Python is often touted as one of the most beginner-friendly programming languages. Hearing this can actually make it sting more when you first try to learn it and don’t succeed. My first attempt at learning Python was a discouraging experience, and the second time wasn’t much better.
However, on my third try, something clicked. I discovered that Python really can be accessible, even for someone like me who doesn’t have a background in coding and is more comfortable with humanities subjects. The key, I realized, is approaching it in the Best Way To Learn Python.
My First Stumble: Learning Python the Hard Way (Literally)
About ten years ago, I decided to learn Python. My goals were vague – maybe automate some tasks, perhaps build a small application. It seemed like a valuable skill to acquire, regardless of the specifics.
I picked up a PDF of the then-free book, Learn Python the Hard Way, and started working through it diligently.
The first major hurdle was setting up Python on my computer. Back then, instructions for this were often geared towards experienced programmers. It felt like an endless struggle to decipher and implement everything.
I wanted to dive into writing Python code, but my initial step involved spending hours wrestling with the command line. This immediately killed my motivation. I felt defeated before I even wrote a single line of meaningful code.
Once I finally got everything configured, I could follow along with the book, writing code. I even started piecing together a very basic text adventure game.
However, when I encountered the inevitable coding challenges – those moments where something just doesn’t work and you’re completely stumped as to why – I gave up.
Pounding my head against the keyboard, trying to decipher cryptic error messages, felt less important than my other responsibilities. Especially when my objective was creating a text adventure game that held little personal interest and would likely never be played by anyone.
Round Two: MOOC Missteps
Years later, I decided to give Python another shot. I was working as a journalist and had become interested in data journalism and, in particular, web scraping.
I knew Python skills would be essential, so I enrolled in a beginner Python course on a well-known online education platform.
Like many Massive Open Online Courses (MOOCs), this one relied heavily on video lectures. I would watch a video explaining a Python concept, take a quiz on the platform to confirm my understanding, and then move onto the next module.
Experienced programmers can probably predict what happened next: when I tried to write Python code independently, I was completely lost.
Watching someone else code in videos and listening to their explanations gave me a false sense of understanding. Scoring perfectly on multiple-choice and fill-in-the-blank quizzes reinforced this illusion that I had grasped the material.
But, predictably, when it came time to apply what I had learned to my own projects, I couldn’t. I could review the videos and copy the instructor’s code, but I struggled to adapt anything to my own needs.
Staying motivated was also difficult because the course material didn’t feel relevant to my goals.
I wanted to learn web scraping. Instead, I was struggling to comprehend video lectures about object-oriented programming (OOP). What did any of this have to do with my aspirations? I wasn’t sure, and that made it easy to quit. Again.
Deconstructing My Python Learning Failures
Looking back, the reasons for my failures are quite clear. In my first attempt, my primary mistakes were:
- Lack of a Clear Goal: Why was I learning Python? I didn’t really have a defined purpose. This made it incredibly easy to give up when faced with challenges – which are inevitable in programming.
- Too Much Initial Difficulty: Setting up Python on my system is a necessary step eventually. However, tackling this complex task with zero prior experience, before even writing a simple
print('Hello world!')statement, was a recipe for frustration and failure.
When learning something challenging, especially as a complete beginner, you need early successes to build confidence and believe in your ability to learn. Starting with a frustrating setup process that didn’t involve actual coding ensured I missed out on these crucial early wins.
In my second attempt, I avoided those initial setup issues, but I made new mistakes:
- Passive Learning: I wasn’t learning by actively doing. Watching videos and taking quizzes gave me the feeling of learning, but I wasn’t actually coding. When I finally tried (and failed) to write code, it was even more discouraging because I thought I already understood it. Constantly rewinding videos to rewatch sections also disrupted my learning flow.
- Goal Disconnection: While I had a clear overall goal (data journalism and web scraping), the learning path wasn’t directly aligned. I was taking a generic introductory Python course, which covered topics that might be crucial for software development but less relevant to data journalism. I struggled to connect the fundamental concepts I was learning in the course with my practical coding aspirations.
