What Is Pandas?

What Is Pandas?

Pandas is an open-source Python library designed for data manipulation and analysis.

The name comes from the term Panel Data, a term used in statistics and economics for structured datasets.

In simple terms:

Why Do We Need Pandas?

Suppose we have customer information:

Name       Age    City
Raj        29     Hyderabad
John       32     London
Sarah      27     New York
David      35     Chicago

We might want to answer questions such as:

• Which customers are older than 30?
• How many customers are from London?
• What is the average age?
• Which city has the most customers?
• Are any values missing?

Pandas provides simple tools for performing these operations.

Pandas vs NumPy

You just completed NumPy, so this distinction is important.

A simple way to remember it:

Installing Pandas

If Pandas is not already installed, you can install it using pip.

pip install pandas

If you are using a virtual environment, make sure the environment is activated before installing Pandas.

Import Pandas

The standard way to import Pandas is with the pd alias.

import pandas as pd

The pd alias is simply a shorter name for Pandas. It allows us to write:

pd.DataFrame()
pd.read_csv()
pd.Series()

Your First Pandas Example

Let's create a small table using Pandas.

import pandas as pd

data = {
    "Name": ["Raj", "John", "Sarah"],
    "Age": [29, 32, 27],
    "City": ["Hyderabad", "London", "New York"]
}

df = pd.DataFrame(data)

print(df)

    Name  Age       City
0    Raj   29  Hyderabad
1   John   32     London
2  Sarah   27   New York

We created a Pandas DataFrame.

A DataFrame is essentially a table with rows and columns. We will study DataFrames in detail in the next lessons.

Understanding the DataFrame

Look at the table again:

    Name  Age       City
0    Raj   29  Hyderabad
1   John   32     London
2  Sarah   27   New York

Here:

• Name is a column.
• Age is a column.
• City is a column.
• Each horizontal line represents a row.
• The numbers on the left are row indexes.

This table-like structure is one of the main reasons Pandas is so useful for Data Science.

Access a Column

We can select one column from the DataFrame.

print(df["Name"])

0      Raj
1     John
2    Sarah
Name: Name, dtype: object

Pandas returns the column as a Series.

We will learn exactly what a Series is in the next lesson.

Why Pandas Is Important for AI

Machine Learning models cannot simply consume a messy CSV file and magically understand it.

Before training a model, we often need to:

• Load the dataset.
• Inspect the data.
• Find missing values.
• Filter unwanted records.
• Sort and organize the data.
• Calculate statistics.
• Prepare columns for Machine Learning.

Pandas is one of the main Python tools used for these tasks.

Real-World Example

Imagine you have an e-commerce dataset containing 100,000 orders:

Order ID | Product | Price | Quantity | Country
1001     | Laptop  | 900   | 1        | USA
1002     | Mouse   | 25    | 2        | UK
1003     | Laptop  | 900   | 1        | India
...

With Pandas, you could load this data and ask:

import pandas as pd

orders = pd.read_csv("orders.csv")

print(orders.head())

Later, you could filter orders, calculate revenue, find missing values, group orders by country, and prepare the dataset for a Machine Learning model.

Those operations are exactly what we will learn in the upcoming Pandas lessons.

How Pandas Builds on NumPy

Pandas is not completely separate from the numerical Python ecosystem. Pandas uses NumPy heavily underneath.

This is why your previous NumPy knowledge is useful.

What You Will Learn in This Pandas Lesson

This lesson introduces Pandas. The following lessons will build the skills step by step:

• Series — one-dimensional labeled data.
• DataFrames — table-like data with rows and columns.
• Reading CSV — load real datasets.
• Filtering Data — select the records you need.
• Sorting Data — organize your dataset.
• Missing Data — handle incomplete values.
• GroupBy — summarize data by categories.
• Pandas Project — combine everything into one project.