d3.csv() loads a comma-separated values file and turns its rows into JavaScript objects that can feed a D3 visualization. Pass the file path as the first argument; use the optional row-conversion callback to prepare each row before chart construction. section 3.1.3 section 3.2.1
Use d3.csv() as a small data-preparation workflow:
- Point to the CSV file, such as d3.csv("../data/data.csv");. section 3.2.1
- Recognize that the first line normally supplies column headers and each later line supplies a data row. section 3.1.3
- Convert imported strings into the types your visualization needs; CSV count values commonly arrive as strings. section 3.2.2
- Return only the key-value pairs needed later when using the row-conversion callback; omitted columns are unavailable after loading. section 3.2.2
- Wait for asynchronous loading to finish before measuring, reorganizing, or binding the dataset to visual elements. section 3.6
Prepare survey counts for a chart
d3.csv("../data/data.csv", d => {\n return {\n technology: d.technology,\n count: +d.count\n };\n});\n\nThe callback receives one row at a time, keeps the technology and count fields, and converts count from text to a number. The resulting records are ready for later visualization work. section 3.2.2 section 3.2.1
Listing 3.1 Tools most used by data practitioners (data.csv)
technology,count ArcGIS,147 D3.js,414 Angular,20 Datawrapper,171 Excel,1078 Flourish,198 ggplot2,435 Gephi,71 Google Data Studio,176 Highcharts,58 Illustrator,426 Java,29 Leaflet,134 Mapbox,167 kepler.gl,24 Observable,157 Plotly,223 Power BI,460 PowerPoint,681 Python,530 QGIS,193 Qlik,61 R,561 React,145 Tableau,852 Vega,48 Vue,51 Web Components,79 WebGL,65 Pen & paper,522 Physical materials,69 Canvas,121 P5/Processing,55
Inspect a complete CSV dataset whose technology and count columns demonstrate the tabular shape consumed by d3.csv(). section 3.2.1
