datapr

datapr is a Python library designed for data profiling and analysis. It aims to provide a comprehensive set of tools for understanding the characteristics of datasets, such as identifying data types, detecting missing values, calculating descriptive statistics, and uncovering potential data quality issues. The library can be used to generate reports that summarize key aspects of a dataset, enabling users to quickly gain insights into its structure and content.

The primary goal of datapr is to automate the often time-consuming process of initial data exploration. By applying various analytical techniques, it helps data scientists and analysts to assess the suitability of data for specific tasks, identify anomalies, and inform data cleaning and preprocessing efforts. The library supports various data formats, including CSV and Pandas DataFrames, making it adaptable to common data analysis workflows.

Key features of datapr include the ability to compute measures like mean, median, standard deviation, quantiles, and frequency distributions. It can also detect categorical and numerical features, identify unique values, and report on the percentage of missing data for each column. The generated reports are typically presented in a human-readable format, often including visualizations to aid in understanding. This makes datapr a valuable tool for both exploratory data analysis and data quality assessment.