Dynamic lists are an essential data structure in the programming world, but the way of defining and using dynamic lists is not always apparent to Python beginners. Years ago, when I first started learning Python ( while still working as a QA engineer ) I was in the same exact position as you, I was asking the same question: “How Do You Create A Dynamic List In Python?”. In this post, I’ll walk you through the ways of creating a dynamic list in Python based on my own experience.

How Do You Create A Dynamic List In Python?

There are several methods for creating a dynamic list in Python, including using the list() function, using square brackets [ ], and using the .append() method.

When working on complex projects, you may encounter a situation where you need to make decisions based on multiple conditions. That’s one of the first things I got to know as a new QA engineer who later became a Python developer.

This is where the elif statement in Python comes in.

In this article, we’ll explore what the elif statement is, how it works, and when to use it in your code.

I’m going to answer the “Should I Use Elif In Python?” question based on my experience.

In many Python programming situations, at some point, as your application grows you want to start storing logs in a file. Since the very early days ( about my Python experience here ) that was the case for me. In this post, I’ll share my ideas for the “How Do I Store Python Logs In A File?” question.

How Do I Store Python Logs In A File?

One way to store these Python logs in a file so you can look at them later is to write them to a file using a built-in logging module. 

Here’s an example of how to use the logging module to store logs in a file in Python:

import logging

# Configure the logging module to use a file
logging.basicConfig(filename='example.log', level=logging.DEBUG)

# Write some log messages
logging.debug('This is a debug message')
logging.info('This is an info message')
logging.warning('This is a warning message')
logging.error('This is an error message')
logging.critical('This is a critical message')

In this example, the basicConfig() function is used to configure the logging module to write logs to a file named ‘example.log’.

The level argument is set to logging.DEBUG which means that all log messages, including debug, info, warning, error, and critical messages, will be written to the file.

Then the code uses different logging levels debug(), info(), warning(), error() and critical() to write different log messages.

When you run this code, it will create a new file called ‘example.log’ in the same directory as your script, and the log messages will be written to it.

Obviously, this is a very simplified version of logging in Python, I’ll get into more details and other examples in the following sections of this post.

The first time you hear about Python Programming you kind of want to explore and understand the capabilities of the language. This is exactly what happened to me when one of my university professors told me about Python and I was just a QA engineer at that time. The first question that came to my mind was: “What Is Python Programming In Short?”. Let me explain…

What Is Python Programming In Short?

As a Python developer who has been using Python for several years now, I can confidently say that it is an incredibly powerful and versatile programming language.

Python is a high-level, interpreted language, which means that it is easy to read and write, and you don’t need to worry about low-level details like memory management and compiling code.

Python is great for beginners who never coded anything before.

I used to be one years ago and all I wanted to do was to just start building a script or a small project, just to start understanding programming from a practical standpoint, not just in theory.