Plotly-Resampler: Effective Visual Analytics for Large Time Series

Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four...

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Bibliographic Details
Published in:IEEE Visualization Conference (Online) pp. 21 - 25
Main Authors: Van Der Donckt, Jonas, Van der Donckt, Jeroen, Deprost, Emiel, Van Hoecke, Sofie
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2022
Subjects:
Benchmark testing
Dash Plotly
Data aggregation
Data science
Data visualization
Open source
Python
Scalability
Time series
Time series analysis
Visual analytics
ISSN:2771-9553
Online Access:Get full text
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Summary:Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four requirements have to be met; a tool should be (1) interactive, (2) scalable to millions of data points, (3) integrable in conventional data science environments, and (4) highly configurable. We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization. As a means to facilitate these requirements, we created Plotly-Resampler, an open source Python library. Plotly-Resampler is an add-on for Plotly's Python bindings, enhancing line chart scalability on top of an interactive toolkit by aggregating the underlying data depending on the current graph view. Plotly-Resampler is built to be snappy, as the reactivity of a tool qualitatively affects how analysts visually explore and analyze data. A benchmark task highlights how our toolkit scales better than alternatives in terms of number of samples and time series. Additionally, Plotly-Resampler's flexible data aggregation functionality paves the path towards researching novel aggregation techniques. Plotly-Resampler's integrability, together with its configurability, convenience, and high scalability, allows to effectively analyze high-frequency data in your day-to-day Python environment.
ISSN:2771-9553
DOI:10.1109/VIS54862.2022.00013