Python for Data Science will be a reference site for some, and a learning site for others. The purpose is to help spread the use of Python for research and data science applications, and explain concepts in an easy to understand way.
If there is content that is not covered, that you wish to be, reach out and make a request! Note: A box that looks like this import researchpy as rp indicates code. Additionaly, some features of this site may not be supported by Internet Explorer.
This is a passion project, meaning there is no large team behind the content. With that, this site does not track you and only has two ads on the right side scroll bar which is powered by Google Adsense. If you enjoy the content and find it helpful, please consider whitelisting this page to support the effort.
For transparency sakes, it should be noted that Researchpy is developed by the same author of this site.
Researchpy is used on this site and is developed to provide the commonly desired statistical information for academic research. It is further designed to be used in conjunction with Statsmodels as Researchpy currently is focusing on univariate and bivariate analyses/information whereas Statsmodels is focused on multivariate and multivariable analyses.
Package documentation for Researchpy and Statsmodels.
Contact information for consults, content/demonstration requests, or anything else is located in the footer.
You can't conduct statistics or data science without data! Data is a valuable thing in the modern world and most companies guard it, or track you over the web and then sell it. Not here, the site doesn't even use cookies. In terms of data for data science, below are some public use datasets.
There are many Python packages available that extend the core functionality of the programming language. Of all of these, the more common packages that are used for statistics and/or data science are: