  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.
