dl2-reports 0.5.0

A Python API for generating Datalys2 reports

Datalys2 Reporting Python API

Version 0.5.0

A Python library to build and compile interactive HTML reports using the Datalys2 Reporting framework.

Note: Compatible with dl2 version 0.4.0 https://github.com/kameronbrooks/datalys2-reporting

Installation

pip install dl2-reports

Quick Start

import pandas as pd
from dl2_reports import DL2Report, KPI, Table

# Create a report
report = DL2Report(title="My Report")

# Add data
df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
report.add_df("my_data", df, compress=True)

# Add a page and visuals (typed component API)
page = report.add_page("Overview")
page.add_row(
    KPI("my_data", value_column="A", title="Metric A"),
    Table("my_data", page_size=10),
)

# Save to HTML or show in Jupyter
report.save("report.html")
report.show()

The Typed Component API (v2)

Since 0.5.0 the recommended way to build reports is with typed component classes — one class per visual, imported from the package root:

KPI, Table, Card, Pie, Bar, Line, Area, Scatter, Checklist, Histogram, Heatmap, Gauge, Boxplot, Tabs, Link, ModalButton

plus typed shapes for structured props:

Threshold, SortSpec, TotalRow, TotalColumn, GaugeRange, AggregateColumn, Tab

from dl2_reports import DL2Report, Line, Table, Tabs, Tab, Threshold, TotalRow, SortSpec

page.add_row(
    Line("sales", x_column="Month", y_columns=["Revenue"],
         threshold=Threshold(value=5000, mode="above")),
)
row = page.add_row()
row.add(Table("sales",
              id="orders-table",
              group_by="Region",
              default_sort=[SortSpec("Amount", "desc")],
              total_row=TotalRow(fns={"Units": "sum", "Amount": "avg"})))
row.add(Tabs(id="views", tabs=[
    Tab("Chart", children=[Line("sales", x_column="Month", y_columns=["Revenue"])]),
    Tab("Data",  children=[Table("sales")]),
]))

Why it's better:

• Typos fail fast. Table("sales", pagesize=20) raises TypeError: unknown prop 'pagesize' (did you mean 'page_size'?) at construction — previously it serialized silently and the viewer ignored it.
• Autocomplete and type checking work everywhere (the package ships py.typed).
• extra={...} passes unmodeled viewer props through explicitly when you need forward compatibility.
• compile() lints legacy calls too: props the viewer doesn't know are reported as [dl2] warnings with suggestions (report.compile(strict=True) turns them into errors).

The legacy row.add_kpi(...) helpers keep working unchanged — they now delegate to the component classes, and their unknown kwargs still pass through (flagged by the compile lint). add() returns the component, so chaining (.add_trend(), .get_value()) works as before.

Migrating existing scripts

A codemod ships with the package (comments and formatting preserved; requires pip install dl2-reports[migrate]):

python -m dl2_reports.migrate my_report.py            # dry run: shows a diff
python -m dl2_reports.migrate my_report.py --write    # apply
python -m dl2_reports.migrate notebooks/ --write      # directories & .ipynb work too

Data Compression

Always use compression for production reports. The Python API provides automatic gzip compression for your datasets, which significantly reduces file size and improves browser performance.

Why Compression Matters

• Large datasets will cause severe performance issues or fail to load entirely without compression
• Compressed reports load faster and consume less memory in the browser
• File sizes can be reduced by 80-90% or more
• The browser automatically decompresses data on-the-fly using the built-in DecompressionStream API

Using Compression

Report-Level Default

When creating a DL2Report, you can set the default compression behavior:

from dl2_reports import DL2Report

# Enable compression by default (recommended)
report = DL2Report(
    title="My Report",
    compress_visuals=True  # This is the default
)

Per-Dataset Control

Control compression for individual datasets using the compress parameter in add_df():

import pandas as pd
from dl2_reports import DL2Report

report = DL2Report(title="Sales Report")

# Compress large datasets (recommended for most data)
large_df = pd.read_csv("sales_data.csv")
report.add_df("salesData", large_df, compress=True)

# Small datasets can be uncompressed for easier debugging
small_df = pd.DataFrame({"kpi": [100]})
report.add_df("kpiData", small_df, compress=False)

How It Works

When you set compress=True, the Python API automatically:

1. Serializes your data to JSON
2. Compresses it using gzip
3. Encodes it as a Base64 string
4. Stores it in a separate <script> tag in the HTML
5. Adds the gc-compressed-data meta tag for automatic memory cleanup

The browser then decompresses the data when the report loads.

