ResidentMario/missingno
Missingno
missingno 是一个用于缺失数据模式可视化和分析的 Python 库。它提供了一套工具来分析数据集的完整性、映射数据缺口并量化变量中空值的数量。
该库通过空值相关性分析器和分层数据聚类工具脱颖而出。这些组件允许通过测量一个变量的缺失如何与另一个变量的缺失相关联，来检测系统性依赖和趋势。
该工具集涵盖了更广泛的数据质量审计和探索性分析功能。它包括使用线性和对数刻度进行列空值汇总的功能，以及用于识别记录中系统性缺口的基于矩阵的映射。
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.
