Added OOP To Basic Python Doc

Docs/Basic/05-oop.md

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+# 05 – Object-Oriented Programming (OOP) in Python
+
+This document explains the basics of **Object-Oriented Programming (OOP)** in Python using simple examples.
+We cover:
+
+* Classes and objects
+* Attributes and methods
+* Class attributes vs instance attributes
+* Inheritance
+* Special (magic) methods
+
+---
+
+## 1. Basic Class, Attribute, and Method
+
+### Code
+
+```python
+class test_class():
+    def __init__(self, input):
+        self.parm = input
+        print("Class Created")
+
+    def result(self):
+        print(f"param is : {self.parm}") 
+
+var = test_class('abbas')
+var.result()
+```
+
+### Explanation
+
+#### Class
+
+* `test_class` is a **class**, which acts as a blueprint for creating objects.
+
+#### `__init__` method (Constructor)
+
+* `__init__` is a **special method** that runs automatically when a new object is created.
+* `input` is a **parameter** passed when creating the object.
+* `self.parm = input` creates an **instance attribute** called `parm`.
+
+#### Attribute
+
+* `parm` is an **attribute** (a variable that belongs to the object).
+* It stores data specific to each object.
+
+#### Method
+
+* `result()` is a **method** (a function that belongs to the class).
+* It uses `self.parm` to access the object’s data.
+
+#### Object Creation
+
+```python
+var = test_class('abbas')
+```
+
+* Creates an object named `var`.
+* Calls `__init__` automatically.
+
+#### Method Call
+
+```python
+var.result()
+```
+
+* Calls the `result` method on the object.
+
+---
+
+## 2. Class Attributes vs Instance Attributes
+
+### Code
+
+```python
+class test_class():
+    test_value = 'abbas'
+
+    def __init__(self, input):
+        self.parm = input
+        print("Class Created")
+
+    def result(self):
+        print(f"param is : {self.parm}") 
+
+var = test_class('abbas')
+var2 = test_class('mmd')
+
+var.result()
+var.test_value
+
+var2.test_value = 'mmd'
+var2.test_value
+var.test_value
+```
+
+### Explanation
+
+#### Class Attribute
+
+```python
+test_value = 'abbas'
+```
+
+* This is a **class attribute**.
+* It belongs to the class itself.
+* Shared by all objects unless overridden.
+
+#### Instance Attribute
+
+```python
+self.parm = input
+```
+
+* This is an **instance attribute**.
+* Each object has its own value.
+
+#### Behavior Analysis
+
+```python
+var.test_value
+```
+
+* Accesses the class attribute → `'abbas'`
+
+```python
+var2.test_value = 'mmd'
+```
+
+* Creates a **new instance attribute** for `var2`.
+* Does not change the class attribute.
+
+```python
+var2.test_value
+```
+
+* Returns `'mmd'` (instance attribute)
+
+```python
+var.test_value
+```
+
+* Still returns `'abbas'` (class attribute)
+
+#### Key Rule
+
+* Instance attributes override class attributes **only for that object**.
+
+---
+
+## 3. Inheritance
+
+### Code
+
+```python
+class class_1():
+    def __init__(self):
+        print("Class 1 Created") 
+
+    def hi(self):
+        print("Hi")
+
+class class_2(class_1):
+    def __init__(self):
+        print("Class 2 Created") 
+        self.hi()
+
+b = class_2()
+```
+
+### Explanation
+
+#### Parent Class
+
+```python
+class class_1():
+```
+
+* `class_1` is the **parent (base) class**.
+
+#### Child Class
+
+```python
+class class_2(class_1):
+```
+
+* `class_2` **inherits** from `class_1`.
+* It automatically has access to all public methods of `class_1`.
+
+#### Method Usage
+
+```python
+self.hi()
+```
+
+* `hi()` is defined in `class_1`.
+* Because of inheritance, `class_2` can call it.
+
+#### Output Order
+
+```text
+Class 2 Created
+Hi
+```
+
+#### Important Note
+
+* `class_1.__init__()` is **not called automatically** here.
+* To call it, you would need:
+
+```python
+super().__init__()
+```
+
+---
+
+## 4. Special (Magic) Methods
+
+### Code
+
+```python
+class class_1():
+    def __init__(self):
+        print("Class 1 Created") 
+
+    def __len__(self):
+        return 1
+
+    def __str__(self):
+        return 'print command on class'
+
+    def __del__(self):
+        return 'on del value'
+```
+
+### Explanation
+
+Special methods start and end with **double underscores (`__`)** and control built-in behavior.
