Project Title

ydata-profiling — One-line data quality profiling and exploratory data analysis for Pandas and Spark DataFrames.

Overview

ydata-profiling is a Python library that provides a quick and efficient way to perform Exploratory Data Analysis (EDA) on Pandas and Spark DataFrames. It offers a one-line profiling experience similar to the df.describe() function in pandas, but with extended analysis capabilities. The tool is designed to deliver a comprehensive overview of a dataset, including time-series and text analysis, and can export the results in various formats such as HTML and JSON.

Key Features

Type Inference: Automatically detects data types of columns (Categorical, Numerical, Date, etc.)
Warnings: Summarizes potential issues in the data, such as missing values, inaccuracies, and skewness.
Univariate Analysis: Provides descriptive statistics and visualizations like distribution histograms.
Multivariate Analysis: Offers correlations, missing data analysis, duplicate rows analysis, and visualizations for variable interactions.
Time-Series Analysis: Includes statistical information for time-dependent data, such as auto-correlation, seasonality, ACF, and PACF plots.
Text Analysis: Analyzes common text categories and provides insights into text data.

Use Cases

Data Scientists: Use ydata-profiling for initial data exploration and to identify data quality issues before deep analysis.
Data Analysts: Quickly generate comprehensive reports on dataset characteristics to inform stakeholders.
Machine Learning Engineers: Profile datasets to understand features better and prepare them for model training.

Advantages

Simplicity: Easy-to-use with a one-line command for generating profiling reports.
Comprehensive Analysis: Covers a wide range of data analysis aspects, from univariate to multivariate and time-series.
Exportable Reports: Supports exporting analysis results in various formats for easy sharing and presentation.

Limitations / Considerations

Customization: While the tool is powerful out-of-the-box, it may lack some advanced customization options compared to more complex EDA tools.
Performance: For extremely large datasets, performance may be a consideration, although ydata-profiling is designed for efficiency.

Pandas Profiling: A similar project that focuses on profiling for pandas DataFrames. ydata-profiling extends this by supporting Spark DataFrames and offering additional features.
Dask: A parallel computing library that can handle larger-than-memory datasets and is often used in data analysis. Unlike ydata-profiling, it does not focus on data profiling but can be used in conjunction with it.
Great Expectations: A tool for data quality testing and profiling. It offers a different approach by focusing on setting expectations for data rather than generating comprehensive reports.

Basic Information

GitHub: https://github.com/ydataai/ydata-profiling
Stars: 13,026
License: Unknown
Last Commit: 2025-07-15

📊 Project Information

Project Name: ydata-profiling
GitHub URL: https://github.com/ydataai/ydata-profiling
Programming Language: Python
⭐ Stars: 13,026
🍴 Forks: 1,725
📅 Created: 2016-01-09
🔄 Last Updated: 2025-07-15

🏷️ Project Topics

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