{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Visualizes missing data patterns in DataFrames through matrix, bar, heatmap, and dendrogram plots to quickly assess data completeness and nullity correlations.","skillfed_tags":["data-exploration","eda","visualization"],"use_cases":["Inspect a newly loaded dataset to identify which columns have missing values and spot patterns before preprocessing","Diagnose whether missingness is random or correlated across variables","Communicate data quality to stakeholders using visual summaries instead of raw null counts","Detect data entry errors by finding unexpected correlations between variables","Profile time-series data by specifying periodicity to see how completeness varies over time"],"what_it_does":"missingno is a lightweight visualization toolkit for understanding missing data patterns. It provides four complementary views: a matrix showing row-by-row nullity patterns with sparklines, a bar chart of nullity by column, a correlation heatmap measuring how the presence of one variable affects another, and a dendrogram using hierarchical clustering to reveal deeper completion relationships. The library is built on numpy, matplotlib, scipy, and seaborn, integrating naturally into standard data-science workflows.\n\nYou use it to quickly spot data quality issues\u2014which columns are sparse, whether missingness is random or patterned, and which variables tend to be missing together. It's most useful in exploratory data analysis when you need a visual summary before deciding on imputation or filtering strategies. The library is in maintenance mode, meaning it receives bugfixes but no major new features.","worth_installing":"Yes, if you work regularly with data exploration and need quick visual assessment of missing data. Low install friction, permissive MIT license, no known vulnerabilities, and a stable API make it a safe choice. Dormant maintenance is not a blocker for a mature visualization tool, but don't expect new features or rapid responses to edge cases. Worth keeping in your toolkit for exploratory data analysis."},"id":"missingno","links":{"html":"https://skillfed.io/packages/missingno","md":"https://skillfed.io/packages/missingno.md","pypi":"https://pypi.org/project/missingno/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-02-26","license_spdx":null,"license_treatment":"permissive","name":"missingno","python_support":"unspecified","summary":"Missing data visualization module for Python."},"popularity":{"monthly_downloads":536975,"position":6122,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.2"}