2 dépôts
Visualizations such as bar, box, and violin plots used to compare distributions across discrete categories.
Distinct from Comparative Analyses: Shortlist candidates focus on logical value comparison or educational analysis, not the specific visual plotting of categorical data.
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This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi
Implements bar, box, and violin plots to compare aggregated values across different data categories.
Plotnine is a data visualization library for Python based on the Grammar of Graphics. It serves as a declarative statistical plotting framework and multi-panel plotting engine, allowing users to create complex charts by mapping data variables to visual properties such as position, color, and size. The project is distinguished by its use of a layered composition model and a statistical transformation engine that performs aggregations and computations before rendering visuals. It features a comprehensive system for multi-panel faceting, which enables the splitting of a single visualization into
Assigns distinct colors or styles to lines based on categorical variables to compare trends.