yhat/ggpyArchived
Ggpy
ggpy is a Python library for statistical data visualization based on the grammar of graphics. It functions as a declarative framework for building complex charts by mapping data variables to visual properties through a structured coordinate system.
The library enables the construction of composite visualizations by layering geometric shapes and statistical summaries. It utilizes a system of continuous and discrete scales to translate raw data into visual attributes and supports facet-based plotting to segment a single visualization into a grid of subplots based on variable categories.
Visual aesthetics are managed through a theme-driven system that separates structural plot logic from styling rules. The framework also includes capabilities for exporting completed plots to local files.
Features
- Grammar of Graphics Renderers - Implements a formal grammar of graphics for creating declarative visualizations by mapping data to layered geometric marks.
- Declarative Statistical Plotting - Functions as a declarative framework for building statistical charts by mapping data to graphical marks.
- Statistical Plotting Libraries - Provides a specialized library for creating complex statistical charts and data distributions using a declarative grammar.
- Declarative Data Visualization - Builds charts by defining independent layers and scales rather than using imperative drawing commands.
- Layered Visualization Composition - Builds complex charts by combining independent geometric, statistical, and coordinate layers.
- Declarative Visualization Grammars - Provides a declarative grammar for building complex charts and graphs by mapping data variables to visual properties.
- Data Visualization Scales - Provides a dedicated system of continuous and discrete scales to translate raw data into visual attributes.
- Declarative Visualization Frameworks - Uses declarative syntax to define multi-layered graphical structures and data-to-visual mappings.
- Visualization Frameworks - Implements a comprehensive framework for building complex charts by mapping data variables to visual properties.
- Grid Plot Arrangements - Arranges multiple plots in grid layouts for subgroup comparison based on data categories.
- Data-to-Pixel Coordinate Transforms - Implements transformations that map data coordinates to pixel positions for chart rendering.
- Multi-panel Faceting - Splits a single visualization into a grid of sub-plots based on categorical variables for comparative analysis.
- Faceted Plotting - Enables splitting visualizations into a grid of small multiples based on discrete variables for comparative analysis.
- Layered Plotting - Supports stacking multiple geometric representations on a single coordinate system to build composite visualizations.
- Faceting Layout Grids - Uses grid systems to partition multifactorial data into separate subplots for comparison.
- Named Theme Application - Separates structural plot logic from visual aesthetics by applying global sets of styling rules.
- Chart Visual Style Customizations - Enables aesthetic modification of data visualization elements to ensure consistent styling across reports.
- Plot Attribute Themes - Allows customization of plot attributes such as background colors, grid lines, and font styles.
- Python Visualization - Python plotting system modeled after R's ggplot2.
- Python Visualization Libraries - Plotting system modeled after R's ggplot2.
