We already discussed that Python is the most powerful programming language especially in the field of Data science and Gen-AI. While working with large data it is really important to understand, how to manipulate (store, manage, and access) data efficiently.
Now that previously we’ve also explored about numbers and strings, and how they are stored in the memory, which you can find here. In this part, we’ll dive deep into the versatility of Python Data Structures and understand the difference between Mutable and Immutable Objects.
Data structure is a type of container which is used to store the data in an organized way that can be accessed and manipulated, whenever it is required. In this article, we’ll discuss the built-in Data Structures of Python — Lists, Arrays, Tuples, Dictionaries, Sets and FrozenSets.
Additionally, here’s the Python notebook, which you can use as a quick syntax reference.
Python, as a high-level programming language, provides a rich set of built-in data structures that make it a powerful tool for developers. These data structures are essential for organizing and storing data efficiently, which is crucial for problem-solving and algorithm development. In this comprehensive guide, we will explore the primary built-in data structures in Python: lists, tuples, dictionaries, sets, and strings. We will cover their characteristics, common operations, and practical use cases.
Lists
Characteristics
- Mutable: Elements can be modified after the list is created.
- Ordered: Elements are stored in a specific order, and that order is maintained.
- Allows Duplicates: Elements can appear multiple times.
Common Operations
- Creating a List:
- Accessing Elements:
- Modifying Elements:
- Appending Elements:
- Inserting Elements:
- Removing Elements:
- List Comprehensions:
Use Cases
- Dynamic Arrays: When you need a flexible array structure that can change in size.
- Ordered Collections: When the order of elements matters, such as in sequences or stacks.
Tuples
Characteristics
- Immutable: Once created, elements cannot be modified.
- Ordered: Elements are stored in a specific order, and that order is maintained.
- Allows Duplicates: Elements can appear multiple times.
Common Operations
- Creating a Tuple:
- Accessing Elements:
- Tuple Unpacking:
- Concatenating Tuples:
Use Cases
- Fixed Collections: When you need a collection of items that should not change.
- Return Multiple Values: When a function needs to return multiple values, tuples can be used for grouping.
Dictionaries
Characteristics
- Mutable: Elements (key-value pairs) can be modified after the dictionary is created.
- Unordered: The order of elements is not maintained (in Python 3.7+, dictionaries maintain insertion order, but it’s not relied upon).
- No Duplicate Keys: Keys must be unique.
Common Operations
- Creating a Dictionary:
- Accessing Values:
- Modifying Values:
- Adding Key-Value Pairs:
- Removing Key-Value Pairs:
- Dictionary Comprehensions:
Use Cases
- Mappings: When you need to map keys to values, such as in a phone book or configuration settings.
- Fast Lookups: When you need fast access to data based on custom keys.
Sets
Characteristics
- Mutable: Elements can be modified after the set is created.
- Unordered: The order of elements is not maintained.
- No Duplicates: Elements must be unique.
Common Operations
- Creating a Set:
- Adding Elements:
- Removing Elements:
- Set Operations (Union, Intersection, Difference):
Python, as a high-level programming language, provides a rich set of built-in data structures that make it a powerful tool for developers. These data structures are essential for organizing and storing data efficiently, which is crucial for problem-solving and algorithm development. In this comprehensive guide, we will explore the primary built-in data structures in Python: lists, tuples, dictionaries, sets, and strings. We will cover their characteristics, common operations, and practical use cases.
Lists
Characteristics
- Mutable: Elements can be modified after the list is created.
- Ordered: Elements are stored in a specific order, and that order is maintained.
- Allows Duplicates: Elements can appear multiple times.
Common Operations
- Creating a List:
- Accessing Elements:
- Modifying Elements:
- Appending Elements:
- Inserting Elements:
- Removing Elements:
- List Comprehensions:
Use Cases
- Dynamic Arrays: When you need a flexible array structure that can change in size.
- Ordered Collections: When the order of elements matters, such as in sequences or stacks.
Tuples
Characteristics
- Immutable: Once created, elements cannot be modified.
- Ordered: Elements are stored in a specific order, and that order is maintained.
- Allows Duplicates: Elements can appear multiple times.
Common Operations
- Creating a Tuple:
- Accessing Elements:
- Tuple Unpacking:
- Concatenating Tuples:
Use Cases
- Fixed Collections: When you need a collection of items that should not change.
- Return Multiple Values: When a function needs to return multiple values, tuples can be used for grouping.
Dictionaries
Characteristics
- Mutable: Elements (key-value pairs) can be modified after the dictionary is created.
- Unordered: The order of elements is not maintained (in Python 3.7+, dictionaries maintain insertion order, but it’s not relied upon).
- No Duplicate Keys: Keys must be unique.
Common Operations
- Creating a Dictionary:
- Accessing Values:
- Modifying Values:
- Adding Key-Value Pairs:
- Removing Key-Value Pairs:
- Dictionary Comprehensions:
Use Cases
- Mappings: When you need to map keys to values, such as in a phone book or configuration settings.
- Fast Lookups: When you need fast access to data based on custom keys.
Sets
Characteristics
- Mutable: Elements can be modified after the set is created.
- Unordered: The order of elements is not maintained.
- No Duplicates: Elements must be unique.
Common Operations
- Creating a Set:
- Adding Elements:
- Removing Elements:
- Set Operations (Union, Intersection, Difference):
I am a data lover and I love to extract and understand the hidden patterns in the data. I want to learn and grow in the field of Machine Learning and Data Science.
