Introduction

Pandas is a powerful open-source data manipulation and analysis library for Python. It is designed to make working with structured (tabular, multidimensional, potentially heterogeneous) data both easy and intuitive. Built on top of the NumPy library, pandas offers a wide range of data manipulation and analysis functionality, including:

• Reading and writing data from/to various formats, including CSV, Excel, and SQL databases;
• Handling missing data and dealing with null values;
• Filtering, grouping, and aggregating data using SQL-like syntax;
• Merging and joining data from multiple sources;
• Manipulating and transforming data using built-in functions and methods;
• Visualizing data using plots and charts.

One of the key features of pandas is the DataFrame, a 2-dimensional labeled data structure with columns that may contain different types. You can think of it as a spreadsheet, an SQL table, or a dictionary of Series objects. It is particularly useful for storing and manipulating large datasets in an organized and efficient manner.

To get started with pandas, you typically need to install it using the following command:

Luckily, we already have it preinstalled, so you can begin by importing it into your Python script with the following syntax: