Adding a Geological Facies Display to a Well Log Plot in Matplotlib and Plotly
Adding a column of colour to a well log plot using Python
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When a Line Chart Becomes Spaghetti
Past a handful of series, a line chart stops letting you follow any of them. Here is what breaks and what to plot instead
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9 Scientific Data Plots and When They Fail
Every chart has a blind spot. Learn where common scientific visualisations start to hide the patterns that matter.
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What Your Scatter Plot Stops Showing You Once It Gets Crowded
Overplotting is easy to miss and easy to fix, once you stop asking your chart to show every point.
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Handling Categorical Geological Data in Machine Learning Models
What label, ordinal, and one hot encoding each tell a model about your rocks
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From Default to Publication-Ready: The Raincloud Plot
Six steps between the chart ptitprince gives you and one you would want to put in a report
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Why Your Jupyter Notebooks Are Harder to Read Than They Should Be
Working code is only half the job. The reasoning needs to be readable too.
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3 Jupyter Notebook Habits That Hold Up When Someone Else Opens Them
Small habits that make notebooks easier to rerun and share
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Boxplots Are Hiding Your Data. Here’s a Simple Fix
How overlaying raw data points reveals what your summary statistics quietly discard
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How to Organise Jupyter Notebooks So Older Work Stays Usable
A lightweight workflow for keeping notebook-heavy projects searchable, readable, and easy to return to
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From Default to Publication-Ready: Transforming Matplotlib Histograms
Seven Steps From a Default Chart to Something Worth Publishing
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When a Jupyter Notebook Is the Wrong Tool: Moving Logic Into Modules and Scripts
Notebooks are great for thinking. They are not always the right place to keep the machinery
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The Project Structure That Makes Good Jupyter Notebook Habits Automatic
Set it up once. Use it on every project.
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6 Jupyter Notebook Habits That Make Your Work Easy to Return To
From messy exploration to a record that still makes sense months later
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Why Whole-Well Completeness Numbers Can Hide Critical Data Gaps
Visualising Well Log Data Availability by Formation Across Multiple Wells
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3 Python Charts You’re Probably Not Using (But Should Be)
Three underused chart types and when to reach for them when working with well log data
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From Default to Publication-Ready: Transforming Matplotlib Heatmaps in 8 Steps
Take a Basic Well Log Data Availability Heatmap and Make It Something Worth Putting in a Report
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Visualising Well Log Data Availability in the Terminal with Python
Building a coverage heatmap for LAS files using rich and lasio
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Inspecting LAS Files in the Terminal with rich and lasio
A practical approach to inspecting LAS files in Python without jumping straight to plots
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Working With LAS Files in Python
A practical look at the LAS format, its structure, and how to work with it using lasio for subsurface well data
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Tables, Pretty Prints, and Sanity: Python Output for Geoscience Data
Three simple libraries to make console output readable, useful and even stylish
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Less Print, More Clarity: Debugging Python with Icecream and Snoop
Two lightweight tools that help you see what your code is doing without the clutter or confusion of print statements
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Bring Your Python Terminal to Life With Colour and Clarity
4 Ways to Enhance Your Python Terminal Output
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Add a Navigation Sidebar to Your Multi-Page Plotly Dash App (With Collapsible Menus)
A quick guide to building a flexible, collapsible navigation sidebar using Dash Bootstrap Components.
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Adding Gradient Backgrounds to Plotly Charts
Using Plotly rectangle shapes to improve data visualisation
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Using Python to Explore and Understand Equations in Petrophysics
A simple way to understand parameters to the Archie Water Saturation equation using Python
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Python in Geoscience: An Essential Skill
How Python can help your daily subsurface geoscience workflows
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7 Useful Pandas Display Options You Need to Know
Improve how you view your dataframes by changing Pandas' display options
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Loading Well Log Data From DLIS using Python
A short tutorial on loading and displaying processed acoustic waveform data using dlisio and matplotlib
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