Pie Charts¶

Source: src/wrappers/plots/pie.py

PieWrapper is the public pie chart plotting 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.pie.

Use this page as a memFrame-specific entry point. For detailed pie chart behavior, donut charts, labels, colors, facets, and examples, see the Plotly documentation:

https://plotly.com/python/pie-charts/

Public API¶

Synchronous	Asynchronous	Purpose
`pie(...)`	`await apie(...)`	Build a Plotly pie chart from the active dataset context

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

Usage Overview¶

dataset = mf.upload_df(frame)

fig = dataset.pie(names="category")
fig.show()

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

fig = dataset.pie(
    names="region",
    values="revenue",
    color="region",
    title="Revenue share by region",
)
fig.show()

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

fig = await dataset.apie(
    names="region",
    values="revenue",
    hole=0.4,
)
fig.show()

Common Parameters¶

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

Parameter	Description
`names`	Column name or values used for slice labels.
`values`	Column name or values used for slice sizes. If omitted, Plotly counts rows by `names`.
`color`	Column name or values used to map slice colors.
`facet_row`, `facet_col`	Column names used to split the chart into facets.
`hover_name`, `hover_data`, `custom_data`	Columns or values included in hover labels or callbacks.
`category_orders`	Ordering rules for categorical values.
`labels`	Display labels for columns.
`color_discrete_sequence`, `color_discrete_map`	Discrete color options passed to Plotly.
`opacity`	Trace opacity.
`hole`	Creates a donut chart when set above `0`.
`title`, `subtitle`, `template`, `width`, `height`	Display and layout options passed to Plotly.
`**kwargs`	Additional `plotly.express.pie` keyword arguments.

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

Return Value¶

pie and apie return the Plotly figure object created by plotly.express.pie.

fig = dataset.pie(names="category", values="score")
fig.update_traces(textposition="inside", textinfo="percent+label")
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.