Charted: Zero-Dependency SVG Charting for Python¶

Charted Logo

Create beautiful, publication-quality SVG charts with zero runtime dependencies.

pip install charted

Why Charted?¶

🚀 Zero Dependencies

Pure Python stdlib. No numpy, pandas, matplotlib, or seaborn required. Charts render as clean SVG strings.

🎨 Beautiful Output

Publication-quality SVG with proper typography, color contrast, and responsive design built-in.

📊 14 Chart Types

Bar, Column, Line, Scatter, Pie, Radar, Area, Bubble, Combo, HeatmapChart, Gantt, Histogram, Polar Area, Box Plot.

🎯 Simple API

Create a chart in 3 lines:

from charted import BarChart
chart = BarChart(data=[120, 180, 210], labels=["Q1", "Q2", "Q3"])
chart.save("sales.svg")

🌙 Theming System

Built-in light/dark/high-contrast themes plus full custom theme support.

📱 Jupyter Ready

Charts render inline automatically. No extra configuration needed.

Quick Start¶

Python

from charted import BarChart, ColumnChart, LineChart

# Bar chart
bar = BarChart(
    data=[120, 180, 210, 150],
    labels=["Q1", "Q2", "Q3", "Q4"],
    title="Sales by Quarter"
)
bar.save("bar.svg")

# Column chart
column = ColumnChart(
    data=[120, 180, 210, 150],
    labels=["Q1", "Q2", "Q3", "Q4"],
    title="Sales by Quarter"
)
column.save("column.svg")

# Line chart
line = LineChart(
    data=[120, 180, 210, 150],
    labels=["Q1", "Q2", "Q3", "Q4"],
    title="Sales Trend"
)
line.save("line.svg")

CLI

# Create a bar chart from CSV
python -m charted create bar sales.svg --data sales.csv

# Batch process multiple files
python -m charted batch ./data ./output

# See all options
python -m charted --help

Jupyter

from charted import BarChart

# Just create a chart: it renders inline
BarChart(
    data=[120, 180, 210, 150],
    labels=["Q1", "Q2", "Q3", "Q4"],
    title="Sales by Quarter"
)

Chart Types¶

ColumnChart

Vertical bars for time series and categorical comparisons.

BarChart

Horizontal bars for long category labels.

LineChart

Line graphs for trends over time with XY mode support.

ScatterChart

Scatter plots for correlations and distributions.

PieChart

Pie and doughnut charts for proportional data.

RadarChart

Multi-axis radar charts for comparison analysis.

HeatmapChart

Heat maps for correlation matrices and density plots.

GanttChart

Gantt charts for project timelines with dependencies.

AreaChart

Area charts with fill support for cumulative data.

BubbleChart

Bubble charts for 3-dimensional data visualization.

ComboChart

Combo charts mixing bars and lines in one view.

Histogram

Histograms for distribution analysis with auto-binning.

PolarAreaChart

Polar area charts for radial distribution data.

BoxPlot

Box plots for statistical quartile and outlier visualization.

Features¶

Multi-Series Support¶

Stacked, side-by-side, or grouped layouts for comparing multiple data series.

from charted import ColumnChart

data = [
    [120, 180, 210, 150],  # 2023
    [130, 190, 220, 160],  # 2024
]

chart = ColumnChart(
    data=data,
    labels=["Q1", "Q2", "Q3", "Q4"],
    series_names=["2023", "2024"],
    title="Sales Comparison"
)
chart.save("multi.svg")

Negative Values Handled¶

Proper zero baseline calculations for data with negative values.

from charted import ColumnChart

chart = ColumnChart(
    data=[120, -80, 210, -150],
    labels=["Q1", "Q2", "Q3", "Q4"],
    title="Profit/Loss"
)
chart.save("negative.svg")

Theming¶

Built-in themes plus full custom theme support.

from charted import BarChart

# Use built-in theme
chart = BarChart(
    data=[120, 180, 210],
    labels=["Q1", "Q2", "Q3"],
    theme="dark"  # or "light", "high-contrast"
)

# Or custom theme
chart = BarChart(
    data=[120, 180, 210],
    labels=["Q1", "Q2", "Q3"],
    theme={
        "colors": ["#FF6B6B", "#4ECDC4", "#45B7D1"],
        "background_color": "#1a1a2e",
        "grid_color": "#ffffff20"
    }
)

Data Loading¶

Load CSV/JSON without pandas.

from charted import load_csv, BarChart

x, y, labels = load_csv("sales.csv", x_col="Quarter", y_col="Revenue")
chart = BarChart(data=y, labels=x, title=labels[0])
chart.save("sales.svg")

Markdown Export¶

Generate embed-ready markdown for documentation.

from charted import BarChart

chart = BarChart(data=[120, 180, 210], labels=["Q1", "Q2", "Q3"], title="Sales")
chart.save("docs/sales.svg")
md = chart.to_markdown(path="docs/sales.svg")
# Output: ![Sales](docs/sales.svg)

Documentation¶

Chart Types

API Reference

Resources

License¶

MIT License: See LICENSE for details.

Community¶

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Built with ❤️ by Andryo Marzuki