- Home
- Teaching
- Visualization Tools and Libraries
Visualization Tools and Libraries
Visualization tools — data exploration and visualization in dashboards:
- Tableau (Visualization environment, Dashboards)
- Visualize Free (Online, Dashboards)
- Glue (Python; multi-dimensional linked-data exploration)
- InstantClue (Visualization design and data exploration environment)
- Voyager 2 (Online, data exploration tool)
- QlikView (Visualization environment, Dashboards)
- Power BI (Visualization environment, Dashboards)
- Keshif Online (Online, Dashboards)
Visualization tools — authoring of individual charts:
- DataWrapper (Visualization environment, single chart)
- RawGraphs (Visualization environment, single chart)
- Make-a-vis (Visualization environment, single or multiple charts)
- Lyra (Online, visualization design environment)
Visualization tools — graph and network data:
- yFiles (+ yEd) (Graph drawing libraries and tools; several platforms)
- Gephi (Network analysis and visualization environment)
- Graphia (Network analysis and visualization environment)
- Cytoscape (Environment for complex networks and multivariate data analysis; several platforms)
- OriGraph (Online graph data wrangling tool)
- kepler.gl (Environment for large-scale geospatial and network analysis; online)
- Nomic Atlas (Environment for AI/ML-based analysis of text, image, and multidimensional data collections; online)
Visualization tools — other specific data types and representations:
- VoyantTools (Environment for text analysis; online)
- StoryMapJS (Authoring tool for map-based storytelling; online)
- TimelineJS (Authoring tool for timeline-based storytelling; online)
- Heatmapper (Environment for geographical map and heatmap visualization; online)
- EdWordle (Authoring tool for word clouds; online)
- Jigsaw (Visual analytic environment for document and multivariate data; several platforms, including WebJigsaw)
- Inviwo (Scientific visualization environment; several platforms, including C++ and Python)
Data analysis and visualization tools (+ libraries):
- Word Rain (Python; semantic-preserving word cloud-like visualization of texts and document collections)
- pyLDAvis (Python; visualization of topic models)
- Scattertext (Python; visualization of text document collections)
- t-SNE (Python and many other languages; dimensionality reduction for multivariate/high-dimensional data)
- UMAP (Python; a recent DR approach comparable, or in some cases, preferred to t-SNE)
- Further manifold learning and DR techniques (scikit-learn) (Python)
- densVis (Python; DR for density-preserving data visualization)
- NetworkX (Python; analysis and visualization of network data)
- py3plex (Python; analysis and visualization of multiplex and multilayer network data)
- muxViz (R; analysis and visualization of multilayer network data)
- ScanPy (Python; analysis and visualization of biological data)
- Yellowbrick (Python; visualization for machine learning)
- OpenRefine (Java; data preprocessing/wrangling)
- Datasette (Python; data preprocessing/wrangling and exploratory data analysis)
Interactive data mining / ML / AI tools:
- Orange (Machine learning and visual analysis environment; Python)
- Textable (Text mining extensions for Orange)
- KNIME Analytics Platform (Interactive data mining environment; several platforms)
- Weka (Interactive data mining environment; Java)
- ELKI (Interactive data mining environment; Java)
Visualization libraries — JavaScript (general-purpose):
Visualization libraries — JavaScript (specific data types and representations):
- Rickshaw (Temporal data; JavaScript; based on D3)
- Leaflet (Geospatial data (maps); JavaScript)
- Mapbox GL JS (Geospatial data (maps); JavaScript)
- Cytoscape.js (Graph/network data; JavaScript)
- Sigma (Graph/network data; JavaScript)
- WebCoLa (cola.js) (Graph/network data; JavaScript; compatible with D3 and Cytoscape.js)
Visualization libraries — Python:
- Dash (Python; generates a web-based dashboard using plotly.js; also available for R, Julia, and F#)
- plotly.py (Python; generates a web-based visualization using plotly.js; also R, Julia, F#, and Matlab)
- HoloViz (Python; a set of high-level packages for interactive visualization)
- Bokeh (Python; generates a web-based bashboard)
- Chartify (Python; generates a web-based visualization using Bokeh)
- Altair (Python; generates a web-based visualization using Vega and Vega-Lite)
- mpld3 (Python; generates a web-based visualization using D3)
- Folium (Python; generates a web-based geographical map using Leaflet.js)
- Geoplot (Python; geospatial data (maps))
Visualization libraries — other languages and platforms:
Visualization libraries — statistical and scientific visualizations:
- ggplot2 (R; statistical graphics)
- Matplotlib (Python; statistical graphics and scientific plotting, static plots with very limited interaction options)
- Seaborn (Python; statistical graphics, based on Matplotlib)
- Chaco (Python; statistical graphics and scientific plotting, based on Matplotlib, further interaction options)
- Veusz (Python; statistical graphics and scientific plotting)
- PyVista (Python; scientific visualization)
Tools and papers for choosing a color map:
Further interactive tools:
Visualization libraries no longer in active development (not recommended for assignments):
Interesting URLs
