Introduction to Pandas

Reviewed & published by Brayan K

Pandas is an open-source Python library for working with tabular data — the rows and columns you see in a spreadsheet — giving you fast, expressive tools to load, clean, analyse, and save real-world datasets.

Part of the free Pandas course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

In this first lesson you'll learn what Pandas is, why analysts everywhere rely on it, how to install and import it, and you'll build your very first table of data.

What You'll Learn in This Lesson

• What Pandas is and the problems it solves
• Why Pandas is built on top of NumPy
• How to install Pandas with pip
• The standard import: import pandas as pd
• Build your first DataFrame from a dictionary
• Check your version with pd.__version__

1 What Is Pandas?

Pandas is a Python library for working with tabular data — data organised in rows and columns, exactly like a spreadsheet or a database table.

📊 Pandas gives you two core objects:

• A Series — a single labelled column of data (1-dimensional)
• A DataFrame — a full table of rows and columns (2-dimensional)

Pandas is built directly on top of NumPy, Python's fast numerical computing library. That means your data is stored in efficient C-backed arrays under the hood, so operations on millions of rows stay quick.

2 Why Use Pandas?

Plain Python lists and dictionaries struggle with real datasets. Pandas was designed to make data work effortless:

• Read any file — CSV, Excel, JSON, SQL, and more in a single line
• Clean messy data — handle missing values, duplicates, and bad types
• Filter and select — grab exactly the rows and columns you need
• Group and summarise — compute totals, averages, and counts by category
• Fast — vectorised operations powered by NumPy, no slow Python loops

3 Installing and Importing Pandas

Install Pandas once from your terminal or command prompt using pip, Python's package installer:

pip install pandas

This also installs NumPy automatically, because Pandas depends on it. If you use Anaconda, Pandas is already included.

Inside your Python file or notebook, import it with the universal alias pd:

import pandas as pd

import pandas as pd

# Check that Pandas is installed and see its version
print("Pandas version:", pd.__version__)

# Output (your version number may differ):
# Pandas version: 2.2.2

# ✅ Expected output:
# Pandas version: 2.3.3

4 Your First DataFrame

The fastest way to create a table is from a dictionary. Each key becomes a column name, and each list of values becomes that column's data.

Pandas automatically adds a numbered index (0, 1, 2, ...) down the left side to label each row.

import pandas as pd

# Each key is a column; each list is that column's values
data = {
    "name": ["Alice", "Bob", "Carol"],
    "age": [25, 30, 35],
    "city": ["London", "Paris", "Berlin"],
}

df = pd.DataFrame(data)
print(df)

# Output:
#     name  age    city
# 0  Alice   25  London
# 1    Bob   30   Paris
# 2  Carol   35  Berlin

# ✅ Expected output:
#     name  age    city
# 0  Alice   25  London
# 1    Bob   30   Paris
# 2  Carol   35  Berlin

import pandas as pd

# 🎯 YOUR TURN: replace each ___ to build a table of 3 products
data = {
    "product": ["Pen", "Notebook", "Eraser"],
    "price": [___, ___, ___],   # use the numbers 2, 5, and 1
}

df = pd.DataFrame(data)
print(df)

# Expected output:
#     product  price
# 0       Pen      2
# 1  Notebook      5
# 2    Eraser      1

📋 Quick Reference

🎯 Mini Challenge: Your Movie List

Build a small DataFrame describing three of your favourite movies:

• Import Pandas as pd
• Create a dictionary with columns title, year, and rating
• Turn it into a DataFrame and print it

import pandas as pd

# 🎯 Build your own movie table — change these to real movies!
movies = {
    "title": ["Inception", "Coco", "Up"],
    "year": [2010, 2017, 2009],
    "rating": [8.8, 8.4, 8.3],
}

df = pd.DataFrame(movies)
print(df)

# Output:
#        title  year  rating
# 0  Inception  2010     8.8
# 1       Coco  2017     8.4
# 2         Up  2009     8.3

# ✅ Expected output:
#        title  year  rating
# 0  Inception  2010     8.8
# 1       Coco  2017     8.4
# 2         Up  2009     8.3

❓ Frequently Asked Questions

Lesson complete — welcome to Pandas!

You now know what Pandas is, why it's built on NumPy, how to install and import it, and you've created your first DataFrame from a dictionary.

Practice quiz

Continue this course

• Next: The Series: Pandas' 1-D Building Block — Create and work with a Series — a labelled one-dimensional array
• Quick reference: Pandas cheat sheet › Creating & Loading

Frequently asked questions

What is Pandas in Python?

Pandas is an open-source Python library for working with tabular data — rows and columns, like a spreadsheet. It provides two core objects, the Series (1-D) and the DataFrame (2-D), plus fast tools for loading, cleaning, analysing, and saving data.

How do I install Pandas?

Run pip install pandas in your terminal. Pandas depends on NumPy, which pip installs automatically. If you use Anaconda, Pandas comes pre-installed.

Why is Pandas so popular for data analysis?

It is built on top of NumPy for speed, reads almost any file format (CSV, Excel, JSON, SQL), and offers concise, expressive methods for filtering, grouping, and summarising data — tasks that would take many lines of plain Python.

What is the difference between Pandas and Excel?

Excel is a visual spreadsheet app; Pandas is code. Pandas handles far larger datasets, is fully reproducible, automatable, and integrates with the wider Python data ecosystem (NumPy, Matplotlib, scikit-learn).