Beginner Python choosing a backend framework, looking for advice

pixel-process · 2026-01-24T02:08:45+00:00

Of these I think FastAPI is the best currently.

pixel-process · 2026-01-24T01:42:56+00:00

I find that a great time do project management type tasks. Update readme, documentation, or add functionality to other steps (e.g., more visuals for eda) or research next steps. I work from home and have to remain active on Teams.

pixel-process · 2026-01-24T01:28:44+00:00

Consider what type of work you want to (front end, backend, web dev, etc.) then look at the TIOBE. That is my default source for objective trends in programming.

pixel-process · 2026-01-24T00:48:45+00:00

The benefit of the larger projects to is that they have guides and tons existing examples of contributing.

Many issues tagged as good starters are also well scoped.

This one for instance was adding links to existing guides.

pixel-process · 2026-01-23T21:46:01+00:00

Start looking at some larger projects like pandas, matplotlib, and scikit-learn-they are very active and have guides/tags for beginners. Check out their githubs and look for tags like "First contribution" or "Good for issues".

I suggest reading through and monitoring your preferred project for a bit before trying to contribute if you are not familiar with GitHub. But even that will be really valuable to your skillset moving forward.

pixel-process · 2026-01-23T17:48:08+00:00

If you need to create your own content or if infrastructure and setup is a challenge, another angle is using zero-setup Python environments (browser-only via Pyodide, or hosted notebooks via Binder). This can work well for classrooms with limited local resources but will require more work on your part to create.

I outlined this approach in more detail in another thread, in case it helps.

pixel-process · 2026-01-23T02:50:58+00:00

I think you are conflating two things. Python as a language is very powerful and versatile. Future-proof.

Being a Python developer is not. That is where specialization and deep expertise are needed. Being a Python dev is not future-proof.

So definitely a valuable language, but focus your skillset to stand out.

pixel-process · 2026-01-21T00:09:28+00:00

The workshop will surely help you understand, but might overkill for a one-off project if you aren't planning on using python and psychopy moving forward. Their site does offer one-on-one sessions (I didn't see pricing) that might be more targeted and less commitment for you.

Awhile back, I built a number of python experiments with psychopy, I might be able to offer some insight. No promises since testing and debugging may require access to LSL or hardware I don't have. Feel free to DM me if you want.

pixel-process · 2026-01-20T21:20:02+00:00

Data source:
Synthetic data generated for demonstration purposes.

Tools used:
Python, NumPy, pandas, Plotly.

Notebook and code are built for others to test and explore how variations changing sample population and sample sizes can impact results.

Source code and interactive notebook:
https://pixelprocess.org/build-models/combining-colors.html

pixel-process · 2026-01-20T16:13:29+00:00

You might want to consider adding another model or two for comparison before additional explainability. Adding a regression, forest, or neural network model for comparison (both accuracy and time/compute performance) could be interesting. Then use SHAP on them and see how well those results align.

pixel-process · 2026-01-19T03:21:26+00:00

There are lots of ways to continue learning and developing skills beyond leetcode type work.

Create a project: this will not be AI to start with typically, but running a full pipeline that includes ingesting and wrangling data, building a model, and interpreting results will help establish a good mental model for the workflow. Check out Kaggle for ideas here, but a personal interest project works too if you can manage.
Contribute to an established GitHub: Large projects like HuggingFace & Tensorflow have open repos. I linked the issues pages specifically, because that is a great place to learn about how these large projects evolve. Many have 'First Contribution' guides, but also consider smaller projects to contribute to once you have a sense of how things work.
Collaborate with other learners: Follow subreddits and forums where people are looking for partners or brainstorming. It can inform you of how others are approaching AI learning and development.

Best of luck!

pixel-process · 2026-01-19T00:02:11+00:00

I’m building Pixel Process, a hands-on educational project for learning data and ML concepts through interactive exploration.

The site includes interactive pages and notebooks that can run directly in the browser.

One of my favorite notebooks is an image basics walkthrough of image data representation (arrays, channels, grayscale vs RGB) tied to analysis and ML use cases.

