Rich Syntax makes Python code look better

If you work with Python in the terminal, Rich can make your output much more readable and visually appealing.

With rich.syntax, you can:

- Highlight Python code with syntax coloring
- Choose different themes such as monokai
- Display code directly in the terminal
- Make debugging and demos easier to read

https://link.amazon/B0exoJjOq

Python Coding Challenge — Day 1274

What is the output of the following Python code?

A small piece of Python code can sometimes produce a surprisingly tricky result.

Before running the code, take a moment to:

Read it carefully

Predict the output

Understand why Python behaves that way

Don’t just guess the answer — explain the reasoning behind it.

Think you know the output?

Check the full challenge here: https://www.clcoding.com/2026/10/python-coding-challenge-day-1274-what.html

Comment your answer before checking the solution.

Python Quiz of the Day

Python Coding Challenge - Question with Answer (ID 051026)

Answer with Explanation: https://www.clcoding.com/2026/10/python-coding-challenge-id-051026.html

🚀 October Python Bootcamp — Day 1 is here!

We’re starting from the ground up with Python Fundamentals.

In Day 1, we’ll cover:

- What is Python?
- Python installation & Jupyter Notebook
- Syntax and indentation
- Variables and naming conventions
- Comments
- print() and input()
- Basic coding exercises

Whether you're completely new to Python or want to strengthen your fundamentals, this session is a great place to start.

Join the live session for FREE and start your Python journey this October.

https://youtube.com/live/VSntnv4yT9U

Let’s learn Python by coding, practicing, and building step by step.

Did You Know?

Python Has a Special Interactive Shell
Python comes with an interactive shell (REPL) that lets you execute Python statements instantly—without creating a .py file.

Python is transforming the way we work with maps, geospatial data, and spatial analysis.

With Python, you can:

Analyze and visualize geospatial data

Work with Shapefiles, GeoJSON, and raster data
Create interactive maps

Perform spatial joins and geoprocessing

Analyze satellite and remote-sensing data

Automate GIS workflows

Build location-based applications

Work with coordinates, projections, and spatial databases

Projects: https://link.amazon/B0f4Cqywq

Prime deals are live, and if you're a developer, this can be a good opportunity to upgrade your workspace, coding setup, and everyday tech accessories.

You don't need to buy everything just because it's discounted. Instead, focus on products that can genuinely improve your productivity, comfort, and workflow.

Here are 10 products worth checking out.

https://x.com/clcoding/status/2107187656824361233?s=20

Understanding Machine Learning: From Theory to Algorithms — Free PDF

Looking to build a strong foundation in Machine Learning?

This 449-page book takes you beyond simply calling ML libraries and focuses on the theory, concepts, and algorithms behind machine learning.

You’ll explore topics such as:

Machine Learning fundamentals

Supervised & unsupervised learning

Classification & regression

Learning algorithms

Generalization and model evaluation

Theoretical foundations of ML

Mathematical concepts behind algorithms

A valuable resource for students, Python developers, aspiring data scientists, and ML enthusiasts who want to understand how Machine Learning actually works.

449 pages of Machine Learning knowledge — available as a free PDF.

Get it here: https://www.clcoding.com/2026/07/understanding-machine-learning-from.html

SHAP makes machine learning models easier to understand.

Instead of just getting a prediction, you can see why the model made that prediction and which features pushed the result higher or lower.

In this example, a Random Forest model predicts a value from the California housing dataset, while a SHAP waterfall plot breaks down the individual contribution of each feature.

For example:

- AveOccup pushes the prediction down
- MedInc pushes it up
- Other features such as Longitude, Latitude, Population, and HouseAge also influence the final prediction

This is the power of Explainable AI (XAI) — moving from “What did the model predict?” to “Why did the model predict it?”

Projects: https://link.amazon/B03Rr0s1k

Python Pattern Challenge — Day 20

Think you know Python loops and nested loops?

Today's pattern challenge is a simple way to test your understanding of:

Nested loops

range()

Pattern logic

Rows and columns

Problem-solving skills

Can you predict the output before running the code?

Try it yourself, then check the solution.

Day 20 is here: https://www.clcoding.com/2026/10/python-pattern-challenge-day-20.html