Pandas profiling

Pandas Profiling is a Python package developed to provide an easy to use interface for quickly exploring and visualizing data sets. It is designed to save time by providing an automated way to view relevant statistical information about a data set and the variables inside it. Through the use of basic descriptive statistics, critical insights can be quickly extracted without manual analysis.

Pandas Profiling is built on top of the popular Pandas library for data manipulation. It provides a data-exploration tool that displays a comprehensive summary of the data set with a few lines of code. Its interactive report includes a wide variety of summaries and plot of variables, which can be used to detect anomalies, patterns and trends in the data. This helps analysts to derive important conclusions and test hypotheses.

The library also offers a number of other features, including an interactive correlation matrix and a configurable selection of plot types. Its detailed technical report contains a number of useful statistics, such as histograms, variable importance scores, outlier scores and a calculator for missing values percentage.

Pandas Profiling has gained popularity for its ease-of-use in data mining and exploratory statistics. Its primary purpose is to help analysts to save time in large data sets, because of the automated nature of the packages’s reports. It also serves as a powerful learning tool for understanding data visualization methods and for teaching them to students.

The library is released as open source and is available for free on the Python Package Index. It is actively maintained and the latest version is well documented and comes with plenty of tutorials to get users started quickly.