Throughout both attempts, I also made a significant mental error. I viewed learning Python in a very binary way. Either I had “learned Python” – all of Python – or I hadn’t.
This made the prospect of learning seem overwhelming. Every challenge was amplified by the daunting idea of some mythical Python “finish line” that felt incredibly distant.
This perspective is, of course, flawed. Like a spoken language, Python (and other programming languages) are not something you ever truly “finish” learning. And just like a spoken language, you don’t need complete fluency in Python to accomplish meaningful tasks.
Just as a first-year exchange student can significantly improve their experience by learning basic phrases like “How much is this?” and “Where’s the bathroom?” in the local language, basic Python skills can be incredibly impactful. You don’t need to know everything – or even that much – to make a real difference in your work and life.
This was a crucial lesson I only learned through a stroke of luck.
How I Finally Started Learning Python Effectively
By 2018, I had essentially given up on learning Python. Two failed attempts felt like enough! However, I then joined Dataquest, a company that teaches data science skills online – including Python programming.
My new role didn’t require coding skills, but I felt it was important to experience the learning platform firsthand. I needed to understand our product and the experiences of our learners. Perhaps, I thought, I could even learn enough to finally pursue web scraping, as I had intended before.
So, with some hesitation, I created an account and started the Python for Data Science course path on Dataquest.
To my surprise, it was enjoyable and felt surprisingly easy. Even more surprisingly, it wasn’t long before I felt capable of building my own projects.
I wrote a small script to organize emails. I used Python to quickly analyze survey data. And, gradually, I developed the web scraping and analysis project I had dreamed of back when I was a journalist.
I was – and still am – actually using Python to improve my workflow and solve data analysis challenges at work. Years later, I’m still a novice coder, but I can create simple scripts to streamline tasks and tackle data-related problems using Python.
This transformation occurred because, more by accident than intention, I had stumbled upon a best way to learn Python that avoided almost all the mistakes I had made previously:
- I started with a clear objective: learn enough Python to perform basic data tasks and better understand our customers.
- I completely bypassed the initial hurdle of installing Python, as Dataquest allows you to learn and write code directly in a web browser.
- I was learning to code by actively writing code, not passively watching someone else.
- I followed a learning path specifically designed for Python data work, ensuring that everything I learned and every exercise felt relevant to my goals.
- I focused on learning what I needed, not trying to conquer all of Python at once.
Alt text: Example of Python code being written and executed within a browser-based learning environment, highlighting the ease of access and immediate feedback for learners.
Key Principles for the Best Way to Learn Python
Reflecting on my failures and my eventual success, I believe the best way to learn python boils down to a few key principles:
First, start with a clear goal. Why do you want to learn Python? What specific projects or tasks do you want to accomplish with it? Without a compelling answer to this question, maintaining motivation will be incredibly difficult.
Second, learn by doing what you actually want to do. If you can find a specialized learning resource, such as a platform that teaches Python specifically for game development, that’s ideal. But even general resources can work if you actively apply what you learn to beginner Python projects as you progress through your studies.
Your learning process must include actively writing code, and ideally, code that directly contributes to something you are genuinely interested in.
Third, eliminate the initial barrier of setting up Python and libraries locally.
Numerous online platforms now allow you to write and run code directly in a web browser. Alternatively, you can use notebooks on platforms like Google Colab or similar services. Prioritize making the initial steps as easy as possible. You can address local setup complexities later, once you have a solid foundation.
Alt text: Screenshot of the Google Colab interface displaying a Python notebook, illustrating a user-friendly platform for writing and executing Python code in the cloud.
Fourth, don’t aim to “learn Python” in its entirety. That’s an immense, long-term objective that is arguably unattainable – even expert Python developers don’t know absolutely everything about the language.
Instead, focus on learning how to use Python to build a simplified version of your target project, or even just a component of that project. Then, learn how to expand that project, or move on to the next step.
Break down large tasks into smaller, manageable chunks. Set goals centered around building tangible outputs, so you experience the psychological reward of completing something concrete as you learn.
By following these guidelines, regardless of your specific reasons for learning Python, I am confident you can achieve your goals without experiencing the frustration of repeated failures along the way!
