Setting up a Jupyter Notebook for the first time to aid with the 100 Days of Python challenge

Installing and using an unofficial Python Unsplash API by salvoventura

Use the Pillow image library to add text and icons layers to an image programmatically.

Learn how to take a markdown file and parse the frontmatter metadata for usage in your scripts.

Use the glob library to create a list of all the files in a directory.

Work through an example of building a simple CLI script with the python-fire library.

Learn the basics of installing and using PyTest with a basic math module example.

Using the OS module recursively make folders on your operating system.

Display your public GitHub repositories in an interactive notebook.

Use PyInquirer to create more interactive and user-friendly command line prompts.

Make debugging Python errors more enjoyable.

Introduce yourself to MoviePy through a simple example to add text to video.

See how the Rich library for Python can make it easy to add color and style to terminal output.

This post will look at how the regex module in python can be used to work with regular expressions.

An overview look at custom exceptions in Python

Learn how to work with the datetime library in Python.

Learn how to set up reliable PyTest unit tests for the Python datetime library with FreezeGun.

Explore how to work with the time module from the Python Standard Library in detail.

Explore PyDoc and understand how it can improve your documentation and how to search it.

Build off previous work on PyTest by adding a GitHub action to run PyTest in CI.

Learn how to deploy your first package to Pip in this straight-forward tutorial.

Use Conventional Commits, pre-commit and Commitizen to automate your versioning based on commit messages.

Learn how to use GitHub Actions to speed up your Pip package deployments to PyPi.

Learn how to generate random data and export it to CSV to use in your projects.

Apply an oil paint effect to images in Python with OpenCV.

The 34 Days of Python series is moving into its second phase with Machine Learning. The journey will start with a first look at project setup and classification.

See how we can visual test the performance capability of our K-Nearest Neighbors classifier using Scikit Learn.

Continuing with our look into supervised learning, today we explore the basics linear regression by applying it to another Scikit Learn toy dataset.

In second blog post on linear regression, we take what we learned in part one and look deeper into the basics of linear regression and applying a train-test-split to our data.

In part three, we look at using k-fold cross-validation to prevent dependency on a particular train/test split.

In our final coverage of regression using SciKit Learn, we look at how we can use regression with regularization.

After 30+ days of my 100 day Python journey, I stop to reflect on the five tips that have been unusual by helpful coming into the language.

An overview on Python higher-order functions, decorators and some practical examples for when decorators can be used.