# Python Data Structures Deep Dive

**1. Overview of Python Data Structures**

Python provides built-in data structures for efficient data storage and manipulation:

* List → Ordered, mutable sequence
* Tuple → Ordered, immutable sequence
* Set → Unordered, unique elements
* Dictionary → Key-value mapping

```python
data = [1, 2, 3]          # List
coords = (10, 20)         # Tuple
unique = {1, 2, 3}        # Set
user = {"id": 101}        # Dictionary
```

Each structure is optimized for specific use-cases.

***

**2. Advanced List Operations**

```python
numbers = [5, 2, 9, 1]

numbers.append(7)
numbers.sort()
numbers.reverse()

print(numbers)
```

Key capabilities:

* Dynamic resizing
* Index-based access
* Rich built-in methods

Common methods:\
`append()`, `extend()`, `remove()`, `pop()`, `sort()`, `copy()`

***

**3. List Comprehension for High Performance**

```python
squares = [x ** 2 for x in range(1, 6)]
print(squares)
```

List comprehensions offer:

* Faster execution
* Cleaner syntax
* Readable transformations

***

**4. Tuple Immutability & Performance**

```python
coordinates = (10, 20, 30)

# coordinates[0] = 5  # Error - Immutable
```

Benefits:

* Faster than lists
* Safer for constant data
* Hashable (usable as dict keys)

***

**5. Set Operations & Mathematical Use**

```python
a = {1, 2, 3, 4}
b = {3, 4, 5}

print(a | b)   # Union
print(a & b)   # Intersection
print(a - b)   # Difference
```

Sets are excellent for:

* Deduplication
* Membership testing
* Mathematical operations

***

**6. Dictionary Deep Dive (Key-Value Engine)**

```python
employee = {
    "name": "Alice",
    "role": "Engineer",
    "salary": 80000
}

employee["department"] = "AI"
print(employee.get("role"))
```

Advanced features:

* Fast lookup (hash-based)
* Dynamic keys
* Nested mapping support

***

**7. Nested Data Structures**

```python
company = {
    "employees": [
        {"name": "Alice", "id": 1},
        {"name": "Bob", "id": 2}
    ]
}

print(company["employees"][0]["name"])
```

Used in:

* JSON structures
* API payloads
* Database models

***

**8. Specialized Data Structures**

**deque (Double-ended Queue)**

```python
from collections import deque

dq = deque([1, 2, 3])
dq.appendleft(0)
dq.append(4)

print(dq)
```

Optimized for fast appends/pops on both ends.

**heapq (Priority Queue)**

```python
import heapq

nums = [5, 1, 3]
heapq.heapify(nums)
heapq.heappush(nums, 0)
print(heapq.heappop(nums))
```

Used in scheduling algorithms and optimized sorting.

***

**9. Comparative Complexity**

| Structure | Access | Insert | Delete |
| --------- | ------ | ------ | ------ |
| List      | O(1)   | O(n)   | O(n)   |
| Tuple     | O(1)   | N/A    | N/A    |
| Set       | O(1)   | O(1)   | O(1)   |
| Dict      | O(1)   | O(1)   | O(1)   |

Understanding complexity is vital for performance-critical systems.

***

**10. Enterprise Example: Data Pipeline Structure**

```python
pipeline = {
    "users": [
        {"id": 1, "name": "Alice", "skills": {"Python", "ML"}},
        {"id": 2, "name": "Bob", "skills": {"Java", "Cloud"}}
    ],
    "metadata": {
        "version": "1.0",
        "record_count": 2
    }
}

print(pipeline["users"][0]["skills"])
```

Demonstrates practical integration of:

* List
* Set
* Dict
* Nested structures

***

### Advanced Data Structure Patterns

| Pattern             | Use Case             |
| ------------------- | -------------------- |
| Nested dict + list  | API responses        |
| List of dicts       | Data records         |
| Set for uniqueness  | Filtering duplicates |
| Tuple for constants | Config values        |
| deque               | Queue processing     |

***

### Best Practices

* Choose structure based on access pattern
* Prefer set for uniqueness check
* Avoid deep nesting when possible
* Use list comprehensions wisely
* Normalize data for scalable systems

***

### Enterprise Applications

Python Data Structures power:

* AI training datasets
* Microservice payloads
* Financial transaction systems
* Configuration management
* Big-data ETL pipelines

Efficient data structure design ensures:

* Performance
* Readability
* Maintainability
* Scalability

***