+
+#### `__init__`
+
+* Runs when an object is created.
+
+#### `__len__`
+
+```python
+len(object)
+```
+
+* Defines the behavior of `len()` on the object.
+* Returns `1` in this example.
+
+#### `__str__`
+
+```python
+print(object)
+```
+
+* Defines the string representation of the object.
+* Used by `print()` and `str()`.
+
+#### `__del__`
+
+* Runs when the object is deleted or garbage-collected.
+* Used rarely in modern Python.
+* Return value is ignored.
+
+---
+
+## Summary
+
+* **Class**: Blueprint for objects
+* **Object**: Instance of a class
+* **Attribute**: Data stored in an object
+* **Method**: Function inside a class
+* **Class Attribute**: Shared across all objects
+* **Instance Attribute**: Unique per object
+* **Inheritance**: Child class reuses parent class logic
+* **Magic Methods**: Customize built-in Python behavior
+

Docs/Libs/Docker/01-Setup.md

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+# Docker SDK for Python – Setup and First Steps
+
+This document introduces the **Docker SDK for Python (`docker` library)** and explains how to connect to the Docker Engine, inspect its status, and authenticate with a registry. The goal is not just to show code, but to clearly explain **what each part does, why it exists, and when you would use it**.
+
+This guide assumes you already understand basic Docker concepts (images, containers, daemon) and are approaching this from a DevOps perspective.
+
+---
+
+## 1. Installing the Docker SDK for Python
+
+Before Python can communicate with Docker, we need to install the official SDK.
+
+```bash
+pip install docker
+```
+
+### What this does
+
+* Installs the **docker** Python package (often called *docker-py*).
+* This package acts as a **client wrapper** around Docker’s REST API.
+* All interactions ultimately talk to the Docker daemon (`dockerd`) over a socket or TCP connection.
+
+Important note:
+
+* Installing the library alone is not enough.
+* Docker **must already be installed and running** on the system.
+
+---
+
+## 2. Connecting to Docker Using Environment Configuration
+
+The simplest and most common way to create a Docker client is by using environment-based configuration.
+
+```python
+import docker
+
+client = docker.from_env()
+version = client.version()
+ping_docker = client.ping()
+
+print(version, ping_docker)
+```
+
+### Step-by-step explanation
+
+#### `import docker`
+
+* Imports the Docker SDK.
+* This module exposes high-level objects for interacting with Docker resources.
+
+#### `docker.from_env()`
+
+* Automatically creates a `DockerClient` instance.
+* Reads Docker connection details from environment variables such as:
+
+  * `DOCKER_HOST`
+  * `DOCKER_TLS_VERIFY`
+  * `DOCKER_CERT_PATH`
+* On Linux, this usually resolves to:
+
+  * `unix:///var/run/docker.sock`
+
+This is why `from_env()` is preferred:
+
+* Works across Linux, macOS, Windows, and CI systems
+* Requires no hardcoded connection strings
+
+#### `client.version()`
+
+* Calls the Docker Engine `/version` API endpoint.
+* Returns detailed metadata such as:
+
+  * Docker Engine version
+  * API version
+  * Go version
+  * OS and architecture
+
+This is commonly used for:
+
+* Debugging
+* Compatibility checks
+* Logging runtime environment details
+
+#### `client.ping()`
+
+* Sends a lightweight request to the Docker daemon.
+* Returns `True` if Docker is reachable and responsive.
+
+This is a **health check**, often used in:
+
+* Startup validation
+* Monitoring scripts
+* CI/CD pipelines
+
+---
+
+## 3. What Does “from_env” Actually Mean?
+
+Using `from_env()` tells the SDK:
+
+> “Figure out how to connect to Docker using the same configuration the Docker CLI uses.”
+
+Behind the scenes, it:
+
+* Detects whether Docker is local or remote
+* Determines socket vs TCP
+* Applies TLS settings if required
+
+### When should you use it?
+
+* Local development
+* Kubernetes nodes
+* CI runners
+* Most production automation
+
+### When not to use it?
+
+* When you need explicit control over connection parameters
+* When connecting to a **remote Docker daemon** with custom networking
+
+---
+
+## 4. Creating a Docker Client Explicitly
+
+Instead of relying on environment detection, you can create a client manually.
