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Pie / Donut (type: "pie")

A pie chart showing each category's share of a total. Set inner_radius for a donut.

Class: dl2_reports.Pie · Legacy helper: row.add_pie(...) · Example: 06_pie.py

Quick start

from dl2_reports import Pie

page.add_row(
    Pie("share", category_column="Browser", value_column="Users",
        show_legend=True),
    Pie("share", category_column="Browser", value_column="Users",
        inner_radius=70, show_legend=True),          # donut
)

Parameters

Parameter Type Description
dataset_id str Required. Dataset id (or a formula datasource).
category_column str | int Column for slice labels.
value_column str | int Column for slice size.
inner_radius int Inner radius in px — nonzero makes a donut.
show_legend bool Show the legend.
enable_export bool (dl2 0.5+) Right-click Export PNG / Export SVG image export (viewer default True).
export_file_name str (dl2 0.5+) Base file name for exported images, no extension (viewer falls back to title → dataset id → chart type).
context_menu bool (dl2 0.5+) Right-click context menu (viewer default True).
extra dict Passthrough props.
**common Common visual properties.

Pies over raw (ungrouped) data

The dataset does not have to be pre-grouped. Attach an aggregate= and the browser groups the rows client-side; value_column then refers to the aggregate's output column:

from dl2_reports import Pie, aggregates as A

# Sum Amount per Region — output column named by as_
Pie("orders",
    category_column="Region",
    value_column="Total",
    aggregate=A.aggregate("Region", A.agg("Amount", "sum", as_="Total")),
    show_legend=True)

# Slice size = row count per group ("count" ignores its column argument)
Pie("orders",
    category_column="Region",
    value_column="Orders",
    aggregate=A.aggregate("Region", A.agg("Region", "count", as_="Orders")))

Without as_, the output column is named "{fn}_{column}" (e.g. sum_Amount). A filter= (or a formula datasource like Pie("orders[Amount >= 100]", ...)) runs before the grouping.

Related

  • Bar — better than a pie when categories are many or values are close.
  • Aggregation — the client-side grouping used above.
  • Chart image export — PNG/SVG export details (dl2 0.5+).