precedent provides a "soup-to-nuts" dynamic workflow system for creating and replicating data analysis. It combines several related projects to support data wrangling, munging, cleaning, exploration, analysis, visualization, and evaluation using a common, straight-forward interface.
precedent's framework supports a wide range of coding styles. You can create complex multiple inheritance structures with mixins galore or simpler, compositional objects. Even though the data structures are necessarily object-oriented, all of the tools to modify them are also available as functions, for those who prefer a more functional approaching to programming.
Data science, and specifically data science in the social sciences is concerned with studying the past, through observation or experiment, to understand the present and the future. This package is designed to aid those engaged in such work.
"Those who cannot remember the past are condemned to repeat it.” – George Santayana
"What has been will be again, what has been done will be done again; there is nothing new under the sun." – Ecclesiastes 1:9
"All of this has happened before. And all of this will happen again." – Cylons
"Whereof what's past is prologue, what to come in yours and my discharge." – William Shakespeare
"Must not what ever can already have passed this way before? Must not what ever can happen, already have happened, been done, passed by before? ... Must we not return eternally?" – Friedrich Nietzsche
"Time is a flat circle. Everything we've ever done or will do, we're gonna do over and over and over again." – Rust Cohle
"History, we know, is apt to repeat herself, and to foist very old incidents upon us with only a slight change of costume." – George Eliot
The project is highly documented so that users and developers can make precedent work with their projects. It is designed for Python coders at all levels. Beginners should be able to follow the readable code and internal documentation to understand how it works. More advanced users should find complex and tricky problems addressed through efficient code.