datachart 0.9.1

Data visualization package, simple to use, highly customizable

Documentation · How-to Guides · API Reference · Source

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. Chart types for trends, comparisons, distributions, relationships, and flows — each created with a single function call from plain lists of dicts. See Charts.
• 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). See Composition.
• Themes & configuration. Predefined themes, each named for its visual trait, plus a global config for tweaking any style attribute — per-chart style overrides included. See Themes.

Quick start

pip install datachart   # or: uv add datachart

Every chart takes a list of series, each a list of dicts. Set a theme once and every chart follows it:

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

config.set_theme(THEME.INK)

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul"]
signups = [420, 465, 430, 510, 560, 545, 610]
churned = [380, 400, 440, 435, 480, 520, 550]

figure = LineChart(
    [
        [{"x": x, "y": y} for x, y in enumerate(signups)],
        [{"x": x, "y": y} for x, y in enumerate(churned)],
    ],
    title="Monthly signups vs. churn",
    subtitle=["Signups", "Churned"],
    xlabel="Month",
    ylabel="Users",
    xticks=list(range(len(months))),
    xticklabels=months,
    show_legend=True,
)
save_figure(figure, "line.png")

figure is a plain matplotlib Figure, so anything matplotlib can do with it still works.

Charts

Every chart is a single function call taking plain lists of dicts, and every function accepts a list of series to overlay or a subplots=True flag to split them apart. The dict keys per chart are listed in each guide.

Composition

Rendered figures compose. Grid arranges them in a grid (nested lists define rows; grids nest), and Panel overlays charts on one plot, with an optional secondary y-axis. Four chart types, one theme, one grid — with a Panel in the last cell:

import numpy as np

from datachart.charts import BarChart, Histogram, LineChart, ScatterChart
from datachart.config import config
from datachart.constants import LEGEND_LOCATION, THEME
from datachart.utils import Grid, Panel, save_figure

config.set_theme(THEME.INK)
rng = np.random.default_rng(0)

line = LineChart(
    [[{"x": x, "y": 50 + a * np.sin(x / 3) + x} for x in range(21)] for a in (20, 12, 5)],
    title="Line", subtitle=["Run 1", "Run 2", "Run 3"], show_legend=True,
)
bars = BarChart(
    [[{"label": f"Bench {b}", "y": y} for b, y in zip("ABCD", ys)]
     for ys in ([66, 59, 77, 83], [65, 58, 78, 82])],
    title="Grouped bar", subtitle=["Model A", "Model B"], show_legend=True,
)
scatter = ScatterChart(
    [[{"x": x, "y": y} for x, y in zip(rng.normal(cx, 1.2, 60), rng.normal(cy, 1, 60))]
     for cx, cy in ((3, 4), (7, 7), (10, 3))],
    title="Scatter", subtitle=["C1", "C2", "C3"], show_legend=True,
)

# Panel overlays figures on one axes; the line gets its own y-axis on the right
samples = np.sort(rng.normal(55, 12, 500))
hist = Histogram([{"x": x} for x in samples], subtitle="Count")
cdf = LineChart(
    [{"x": x, "y": 100 * i / len(samples)} for i, x in enumerate(samples, 1)],
    subtitle="Cumulative %",
)
config.update_config({"plot_legend_location": LEGEND_LOCATION.UPPER_LEFT})
overlay = Panel(
    [hist, cdf], title="Histogram + line", ylabel_right="%",
    auto_secondary_axis=1, show_legend=True,
)

# Grid arranges figures in cells; nested lists define the rows
figure = Grid([[line, bars], [scatter, overlay]], title="INK theme")
save_figure(figure, "grid.png")

Themes

Predefined themes, applied with config.set_theme(THEME.<NAME>). Any attribute can then be tweaked globally via config.update_config(...) or per chart via the style argument — see the themes, config, and theme gallery guides.

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

Install

Requires python 3.10 or higher.

pip install -U datachart

With uv:

uv add datachart

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

Contributing

Bug reports, feature requests, and pull requests are welcome — open an issue to report a problem or propose a chart, theme, or option you are missing.

To work on the package locally:

git clone https://github.com/eriknovak/datachart.git
cd datachart
uv sync --group dev                  # package + dev dependencies
python -m unittest discover test     # unit tests
pytest                               # documentation notebooks
mkdocs serve                         # docs at http://127.0.0.1:8000

Code is formatted with black; the pre-commit hook runs it for you.