Introduction to Pandas in Python

Introduction

Pandas is a powerful and flexible open source data analysis and manipulation tool built on top of the Python programming language. It provides fast and efficient tools for slicing and dicing, aggregating, and filtering large datasets, and it’s particularly useful for working with tabular or structured data, such as data in a CSV file or a SQL database.

DataFrame: A Pandas DataFrame is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It is generally the most commonly used pandas object.

Series: A Series is a 1-dimensional labeled array capable of holding any data type. It is similar to a column in a spreadsheet or a field in a SQL table.

Index: An Index is an immutable array-like object that stores the axis labels for a data structure. It is an essential component of a Series or DataFrame, and can be used to filter, select, or transform data.

Key features of pandas:

1. Handling large and diverse datasets: Pandas is designed to work with large and diverse datasets, including tabular data, time series data, and unstructured data. It can load data from various sources, including CSV, Excel, and SQL…