the_information_nexus/tech_docs/python/data_structures.md

Sure, here's a detailed look at common data structures in Python and the types of data they are typically used for:

1. Lists

• Common Uses:

  • Ordered collections of items where the order matters.
  • Storing items that can be changed, added, or removed.
  • Examples: To-do lists, sequences of numbers, names of students, etc.

• Typical Data:

  • Numbers: [1, 2, 3, 4, 5]
  • Strings: ['apple', 'banana', 'cherry']
  • Mixed types: [1, 'apple', 3.14, True]

2. Dictionaries

• Common Uses:

  • Mapping relationships between keys and values.
  • Fast lookups of values based on unique keys.
  • Examples: Phone books (name to number), configuration settings, JSON-like data structures.

• Typical Data:

  • Strings to strings: {'name': 'Alice', 'city': 'New York'}
  • Strings to numbers: {'apples': 10, 'bananas': 5}
  • Mixed keys and values: {1: 'one', 'two': 2, (3, 4): 'tuple_key'}

3. Tuples

• Common Uses:

  • Fixed collections of items where the order matters and should not change.
  • Returning multiple values from a function.
  • Storing related but different pieces of data.
  • Examples: Coordinates (x, y, z), RGB color values, database records.

• Typical Data:

  • Numbers: (1, 2, 3)
  • Strings: ('red', 'green', 'blue')
  • Mixed types: (1, 'apple', 3.14)

4. Sets

• Common Uses:

  • Collections of unique items.
  • Membership tests, eliminating duplicates.
  • Mathematical set operations like union, intersection, and difference.
  • Examples: Unique user IDs, unique items in a collection, tags or keywords.

• Typical Data:

  • Numbers: {1, 2, 3, 4, 5}
  • Strings: {'apple', 'banana', 'cherry'}
  • Mixed types (less common due to unhashable types): {1, 'apple', (3, 4)}

5. Strings

• Common Uses:

  • Storing and manipulating text.
  • Examples: Sentences, file paths, URLs, JSON data.

• Typical Data:

  • Sentences: "Hello, world!"
  • Paths: "/usr/bin/python"
  • JSON data: '{"name": "Alice", "age": 25}'

6. Arrays (from the array module)

• Common Uses:

  • Storing large amounts of numeric data efficiently.
  • Arrays provide more efficient storage and faster access for numeric data.
  • Examples: Numerical computations, scientific data.

• Typical Data:

  • Numbers: array.array('i', [1, 2, 3, 4, 5])

7. Namedtuples (from the collections module)

• Common Uses:

  • Creating simple classes to group related data.
  • Similar to tuples, but with named fields for better readability.
  • Examples: Representing rows from a database, simple data structures.

• Typical Data:

  • Custom data structures:

    from collections import namedtuple
    Point = namedtuple('Point', ['x', 'y'])
    point = Point(1, 2)
    

8. Deques (from the collections module)

• Common Uses:

  • Double-ended queues for fast appends and pops from both ends.
  • Examples: Implementing queues, stacks, and other data structures.

• Typical Data:

  • Items in a queue or stack:

    from collections import deque
    my_deque = deque([1, 2, 3, 4, 5])
    

9. Defaultdict (from the collections module)

• Common Uses:

  • Dictionaries with a default value for non-existent keys.
  • Examples: Counting occurrences, grouping data.

• Typical Data:

  • Dictionaries with default values:

    from collections import defaultdict
    my_defaultdict = defaultdict(int)
    my_defaultdict['a'] += 1
    

Examples of Usage

List Example

# List of student names
students = ['Alice', 'Bob', 'Charlie']

Dictionary Example

# Dictionary of student grades
grades = {'Alice': 'A', 'Bob': 'B', 'Charlie': 'C'}

Tuple Example

# Coordinates of a point
point = (10, 20)

Set Example

# Set of unique fruits
unique_fruits = {'apple', 'banana', 'cherry'}

String Example

# A sentence
sentence = "The quick brown fox jumps over the lazy dog."

Array Example

import array
# Array of integers
int_array = array.array('i', [1, 2, 3, 4, 5])

Namedtuple Example

from collections import namedtuple
# Named tuple for representing a point
Point = namedtuple('Point', ['x', 'y'])
point = Point(1, 2)

Deque Example

from collections import deque
# Deque for queue operations
my_deque = deque(['a', 'b', 'c'])
my_deque.append('d')
my_deque.appendleft('z')

Defaultdict Example

from collections import defaultdict
# Defaultdict for counting
count = defaultdict(int)
count['apple'] += 1

Understanding these common data structures and their typical use cases will help you choose the right one for your needs in different programming scenarios.