Scatter Plots¶

Source: src/wrappers/plots/scatter.py

ScatterWrapper is the public scatter plot interface exposed through a ContextManager. It fetches the active backend table into a pandas DataFrame for the requested columns, then delegates chart construction to plotly.express.scatter.

Use this page as a memFrame-specific entry point. For detailed scatter plot behavior, marker sizing, symbols, colors, facets, animations, and examples, see the Plotly documentation:

https://plotly.com/python/line-and-scatter/

Public API¶

Synchronous	Asynchronous	Purpose
`scatter(...)`	`await ascatter(...)`	Build a Plotly scatter plot from the active dataset context

dataset.scatter(...) is also callable directly because dataset.scatter resolves to the scatter plotting wrapper.

Usage Overview¶

dataset = mf.upload_df(frame)

fig = dataset.scatter(x="score", y="salary")
fig.show()

dataset = mf.upload_csv("data/sales.csv")

fig = dataset.scatter(
    x="revenue",
    y="profit",
    color="region",
    size="orders",
    title="Revenue vs profit",
)
fig.show()

dataset = await mf.aupload_csv("data/sales.csv")

fig = await dataset.ascatter(
    x="revenue",
    y="profit",
    color="region",
)
fig.show()

Common Parameters¶

Most parameters are passed through to plotly.express.scatter.

Parameter	Description
`x`	Column name or values used for the x-axis.
`y`	Column name or values used for the y-axis.
`color`	Column name or values used to map marker colors.
`symbol`	Column name or values used to map marker symbols.
`size`	Column name or values used to scale marker sizes.
`hover_name`, `hover_data`, `custom_data`	Columns or values included in hover labels or callbacks.
`text`	Column name or values used as text labels.
`facet_row`, `facet_col`	Column names used to split the chart into facets.
`error_x`, `error_x_minus`, `error_y`, `error_y_minus`	Columns or values used for error bars.
`animation_frame`, `animation_group`	Columns or values used for animated charts.
`**kwargs`	Additional `plotly.express.scatter` keyword arguments.

Do not pass data_frame; memFrame derives it from the active dataset context.

Return Value¶

scatter and ascatter return the Plotly figure object created by plotly.express.scatter.

fig = dataset.scatter(x="score", y="salary", color="category")
fig.update_layout(xaxis_title="Score", yaxis_title="Salary")
fig.show()

Because the return value is a Plotly figure, use Plotly figure methods such as update_layout, update_traces, and show for final presentation changes.