ResidentMario/missingno
Missingno
missingno is a Python library for the visualization and analysis of missing data patterns. It provides a set of tools to profile dataset completeness, map data gaps, and quantify the volume of null values across variables.
The library differentiates itself through a nullity correlation analyzer and a hierarchical data clustering tool. These components allow for the detection of systemic dependencies and trends by measuring how the absence of one variable relates to the absence of another.
The toolset covers broader data quality auditing and exploratory analysis capabilities. It includes features for column nullity summarization using linear and logarithmic scales, as well as matrix-based mapping to identify systemic gaps in records.
Features
- Missing Data Analysis - Identifies patterns and systemic gaps in datasets using Python to determine how and where information is missing.
- Hierarchical Clustering - Implements hierarchical clustering to group variables based on similar missingness patterns using dendrograms.
- Feature Correlation Analysis - Measures statistical relationships between the absence of one variable and another using correlation heatmaps.
- Data Analysis & Visualization - Provides tools for the visual analysis and mapping of data completion patterns to identify systemic gaps.
- Dataframe Visualizers - Provides a pipeline that transforms tabular pandas dataframes into static visual representations for missing data exploration.
- Exploratory Data Analysis - Enables exploratory data analysis by visualizing the distribution and volume of null values.
- Missing Data Clustering - Groups variables using hierarchical clustering to reveal deep trends and dependencies in how data is missing across a dataset.
- Completeness Profilers - Provides a visual summary of missing value volumes per column using linear and logarithmic scaling.
- Completeness Summaries - Displays the volume of missing values for each variable using linear or logarithmic scales to compare completeness.
- Correlation Matrices - Generates correlation matrices to compute the statistical relationship between missingness in different variables.
- Nullity Correlation Analyzers - Provides a visualization tool that measures how the absence of one variable relates to the absence of another.
- Nullity Masks - Converts dataframes into binary masks of presence and absence to identify systemic gaps through visual patterns.
- Dataset Quality Analysis - Analyzes nullity correlations and dependencies between variables to detect biases or errors in data collection.
- Data Preprocessing for Modeling - Prepares datasets for machine learning by analyzing missingness patterns to inform imputation or removal strategies.
- Logarithmic Axis Scales - Supports logarithmic axis scales to visualize and compare null counts across variables with vastly different volumes.
- Automated EDA and Visualization - Visualize missing data patterns.
- Data Processing Libraries - Visualizing and diagnosing missing data patterns.
- Data Visualization - Visualization module for missing data patterns.
- Python Visualization - Visual utility for assessing dataset completeness.
- Python Visualization Libraries - Visualization utilities for checking dataset completeness.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
