Metadata-Version: 2.2
Name: megaprofiler
Version: 0.2.2
Summary: megaprofiler is a highly customizable and extensible data profiling library designed to help data scientists and engineers understand their datasets before performing analysis or building models.
Home-page: https://github.com/sidkris/megaprofiler
Author: Siddharth Krishnan
Author-email: sid@sidkrishnan.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: numpy
Requires-Dist: tabulate
Requires-Dist: scikit-learn
Requires-Dist: imbalanced-learn
Requires-Dist: statsmodels
Requires-Dist: scipy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

[![Downloads](https://img.shields.io/pypi/dm/megaprofiler)](https://pypi.org/project/megaprofiler/)

When working with large datasets, it’s often necessary to understand data types, distributions, and potential issues (e.g., missing values, outliers) before analysis. While libraries like pandas-profiling exist, there is still room for an extensible, easy-to-use, and highly customizable profiler that integrates data validation.

Key Features:
Automatic Data Summaries: Provide insights like distribution, unique values, missing values, and more for each column.
Anomaly Detection: Automatically flag columns or rows with unusual distributions, outliers, or inconsistent data.
Data Validation: Set validation rules (e.g., no missing values in specific columns, data type constraints) and get alerts if the data violates these rules.
Custom Reports: Generate visual reports (e.g., HTML, PDF) with configurable thresholds for what counts as an anomaly.
Data Drift Detection: Track changes in data distributions over time to identify shifts in data quality or content.
Benefits:
DataProfiler would be invaluable to data scientists and engineers dealing with exploratory data analysis, data quality checks, and ETL pipelines, reducing manual data investigation.


To Use :

'from megaprofiler import MegaProfiler'


