{"repo":"Data-Centric-AI-Community/fg-data-profiling","free":true,"listed":false,"github":"https://github.com/Data-Centric-AI-Community/fg-data-profiling","clone":"git clone https://github.com/Data-Centric-AI-Community/fg-data-profiling.git","description":"1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.","language":"Python","stars":13674,"topics":["pandas-profiling","pandas-dataframe","statistics","jupyter-notebook","exploration","data-science","python","pandas","machine-learning","deep-learning"],"license":"MIT","category":"machine-learning","readme_excerpt":"fg-data-profiling ydata-profiling is now fg-data-profiling . This package has been renamed to fg-data-profiling . Please follow the Migration Guide as soon as possible — the old package will no longer receive updates or bug fixes. Documentation Discord Stack Overflow Latest changelog Do you like this project? Show us your love and give feedback! fg-data-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, fg-data-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json . The package outputs a simple and digested analysis of a dataset, including time-series and text . Looking for a scalable solution that can fully integrate with your database systems? Leverage YData Fabric Data Catalog to connect to different databases and storages (Oracle, snowflake, PostGreSQL, GCS, S3, etc.) and leverage an interactive and guided profiling experience in Fabric. Check out the Community Version. Migration Guide 1. Uninstall the old package 2. Install the new package 3. Update your imports Find and replace all occurrences of the old import in your codebase: You can use this one-liner to find all affected files: ▶️ Quickstart Install or Start profiling Start by loading your pandas DataFrame as you normally would, e.g. by using: To generate the standard profiling report, merely run: ","default_branch":null,"files":null,"tree":[],"storefront":"/r/Data-Centric-AI-Community","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Data-Centric-AI-Community/fg-data-profiling/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}