+
+```python
+import docker
+
+client = docker.DockerClient(base_url='unix://var/run/docker.sock')
+version = client.version()
+ping_docker = client.ping()
+
+print(version, ping_docker)
+```
+
+### Explanation
+
+#### `docker.DockerClient(...)`
+
+* Directly instantiates a Docker client object.
+* Requires you to specify how to reach the Docker daemon.
+
+#### `base_url='unix://var/run/docker.sock'`
+
+* Points to the Unix socket used by Docker on Linux systems.
+* This socket is owned by root and the `docker` group.
+
+Important implications:
+
+* Your Python process must have permission to access the socket
+* Usually means running as root or a user in the `docker` group
+
+### When this approach is useful
+
+* Controlled environments
+* Educational examples
+* Explicit infrastructure scripts
+
+### Why it is less common
+
+* Not portable across OSes
+* Hardcodes infrastructure assumptions
+
+---
+
+## 5. Inspecting Docker and Authenticating
+
+Once connected, the client can retrieve detailed system information and authenticate with registries.
+
+```python
+import docker
+
+client = docker.DockerClient(base_url='unix://var/run/docker.sock')
+version = client.version()
+ping_docker = client.ping()
+information = client.info()
+
+client.login(
+    username='user',
+    password='password',
+    registry='registry_link'
+)
+
+print(information, ping_docker)
+```
+
+### `client.info()`
+
+* Calls the Docker `/info` endpoint.
+* Returns a comprehensive snapshot of the Docker host, including:
+
+  * Number of containers (running/stopped)
+  * Number of images
+  * Storage driver
+  * Cgroup and kernel features
+  * Security options (AppArmor, seccomp)
+
+This is extremely valuable for:
+
+* Capacity planning
+* Debugging runtime issues
+* Auditing host configuration
+
+### `client.login(...)`
+
+* Authenticates Docker with a container registry.
+* Stores credentials in Docker’s credential store.
+
+Parameters:
+
+* `username`: registry account username
+* `password`: registry password or access token
+* `registry`: registry URL (e.g. Docker Hub or private registry)
+
+Common use cases:
+
+* Pulling private images
+* Pushing images in CI/CD pipelines
+* Automating registry interactions
+
+Security note:
+
+* Avoid hardcoding credentials in source code
+* Prefer environment variables or secret managers
+
+---
+
+## 6. High-Level vs Low-Level API (Important Concept)
+
+What you are using here is the **high-level Docker client**.
+
+Characteristics:
+
+* Pythonic object model
+* Resource-oriented (containers, images, networks)
+* Easier to read and maintain
+
+The SDK also exposes a **low-level API**:
+
+* Direct access to Docker REST endpoints
+* More control, less abstraction
+
+As a DevOps engineer, you will typically:
+
+* Use high-level API for automation
+* Drop to low-level API for advanced edge cases
+
+---
+
+## 7. Summary
+
+In this setup phase, you learned how to:
+
+* Install the Docker SDK for Python
+* Connect to Docker using environment-based configuration
+* Explicitly define Docker connection settings
+* Verify Docker availability
+* Retrieve system-level Docker information
+* Authenticate with container registries
+
+This foundation is critical before moving on to:
+
+* Managing containers
+* Building images
+* Working with volumes and networks
+
+---
+
+## References
+
+Official Docker SDK for Python documentation:
+
+* [https://docker-py.readthedocs.io/](https://docker-py.readthedocs.io/)

Docs/Libs/Docker/02-Images.md

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+# Docker SDK for Python – Working with Images
+
+This document explains how to **manage Docker images** using the Docker SDK for Python. Instead of treating the SDK as a set of function calls, we’ll approach images the same way Docker itself does: as immutable artifacts that are pulled, built, tagged, pushed, inspected, and eventually cleaned up.
+
+All examples assume:
+
+* Docker is installed and running
+* The Python process has access to the Docker socket
+
+---
+
+## 1. Creating the Docker Client
+
+```python
+import docker
+
+client = docker.DockerClient(base_url='unix://var/run/docker.sock')
+ping_docker = client.ping()
+```
+
+### Explanation
+
+* `DockerClient` establishes a connection to the Docker daemon.
+* `ping()` verifies that Docker is reachable before doing any real work.
+
+This is a common pattern in automation:
+
+* Fail fast if Docker is unavailable
+* Avoid partial execution later in the script
+
+---
+
+## 2. Pulling Images from a Registry
+
+```python
+def pull_image(name_image, tag_image):
+    image = client.images.pull(name_image, tag=tag_image)
+    print(image)
+```
+
+### What this does
+
+* Downloads an image from a registry (Docker Hub or private registry).
