# Django Slick Reporting ### The one-stop reporting engine for Django — analytics, charts & dashboards in a few lines of code. [](https://pypi.org/project/django-slick-reporting) [](https://pypi.org/project/django-slick-reporting) [](https://django-slick-reporting.readthedocs.io/) [](https://codecov.io/gh/ra-systems/django-slick-reporting) [](https://github.com/ra-systems/django-slick-reporting/blob/develop/LICENSE.md) Grouped totals, time-series, crosstabs and pivots — each one a small Python class, each one chartable with a single line, in **Highcharts, Chart.js or ApexCharts**. [**Live Demo**](https://django-slick-reporting.com/) · [**Documentation**](https://django-slick-reporting.readthedocs.io/) · [**Quickstart**](#quickstart)

One report class, every chart

Declare the calculation once. Switch the visualization by changing a single argument — no new query, no new template.

Why Django Slick Reporting?

• Every report shape — simple aggregates, group-by, time-series, crosstab/pivot, and combinations of them.
• Charts included — Highcharts, Chart.js and ApexCharts wrappers. Bar, column, line, area, pie — stacked or totalled — from one Chart(...) line.
• Custom calculations — build reusable computation fields, chain dependencies, compute percentages and balances.
• Dashboards — drop any report onto a page as a self-contained widget with a template tag.
• Model-optional — report against a Django model, a traversed relation, a raw SQL table, or precomputed/aggregated data.
• Fast & extendable — optimized queries, CSV export out of the box, and hooks for everything.

Installation

pip install django-slick-reporting

Add it to INSTALLED_APPS and you're ready:

INSTALLED_APPS = [
    ...,
    "crispy_forms",
    "crispy_bootstrap5",
    "slick_reporting",
]

Quickstart

Given a typical SalesTransaction model, here is a group-by report — total value sold per product — with a bar chart:

# views.py from django.db.models import Sum from slick_reporting.views import ReportView, Chart from slick_reporting.fields import ComputationField from .models import SalesTransaction class ProductSales(ReportView): report_model = SalesTransaction date_field = "date" group_by = "product" columns = [ "name", ComputationField.create( Sum, "value", name="value__sum", verbose_name="Total sold $", ), ] chart_settings = [ Chart( "Total sold $", Chart.BAR, data_source=["value__sum"], title_source=["name"], ), ] # urls.py path("product-sales/", ProductSales.as_view()),

That's the whole report — filter form, chart, sortable data table and CSV export are generated for you.

Same data, any visualization

The chart is just configuration. Keep the report, change the Chart(...) line:

Chart( "Sales", Chart.BAR, data_source=["value__sum"], title_source=["name"], )

Chart( "Sales", Chart.PIE, data_source=["value__sum"], title_source=["name"], plot_total=True, )

…and any chart engine

Set chart_engine on the report (or per Chart). The same data renders through your engine of choice:

Highcharts chart_engine="highcharts"	Chart.js chart_engine="chartsjs"	ApexCharts chart_engine="apexcharts"

Report types

class MonthlyProductSales(ReportView):
    report_model = SalesTransaction
    date_field = "date"
    group_by = "product"
    columns = ["name", "sku"]

    time_series_pattern = "monthly"          # daily / weekly / yearly / custom
    time_series_columns = [
        ComputationField.create(Sum, "value", name="value", verbose_name="Sales"),
    ]

    chart_settings = [
        Chart("Sales Monthly", Chart.COLUMN, data_source=["value"], title_source=["name"]),
    ]

Crosstab / Pivot — matrix reports with rows, columns and intersecting totals

class ClientProductMatrix(ReportView):
    report_model = SalesTransaction
    group_by = "client"
    crosstab_field = "product"
    crosstab_columns = [
        ComputationField.create(Sum, "value", verbose_name="Value"),
    ]
    crosstab_compute_remainder = True         # a column capturing "everything else"

    columns = [
        "name",
        "__crosstab__",                       # where the matrix columns land
        ComputationField.create(Sum, "value", verbose_name="Total Value"),
    ]

Crosstab and time-series can even be combined for a matrix over periods. Already-aggregated data? Set `crosstab_precomputed=True`.

from slick_reporting.views import ListReportView

class LastTenSales(ListReportView):
    report_model = SalesTransaction
    date_field = "date"
    columns = ["product__name", "client__name", "date", "quantity", "value"]
    default_order_by = "-date"
    limit_records = 10

from slick_reporting.generator import ReportGenerator

report = ReportGenerator(
    report_model=SalesTransaction,
    group_by="product",
    columns=["title", "__total__"],
)
report.get_report_data()
# -> [{'title': 'Product 1', '__total__': 56}, {'title': 'Product 2', '__total__': 43}, ...]

Dashboards

Compose any report into a page as a self-contained widget — chart, table, or both — with one template tag:

{% load slick_reporting_tags %}

{% get_widget_from_url url_name="product-sales" %}
{% get_widget_from_url url_name="monthly-product-sales" chart_id=1 display_table=False title="Chart only" %}

See the live dashboard example.

Demo site

Live at django-slick-reporting.com, or run it locally:

git clone https://github.com/ra-systems/django-slick-reporting.git
python -m venv .venv && source .venv/bin/activate

cd django-slick-reporting/demo_proj
pip install -r requirements.txt
python manage.py migrate
python manage.py create_entries   # generates demo data
python manage.py runserver

Documentation

Full documentation lives on Read the Docs. Build it locally with:

cd docs
pip install -r requirements.txt
sphinx-build -b html source build

Running the tests

git clone [email protected]:ra-systems/django-slick-reporting.git
cd django-slick-reporting/tests
python -m pip install -e ..
python runtests.py
# coverage:
coverage run --include=../* runtests.py && coverage html

Contributing

PRs and reviews are most welcome. We follow Django's contributing guidelines. If the project is useful to you, please consider giving it a star — it keeps the project visible and motivated.

Authors

• Ramez Ashraf — Initial work — @RamezIssac

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