Pandas - lesson 2 | HackIndia Skills

Key point

One clean setup

Use one project folder and one Python environment for this track. It keeps your cleaning code, raw files, outputs, and dependency notes together.

pandas gives you DataFrames for table work. NumPy supports pandas and gives you arrays, missing-value sentinels, and fast vector operations.

What you need

For this track, your base stack is pandas 3.0.6 and NumPy 2.5.3. pandas is the main tool for loading, inspecting, cleaning, grouping, joining, and saving tabular data.

A DataFrame is pandas’ table object. Think of it like a spreadsheet or database table you can control with code.

Your to-do

Make the folder

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.

Create a folder named clean-data-with-pandas.
Create subfolders named data_raw, data_clean, notebooks, and charts.
Create a Python file named check_setup.py.
Keep this folder as the start of your public HackIndia repo.

0 of 4 done

Example

Install and check

Install pandas and NumPy inside your project environment. If your college laptop already has Python, use the option your setup supports.

pip is the common option. conda is also fine if you already use it.

pip install pandas
pip install numpy

# conda option
conda install -c conda-forge pandas
pip install numpy

Example

Verify imports

Put this in check_setup.py and run it. This proves your project can import the two libraries before you start cleaning real files.

Do not skip this. It catches broken installs early, before you are rushing near a hackathon deadline.

import pandas as pd
import numpy as np

print("pandas", pd.__version__)
print("numpy", np.__version__)

sample = pd.DataFrame({
    "city": ["Pune", "Bengaluru", "Delhi"],
    "orders": [120, 180, 95],
    "revenue": np.array([24000, 54000, 19000])
})

print(sample)
print(sample.dtypes)

Common mistake

Old tutorial traps

Many older pandas tutorials assume string columns are stored as NumPy object dtype. In pandas 3.0, string data is inferred as a dedicated str dtype by default.

Also avoid chained assignment examples like df[df["city"] == "Pune"]["orders"] = 0. Copy-on-Write means this pattern no longer works the way older lessons suggest. You will learn safe editing later.

Tip

Use AI carefully

You can ask ChatGPT, Gemini, or Claude to explain install errors. Paste the exact error, your operating system, Python version, and the command you ran.

Check what the AI suggests before running it. Avoid random global uninstall commands. Prefer fixes inside your project environment.