has2k1/plotnine
Plotnine
Plotnine 是一个基于“图形语法”（Grammar of Graphics）的 Python 数据可视化库。它作为一个声明式统计绘图框架和多面板绘图引擎，允许用户通过将数据变量映射到位置、颜色和大小等视觉属性来创建复杂的图表。
该项目的特点在于其分层组合模型和统计转换引擎，后者在渲染视觉效果前执行聚合和计算。它具有全面的多面板分面（faceting）系统，能够根据分类变量将单个可视化图表拆分为子图网格。
该库涵盖了广泛的功能，包括用于分布图、面积图和散点图的多种几何表示，以及用于渲染地理边界的地理空间可视化。它提供了丰富的工具用于比例映射、坐标投影和基于主题的样式设置，从而将数据驱动元素与非数据美学属性分离开来。
该框架利用 Matplotlib 后端进行渲染，并通过管道操作与表格数据框（DataFrames）集成。
Features
- Grammar of Graphics Renderers - Implements a formal grammar of graphics that maps data variables to layered geometric marks.
- Layered Visual Composition - Constructs complex plots by stacking multiple geometric representations and statistical transformations on a single set of axes.
- Declarative Visualization Grammars - Employs a declarative grammar of graphics to build complex visualizations by mapping data variables to layered geometric marks.
- Visualization Transformation Engines - Performs data aggregations and computations, such as kernel density or regression, before rendering visuals.
- Visual Grouping Strategies - Assigns distinct colors or styles to lines based on categorical variables to compare trends.
- Statistical Plotting Libraries - Provides specialized functions for creating complex statistical charts and visualizing data distributions.
- Faceted Plotting Systems - Implements a system for partitioning data into multi-panel layouts across rows and columns based on categorical variables.
- Geometric Data Mappings - Maps raw data points to various geometric shapes such as points, lines, bars, and polygons for rendering.
- Visual Property Mappings - Maps data fields to visual properties such as color, size, and shape to define how data is visually represented.
- Layered Visualization Composition - Constructs complex graphics by combining independent geometric, statistical, and coordinate layers on a single system.
- Multi-panel Faceting - Splits visualizations into grids of smaller sub-plots based on categorical variables for side-by-side comparison.
- Data Visualization Scales - Provides mapping functions that translate abstract data domains into visual ranges using various scale types.
- Coordinate Projections - Provides mathematical utilities for calculating scales and coordinate projections to map data space to visual space.
- Layered Plotting - Deno X stacks multiple geometric representations on a single set of axes to combine different data views.
- Statistical Data Visualizations - Uses visual representations like boxplots and density plots to analyze relationships and patterns in numerical data.
- Statistical Transformations - Performs data aggregations and computations such as density estimates and boxplot statistics before rendering.
- Bar Charts - Represents categorical data using rectangular bars of varying heights or lengths to compare quantities.
- Stacked and Grouped Bar Plots - Provides specialized bar charts that support grouped and stacked layouts for comparative data analysis.
- Plot Axis Customizers - Configures titles, subtitles, captions, and axis labels to provide context to the visualization.
- Visualization Coordinate Mapping - Implements mappings that translate data values to visual coordinates for chart rendering.
- Geographic Data Mapping - Provides capabilities for rendering spatial data onto regional boundaries to visualize distribution.
- DataFrame Ingestion - Integrates tabular dataframes via piping operations, converting external pandas or polars objects into internal plotting formats.
- Matplotlib - Utilizes Matplotlib's object-oriented API as the rendering backend to draw final geometric shapes and annotations.
- Area Charts - Visualizes the magnitude of a variable over time or a continuous scale using filled regions.
- Coordinate Projections - Implements mathematical coordinate projections to control axis spacing and geometric mapping.
- Element Jittering - Deno X modifies the placement of visual elements by adding jitter to points to prevent overlap.
- Geospatial Visualizations - Renders data across geographic regions using map projections and custom geometries.
- Trend Line Smoothing - Calculates and draws conditional mean lines over data points using local regression.
- Density-Based Color Gradients - Maps numerical data values to continuous color scales to visualize density or intensity.
- Scatter Plot Rendering - Renders scatter plots mapping variables to point positions, colors, and sizes to visualize multidimensional relationships.
- Text Annotations - Deno X places labels, letters, or custom text elements within a plot to provide context or identify data points.
- Plot Layout Optimizations - Removes clutter and adjusts figure dimensions to control the overall aesthetic and layout of a visualization.
- Composite Visualizations - Combines several distinct chart types into a single unified visual composition.
- Geospatial Visualizations - Renders geographic boundaries and spatial coordinates to analyze and compare regional data distributions.
- Scale Customizations - Adjusts how data values translate to visual properties via color palettes and axis limit modifications.
- Visualization Themes - Separates the data-driven visual elements from the non-data stylistic properties of the chart using themes.
- Density Plots - Visualizes the probability density of a continuous variable using smoothed curves or ridgeline variants.
- Distribution Plots - Shows the distribution of quantitative data through quartiles, whiskers, or kernel density estimations.
- Composite Plot Types - Mixes multiple geometric representations, such as points and lines, on a single set of axes.
- Continuous Axis Configurations - Provides configuration options for rendering continuous numeric ranges on chart axes.
- Heatmaps - Renders grids of colored tiles where each cell can contain a text label to represent values.
- Line Charts - Generates line charts to represent data trends and time series as connected points.
- Visual Overlap Adjustments - Implements dodging and jittering strategies to shift data points and prevent overlap in crowded plots.
- Chart Visual Style Customizations - Provides thematic and aesthetic modification of data visualization elements through themes and scales.
- Visual Themes - Sets a consistent visual identity and look-and-feel across all plots in a composition.
- Minimalist Themes - Removes borders, axis lines, and grids to maximize the data-ink ratio and minimize visual clutter.
- Visualization Styling - Deno X controls non-data visual elements including text size, axis colors, and legend placement through themes.
- Data Visualization - Grammar of graphics implementation for statistical plotting.
- Data Visualization - Grammar of graphics for Python.
- Visualization - Grammar of graphics implementation for Python.
