Plotting¶
marimo supports most major plotting libraries, including Matplotlib, Seaborn, Plotly, and Altair. Just import your plotting library of choice and use it as you normally would.
For more information about plotting, see the plotting guide.
Reactive charts with Altair¶
Disabling automatic selection¶
marimo automatically adds a default selection based on the mark type, however, you may want to customize the selection behavior of your Altair chart. You can do this by setting chart_selection
and legend_selection
to False
, and using .add_params
directly on your Altair chart.
# Create an interval selection
brush = alt.selection_interval(encodings=["x"])
_chart = (
alt.Chart(traces, height=150)
.mark_line()
.encode(x="index:Q", y="value:Q", color="traces:N")
.add_params(brush) # add the selection to the chart
)
chart = mo.ui.altair_chart(
_chart,
# disable automatic selection
chart_selection=False,
legend_selection=False
)
chart # You can now access chart.value to get the selected data
- marimo.ui.altair_chart(chart: altair.Chart, chart_selection: Literal['point'] | Literal['interval'] | bool = True, legend_selection: list[str] | bool = True, *, label: str = '', on_change: Callable[[ChartDataType], None] | None = None) None ¶
Make reactive charts with Altair
Use
mo.ui.altair_chart
to make Altair charts reactive: select chart data with your cursor on the frontend, get them as a dataframe in Python!For Polars DataFrames, you can convert to a DataFrame. However the returned DataFrame will still be a DataFrame, so you will need to convert back to a Polars DataFrame if you want.
Example.
import altair as alt import marimo as mo from vega_datasets import data chart = ( alt.Chart(data.cars()) .mark_point() .encode( x="Horsepower", y="Miles_per_Gallon", color="Origin", ) ) chart = mo.ui.altair_chart(chart)
# View the chart and selected data as a dataframe mo.hstack([chart, chart.value])
Attributes.
value
: a dataframe of the plot data filtered by the selectionsdataframe
: a dataframe of the unfiltered chart dataselections
: the selection of the chart; this may be an interval along the name of an axis or a selection of points
Initialization Args.
chart
: Analtair.Chart
chart_selection
: optional selection type,"point"
,"interval"
, or a bool; defaults toTrue
which will automatically detect the best selection typelegend_selection
: optional list of legend fields (columns) for which to enable selection,True
to enable selection for all fields, orFalse
to disable selection entirelylabel
: optional markdown label for the elementon_change
: optional callback to run when this element’s value changes
Performance and Data Transformers¶
Altair has a concept of data transformers, which can be used to improve performance.
Such examples are:
pandas Dataframe has to be sanitized and serialized to JSON.
The rows of a Dataframe might need to be sampled or limited to a maximum number.
The Dataframe might be written to a
.csv
or.json
file for performance reasons.
By default, Altair uses the default
data transformer, which is the slowest in marimo. It is limited to 5000 rows (although we increase this to 20_000
rows as marimo can handle this). This includes the data inside the HTML that is being sent over the network, which can also be limited by marimo’s maximum message size.
It is recommended to use the marimo_csv
data transformer, which is the most performant and can handle the largest datasets: it converts the data to a CSV file which is smaller and can be sent over the network. This can handle up to +400,000 rows with no issues.
When using mo.ui.altair_chart
, we automatically set the data transformer to marimo_csv
for you. If you are using Altair directly, you can set the data transformer using the following code:
import altair as alt
alt.data_transformers.enable('marimo_csv')
Reactive plots with Plotly¶
mo.ui.plotly only supports scatter plots, treemaps charts, and sunbursts charts.
marimo can render any Plotly plot, but mo.ui.plotly
only
supports reactive selections for scatter plots, treemaps charts, and sunbursts charts. If you require other kinds of
selection, consider using mo.ui.altair_chart
.
- marimo.ui.plotly(figure: go.Figure, config: Dict[str, Any] | None = None, renderer_name: str | None = None, *, label: str = '', on_change: Callable[[JSONType], None] | None = None) None ¶
Make reactive plots with Plotly.
Use
mo.ui.plotly
to make plotly plots reactive: select data with your cursor on the frontend, get them as a list of dicts in Python!This function currently only supports scatter plots, treemaps charts, and sunbursts charts.
Example.
import plotly.express as px import marimo as mo from vega_datasets import data _plot = px.scatter( data.cars(), x="Horsepower", y="Miles_per_Gallon", color="Origin" ) plot = mo.ui.plotly(_plot)
# View the plot and selected data mo.hstack([plot, plot.value])
Or with custom configuration:
plot = mo.ui.plotly( _plot, config={"staticPlot": True}, )
Attributes.
value
: a dict of the plot dataranges
: the selection of the plot; this may be an interval along the name of an axis
Initialization Args.
figure
: Aplotly.graph_objects.Figure
config
: optional configuration for the plot This is a dictionary that is passed directly to the plotly. See the plotly documentation for more information: https://plotly.com/javascript/configuration-options/ This takes precedence over the default configuration of the renderer.renderer_name
: optional renderer to use for the plot. If this is not provided, the default renderer (pio.renderers.default) is used.label
: optional markdown label for the elementon_change
: optional callback to run when this element’s value changes
Interactive matplotlib¶
- marimo.mpl.interactive(figure: Figure | Axes) Html ¶
Render a matplotlib figure using an interactive viewer.
The interactive viewer allows you to pan, zoom, and see plot coordinates on mouse hover.
Example:
plt.plot([1, 2]) # plt.gcf() gets the current figure mo.mpl.interactive(plt.gcf())
Args:
figure: a matplotlib
Figure
orAxes
object
Returns:
An interactive matplotlib figure as an
Html
object
Other plotting libraries¶
You can use all the popular plotting libraries with marimo. Such as: