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Pre-Class Self-Study: Introduction to Pandas

⏱️ Estimated Time: 45-60 minutes

📅 Complete Before: Lesson Start

🎯 What You'll Learn Before Class

By completing this pre-class work, you will:

  • Understand the difference between a list, a dictionary, and a Pandas DataFrame.
  • Identify the two core Pandas structures: Series and DataFrame.
  • Recognize how to import Pandas and Numpy.

🎬 Brief intro of Pandas

💻 Part 1: Environment Setup (15-20 min)

Prerequisites

For Google Colab User

If you use Google Colab to run the Jupyter notebook, no installation is required.

For VS Code User

Please follow the Installation Instructions below

Ensure you have Python 3.8+ and Jupyter Notebook/Lab installed. Open your terminal or command prompt and run:

Install the prescribed conda environment before you begin.

conda env create -f environment.yml

To start Jupyter Notebook, open your terminal (or anaconda prompt), then activate the conda environment:

conda activate pds
pip install pandas numpy
# Or using conda:
conda install pandas numpy

Verification

Create a new Jupyter Notebook and run this cell:

import pandas as pd
import numpy as np

print(f"Pandas version: {pd.__version__}")
print("✅ Environment is ready!")

📚 Part 2: Course Introduction (10-15 min)

What Is Pandas?

Pandas is the Excel of Python. It takes data that might look like a messy pile of lists or dictionaries and organizes it into a clean, tabular format called a DataFrame.

Real-World Example:

Imagine you have sales data. In standard Python, calculating the "Average Sales per Region" requires loops and complex logic. In Pandas, it's often a single line of code.

Why This Matters

  • Efficiency: Handle millions of rows in seconds.
  • Integration: Works perfectly with visualization tools and machine learning libraries.
  • Standard: It is the industry standard for data manipulation in Python.

🧠 Part 3: Basic Concepts (20-25 min)

Concept 1: The Series

Definition: A Series is a one-dimensional labeled array.

Analogy: Think of a Series like a single column in an Excel sheet. It has values, and it has row labels (the index).

Try It Yourself:

import pandas as pd
# A simple list of data
data = [10, 20, 30, 40]
# Converting it to a Pandas Series
s = pd.Series(data, index=['a', 'b', 'c', 'd'])
print(s)

Concept 2: The DataFrame

Definition: A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types.

Analogy: Think of a DataFrame like the whole Spreadsheet. It is essentially a collection of Series (columns) that share the same index (rows).

Try It Yourself:

# A dictionary where keys are column names
data = {
    'Apples': [3, 2, 0, 1],
    'Oranges': [0, 3, 7, 2]
}
df = pd.DataFrame(data)
print(df)

✅ Pre-Class Self-Check

Before Zoom class, make sure you can:

  • Import pandas as pd.
  • Create a simple Series from a list.
  • Explain the difference between a Series and a DataFrame.

❓ Pre-Class Questions to Consider

  1. Why might we want to label our data (using an Index) instead of just using numbered positions (0, 1, 2)?
  2. If a DataFrame is a collection of Series, what happens if we extract just one column from a DataFrame?