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Description:

boredcharts 0.13.1

bored-charts
Build easy, minimal, PDF-able data reports with markdown and python.
The idea is you do your analysis in Python, as you normally would, and dumping your
figures into a nice report written in markdown is now super low-effort: you decorate
the function to generate the figure (that you already wrote when doing your analysis)
and it becomes available to bored-charts so you can present your findings clearly.
Minimal example
Install bored-charts and uvicorn:
pip install bored-charts uvicorn

Create your app
# main.py

from pathlib import Path

import plotly.express as px
import plotly.graph_objects as go
from boredcharts import FigureRouter, boredcharts

figures = FigureRouter()

@figures.chart("population")
async def population(country: str) -> go.Figure:
df = px.data.gapminder().query(f"country=='{country}'")
fig = px.bar(df, x="year", y="pop")
return fig

app = boredcharts(pages=Path(__file__).parent, figures=figures)

Write a markdown report


## Populations

USA's population has been growing linearly for the last 70 years:

{{ figure("population", country="United States") }}

Run your app
uvicorn main:app --reload

🎉Now you can view your reports at http://localhost:8000!
Going further
A more full project structure might look like this
(see the full example here):
my-reports
├── analysis <-- do your analysis and define your figures
│   ├── __init__.py
│   ├── figures.py
│   └── ...
├── pages <-- write your markdown reports
│   ├── example.md
│   └── ...
├── app.py <-- spin up the bored-charts app
├── pyproject.toml
└── README.md

Supported charting libraries

matplotlib
plotly
vega-altair
seaborn

Extensibility
The bored-charts app is just a FastAPI (ASGI) app,
so you can integrate it into your existing projects or extend it as needed
with existing solutions (e.g., adding authentication).
Roadmap
See the Github repo