Course Introduction

The ever-growing popularity of Python has democratized the application of data science and analytics for individuals and companies alike. This phenomenon is driven by one of the essential Python software libraries used for data manipulation and analysis—pandas.

The pandas library is an open-source Python library that serves as an easy-to-use tool kit for data manipulation and analysis.

To make the most of this advanced course, you will need some familiarity with Python and the primary uses of pandas, e.g., reading data from CSV, accessing specific rows, etc.

Beyond the fundamental functionalities of pandas, a wide range of advanced capabilities are available for us to work more effectively and intelligently with data.

Course outline

The course covers a comprehensive range of pandas functionalities for working with different data types. We'll start by learning to import different types of data in pandas to put the data in the correct data structure (most commonly pandas DataFrames) before downstream operations.

We'll then discover how to manipulate the data within pandas DataFrames by combining, reshaping, and transforming them. Following that, we'll learn how to convert and encode data into specific types before learning how to handle various data types and structures.

Beyond data manipulation, we'll also explore how to use pandas to generate plots and charts for data visualization. Finally, we'll learn about additional pandas features and extended libraries that simplify and augment our analyses.

Intended audience

The intended audience for this course on pandas functionalities includes data analysts, scientists, and researchers who work with diverse datasets. It is suitable for individuals seeking to enhance their skills in data manipulation, transformation, and visualization using pandas, especially those with a foundational understanding of Python and data analysis concepts.

What learners can expect

In this course, we’ll learn about the advanced pandas functions and capabilities at our disposal, including lesser known pandas features that are highly useful for data analysis.

We'll also learn how to:

• Leverage pandas to implement versatile data manipulation.

• Analyze and handle the different data types, such as numerical, categorical, sparse, time series, and missing data.

• Utilize the powerful extended libraries of pandas.

The course comprises a mix of theoretical explanations, practical demonstrations, and hands-on exercises to get a comprehensive learning experience. It’s also designed with pragmatic implementation in mind, based on real-world use cases. Therefore, the course isn’t structured like an academic exercise where we tediously go through every detail of pandas.

Upon completing the course, we’ll be equipped with skills to add to our tool kit as competent data practitioners, possessing the know-how to wield the powerful abilities of pandas for data manipulation and analysis.