Best Practices

• ✅ Always compress in production - essential for performance and reliability
• ✅ Compress any dataset with more than a few rows - the overhead is minimal
• ❌ Only disable compression when:
  • Debugging and you need to inspect the raw JSON in the HTML file
  • Working with extremely small datasets (single-row KPI values) during development

Features

Jupyter Notebook Support

You can render reports directly inside Jupyter Notebooks (including VS Code and JupyterLab).

• report.show(height=800): Displays the report in an iframe.
• Automatic Rendering: Simply placing the report object at the end of a cell will render it automatically.

Requirements:

• IPython must be installed in your environment.

Available Visuals

All visuals are added to a layout row using row.add_<type>(...).

Common Visual Keyword Arguments

All Layout.add_* visual helpers accept **kwargs which are passed through to the viewer as visual properties.

Common properties supported by the viewer include:

• padding: number (px)
• margin: number (px)
• border: bool or CSS border string
• shadow: bool or CSS box-shadow string
• flex: number (flex grow)
• modal_id: string (global modal id opened via the expand icon)

The Python API accepts these in snake_case; the compiled report JSON uses camelCase.

Layout Visual Helper APIs

Below are the current convenience helpers available on Layout (rows are layouts).

Visual Types (Quick Summary)

These are the visual types you can add via the Layout helpers:

• kpi (via add_kpi)
• table (via add_table)
• card (via add_card)
• pie (via add_pie)
• clusteredBar / stackedBar (via add_bar(stacked=...))
• scatter (via add_scatter)
• line (via add_line)
• area (via add_area)
• checklist (via add_checklist)
• histogram (via add_histogram)
• heatmap (via add_heatmap)
• boxplot (via add_boxplot)
• modal (via add_modal_button)
• tabs (via add_tabs, dl2 0.3+)
• link (via add_link, dl2 0.4+)

You can also add any viewer-supported visual type directly using add_visual(type=..., ...).

Generic Visual

Use this when you want to pass through viewer props that don't have a dedicated helper yet.

Parameter	Type	Default	Description
type	str	(required)	Visual type (e.g., 'kpi', 'table', 'line', 'scatter').
dataset_id	str | None	None	Dataset id to bind to this visual (required for most chart/data visuals).
**kwargs	Any	—	Additional visual properties (serialized to JSON). Common ones include padding, margin, border, shadow, flex, modal_id.

KPI

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
value_column	str | int	(required)	Column for the main KPI value.
title	str | None	None	Optional KPI card title.
comparison_column	str | int | None	None	Column for the comparison value.
comparison_row_index	int | None	None	Row index to use for comparison (supports negative indices). If not provided, the viewer uses the same row as row_index.
comparison_text	str	The comparison text to show alongside the comparison value. Ex. ("Last Month", "Yesterday", etc.)
row_index	int | None	None	Row index to display (supports negative indices).
format	str | None	None	'number', 'currency', 'percent', 'date', 'hms'.
`
currency_symbol	str | None	None	Currency symbol (viewer default is usually '$').
good_direction	str | None	None	Which direction is “good” ('higher' or 'lower').
breach_value	float | int | None	None	Value that triggers a breach indicator.
warning_value	float | int | None	None	Value that triggers a warning indicator.
description	str | None	None	Optional description text.
width	int | None	None	Optional width.
height	int | None	None	Optional height.
**kwargs	Any	—	Additional common visual properties (e.g., modal_id, padding/margins, etc.).

