Tutorials · mbostock/d3 Wiki. Wiki ▸ Tutorials Please feel free to add links to your work!!
Tutorials may not be up-to-date with the latest version 4.0 of D3; consider reading them alongside the latest release notes, the 4.0 summary, and the 4.0 changes. Introductions & Core Concepts Specific Techniques D3 v4 Blogs Books Courses D3.js in Motion (Video Course)Curran Kelleher, Manning Publications, September 2017D3 4.x: Mastering Data Visualization Nick Zhu & Matt Dionis, Packt. Talks and Videos Meetups Research Papers D3: Data-Driven DocumentsMichael Bostock, Vadim Ogievetsky, Jeffrey HeerIEEE Trans. Digital Geography.
Many points with Leaflet WebGL. RAW. Folium: Python Data. Leaflet.js Maps. — Folium 0.1.2 documentation. Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library.
Manipulate your data in Python, then visualize it in on a Leaflet map via Folium. Concept Folium makes it easy to visualize data that’s been manipulated in Python on an interactive Leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing Vincent/Vega visualizations as markers on the map. The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys.
Base Maps To create a base map, simply pass starting coordinates to Folium, then create the map: import foliummap_osm = folium.Map(location=[45.5236, -122.6750])map_osm.create_map(path='osm.html') Live example Folium defaults to 960 x 500 pixels (to make it easy to generate maps for bl.ocks ). New Coder – five life jackets to throw to the new coder.
Method: Data visualization with D3.js and python - part 1 - Next Genetics. View the demo hereHTML source is at the bottom of the post Computers and the internet have changed academia in dramatic ways from greater sharing of data to a larger sense of community.
Science journals are now all digitized and available online either through your web browser or downloadble as a .pdf. Even with all the technology available for presenting data, most published papers still only contain static figures. I am not undervaluing the importance of having nicely formatted figures and graphs. But I do want to show how data can be presented with all the tools available now.
Science papers are generally viewed on a computer through a web browser like Chrome, Firefox, or Safari which use javascript/html/css for displaying information. Here are a bunch of examples of interactive figures made using browser technologies, specifically D3.js. How a web page displays information A brief primer on javascript, html, css, and how they are interpreted by the browser: Les 20 meilleurs outils de datavisualisation au banc d’essai.
Que vous soyez absolument novice ou codeur amateur, il existe aujourd’hui sur le web une impressionnante palette d’outils (presque) gratuits pour réaliser des datavisualisations.
Banc d’essai. Note : j’ai volontairement éliminé les outils (a) entièrement payants (b) trop moches pour être utilisés dans des rédactions (c) en Flash. Ce billet n’est consacré qu’aux outils de “visualisation”, et non de scraping ou de traitement des données (un autre billet suivra bientôt). ↑1 » Pour les novices/pressés : le clé-en-mains Ces outils gratuits ou freemium permettent de générer des graphiques ultra-rapidement en copiant-collant des données d’un tableur. Le meilleur – Datawrapper : Simple d’utilisation, sobre, rapide, Datawrapper est tout à fait satisfaisant pour la plupart des visualisations courantes. Les + : la possibilité de personnaliser les couleurs, de mettre en évidence une série, la navigation par onglets entre les différentes séries. Les challengers : Ils ne nous ont pas convaincu :
Gallery · mbostock/d3 Wiki. Wiki ▸ Gallery Welcome to the D3 gallery!
More examples are available for forking on Observable; see D3’s profile and the visualization collection. Please share your work on Observable, or tweet us a link! Visual Index Basic Charts Techniques, Interaction & Animation Maps Statistics Examples Collections The New York Times visualizations Jerome Cukier. Mbostock/d3. Github Visualizer. Data Visualizations, Challenges, Community. GitHub Visualization. About GitHub is one of the most popular sites for online code repositories.
With a large user base GitHub generates lot of activity. This activity, in the form of commits, contains the relations between repositories and contributors. With the use of appropriate visualization techniques we can effectively map the relation between projects and contributors onto visual graphs. These graphs can reveal some interesting trends and insights that are not apparent using text and tables. Existing Visualizations GitHub itself has some interesting graphs that map the activity occurring on each repository. Design The data generated on GitHub is essentially temporal in nature. Ideation and initial mockups After going through few design iterations we finalized upon few graphs for evaluation. Application. Datavisualization.ch Selected Tools. Passion dataviz.