Exploratory Data Analysis
Showing 24 of 24 projects
A Python visualization library based on matplotlib for creating attractive statistical graphics with a high-level interface.
Generate comprehensive data quality profiles and exploratory data analysis reports for Pandas and Spark DataFrames with a single line of code.
Generate comprehensive data quality profiling and exploratory data analysis reports for Pandas and Spark DataFrames with a single line of code.
A Python library that automates data visualization and exploration for pandas dataframes in Jupyter notebooks.
An open-source augmented analytics platform that automates exploratory data analysis and visualization with AI-powered insights.
A Python library for visualizing missing data in pandas DataFrames using matrix, bar, heatmap, and dendrogram plots.
A Python library for automated exploratory data analysis (EDA) with high-density visualizations and target analysis in two lines of code.
An open-source Python library for low-code data preparation, offering fast EDA, data cleaning, and collection from APIs and databases.
An R package that extends ggplot2 to create publication-ready graphics with statistical details embedded directly in the plots.
Automatically visualize any dataset with a single line of code, including data quality assessment and fixes.
An interactive grammar of graphics for R that combines ggplot2's grammar with reactive programming from Shiny.
A Python library that automates the tedious parts of exploratory data analysis with cleaning, feature engineering, visualization, and versioning.
A tutorial series comparing how to implement data science concepts and build applications in both Python and R ecosystems.
A JupyterLab extension that breaks the linear presentation of notebooks by enabling sticky, floating cells for interactive dashboards.
An R package that automates exploratory data analysis and data treatment with one-line reports and visualizations.
A lightweight Python tool for generating rich summary statistics of pandas and Polars dataframes directly in the console.
An R package for visualizing correlation matrices with automatic variable reordering to reveal hidden patterns.
A JupyterLab extension to visualize CSV and JSON data interactively using Voyager 2.
A charting library designed for interactive data visualization in F# scripting environments.
A Python package for automated univariate and bivariate data analysis and visualization to streamline machine learning workflows.
A Julia package providing kernel density estimation (KDE) for univariate and bivariate data with flexible bandwidth selection and interpolation.
A high-level Python toolbox for topic modeling with easy-to-use functions and command-line interface.
A Clojure library for data processing, cleanup, and interactive visualization using D3.
A powerful high-level data visualization system for R, inspired by Trellis graphics, with emphasis on multivariate data and conditioning.
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