Comprehensive Guide to Python Built-in Data Structures - Analytics Vidhya

Introduction

Python excels as a programming language, particularly in data science and generative AI. Efficient data manipulation (storage, management, and access) is crucial when dealing with large datasets. We've previously covered numbers and strings and their memory representation (link to previous article). This article delves into Python's built-in data structures and the distinction between mutable and immutable objects.

Key Concepts

• Python's Strengths: Python's versatility shines in data science and generative AI applications.
• Data Structures Overview: This section explores built-in data structures: lists, arrays, tuples, dictionaries, sets, and frozen sets.
• Lists: Mutable, dynamic arrays capable of holding diverse data types; offering extensive manipulation methods.
• Arrays vs. Lists: Arrays are homogeneous (same data type) and memory-efficient; lists provide greater flexibility with mixed data types.
• Tuples: Immutable sequences; faster and more memory-efficient than lists; ideal for unchanging collections.
• Dictionaries: Key-value pairs; mutable and highly versatile; useful for tasks like counting, reversing, memoization, and sorting complex data.

Table of contents

• What are Python's Built-in Data Structures?
• A. Working with Lists
  • List Literals
  • List Creation
  • Arrays in Python
  • Arrays vs. Lists (Dynamic Arrays)
  • Reversing Lists with Slicing
  • List Traversal Methods
  • Lists and Diverse Data Types
  • Reversing Lists with reverse()
  • The reversed() Function
  • In-place Methods
  • Replacing Lists vs. Modifying List Contents
  • Copying Lists using Slicing
  • Copying Lists using copy()
  • Copying Lists using deepcopy()
  • List Concatenation with the Operator
  • Generating Lists with range()
  • List Comprehensions
  • Nested if with List Comprehensions
  • Flattening Nested Lists
  • Converting Space-Separated Numbers to Integer Lists
  • Combining Lists into a List of Lists
  • Converting Lists of Tuples to Lists of Lists
• B. Working with Tuples
  • Tuple Literals
  • Lists vs. Tuples: A Comparison
  • Performance: Speed and Memory
  • Error Handling
  • Returning and Assigning Multiple Values
  • Tuple Creation using Generators
  • The zip() Function with Tuples
• C. Working with Dictionaries
  • Dictionary Literals
  • Nested Dictionaries (JSON)
  • Adding Key-Value Pairs to Nested Dictionaries
  • Removing Key-Value Pairs from Nested Dictionaries
  • Dictionaries as Counters
  • Inverting Dictionaries
  • Memoized Fibonacci
  • Sorting Complex Iterables with sorted()
  • Defining Default Values with .get() and .setdefault()
  • Merging Dictionaries with **
  • Creating Dictionaries with zip()
  • Dictionary Comprehensions
  • Creating Dictionaries from Existing Dictionaries
• D. Working with Sets
  • Set Literals
  • Removing Duplicates from Lists using Sets
  • Set Operations
  • isdisjoint(), issubset(), issuperset()
  • Set Comprehensions
  • Operations on Frozen Sets
• Frequently Asked Questions

What are Python's Built-in Data Structures?

Data structures organize and store data for efficient access and manipulation. This article covers Python's built-in data structures: lists, arrays, tuples, dictionaries, sets, and frozen sets.

A companion Python notebook (link to notebook) serves as a quick syntax reference.

A. Working with Lists

List Literals

Lists are built-in Python data types storing items of various data types within square brackets [], separated by commas. They are dynamic arrays, meaning their size can change.

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