datachart

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Data visualization package, simple to use, highly customizable

Documentation: https://eriknovak.github.io/datachart

Source code: https://github.com/eriknovak/datachart

The datachart package is a python package for creating data visualizations, built on top of matplotlib. It is designed to be simple to use and highly customizable, i.e. it is easy to change the look and feel of the charts.

Features:

Charts. Bar charts, line charts, scatter charts, histograms, heatmaps, box plots, pyramid charts, radial charts, and parallel coordinates — each created with a single function call from plain lists of dicts.
Composition. Combine rendered charts with Panel (overlay charts on a single plot, with optional dual y-axes) and Grid (arrange charts in a grid; grids nest).
Themes & configuration. Six predefined themes, each named for its visual trait, plus a global config for tweaking any style attribute — per-chart style overrides included.

Requirements

Before starting the project make sure these requirements are available:

python. The python programming language (v3.10 or higher).

Install

pip install datachart

Upgrade

pip install datachart --upgrade

Example

Set a theme once and every chart follows it. The example below uses the INK theme:

from datachart.charts import LineChart
from datachart.config import config
from datachart.constants import THEME

config.set_theme(THEME.INK)

figure = LineChart(
    [
        [{"x": x, "y": y} for x, y in enumerate([40, 45, 43, 50, 56, 54, 61])],
        [{"x": x, "y": y} for x, y in enumerate([38, 40, 44, 43, 48, 52, 55])],
    ],
    title="Line",
    subtitle=["Run 1", "Run 2"],
    show_legend=True,
)

The same theme, across chart types and composed with Grid:

INK theme example charts

More examples on how to use the datachart package are available on the official How-to Guides.

Using with LLMs

The documentation is available in LLM-friendly formats:

llms.txt — index of the documentation with descriptions
llms-full.txt — full documentation in a single file
Every documentation page is also available as plain markdown by appending index.md to its URL, e.g. how-to-guides/charts/linechart/index.md

You can also connect your AI assistant directly:

Context7 — up-to-date, version-aware docs for AI coding assistants
GitMCP — an MCP server serving this repository's documentation

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