# Plotly-Resampler: Effective Visual Analytics for Large Time Series

arXiv: https://arxiv.org/abs/2206.08703 · HTML: https://mlsanity.com/paper/2206.08703/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [tsflex: flexible time series processing & feature extraction](https://mlsanity.com/paper/2111.12429/) (2021)
- [Big Data Visualization Tools](https://mlsanity.com/paper/1801.08336/) (2018)
- [Periphery Plots for Contextualizing Heterogeneous Time-Based Charts](https://mlsanity.com/paper/1906.07637/) (2019)

## Built on by (most cited first, 3 of 3)

- [Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring](https://mlsanity.com/paper/2207.07753/) (2022)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities](https://mlsanity.com/paper/2407.11048/) (2024)

## Abstract (arXiv)

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.