Table

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
title	str | None	None	Optional table title.
columns	list[str] | None	None	Optional list of columns to display.
page_size	int | None	None	Rows per page (groups per page while grouped).
table_style	str | None	None	'plain', 'bordered', or 'alternating'.
show_search	bool | None	None	Whether to show the search box.
sortable	bool | None	None	Type-aware sorting; Shift+click multi-sort (viewer default true).
default_sort	list[dict] | None	None	Initial sort, e.g. [{"column": "Amount", "direction": "desc"}].
hidden_columns	list[str] | None	None	Columns hidden initially.
allow_column_hiding	bool | None	None	Runtime Columns menu (viewer default true).
group_by	str | None	None	Initial grouping column (collapsible groups).
group_aggregates	list[dict] | None	None	Per-group aggregates, e.g. [aggregates.agg("Amount", "sum")].
groups_collapsed	bool | None	None	Whether groups start collapsed.
enable_export	bool | None	None	CSV export / clipboard copy (viewer default true).
export_file_name	str | None	None	File name for CSV export.
context_menu	bool | None	None	Right-click context menus (viewer default true).
max_height	int | None	None	Max body height in px (scrollable body + sticky header).
sticky_header	bool | None	None	Viewer default: true when max_height is set.
total_row	bool | dict | None	None	True or {"label": ..., "fns": {"<col>": "<fn>"}} — grand-total row (dl2 0.4+).
total_column	bool | dict | None	None	True or {"label": ..., "columns": [...]} — per-row total column (dl2 0.4+).
row_modal	bool | None	None	Built-in row detail modal on double-click (dl2 0.4+).
row_modal_id	str | None	None	Open a custom modal instead; cards can use {{ row.Col }} templates (dl2 0.4+).
row_modal_columns	list[str] | None	None	Columns listed in the built-in detail modal.
row_modal_title	str | None	None	Title of the built-in detail modal.
id	str | None	None	Stable element id (persistence + link targeting).
persist_state	bool | None	None	Persist sort/columns/grouping (viewer default: true when id is set).
**kwargs	Any	—	Additional common visual properties (e.g. filter=, aggregate=).

Card

Parameter	Type	Default	Description
title	str | None	(required)	Optional title (supports template syntax in the viewer).
text	str	(required)	Main card text (supports template syntax in the viewer).
**kwargs	Any	—	Additional common visual properties.

Pie / Donut

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
category_column	str | int	(required)	Column for slice labels.
value_column	str | int	(required)	Column for slice values.
inner_radius	int | None	None	Inner radius for donut styling.
show_legend	bool | None	None	Whether to show the legend.
**kwargs	Any	—	Additional common visual properties.

Bar (Clustered / Stacked)

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
x_column	str | int	(required)	Column for X-axis categories.
y_columns	list[str]	(required)	Series columns for Y values.
stacked	bool	False	If True, uses stacked bars; otherwise clustered.
threshold	dict | None	None	Optional pass/fail coloring (see Threshold Configuration).
x_axis_label	str | None	None	Optional X-axis label.
y_axis_label	str | None	None	Optional Y-axis label.
show_legend	bool | None	None	Whether to show the legend.
show_labels	bool | None	None	Whether to show value labels.
horizontal	bool | None	None	Whether to render bars horizontally (viewer-dependent).
**kwargs	Any	—	Additional common visual properties.

Scatter

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
x_column	str | int	(required)	Column for numeric X values.
y_column	str | int	(required)	Column for numeric Y values.
category_column	str | int | None	None	Optional column for coloring points by category.
show_trendline	bool | None	None	Whether to show a trendline.
show_correlation	bool | None	None	Whether to show correlation stats.
point_size	int | None	None	Point size.
x_axis_label	str | None	None	Optional X-axis label.
y_axis_label	str | None	None	Optional Y-axis label.
**kwargs	Any	—	Additional common visual properties.

