Sparklines

Sparklines brings Edward Tufte's sparklines to your terminal — compact Unicode bar charts like ▃▁▄▁▅█▂▅, right in your shell or Python code. Originally built for sanity-checking sensor data in IoT networks, it works anywhere you need a quick visual summary of a sequence of numbers.

Example usecase for such "sparklines" on the command-line, showing IoT sensor values (generating code not included here).

Positive values work best, but negatives are fully supported: mixed data auto-splits into two rows, all-negative data renders as downward bars (see Mixed and negative datasets below). True line-style sparklines would require a dedicated font — out of scope here. We use "▁▂▃▄▅▆▇█" for values and a blank for missing ones.

Use cases

Finance — stock price history, daily P&L (bipolar: gains above, losses below), trading volume spikes, bid-ask spread over a session.

IoT — temperature and humidity sensors, power consumption per circuit, air quality index, battery charge on remote devices.

DevOps & SRE — request rate and error rate per minute, CI build duration trend, queue depth, replica lag.

Data Science & ML — training loss and validation accuracy per epoch, gradient norms, data drift score, inference latency after a deployment.

Agentic computing — token usage and cost per API call, context window fill level, cache hit rate, tool-call frequency per agent loop iteration.

Health & Fitness — heart rate during exercise, sleep quality over weeks, blood glucose across a day, daily caloric balance.

Sample output

A recorded demo session — click the image to play it on Asciinema:

Installation

From PyPI

pip install sparklines

On macOS with Homebrew

brew tap deeplook/sparklines
brew install sparklines

With uv

uvx sparklines 2 7 1 8 2 8 1 8

Or install it into your uv tool environment:

uv tool install sparklines

From Source

git clone https://github.com/deeplook/sparklines.git
cd sparklines
pip install .

Development

git clone https://github.com/deeplook/sparklines.git
cd sparklines
pip install -e ".[dev]"
pytest tests

Usage

Python

from sparklines import sparklines

for line in sparklines([1, 2, 3, 4, 5.0, None, 3, 2, 1]):
    print(line)
# ▁▃▅▆█ ▅▃▁

for line in sparklines([1, 2, 3, 4, 5.0, None, 3, 2, 1], num_lines=2):
    print(line)
#   ▁▅█ ▁
# ▁▅███ █▅▁

Mixed and negative datasets

Mixed positive/negative data is split automatically — no flags needed:

from sparklines import sparklines

data = [50, 30, 80, -20, -60, -10, 40, 10]
for line in sparklines(data):
    print(line)
# ▅▄█   ▄▂
#    ▔▀▔

All-negative data renders as inverted (downward) bars automatically.

-n / --num-lines

Form	Behaviour
integer (default 1)	total rows, split proportionally; shared scale
auto	smallest row count for an exact proportional split
up:down (e.g. 2:1)	explicit per-side layout; independent scaling

$ sparklines -n auto 1 2 3 -1 -2 -3 0 4 5 6
       ▃▆█
▄▆█   ▁███
   ▔▔▀
$ sparklines -n 2:1  1 2 3 -1 -2 -3 0 4 5 6
       ▃▆█
▄▆█   ▁███
   ▔▀█

--zero: up (default) places zeros on the positive baseline; none renders them as gaps.

$ sparklines --zero up   0 1 2 -1 -2 0
▁▄█  ▁
   ▀█
$ sparklines --zero none 0 1 2 -1 -2 0
 ▄█
   ▀█

Downward bars use ANSI reverse video for full 8-level resolution. Falls back to ▔▀█ when NO_COLOR, ANSI_COLORS_DISABLED, or TERM=dumb is set.

References

Inspired by Zach Holman's spark, with prior Python ports by Kenneth Reitz (spark.py), RedKrieg (pysparklines), and Roger Allen (shorter spark.py).

This package adds:

• multi-line rendering for higher resolution (-n)
• gaps for missing values (None)
• auto-split for mixed positive/negative data
• inverted bars for all-negative data
• proportional row allocation (-n auto)
• explicit per-side layout (-n up:down)
• zero handling (--zero up / --zero none)
• colour emphasis via --emphasize
• line wrapping via --wrap