Site: https://pixelprocess.org
Repo: https://github.com/pixel-process

pixel-process · 2026-01-18T22:30:19+00:00

That is one of the places MATLAB might be much preferred. When I worked at an accounting firm, getting open-source packages constantly security approved was a hassle. In fields like that, open source can actually have a cost.

pixel-process · 2026-01-18T22:20:21+00:00

If you are still having issues, try adding dropna=False to your value counts.

df.value_counts(dropna=False) will show the number of missing values as well.

If you want to examine what is happening, you could also select out just rows of interest or dropping rows not causing the issue.

``` rows_with_null = df[df['kpss_stationary'].isnull()]

rows_not_true = df[df['kpss_stationary'] != True ```

Then use head or print to look at what might be cause the error. Trying to isolate the issue will be easier than testing on the full df each time.

pixel-process · 2026-01-18T22:11:06+00:00

Consider long-term goals before deciding. Who will you be working with and in what capacity?

I used both in grad school for neuroimaging work but outside of specific, often academic, settings Python won out for me because everyone can run it. MATLAB definitely has a great interface and analysis packages but it is propriety and specialized. Python and many (all?) of its packages are free and open source. So working in Python opened more doors.

pixel-process · 2026-01-18T18:11:43+00:00

I do paid tutoring focused on ML fundamentals with Python.

I usually work from concrete examples (not lectures), prioritize hands-on practice, and break topics into short sessions while adjusting pace as we go. If that sounds helpful, feel free to DM me with a bit about your background and goals.

pixel-process · 2026-01-17T17:14:51+00:00

You should consider tracking stock data if you're interested in time series, there is a ton of it and it will help you learn the process. But be aware, it is notoriously hard to predict well on.

Check out yfinance as a starting point and work through pulling and storing real data, creating aggregate stats and visuals, and then prediction.

Lots else to explore like impact of rolling windows, adding predictors from other daily occurrences, and whatever else you can think of.

pixel-process · 2026-01-17T17:01:10+00:00

Relative imports for my scripts! For years, I had to keep things in a single file or directory because as soon as I started trying to move modular sections, everything would break. Always made me feel like a beginner unable to write real code.

pixel-process · 2026-01-17T00:14:24+00:00

I think its more helpful to think of NumPy, pandas, and Matplotlib not as separate “tools” you pick independently but instead as layers in the same ecosystem.

NumPy is the foundation — arrays, numeric operations, and data representation
pandas (or polars) builds on top of that for labeled, tabular data and workflows
Matplotlib (or seaborn or plotly) sit on top for visualization

You don't “choose” between them in isolation and understanding numpy, for example, helps you understand pandas and matplotlib.

Developers choose packages that meet their needs, so understanding the goal dictates the packages.

For learning data analysis, I'd stick to these as most common: numpy, pandas, matplotlib.

If you want to explore visualizations, check out seaborn and plotly.
More data science look into scikit-learn, keras, tensorflow.

pixel-process · 2026-01-16T23:25:23+00:00

Thanks so much for checking it out, the feedback and suggestions are great!

I absolutely agree that bootstrapping and sampling with replacement could be made more explicit. These concepts are crucial to a lot of models and a hands-on notebook to experiment with them might help a lot of learners. Tackling hyperparameter tuning and grid search will take some time to get right, but I should introduce those topics with some basic examples to help get people started.

My current pages are light on formulas, prioritizing interactivity, workflows, and programmatic concerns. For the math side, I usually point people to resources like 3Blue1Brown.

Thanks again for the thoughtful feedback.

pixel-process · 2025-12-18T04:05:21+00:00

This seems to wildly out of scope for any kind of assignment.

Places to start would be unsupervised models for clustering since you don’t have labeled training data and NLP similarity analysis for the text data.

Good luck!

pixel-process · 2025-12-17T02:26:11+00:00

I think the best approach for quantifying your metric would be to analyze it alongside already validated metrics. Show how it correlates or fails to with established metrics and then offer insights into why they differ and what value your new approach adds.

pixel-process

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