Line

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
x_column	str | int	(required)	Column for X values (time or category).
y_columns	list[str] | str	(required)	Column(s) for Y series.
smooth	bool | None	None	Whether to render smooth curves.
show_legend	bool | None	None	Whether to show the legend.
show_labels	bool | None	None	Whether to show value labels.
min_y	float | int | None	None	Optional minimum Y.
max_y	float | int | None	None	Optional maximum Y.
colors	list[str] | None	None	Optional list of series colors.
threshold	dict | None	None	Optional pass/fail coloring (see Threshold Configuration).
x_axis_label	str | None	None	Optional X-axis label.
y_axis_label	str | None	None	Optional Y-axis label.
**kwargs	Any	—	Additional common visual properties.

Area

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
x_column	str | int	(required)	Column for X values.
y_columns	list[str] | str	(required)	Column(s) for Y series.
smooth	bool | None	None	Whether to render smooth curves.
show_line	bool | None	True	Show line stroke on top of fill.
show_markers	bool | None	True	Show interactive marker points.
fill_opacity	float | None	0.3	Area fill opacity (0-1).
show_legend	bool | None	None	Whether to show the legend.
show_labels	bool | None	None	Whether to show value labels.
min_y	float | int | None	None	Optional minimum Y.
max_y	float | int | None	None	Optional maximum Y.
colors	list[str] | None	None	Optional list of series colors.
threshold	dict | None	None	Optional pass/fail coloring (see Threshold Configuration).
x_axis_label	str | None	None	Optional X-axis label.
y_axis_label	str | None	None	Optional Y-axis label.
**kwargs	Any	—	Additional common visual properties.

Checklist

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
status_column	str	(required)	Column containing a truthy completion value.
warning_column	str | None	None	Optional date column to evaluate for warnings.
warning_threshold	int | None	None	Days before due date to trigger warning.
columns	list[str] | None	None	Optional subset of columns to display.
page_size	int | None	None	Rows per page.
show_search	bool | None	None	Whether to show the search box.
**kwargs	Any	—	Additional common visual properties.

Histogram

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
column	str | int	(required)	Numeric column to bin.
bins	int | None	None	Number of bins.
color	str | None	None	Bar color.
show_labels	bool | None	None	Whether to show count labels.
x_axis_label	str | None	None	Optional X-axis label.
y_axis_label	str | None	None	Optional Y-axis label.
**kwargs	Any	—	Additional common visual properties.

Heatmap

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
x_column	str | int	(required)	Column for X categories.
y_column	str | int	(required)	Column for Y categories.
value_column	str | int	(required)	Column for cell values.
show_cell_labels	bool | None	None	Whether to show values inside cells.
min_value	float | int | None	None	Optional minimum for the color scale.
max_value	float | int | None	None	Optional maximum for the color scale.
color	str | list[str] | None	None	D3 interpolator name (e.g., 'Viridis') or custom colors.
x_axis_label	str | None	None	Optional X-axis label.
y_axis_label	str | None	None	Optional Y-axis label.
**kwargs	Any	—	Additional common visual properties.

Boxplot

Supports two modes:

• Data mode: provide data_column (and optional category_column)
• Pre-calculated mode: provide min_column, q1_column, median_column, q3_column, max_column (and optional mean_column)

Parameter	Type	Default	Description
dataset_id	str	(required)	The dataset id.
data_column	str | int | None	None	Raw values column (data mode).
category_column	str | int | None	None	Grouping/label column.
min_column	str | int | None	None	Pre-calculated minimum column.
q1_column	str | int | None	None	Pre-calculated Q1 column.
median_column	str | int | None	None	Pre-calculated median column.
q3_column	str | int | None	None	Pre-calculated Q3 column.
max_column	str | int | None	None	Pre-calculated maximum column.
mean_column	str | int | None	None	Pre-calculated mean column (optional).
direction	str | None	None	'vertical' or 'horizontal'.
show_outliers	bool | None	None	Whether to show outliers.
color	str | list[str] | None	None	Fill color or scheme.
x_axis_label	str | None	None	Optional X-axis label.
y_axis_label	str | None	None	Optional Y-axis label.
**kwargs	Any	—	Additional common visual properties.

