Day 15 : Python libraries for DevOps

Introduction

Python is one of the best programming language and its powerful tool is several impactful libraries in which Python have to perform a variety of tasks like configuration, development, and Deployment in computer engineering.

Python offers a set of libraries for DevOps engineers to practice a wide range of day-to-day activities like JSON and YAML. In this blog, we will explore these.

DevOps Engineer should be familiar with these libraries include os, sys, json, yaml, and some more.

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.

Thanks for reading my blog.