Data Structures in Python

Overview

In this course, you’ll explore data structures in Python, which are methods of storing and organizing data in a computer. You’ll focus on data structures that are among the most useful for data professionals: lists, tuples, dictionaries, sets, and arrays. You’ll also discover how to categorize data using data loading, cleaning, and binning. Lastly, you’ll learn about two of the most widely used and important Python tools for advanced data analysis: NumPy and pandas. By the end of this course, you will be able to: • Explain how to manipulate dataframes using techniques such as selecting and indexing, boolean masking, grouping and aggregating, and merging and joining • Describe the main features and methods of core pandas data structures such as dataframes • Describe the main features and methods of core NumPy data structures such as arrays and series • Define Python tools such as libraries, packages, modules, and global variables • Describe the main features and methods of built-in Python data structures such as lists, tuples, dictionaries, and sets

Syllabus

Lists and tuples

In this module, you will explore data structures in Python, which are methods of storing and organizing data in a computer. You’ll focus on lists and tuples, data structures that are among the most useful for data professionals.

Dictionaries and sets

In this module, you will focus on dictionaries and sets, some more data structures that are among the most useful for data professionals.

Arrays and vectors with NumPy

In this module, you will focus on arrays. You’ll learn about one of the most widely used and important Python tools for advanced data analysis: NumPy.

Dataframes with pandas

In this module, you will learn about one of the most widely used and important Python tools for advanced data analysis: pandas. You’ll also discover how to categorize data using data loading, cleaning, and binning.