Tuple from other iterables

Tuple Operations and Methods

| Operation | Syntax | Description | Example | |-----------|--------|-------------|---------| | Access | tuple[index] | Get element at index | coordinates[0] returns 3 | | Slice | tuple[start:end] | Get subset of tuple | mixed_tuple[1:3] | | Count | tuple.count(item) | Count occurrences | coordinates.count(3) | | Index | tuple.index(item) | Find index of item | coordinates.index(4) | | Length | len(tuple) | Get tuple length | len(coordinates) | | Membership | item in tuple | Check if item exists | 3 in coordinates |

Detailed Tuple Examples

`python

Creating and using tuples

Accessing elements

Tuple unpacking

Partial unpacking with asterisk

Nested tuples

Tuple methods

Advanced Tuple Usage

`python

Named tuples (requires import)

Define a named tuple

Tuple as dictionary keys (immutable, so hashable)

Swapping variables using tuples

Dictionaries

Overview

Dictionaries are unordered, mutable collections that store key-value pairs. They provide fast lookup times and are ideal for representing structured data where each value is associated with a unique key.

Creating Dictionaries

`python

Empty dictionary

Dictionary with initial values

Dictionary from keyword arguments

Dictionary from list of tuples

Dictionary comprehension

Dictionary Operations and Methods

| Operation | Syntax | Description | Example | |-----------|--------|-------------|---------| | Access | dict[key] | Get value by key | student["name"] | | Get | dict.get(key, default) | Safe access with default | student.get("email", "N/A") | | Set | dict[key] = value | Set key-value pair | student["email"] = "alice@email.com" | | Delete | del dict[key] | Remove key-value pair | del student["grade"] | | Pop | dict.pop(key, default) | Remove and return value | student.pop("age", 0) | | Keys | dict.keys() | Get all keys | student.keys() | | Values | dict.values() | Get all values | student.values() | | Items | dict.items() | Get key-value pairs | student.items() | | Update | dict.update(other) | Merge dictionaries | student.update({"year": 2023}) | | Clear | dict.clear() | Remove all items | student.clear() |

Detailed Dictionary Examples

`python

Creating and manipulating dictionaries

print(f"Initial inventory: {inventory}")

Accessing values

Safe access with get()

Adding new items

Updating existing items

Removing items

Iterating through dictionary

Dictionary methods

Advanced Dictionary Operations

`python

Nested dictionaries

Accessing nested data

Dictionary merging (Python 3.9+)

Dictionary comprehensions with conditions

Counting with dictionaries

Using defaultdict for automatic default values

Comparison of Data Structures

Characteristics Comparison

| Feature | List | Tuple | Dictionary | |---------|------|-------|------------| | Mutability | Mutable | Immutable | Mutable | | Ordering | Ordered | Ordered | Unordered (3.7+ maintains insertion order) | | Indexing | Integer index | Integer index | Key-based | | Duplicates | Allowed | Allowed | Keys must be unique | | Syntax | [1, 2, 3] | (1, 2, 3) | {"a": 1, "b": 2} | | Performance | Good for append/pop | Faster access | Fast key lookup |

Performance Comparison

| Operation | List | Tuple | Dictionary | |-----------|------|-------|------------| | Access by index/key | O(1) | O(1) | O(1) average | | Search for value | O(n) | O(n) | O(n) for values | | Insert at end | O(1) amortized | N/A | O(1) average | | Insert at beginning | O(n) | N/A | O(1) average | | Delete | O(n) | N/A | O(1) average | | Memory usage | Moderate | Low | Higher |

When to Use Each Structure

`python

Use Lists when:

- You need an ordered collection that can change

- You need to append/remove items frequently

- You need to access items by position

Use Tuples when:

- You need an ordered collection that won't change

- You want to ensure data integrity

- You need to use the collection as a dictionary key

Use Dictionaries when:

- You need fast lookups by key

- You have associated pairs of data

- You need to represent structured data

Best Practices

List Best Practices

`python

Use list comprehensions for simple transformations

Over:

Use enumerate() when you need both index and value

Use zip() to combine lists

Avoid modifying list while iterating

Wrong way:

Right way:

Tuple Best Practices

`python

Use tuples for fixed collections

name, age = get_name_age() # Tuple unpacking

Use named tuples for better readability

Use tuples as dictionary keys when you need composite keys

Dictionary Best Practices

`python

Use get() method to avoid KeyError

Use defaultdict for automatic initialization

Use dictionary comprehensions for transformations

Use items() for key-value iteration

Check for key existence efficiently

Avoid:

Common Use Cases

Data Processing Examples

`python

Processing CSV-like data with lists

Extract header and data

Convert to list of dictionaries

print("Records:", records)

Group data by city using dictionaries

print("Grouped by city:", dict(city_groups)) `

Configuration Management

`python

Application configuration using nested dictionaries

Accessing configuration

Data Analysis Examples

`python

Sales data analysis

Calculate total revenue per product

Find top selling product by revenue

Summary statistics

summary = { "total_revenue": total_revenue, "total_quantity": total_quantity, "average_price": average_price, "product_count": len(sales_data) }

print("Sales Summary:", summary) `

This comprehensive guide covers the fundamental aspects of Python's three primary data structures. Understanding when and how to use lists, tuples, and dictionaries effectively is essential for writing efficient and maintainable Python code. Each structure has its strengths and optimal use cases, and mastering them will significantly improve your Python programming capabilities.