Python libraries for DevOps

Python is a vast language that is used for various tasks of DevOps such as:-

1. Automation

2. Configuration management

3. Continuous integration continuous deployment (CI/CD)

Some popular Python libraries and tools commonly used in DevOps are:-

1. Ansible:- It is an open-source automation tool that is used for configuration management, task automation and application deployment. Ansible itself is not a Python library but you can write playbooks in YAML and use Python as a part of automation tasks.

2. BOTO3:- It is the AWS SDK for Python, it allows you to interact with various Amazon Web services and resources. It is used for automating cloud infrastructure tasks such as managing EC2 instances S three bucket and many more.

3. Terraform and Terrascript:- Terraform is the popular infrastructure as Code(iaC) tool for provisioning and managing infrastructure. Terrascript is a Python library that allows you to generate terraform configurations using Python code.

4. PyYAML:- it is a library used for parsing and working with YAML, which is commonly used for configuration files in DevOps. It is a tool like Ansible and Kubernetes.

5. Docker-py:- If you work with Docker containers the Docker-py Library allows you to interact with the Docker Daemon from Python you can create manage and control Dogger containers and images using this library.

6. Kubernetes Python Client(pykube):- For managing clusters in Kubernetes pycube is a Python library that provides a convenient way to interact with the Kubernetes API it allows you to update create and manage resources in a Kubernetes cluster.

Read JSON file:-

JSON (JavaScript Object Notation) is a lightweight data interchange format that is widely used for data serialization and configuration files. Python has built-in support for working with JSON through the json module, which allows you to encode Python data structures into JSON format (serialization) and decode JSON data into Python objects (deserialization).

Steps:-

1. Import the json module: import json

2. JSON Serialization (Encoding): To convert a Python data structure (e.g., dictionary, list) into a JSON string, you can use the json.dumps() function

3. JSON Deserialization (Decoding):

   To parse a JSON string and convert it back into a Python data structure, you can use the json.loads() function

4. Reading and Writing JSON to Files:

   You can also read and write JSON data from/to files using the json.dump() and json.load() functions

5. Handling JSON with Nested Structures:

   JSON can represent complex data structures with nested objects and arrays. Python's json module can handle nested structures as well.

Read YAML file:-

YAML is a human readable data serialization format. It is used for confrigation files and data exchange. Python has support for working with YAML through various libraries. Most popular libraries for helping YAML is PyYAML.

Steps:-

1. Install PyYAML: pip install pyyaml

2. Import the PyYAML module:

   Import the PyYAML module in your Python script.

3. YAML Serialization (Dumping):

   To convert a Python data structure (e.g., dictionary, list) into a YAML-formatted string, you can use the yaml.dump() function.

4. YAML Deserialization (Loading):

   To parse a YAML string and convert it back into a Python data structure, you can use the yaml.load() function

5. Reading and Writing YAML to Files:

   You can also read and write YAML data to/from files using the yaml.dump() and yaml.safe_load() functions.

6. Handling YAML with Complex Structures:

   YAML can represent complex data structures, including nested objects and lists, similar to JSON. PyYAML can handle these structures as well.

Today Task:-

1. Create a Dictionary in Python and write it to a json File.

2. Read YAML file using python, file services.yaml and read the contents to convert yaml to json.

Day 15 of #90daysofDevOps

Thanks for reading

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