Modal Button

Parameter	Type	Default	Description
modal_id	str	(required)	The global modal id to open.
button_label	str	(required)	Button label text.
**kwargs	Any	—	Additional common visual properties.

Note: Most visuals also support modal_id as a keyword argument to enable an "expand" icon that opens a modal on click.

Threshold Configuration

Color chart elements based on whether values pass or fail a threshold. Applies to Line, Area, and Clustered Bar.

Property	Type	Default	Description
value	number	(required)	The threshold value to compare against.
pass_color	str	#22c55e	Color for passing values.
fail_color	str	#ef4444	Color for failing values.
mode	'above'|'below'|'equals'	'above'	How to determine pass/fail.
show_line	bool	True	Show reference line at threshold.
line_style	'solid'|'dashed'|'dotted'	'dashed'	Threshold line style.
blend_width	number	5	Gradient blend zone width (line/area only).
apply_to	'both'|'markers'|'lines'	'both'	Which elements get threshold colors.

Mode Options:

• 'above' - Values >= threshold pass
• 'below' - Values <= threshold pass
• 'equals' - Only exact matches pass

Tabs (dl2 0.3+)

row.add_tabs() returns a Tabs container; tabs.add_tab(title) returns a full Layout, so every add_* helper works inside a tab. Tabs can be nested.

tabs = page.add_row().add_tabs(id="sales-tabs", default_tab=0, title="Sales Views")
tabs.add_tab("Chart").add_line("sales", x_column="Month", y_columns=["Revenue"])
tabs.add_tab("Data", direction="column").add_table("sales", id="sales-table")

Parameter	Type	Default	Description
id	str | None	auto	Stable id — enables active-tab persistence (dl2 0.4+) and link targeting.
default_tab	int | None	None	Index of the initially active tab (viewer default 0).
title	str | None	None	Optional title above the tab strip.
**kwargs	Any	—	Container props (padding, border, shadow, flex, persist_state, ...).

add_tab(title, direction="column", **layout_kwargs) — the kwargs are layout props (gap, wrap, columns, ...).

Link (dl2 0.4+)

Navigate to any visual with an id (switches page, activates containing tabs, scrolls, flashes) or to an external URL. Exactly one of target_id / href is required.

row.add_link(target_id="sales-table", label="Jump to data", link_style="button")
row.add_link(href="https://example.com", label="Docs")

Filtering & Aggregation (dl2 0.3+)

Any visual accepts filter= and aggregate= — several visuals can show different client-side slices of one shared dataset, with no extra data embedded in the HTML. Use the filters and aggregates builder modules (plain dicts work too); invalid operators/functions raise ValueError at build time.

from dl2_reports import DL2Report, aggregates as A, filters as F

# Filter: leaf conditions + and_/or_/not_ composition
row.add_table(
    "sales",
    filter=F.and_(F.gte("Amount", 200), F.isin("Region", ["South", "West"])),
)

# Aggregate: groupBy + aggregate fns (applied after the filter)
row.add_bar(
    "sales",
    x_column="Region",
    y_columns=["sum_Amount"],   # default output name is "{fn}_{column}"
    aggregate=A.aggregate("Region", A.agg("Amount", "sum")),
)

Filter shortcuts: eq, neq, gt, gte, lt, lte, isin, notin, contains, starts_with, ends_with, between, is_null, not_null (plus generic where(column, op, ...)). Aggregate fns: sum, avg, min, max, count, countDistinct, first, last. Use A.agg("Amount", "sum", as_="Total") to name the output column.

Derived Datasets (dl2 0.3+)

Declare a dataset computed in the browser from another dataset — filtered and/or aggregated at load time. Chains are supported and declaration order doesn't matter (sources are checked at compile()).

report.add_derived_dataset(
    "north_by_category",
    source="sales",
    filter=F.eq("Region", "North"),
    aggregate=A.aggregate("Category", A.agg("Amount", "sum", as_="Total")),
)
page.add_row().add_table("north_by_category")

Note: derived values are not available to report.get_value() at compile time (they only exist in the browser) — compute with pandas if you need them while building.

