Pandas - lesson 8 | HackIndia Skills

Key point

The proof matters

Your repo is the proof, not your notebook screenshot. A reviewer should be able to open it, see the raw files, run your cleaning script, and find the cleaned output and charts.

Keep the project small. Two raw files are enough if you clean them properly and show one join or group result.

What to include

Use a public repository. Put data in clear folders so a classmate or recruiter does not have to guess where things are.

Your repo should show the full path from messy data to usable data: load, inspect, clean, join or group, chart, save.

Example

Good repo shape

This structure is simple and easy to review on a phone. Names can change, but keep the same idea.

clean-data-project/
  README.md
  requirements.txt
  data/
    raw/
      orders.csv
      customers.json
    cleaned/
      cleaned_orders.csv
      customer_summary.csv
  charts/
    orders_by_city.png
    payment_status_bar.png
  src/
    clean_data.py

Your to-do

Write your README

Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.

State the problem in 2 to 3 lines, like cleaning orders and customer data for a small D2C store.
List the raw files and what each row means.
Explain the cleaning done: missing values, bad types, duplicates, invalid rows, and renamed columns.
Mention the join or group result you created.
Add exact run steps and where outputs appear.

0 of 5 done

Example

README run steps

Do not write vague lines like run the code. Give commands and expected outputs. Use the same file names that exist in your repo.

Project: Clean order and customer data

Raw files:
- data/raw/orders.csv: one row per order
- data/raw/customers.json: one row per customer

Cleaning done:
- Standardised column names
- Converted order_date to datetime
- Filled missing city as Unknown
- Removed orders with negative amount
- Joined orders with customer city
- Created city-level order summary

How to run:
1. Install the packages in requirements.txt
2. Run: python src/clean_data.py
3. Check data/cleaned/ for CSV outputs
4. Check charts/ for PNG charts

Common mistake

Avoid hidden work

Do not clean data manually in Excel and then upload only the final CSV. That does not prove your pandas skill.

Do not commit secrets, private college data, phone numbers, or raw UPI details. If your dataset has personal data, remove or mask it before uploading.

Tip

Use AI carefully

You can ask an AI tool to review your README or suggest folder names. You can also paste your script and ask it to find unclear steps.

Check everything it returns. Make sure commands are real, file paths match your repo, and it does not invent charts, columns, packages, or results.