+* If the image already exists locally, Docker may reuse layers.
+
+Parameters:
+
+* `name_image`: repository name (e.g. `alpine`, `nginx`, `myrepo/app`)
+* `tag_image`: specific version or variant (e.g. `3.20`, `latest`)
+
+Returned value:
+
+* An `Image` object representing the pulled image
+
+Why this matters:
+
+* Pulling explicitly avoids relying on implicit image downloads
+* Makes automation predictable and repeatable
+
+---
+
+## 3. Building Images from a Dockerfile
+
+### Basic build
+
+```python
+def build_image():
+    image, logs = client.images.build(
+        path=".",
+        tag="myapp:1.0"
+    )
+
+    for log in logs:
+        if "stream" in log:
+            print(log["stream"].strip())
+```
+
+### Explanation
+
+* `path="."` tells Docker to use the current directory as the build context.
+* Docker automatically looks for a file named `Dockerfile`.
+* `tag="myapp:1.0"` assigns a name and version to the resulting image.
+
+The build process returns:
+
+* `image`: the final built image object
+* `logs`: a stream of build output messages
+
+Printing build logs is important because:
+
+* Docker build failures are only visible in logs
+* CI pipelines rely on this output for debugging
+
+---
+
+## 4. Advanced Build with Custom Dockerfile and Build Arguments
+
+```python
+def build_image_2():
+    image, logs = client.images.build(
+        path=".",
+        dockerfile="Dockerfile.prod",
+        tag="myapp:prod",
+        buildargs={
+            "APP_ENV": "production",
+            "VERSION": "1.0.0"
+        }
+    )
+```
+
+### Explanation
+
+This version adds more control:
+
+* `dockerfile="Dockerfile.prod"`
+
+  * Allows multiple Dockerfiles per project
+  * Common for dev vs prod builds
+
+* `buildargs`
+
+  * Passed to `ARG` instructions inside the Dockerfile
+  * Enables parameterized builds without editing the Dockerfile
+
+Typical DevOps use cases:
+
+* Environment-specific builds
+* Injecting version numbers
+* Feature flags during build time
+
+---
+
+## 5. Tagging Images
+
+```python
+def tag_image():
+    image = client.images.get("myapp:1.0")
+    image.tag("myrepo/myapp", tag="latest")
+```
+
+### Explanation
+
+* Docker images are immutable, but tags are not.
+* This creates an additional reference to the same image ID.
+
+Why tagging is important:
+
+* One image can have multiple tags
+* Tags represent lifecycle stages (`1.0`, `prod`, `latest`)
+
+This is how promotion pipelines work:
+
+* Build once
+* Tag many times
+* Push selectively
+
+---
+
+## 6. Removing Images
+
+```python
+def remove_image():
+    client.images.remove("myapp:1.0", force=True)
+```
+
+### Explanation
+
+* Removes the image reference from the local Docker host.
+* `force=True` removes the image even if it is in use by stopped containers.
+
+Use with care:
+
+* Running containers still prevent deletion
+* Forced removal is destructive
+
+---
+
+## 7. Pushing Images to a Registry
+
+```python
+def push_image():
+    client.images.push(
+        repository="myrepo/myapp",
+        tag="latest"
+    )
+```
+
+### Explanation
+
+* Uploads the image layers to a registry.
+* Requires prior authentication (`client.login`).
+
+Important notes:
+
+* Only new or changed layers are pushed
+* Tags determine what remote users pull
+
+This step is usually automated in:
+
+* CI/CD pipelines
+* Release workflows
+
+---
+
+## 8. Cleaning Up Unused Images
+
+```python
+def prune_images():
+    result = client.images.prune()
+    print(result)
+```
+
+### Explanation
+
+* Removes dangling images (untagged and unused).
+* Helps reclaim disk space on build servers.
+
+The result includes:
+
+* Number of images removed
+* Amount of disk space freed
+
+This is essential for:
+
+* CI runners
+* Long-lived build machines
+
+---
+
+## 9. Inspecting Image Metadata
+
+```python
+def inspect_image():
+    alpine_image = client.images.get("alpine:3.20")
+
+    print(alpine_image.attrs)
+    print(alpine_image.attrs["Id"])
+    print(alpine_image.attrs["Size"])
+    print(alpine_image.attrs["Config"]["Env"])
+    print(alpine_image.attrs["Config"]["Cmd"])
+```
+
+### Explanation
+
+* `attrs` exposes the raw Docker image inspection data.