Table Totals & Row Detail Modals (dl2 0.4+)

page.add_row().add_table(
    "orders",
    id="orders-table",
    total_row={"label": "Totals", "fns": {"Units": "sum", "Amount": "avg"}},
    total_column={"columns": ["Units", "Amount"]},
    row_modal_id="order-detail",       # or row_modal=True for the built-in modal
)

modal = report.add_modal("order-detail", "Order Details")
modal.add_row().add_card(
    title="Order — {{ row.Region }}",
    text="**Rep:** {{ row.Rep }}\n**Amount:** {{ formatCurrency(row.Amount) }}",
    content_type="md",
)

The column names in total_row["fns"] are preserved exactly as written (they are not snake_case→camelCase converted). For your own passthrough props whose dict keys are column names, wrap them in dl2_reports.RawDict to get the same protection.

Persistent View State (dl2 0.4+)

Runtime view changes (table sort/hidden columns/grouping, active tabs) are saved to localStorage per report + visual id and restored on reload.

• Give tables/tabs a stable id= and the viewer persists them automatically; opt out per visual with persist_state=False.
• Set a stable report identity so state survives title changes: DL2Report(title, report_id="my-report") or report.set_report_id("my-report") (emits <meta name="report-id">).
• Users can reset via right-click → Reset view, or the report-wide Reset view button.

Layout Options (dl2 0.3+)

Layouts own spacing now (gap defaults to 10px; visuals default to margin: 0). Rows/columns accept wrap=True, align=..., justify=...; grids accept min_child_width=250 for responsive auto-fit columns. flex=0 and padding=0/margin=0 are respected.

page.add_row(wrap=True, gap=16, justify="space-between")
page.add_row(direction="grid", min_child_width=250)

Modals

Create detailed overlays that can be triggered from any element.

# Define a modal
modal = report.add_modal("details", "Detailed View")
modal.add_row().add_table("my_data")

# Trigger from a visual
page.add_row().add_kpi("my_data", "A", "Metric", modal_id="details")

# Or add a dedicated button
page.add_row().add_modal_button("details", "Open Details")

Visual Elements (Annotations)

Add trend lines, markers, and custom axes to your charts.

Trend Lines

You can add a trend line using the .add_trend() method. It can automatically calculate linear or polynomial regression if you don't provide coefficients.

chart = page.add_row().add_scatter("my_data", "A", "B")

# Auto-calculate linear trend (degree 1)
chart.add_trend(color="red")

# Auto-calculate polynomial trend (e.g., degree 2)
chart.add_trend(coefficients=2, color="blue", line_style="dashed")

# Manually provide coefficients [intercept, slope, ...]
chart.add_trend(coefficients=[0, 1.5], color="green")

Other Elements

Use .add_element(type, **kwargs) for other annotations.

Element Type	Description	Key Arguments
xAxis	Vertical line at a specific X value.	value, color, label, line_style
yAxis	Horizontal line at a specific Y value.	value, color, label, line_style
marker	A point marker at a specific value.	value, size, shape (circle, square, triangle), color
label	A text label at a specific value.	value, label, font_size, font_weight

chart.add_element("yAxis", value=100, label="Target", color="green")

Tree Traversal

All components (Pages, Rows, Layouts, Visuals) are part of a tree. You can access the root report from any component using .get_report().

visual = layout.add_visual("line", "my_data")
report = visual.get_report()
print(report.title)

Reading Values

The API provides two ways to read scalar values back from your data after the report is built. These are useful for conditional layout logic, threshold checks, or computing derived metrics without re-querying the original DataFrame.

report.get_value()

Query a value from any registered dataset by name — no visual reference required.

report.get_value(data_source_name, column_name, row_index=-1)

Parameter	Type	Default	Description
data_source_name	str	(required)	The dataset name passed to add_df().
column_name	str	(required)	The column to read from.
row_index	int	-1	Row index. Negative indices count from the end (e.g. -1 = last row).

