Datalys2 Reporting Python API

Version 0.2.6

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

Note: Compatible with dl2 version 0.2.6 https://github.com/kameronbrooks/datalys2-reporting

Installation

pip install dl2-reports

Quick Start

import pandas as pd
from dl2_reports import DL2Report

# 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 visual
page = report.add_page("Overview")
page.add_row().add_kpi("my_data", value_column="A", title="Metric A")

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

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)
• checklist (via add_checklist)
• histogram (via add_histogram)
• heatmap (via add_heatmap)
• boxplot (via add_boxplot)
• modal (via add_modal_button)

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.
table_style	str | None	None	'plain', 'bordered', or 'alternating'.
show_search	bool | None	None	Whether to show the search box.
**kwargs	Any	—	Additional common visual properties.

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.
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.
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.

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)

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