lovely-numpy
💟 Lovely numpy
Decision gist · record as of 2026-08-14
Yes. Low install friction, active maintenance, MIT license, and no known vulnerabilities make it a safe addition. It directly solves a real debugging pain point—making NumPy array inspection practical in notebooks—and the visualization features add value for data exploration. Useful for anyone working with NumPy in interactive environments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with three common dependencies (numpy, fastcore, matplotlib).
- Active maintenance with a recent release and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open and closed projects.
last release 2026-05-15 (91 days) · last repo commit 2026-05-15 · 76 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 98,246 downloads/mo, #13,098 on PyPI
Alternatives
Verify before relying
pip install lovely-numpy
import numpy as np
from lovely_numpy import lo
arr = np.random.randn(100, 100)
lo(arr)- Whether the package works with NumPy versions beyond the current ecosystem (specific version constraints not stated in fact sheet).
- Performance characteristics when inspecting very large arrays or nested structures.
What it is and what it does
Lovely NumPy wraps NumPy arrays to display concise, human-readable summaries instead of raw output. When you call `lo(array)`, it shows shape, dtype, element count, memory size, value range with a histogram, mean, standard deviation, and flags for NaN or infinity values—all in a single line. For smaller arrays, it still prints the actual values.
The package also provides methods to explore nested structures (`.deeper`), visualize arrays as RGB images (`.rgb` with optional denormalization), display individual channels (`.chans`), and generate matplotlib histograms (`.plt`). It's designed for interactive debugging in Jupyter notebooks and similar environments, reducing the cognitive load of scanning large numerical outputs.
Use it for
- Inspect tensor shapes and statistics during model training or data preprocessing without scrolling through raw arrays.
- Quickly spot NaN, infinity, or all-zero arrays that indicate bugs in numerical computations.
- Visualize image-like arrays as RGB or channel views to verify data normalization and transformations.
- Explore multi-dimensional nested structures layer-by-layer to understand data organization.
- Generate and save matplotlib plots of array distributions for documentation or reports.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Low install friction, active maintenance, MIT license, and no known vulnerabilities make it a safe addition. It directly solves a real debugging pain point—making NumPy array inspection practical in notebooks—and the visualization features add value for data exploration. Useful for anyone working with NumPy in interactive environments.
Install
lovely-numpy on PyPI
Before you install
Low install friction with three common dependencies (numpy, fastcore, matplotlib). Active maintenance with a recent release and no known vulnerabilities.
License in practice
MIT license permits unrestricted use, modification, and distribution in both open and closed projects.
Quickstart
pip install lovely-numpy
import numpy as np
from lovely_numpy import lo
arr = np.random.randn(100, 100)
lo(arr)Verify before relying
- Whether the package works with NumPy versions beyond the current ecosystem (specific version constraints not stated in fact sheet).
- Performance characteristics when inspecting very large arrays or nested structures.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpyfastcorematplotlib |
| Maintenance | Actively maintained 91 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 98,246 / month, #13,098 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: Only |
Evidence: lovely_numpy-0.2.24-py3-none-any.whl
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