{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Provides a human-readable summary of NumPy arrays with shape, dtype, statistics, and special values, plus visualization methods for debugging and exploration.","skillfed_tags":["jupyter-friendly","array-debugging","data-visualization"],"use_cases":["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."],"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\u2014all in a single line. For smaller arrays, it still prints the actual values.\n\nThe 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.","worth_installing":"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\u2014making NumPy array inspection practical in notebooks\u2014and the visualization features add value for data exploration. Useful for anyone working with NumPy in interactive environments."},"id":"lovely-numpy","links":{"html":"https://skillfed.io/packages/lovely-numpy","md":"https://skillfed.io/packages/lovely-numpy.md","pypi":"https://pypi.org/project/lovely-numpy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-15","license_spdx":"MIT","license_treatment":"permissive","name":"lovely-numpy","python_support":"supports_current","summary":"\ud83d\udc9f Lovely numpy"},"popularity":{"monthly_downloads":98246,"position":13098,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.24"}