added data structures
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tech_docs/python/data_structures.md
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tech_docs/python/data_structures.md
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Sure, here's a detailed look at common data structures in Python and the types of data they are typically used for:
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### 1. Lists
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- **Common Uses**:
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- Ordered collections of items where the order matters.
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- Storing items that can be changed, added, or removed.
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- Examples: To-do lists, sequences of numbers, names of students, etc.
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- **Typical Data**:
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- Numbers: `[1, 2, 3, 4, 5]`
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- Strings: `['apple', 'banana', 'cherry']`
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- Mixed types: `[1, 'apple', 3.14, True]`
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### 2. Dictionaries
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- **Common Uses**:
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- Mapping relationships between keys and values.
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- Fast lookups of values based on unique keys.
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- Examples: Phone books (name to number), configuration settings, JSON-like data structures.
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- **Typical Data**:
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- Strings to strings: `{'name': 'Alice', 'city': 'New York'}`
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- Strings to numbers: `{'apples': 10, 'bananas': 5}`
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- Mixed keys and values: `{1: 'one', 'two': 2, (3, 4): 'tuple_key'}`
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### 3. Tuples
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- **Common Uses**:
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- Fixed collections of items where the order matters and should not change.
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- Returning multiple values from a function.
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- Storing related but different pieces of data.
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- Examples: Coordinates (x, y, z), RGB color values, database records.
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- **Typical Data**:
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- Numbers: `(1, 2, 3)`
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- Strings: `('red', 'green', 'blue')`
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- Mixed types: `(1, 'apple', 3.14)`
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### 4. Sets
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- **Common Uses**:
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- Collections of unique items.
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- Membership tests, eliminating duplicates.
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- Mathematical set operations like union, intersection, and difference.
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- Examples: Unique user IDs, unique items in a collection, tags or keywords.
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- **Typical Data**:
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- Numbers: `{1, 2, 3, 4, 5}`
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- Strings: `{'apple', 'banana', 'cherry'}`
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- Mixed types (less common due to unhashable types): `{1, 'apple', (3, 4)}`
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### 5. Strings
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- **Common Uses**:
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- Storing and manipulating text.
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- Examples: Sentences, file paths, URLs, JSON data.
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- **Typical Data**:
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- Sentences: `"Hello, world!"`
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- Paths: `"/usr/bin/python"`
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- JSON data: `'{"name": "Alice", "age": 25}'`
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### 6. Arrays (from the `array` module)
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- **Common Uses**:
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- Storing large amounts of numeric data efficiently.
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- Arrays provide more efficient storage and faster access for numeric data.
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- Examples: Numerical computations, scientific data.
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- **Typical Data**:
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- Numbers: `array.array('i', [1, 2, 3, 4, 5])`
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### 7. Namedtuples (from the `collections` module)
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- **Common Uses**:
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- Creating simple classes to group related data.
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- Similar to tuples, but with named fields for better readability.
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- Examples: Representing rows from a database, simple data structures.
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- **Typical Data**:
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- Custom data structures:
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```python
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from collections import namedtuple
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Point = namedtuple('Point', ['x', 'y'])
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point = Point(1, 2)
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```
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### 8. Deques (from the `collections` module)
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- **Common Uses**:
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- Double-ended queues for fast appends and pops from both ends.
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- Examples: Implementing queues, stacks, and other data structures.
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- **Typical Data**:
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- Items in a queue or stack:
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```python
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from collections import deque
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my_deque = deque([1, 2, 3, 4, 5])
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```
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### 9. Defaultdict (from the `collections` module)
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- **Common Uses**:
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- Dictionaries with a default value for non-existent keys.
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- Examples: Counting occurrences, grouping data.
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- **Typical Data**:
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- Dictionaries with default values:
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```python
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from collections import defaultdict
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my_defaultdict = defaultdict(int)
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my_defaultdict['a'] += 1
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```
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### Examples of Usage
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#### List Example
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```python
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# List of student names
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students = ['Alice', 'Bob', 'Charlie']
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```
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#### Dictionary Example
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```python
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# Dictionary of student grades
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grades = {'Alice': 'A', 'Bob': 'B', 'Charlie': 'C'}
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```
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#### Tuple Example
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```python
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# Coordinates of a point
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point = (10, 20)
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```
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#### Set Example
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```python
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# Set of unique fruits
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unique_fruits = {'apple', 'banana', 'cherry'}
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```
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#### String Example
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```python
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# A sentence
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sentence = "The quick brown fox jumps over the lazy dog."
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```
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#### Array Example
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```python
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import array
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# Array of integers
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int_array = array.array('i', [1, 2, 3, 4, 5])
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```
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#### Namedtuple Example
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```python
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from collections import namedtuple
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# Named tuple for representing a point
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Point = namedtuple('Point', ['x', 'y'])
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point = Point(1, 2)
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```
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#### Deque Example
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```python
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from collections import deque
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# Deque for queue operations
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my_deque = deque(['a', 'b', 'c'])
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my_deque.append('d')
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my_deque.appendleft('z')
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```
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#### Defaultdict Example
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```python
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from collections import defaultdict
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# Defaultdict for counting
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count = defaultdict(int)
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count['apple'] += 1
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```
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Understanding these common data structures and their typical use cases will help you choose the right one for your needs in different programming scenarios.
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