Module 1: Introduction to Pandas

Pandas is the "Excel for Python". It provides high-performance, easy-to-use data structures and data analysis tools. If you are doing Data Science in Python, you will use Pandas.

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Lesson 1: Why Pandas?

Pandas vs. Excel

• Scalability: Pandas handles millions of rows; Excel struggles after ~100k.
• Automation: Pandas workflows are code, so they are repeatable and automatable.
• Integration: Pandas connects natively to NumPy, Scikit-Learn, and SQL databases.

Installation

Pandas is built on top of NumPy.

pip install pandas

Importing

The standard alias is pd.

import pandas as pd
import numpy as np

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Lesson 2: Core Data Structures

Pandas has two main objects: Series (1D) and DataFrame (2D).

1. The Series (1D)

Think of a Series as a single column of data, but with a powerful index.

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])

2. The DataFrame (2D)

A DataFrame is a table. It's a collection of Series that share the same index.

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Practice: Create Your Own Data

Challenge: creates a DataFrame representing a small inventory system.

1. Columns: Product, Price, Stock.
2. Rows: 3 items of your choice.
3. Print the 'Price' column only.

Key Takeaways

✅ DataFrame is your main tool: it's a programmable spreadsheet.
✅ Series is a single column of a DataFrame.
✅ Always import as pd.