(5)

🔤 4.2 Count of Reviews Containing “good”
javascript db.reviews.countDocuments({ review: /good/i })

🏪 4.3 Get Reviews for a Specific Business ID
javascript db.reviews.find({ business_id: "B005" })

✏️ Step 5: Update and Delete
✏️ Update a Review
javascript db.reviews.updateOne( { business_id: "B005" }, { $set: { rating: 4.3, review: "Updated: Great taste and fresh ingredients!" } } )

🗑️ Delete a Record
javascript db.reviews.deleteOne({ business_id: "B010" })

📤 Step 6: Export Data to JSON/CSV
Exit Mongo shell:

bash exit

Then run the following from PowerShell (not inside mongosh) 👇

📄 Export as CSV
bash mongoexport --uri="mongodb+srv://22cs098_db_user:NAVEEN@m0.wpjmxqh.mongodb.net/businessDB" --collection=reviews --type=csv --fields=business_id,name,rating,review,date --out=reviews.csv

📦 Export as JSON
bash mongoexport --uri="mongodb+srv://22cs098_db_user:NAVEEN@m0.wpjmxqh.mongodb.net/businessDB" --collection=reviews --out=reviews.json

📊 Step 7: View the Exported Files
Open reviews.csv in Excel or VS Code.
Open reviews.json in any text editor.

✅ Step 8: Summary

Operation	Command	Example
Insert	insertMany()	Add 10 review records
Query	find(), countDocuments()	Retrieve data
Update	updateOne()	Modify fields
Delete	deleteOne()	Remove a record
Export	mongoexport	Generate CSV/JSON files

🎯 Final Thoughts
MongoDB Atlas makes it easy to:

Manage cloud-hosted databases
Perform CRUD operations
Export results in multiple formats
This project demonstrates all essential MongoDB operations — perfect for Data Engineering and Database Management learning tasks.

Forem: jayanth anbu

Hands-on Data Cleaning Using Pandas in Google Colab

Exploring NoSQL Data Analysis: A Practical Study Using a Kaggle Dataset