plotbin
PlotBin: Plotting Binned Maps and Other Utilities
Decision gist · record as of 2026-08-14
Yes, if your use case is non-commercial or internal research. The package is actively maintained, has low install friction, and provides specialized map-plotting utilities on top of standard scientific libraries. However, do not install if you intend to redistribute the code or integrate it into a commercial product—the custom license explicitly prohibits redistribution. Verify the license terms match your use case first.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction; a pure-Python wheel with only three standard scientific dependencies.
- Last release 35 days ago with active maintenance status.
License · maintenance · safety
Other/Proprietary License (unclear) — License treatment is unclear: the package uses a custom proprietary license that permits non-commercial use and personal modification, but explicitly prohibits redistribution. Verify your use case against the copyright notice (2013-2026 Michele Cappellari) before integrating into commercial or redistributed software.
last release 2026-07-10 (35 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 289,163 downloads/mo, #8,005 on PyPI
Alternatives
Verify before relying
pip install plotbin
import plotbin
import numpy as np
import matplotlib.pyplot as plt- Whether the non-commercial restriction applies to your intended use case
- Exact Python version support (requires_python is unspecified in metadata)
- Specific capabilities and API surface beyond general binned map plotting
What it is and what it does
PlotBin is a scientific plotting library that specializes in visualizing binned two-dimensional maps and related spatial data. It wraps Matplotlib, NumPy, and SciPy to provide utilities for creating and displaying binned map visualizations.
The package is lightweight, distributed as a pure-Python wheel, and requires only the three core scientific Python libraries. It is actively maintained with a release 35 days ago and has been in production use since 2018. However, its license is custom and non-standard: it permits non-commercial use and personal modification but explicitly prohibits redistribution, which may restrict its use in commercial products or shared codebases.
Use it for
- Visualize binned spatial data or density maps using Matplotlib as the rendering backend.
- Create two-dimensional map plots with built-in binning utilities for scientific research.
- Build internal research tools that process and display spatially binned data without redistribution concerns.
- Prototype spatial data visualization workflows using NumPy and SciPy arrays as input.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if your use case is non-commercial or internal research.
The package is actively maintained, has low install friction, and provides specialized map-plotting utilities on top of standard scientific libraries. However, do not install if you intend to redistribute the code or integrate it into a commercial product—the custom license explicitly prohibits redistribution. Verify the license terms match your use case first.
Install
plotbin on PyPI
Before you install
Low install friction; a pure-Python wheel with only three standard scientific dependencies. Last release 35 days ago with active maintenance status.
License in practice
License treatment is unclear: the package uses a custom proprietary license that permits non-commercial use and personal modification, but explicitly prohibits redistribution. Verify your use case against the copyright notice (2013-2026 Michele Cappellari) before integrating into commercial or redistributed software.
Quickstart
pip install plotbin
import plotbin
import numpy as np
import matplotlib.pyplot as plt
Verify before relying
- Whether the non-commercial restriction applies to your intended use case
- Exact Python version support (requires_python is unspecified in metadata)
- Specific capabilities and API surface beyond general binned map plotting
Package facts
| License | Other/Proprietary License unclear |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpyscipymatplotlib |
| Maintenance | Actively maintained 35 days since the last release |
| First released | |
| Downloads | 289,163 / month, #8,005 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: plotbin-3.2.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “binned map plotting”
- plotbinPlotBin provides utilities to create and display binned maps and…
- correctionlibProvides a JSON-based format and evaluator for correction…
- basemapBasemap renders geographic data on matplotlib figures using map…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also splot · highcharts-maps · Cartopy · matplotlib · xtgeoviz · basemap-data · mapclassify · basemap · leafmap · matplotlib-venn