Pandas is one of the most important Python libraries for anyone who wants to learn data analysis, data science, machine learning, or artificial intelligence. When you start working with real-world datasets, you quickly realize that handling data is not only about performing mathematical calculations. Real datasets can contain thousands or millions of rows, different types of information, missing values, duplicate records, text, dates, categories, and many other problems that need to be handled before meaningful analysis can begin.
This is where Pandas becomes extremely useful.
Pandas provides powerful data structures and tools that make it easier to work with labeled and tabular data. The official Pandas documentation describes it as a Python package designed for fast, flexible, and expressive data structures and data analysis tools, with Series and DataFrame serving as its two primary data structures.
In this Day 37 lesson, we will build a strong foundation in Pandas. We will understand what Pandas is, why it is important in data science, how it differs from NumPy, how to install and import it, what Series and DataFrame mean, how to create our first Pandas objects, where Pandas is used in the real world, how a typical Pandas workflow works, and which basic terms every beginner should understand.
What Is Pandas in Python?
Pandas is an open-source Python library used primarily for working with structured and labeled data. It provides convenient tools for loading, organizing, inspecting, cleaning, transforming, analyzing, and preparing data.
The name Pandas is commonly associated with the phrase “Panel Data,” although today the library is much broader than that original concept.
If you have worked with Excel spreadsheets before, you can think of a Pandas DataFrame as a programmable table that you can control using Python.
For example, imagine that a company has the following student-like dataset.