+* This is equivalent to `docker image inspect`.
+
+Useful fields:
+
+* `Id`: content-addressable image hash
+* `Size`: image size in bytes
+* `Config.Env`: environment variables baked into the image
+* `Config.Cmd`: default command
+
+This information is often used for:
+
+* Debugging unexpected behavior
+* Auditing images
+* Validating build output
+
+---
+
+## 10. Listing Local Images
+
+```python
+def image_list():
+    images = client.images.list()
+    for item in images:
+        print(item.id, item.tags)
+```
+
+### Explanation
+
+* Lists all images stored locally.
+* Each image may have multiple tags or none.
+
+This mirrors:
+
+* `docker images`
+
+---
+
+## 11. Ensuring an Image Exists
+
+```python
+def ensure_image(name):
+    try:
+        client.images.get(name)
+        print(f"{name} exists")
+    except docker.errors.ImageNotFound:
+        print(f"Pulling {name}")
+        client.images.pull(name)
+```
+
+### Explanation
+
+This is a very common DevOps pattern:
+
+* Check if the image exists locally
+* Pull it only if necessary
+
+Benefits:
+
+* Avoids unnecessary network calls
+* Makes scripts idempotent
+
+---
+
+## 12. Important Exceptions to Know
+
+When working with images, you must handle failures explicitly.
+
+### `docker.errors.ImageNotFound`
+
+* Raised when an image does not exist locally
+* Common when calling `get()`
+
+### `docker.errors.BuildError`
+
+* Raised when an image build fails
+* Usually due to Dockerfile errors or missing files
+
+### `docker.errors.APIError`
+
+* Raised for general Docker API failures
+* Includes permission issues, daemon errors, and network problems
+
+Catching these exceptions is critical for:
+
+* Reliable automation
+* Meaningful error reporting
+* CI/CD stability
+
+---
+
+## 13. Summary
+
+In this section, you learned how to:
+
+* Pull images from registries
+* Build images using Dockerfiles
+* Use build arguments and multiple Dockerfiles
+* Tag and push images
+* Inspect and list images
+* Clean up unused images
+* Handle common Docker image errors
+
+This image workflow is the backbone of:
+
+* CI pipelines
+* Release automation
+* Platform engineering systems
+
+---
+
+## References
+
+Official Docker SDK for Python documentation:
+
+* [https://docker-py.readthedocs.io/](https://docker-py.readthedocs.io/)

Docs/Libs/Docker/03-Container-Lifecycle.md

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+# Docker SDK for Python – Working with Containers
+
+This document explains how to **manage Docker containers** using the Docker SDK for Python. Containers are the runtime unit of Docker, so this section focuses on lifecycle management: listing, creating, running, starting, stopping, restarting, pausing, and inspecting logs.
+
+The goal is to understand **how container state changes**, how the Python SDK maps to Docker CLI behavior, and how these operations are typically used in real DevOps automation.
+
+---
+
+## 1. Creating the Docker Client
+
+```python
+import docker
+import time
+
+client = docker.DockerClient(base_url='unix://var/run/docker.sock')
+ping_docker = client.ping()
+```
+
+### Explanation
+
+* Establishes a connection to the Docker daemon via the Unix socket.
+* `ping()` is used as a sanity check before executing container operations.
+
+In production-grade scripts, this check prevents:
+
+* Silent failures
+* Partial execution when Docker is down
+
+---
+
+## 2. Listing Running Containers
+
+```python
+def get_containers_list():
+    containers = client.containers.list()
+
+    for c in containers:
+        print(c.id, c.name, c.status)
+```
+
+### Explanation
+
+* `containers.list()` returns **only running containers** by default.
+* Each returned object represents a live container instance.
+
+Common attributes:
+
+* `id`: unique container identifier
+* `name`: human-readable container name
+* `status`: runtime state (running)
+
+This is equivalent to:
+
+* `docker ps`
+
+---
+
+## 3. Listing All Containers (Including Stopped)
+
+```python
+def get_all_containers_list():
+    containers = client.containers.list(all=True)
+
+    for c in containers:
+        print(c.id, c.name, c.status)
+```
+
+### Explanation
+
+* `all=True` includes stopped, exited, and created containers.