This is the right tool when you need to inspect a value before or without creating a visual — for example, deciding whether to add a row at all:

report = DL2Report(title="Sales Report")
report.add_df("sales", sales_df, format="records", compress=False)

page = report.add_page("Overview")

# Add a bar chart
bar_row = page.add_row()
bar_row.add_bar(dataset_id="sales", x_column="region", y_columns=["revenue"])

# Only add a warning card if the worst region is below target
TARGET = 120_000
revenues = [report.get_value("sales", "revenue", i) for i in range(len(sales_df))]
worst = min(revenues)

if worst < TARGET:
    warning_row = page.add_row()
    warning_row.add_card(
        title="Warning: underperforming region detected",
        text=f"Lowest revenue is ${worst:,} — below the ${TARGET:,} target.",
        content_type="md",
    )

visual.get_value()

Read the scalar value that a specific visual represents, directly from its backing DataFrame. The visual must be part of the report tree (i.e. already added to a row) and its props must include row_index and value_column.

value = visual.get_value()

This is the right tool when you already have a visual reference and want to inspect or act on its value:

kpi = page.add_row().add_kpi(
    dataset_id="sales",
    value_column="revenue",
    row_index=0,
    title="Revenue – North",
    format="currency",
)

north_revenue = kpi.get_value()
print(f"North revenue: {north_revenue:,}")

visual.copy()

Create a duplicate of a visual with the same type, dataset_id, props, and annotations, but a new unique ID. Use this to stamp the same visual configuration into multiple rows without re-specifying every argument.

copied_visual = visual.copy()

After copying, add the copy back into any layout row using row.add_visual(copy.type, visual=copy). You can then mutate copy.props to override only what differs:

# Build a prototype KPI once
proto = row.add_kpi(
    dataset_id="sales",
    value_column="revenue",
    row_index=0,
    title="Revenue – North",
    format="currency",
)

# Stamp copies for remaining regions
for i, region in enumerate(["South", "East", "West"], start=1):
    copy = proto.copy()
    copy.props["row_index"] = i
    copy.props["title"] = f"Revenue – {region}"
    row.add_visual(copy.type, visual=copy)

# Each copy exposes get_value() once it is in the tree
total = proto.get_value() + sum(
    row.children[i].get_value() for i in range(1, 4)
)

Conditional Layout

Use layout.on_condition() to conditionally add visuals to a row at report-build time. This is a compile-time guard — it evaluates a plain Python bool and either delegates the add_* call to the real layout or silently discards it.

layout.on_condition(condition).add_<visual>(...)

Parameter	Type	Description
condition	bool	If True, the visual is added normally and returned. If False, nothing is added and None is returned.

How It Works

• When condition is True, the call is forwarded to the parent layout exactly as if you had called layout.add_<visual>(...) directly.
• When condition is False, no visual is created, no element ID is consumed, and None is returned.
• The wrapper is not a tree node — it never occupies a slot in the report tree regardless of the condition.

Example: Show a warning card only when a threshold is breached

report = DL2Report(title="Sales Report")
report.add_df("sales", sales_df, format="records", compress=False)

page = report.add_page("Overview")
row = page.add_row()
row.add_bar(dataset_id="sales", x_column="region", y_columns=["revenue"])

TARGET = 120_000
worst = min(report.get_value("sales", "revenue", i) for i in range(len(sales_df)))

warning_row = page.add_row()
warning_row.on_condition(worst < TARGET).add_card(
    title="Warning: underperforming region detected",
    text=f"Lowest revenue is ${worst:,} — below the ${TARGET:,} target.",
)

Example: Toggle a chart based on a flag

show_details = True  # could come from any Python logic

detail_row = page.add_row()
detail_row.on_condition(show_details).add_table("sales", title="Detail View")

Tip: Since 0.5.0, on_condition(False) returns a falsy NullComponent instead of None — chained calls like .add_trend() are silently absorbed, so no guard is needed. Truthiness checks (if result:) keep working; is None checks should become truthiness checks.

Datalys2 Documentation

For detailed information on available visuals and configuration, see DOCUMENTATION.md.

Or see the github repo at https://github.com/kameronbrooks/datalys2-reporting