How to Master Seaborn Color Palettes, Boxplots, and Clustermaps

Master three essential Seaborn visualization techniques: create perceptually uniform color palettes with sns.color_palette() for qualitative, sequential, and diverging data; build statistical boxplots using sns.boxplot() to show distribution quartiles and outliers; and generate hierarchically-clustered heatmaps with sns.clustermap() to reveal data patterns through dendrogram-based clustering—all with practical code examples and statistical best practices.

Complete Seaborn tutorial: master statistical data visualization with Python

Seaborn is Python’s premier statistical visualization library, built on matplotlib with a high-level, dataset-oriented API that makes complex statistical plots accessible in just a few lines of code; install with pip install seaborn, load data into pandas DataFrame, use functions like sns.heatmap(), sns.pairplot(), and sns.boxplot() with built-in themes and color palettes for publication-ready graphics that Continue reading

How to change plotly figure size

Plotly allows you to create interactive, publication-quality figures in Python. You can customize the width and height of your figures through multiple approaches: using update_layout, Plotly Express parameters, default templates, or configuration options when exporting. This guide covers each method with code examples.

Tkinter Grid Layout: Understanding Sticky

The Tkinter grid layout manager is a powerful tool for arranging widgets in a table-like structure of rows and columns. One of its most important options is sticky, which controls how a widget is aligned and stretched within its grid cell. Understanding sticky is crucial for creating responsive and well-aligned Tkinter GUIs.