+* Useful for cleanup, auditing, and lifecycle reconciliation.
+
+Equivalent CLI command:
+
+* `docker ps -a`
+
+---
+
+## 4. Creating a Container (Without Starting It)
+
+```python
+def create_container():
+    container = client.containers.create(
+        image="nginx:latest",
+        name="my_nginx",
+        ports={"80/tcp": 8080},
+        detach=True
+    )
+```
+
+### Explanation
+
+This creates a container in the **created** state.
+
+Key parameters:
+
+* `image`: base image for the container
+* `name`: explicit container name
+* `ports`: port mapping (container → host)
+* `detach=True`: container runs in background when started
+
+Important distinction:
+
+* `create()` does **not** start the container
+* This mirrors `docker create`
+
+Use cases:
+
+* Deferred startup
+* Multi-step initialization
+
+---
+
+## 5. Running a Container (Create + Start)
+
+```python
+def run_container():
+    container = client.containers.run(
+        "nginx:latest",
+        name="nginx_run",
+        ports={"80/tcp": 8081},
+        detach=True
+    )
+```
+
+### Explanation
+
+* `run()` is a convenience method.
+* Internally performs `create()` followed by `start()`.
+
+Equivalent CLI command:
+
+* `docker run -d -p 8081:80 nginx:latest`
+
+This is the most common approach for:
+
+* Short-lived workloads
+* Simple services
+
+---
+
+## 6. Managing Container Lifecycle
+
+```python
+def start_container():
+    container = client.containers.get("my_nginx")
+
+    container.start()
+    time.sleep(1000)
+
+    container.restart()
+    time.sleep(1000)
+
+    container.pause()
+    container.unpause()
+    time.sleep(1000)
+
+    container.stop(timeout=10)
+```
+
+### Explanation
+
+This function demonstrates multiple lifecycle transitions.
+
+#### `containers.get(name)`
+
+* Retrieves an existing container by name or ID.
+* Raises an exception if the container does not exist.
+
+#### `start()`
+
+* Transitions container from `created` or `stopped` → `running`.
+
+#### `restart()`
+
+* Stops and immediately starts the container.
+* Often used after configuration changes.
+
+#### `pause()` / `unpause()`
+
+* Freezes container processes using cgroups.
+* Network and filesystem state remain intact.
+
+#### `stop(timeout=10)`
+
+* Sends SIGTERM, then SIGKILL after timeout.
+* Allows graceful shutdown.
+
+The `sleep()` calls simulate:
+
+* Long-running services
+* Observing container behavior between states
+
+---
+
+## 7. Reading Container Logs
+
+```python
+def log_container():
+    container = client.containers.get("my_nginx")
+    logs = container.logs(tail=20)
+    print(logs.decode())
+```
+
+### Explanation
+
+* Retrieves logs from the container’s stdout/stderr.
+* `tail=20` limits output to the last 20 lines.
+
+Important details:
+
+* Logs are returned as bytes
+* Decoding is required for readable output
+
+Equivalent CLI command:
+
+* `docker logs --tail 20 my_nginx`
+
+This is critical for:
+
+* Debugging runtime issues
+* Health checks
+* Observability tooling
+
+---
+
+## 8. Conditional Execution Based on Docker Availability
+
+```python
+if ping_docker:
+    get_containers_list()
+```
+
+### Explanation
+
+* Ensures container operations only run if Docker is reachable.
+* Prevents unhandled API errors.
+
+This pattern is common in:
+
+* Entry-point scripts
+* Health probes
+* Automation jobs
+
+---
+
+## 9. Container States (Conceptual Model)
+
+Understanding container states is essential:
+
+* `created`: container exists but is not running
+* `running`: container processes are active
+* `paused`: execution is suspended
+* `exited`: container stopped normally
+* `dead`: container failed unexpectedly
+
+Every method in this document transitions the container between these states.
+
+---
+
+## 10. Summary
+
+In this section, you learned how to:
+
+* List running and stopped containers
+* Create containers separately from starting them
+* Run containers in a single step
+* Control container lifecycle states
+* Read container logs
+* Safely execute operations based on Docker availability
+
+This container-level control is the foundation for:
+
+* Service orchestration
+* CI/CD job execution
+* Debugging and recovery automation
+
+---
+
+## References
+
+Official Docker SDK for Python documentation:
+
+* [https://docker-py.readthedocs.io/](https://docker-py.readthedocs.io/)