Pandas - lesson 1 | HackIndia Skills

Key point

Main idea

Your job is not to make pandas run. Your job is to make messy data safe enough to trust.

Most project bugs come from wrong types, missing values, duplicate rows, broken dates, mixed spellings, and tables that do not join cleanly.

Why it takes time

Real data is collected by forms, apps, admins, vendors, sensors, and people in a hurry. A college event sheet may have “CSE”, “cse”, “Computer Science”, and blank department values in the same column.

pandas is used because it works well with tables. A table in pandas is called a DataFrame. You use it to load, explore, clean, process, join, group, and write data.

Example

Messy looks normal

This tiny CSV already has problems. It looks usable, but it is not ready for analysis.

student_id,name,dept,fee_paid,joined_on
101,Asha,CSE,500,2026-08-01
102,Rahul,cse,,01/08/26
102,Rahul,CSE,500,2026-08-01
103,Meena,Computer Science,₹500,
104,,ECE,no,2026/08/03

Your to-do

What you check

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.

Check column names and what each row means.
Check missing values in important columns.
Check data types like dates, numbers, and text.
Check duplicate rows and duplicate IDs.
Check category spelling, casing, and units.
Check whether tables can join using a stable key.

0 of 6 done

Common mistake

Common beginner mistake

Do not jump straight to charts. A bar chart from dirty data can look correct and still be wrong.

For example, “CSE”, “cse”, and “Computer Science” may become three bars. The chart is neat, but the answer is false.

Tip

Use AI carefully

You can ask ChatGPT, Gemini, or Claude to review a sample of raw data and suggest cleaning checks. This saves time when column names are unclear.

But verify every suggestion. AI may assume meanings, invent missing business rules, or miss local details like rupee symbols, Indian date formats, or mixed phone number formats.

Prompt to use:
I am cleaning this dataset for a pandas project. List likely data quality issues and checks I should run. Do not write final code yet. Here are 10 sample rows:
[paste rows]

Your proof target

By the end of this track, your HackIndia submission should prove the full cleaning flow. It should not be just a notebook screenshot.

A strong repo has at least two raw files, code that loads them, cleaning steps with pandas and NumPy, a join or group summary, simple charts, cleaned output files, and clear run steps.