Hi, I'm Andy!

I help geoscience professionals become familiar with data science, AI, Machine Learning and Python

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About Andy McDonald

I am a petrophysicist (a special type of geoscientist) who enjoys exploring the world of Python development, data science and machine learning, and bringing that knowledge into the geoscience domain and applying it to domain-specific problems.

Since 2020 I have been sharing numerous articles on how python and machine learning can be applied to the geoscience domain. In 2021 I launched my official YouTube channel as a way to share knowledge with a wider audience.

Feel free to check out my projects, blog posts and explore the website. If you want to get in touch you can send a message via one of my social media channels or directly via email.

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Latest YouTube Content

Since 2021, I have been running my YouTube channel that explores various topics in applying Python to geoscience and petrophysical applications. Check out my latest videos below, and if you enjoy the content, consider subscribing to be kept updated when new videos are released.

This video follows my previous video where we created a basic waffle chart using PyWaffle. In this video, I share how you can quickly and easily display waffle charts as subplots in matplotlib, which can make it easier to understand the variances between different categories. ⭐️ If you haven't already, make sure you subscribe to the channel: https://www.youtube.com/channel/UCn1O_4_ApzbYwrsUdRoMmOg?sub_confirmation=1 ▼ ---- SUPPORT THE CHANNEL ---- ▼ ☕️ BUY ME A COFFEE: https://www.buymeacoffee.com/andymcdonaldgeo ▼ ---- SOCIAL CHANNELS ---- ▼ Thanks for watching, if you want to connect you can find me at the links below: https://andymcdonaldgeo.medium.com/ https://www.linkedin.com/in/andymcdonaldgeo/ https://www.andymcdonald.scot/ #datascience #petrophysics #python #datavisualization #eda
Waffle charts are a great way to visualise categorical data, are aesthetically pleasing and easy for readers to understand — which is one of the key goals of effective data visualisations. They also provide a nicer looking alternative to pie charts. Waffle charts are square or rectangular displays made up of smaller squares in a grid pattern. Most commonly, it is a 10 x 10 grid, but they can be any dimension you want them to be, and this will depend on the data you are looking to display. Each square within the grid is coloured based on a category and represents a portion of the whole. From these plots, we can see contributions of individual categories or display progress towards a goal. Advantages and Disadvantages of Waffle Charts As with any data visualisation tool, there are advantages and disadvantages to displaying data in specific formats. Waffle charts have several advantages for displaying data, including: A great alternative to the pie chart Interesting and aesthetically pleasing to look at Easy to read and interpret Data is not distorted Great way to visualise a small number of categories Easy to visualise progress towards a goal Even with these advantages, there are a few disadvantages to using waffle charts: It can be hard to read if you have a large number of categories to display It can be hard to compare non-adjacent categories Can’t include text or numbers within the squares due to their size Accuracy can be impacted if the values are smaller than the square’s representative value. eg. if a square equals one unit, and you have a half unit Within this video, we will use geological lithology data to see how we can use Python to display Waffle charts. ⭐️ If you haven't already, make sure you subscribe to the channel: https://www.youtube.com/channel/UCn1O_4_ApzbYwrsUdRoMmOg?sub_confirmation=1 ▼ ---- SUPPORT THE CHANNEL ---- ▼ ☕️ BUY ME A COFFEE: https://www.buymeacoffee.com/andymcdonaldgeo ▼ ---- GET THE CODE --- ▼ ▼ ---- SOCIAL CHANNELS ---- ▼ Thanks for watching, if you want to connect you can find me at the links below: https://andymcdonaldgeo.medium.com/ https://www.linkedin.com/in/andymcdonaldgeo/ https://www.andymcdonald.scot/ #datascience #petrophysics #python #datavisualization #eda
When we create infographics or posters containing data, we want to catch the reader’s attention and make it aesthetically pleasing to look at whilst telling a convincing story. Within Python, we have numerous plotting libraries that allow us to create charts — one such library is the well-known matplotlib library. However, out of the box, the plots generated by matplotlib are often seen as boring, and getting them to something visually appealing can be time-consuming. Cyberpunk has become a widely popular subgenre of science fiction, characterised by its portrayal of dystopian societies, highly advanced technology, and countercultural themes. These settings are typically depicted with a futuristic look, highlighted by neon lights and vibrant, bold colours. In this tutorial we will see how we can use the popular mplcyberpunk (https://github.com/dhaitz/mplcyberpunk) library to take boring matplotlib figures to the next level. ⭐️ If you haven't already, make sure you subscribe to the channel: https://www.youtube.com/channel/UCn1O_4_ApzbYwrsUdRoMmOg?sub_confirmation=1 ▼ ---- SUPPORT THE CHANNEL ---- ▼ ☕️ BUY ME A COFFEE: https://www.buymeacoffee.com/andymcdonaldgeo ▼ ---- SOCIAL CHANNELS ---- ▼ Thanks for watching, if you want to connect you can find me at the links below: https://andymcdonaldgeo.medium.com/ https://www.linkedin.com/in/andymcdonaldgeo/ https://www.andymcdonald.scot/ #datascience #petrophysics #python #matplotlib #cyberpunk
Streamlit provides a quick and easy way to build interactive applications and dashboards for data analysis and machine learning. If we are looking to build a data analysis app within Streamlit that uses data containing location information, one of the first visualisations we may want to consider adding is a map. Having an interactive map within our app allows us to visualise where the data points are located, and in turn, we can identify patterns or dig into the data in more depth. This tutorial will show how to integrate plotly express maps directly in a Streamlit app. Article Version: https://medium.com/towards-data-science/3-easy-ways-to-include-interactive-maps-in-a-streamlit-app-b49f6a22a636 Dataset The data we are using for this article comes from the Norwegian Petroleum Directorate website and contains the locations of all wells that have been drilled on the Norwegian Continental Shelf. The full dataset can be downloaded here: https://factpages.npd.no/en/wellbore/tableview/exploration/all ⭐️ If you haven't already, make sure you subscribe to the channel: https://www.youtube.com/channel/UCn1O_4_ApzbYwrsUdRoMmOg?sub_confirmation=1 ▼ ---- SUPPORT THE CHANNEL ---- ▼ ☕️ BUY ME A COFFEE: https://www.buymeacoffee.com/andymcdonaldgeo ▼ ---- GET THE CODE --- ▼ https://github.com/andymcdgeo/andymcdgeo_examples/tree/main/streamlit/streamlit_plotly_mapbox ▼ ---- SOCIAL CHANNELS ---- ▼ Thanks for watching, if you want to connect you can find me at the links below: https://andymcdonaldgeo.medium.com/ https://www.linkedin.com/in/andymcdonaldgeo/ https://www.andymcdonald.scot/ #datascience #petrophysics #python #streamlit #eda
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