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Cookiecutter Data Science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

We're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards — ultimately, data science code quality is about correctness and reproducibility.

While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them.

And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards — ultimately, data science code quality is about correctness and reproducibility.

Tentative experiments and rapidly testing approaches that might not work out are all part of the process for getting to the good stuff, and there is no magic bullet to turn data exploration into a simple, linear progression.

That being said, once started it is not a process that lends itself to thinking carefully about the structure of your code or project layout, so it's best to start with a clean, logical structure and stick to it throughout.

well-defined, standard project structure means that a newcomer can begin to understand an analysis without digging in to extensive documentation.

Because that default project structure is logical and reasonably standard across most projects, it is much easier for somebody who has never seen a particular project to figure out where they would find the various moving parts.

That means a Red Hat user and an Ubuntu user both know roughly where to look for certain types of files, even when using each other's system — or any other standards-compliant system for that matter!

Here are some questions we've learned to ask with a sense of existential dread: These types of questions are painful and are symptoms of a disorganized project.

A good project structure encourages practices that make it easier to come back to old work, for example separation of concerns, abstracting analysis as a DAG, and engineering best practices like version control.

You shouldn't have to run all of the steps every time you want to make a new figure (see Analysis is a DAG), but anyone should be able to reproduce the final products with only the code in src and the data in data/raw.

For example, notebooks/exploratory contains initial explorations, whereas notebooks/reports is more polished work that can be exported as html to the reports directory.

Since notebooks are challenging objects for source control (e.g., diffs of the json are often not human-readable and merging is near impossible), we recommended not collaborating directly with others on Jupyter notebooks.

You can import your code and use it in notebooks with a cell like the following: Often in an analysis you have long-running steps that preprocess data or train models.

Both of these tools use text-based formats (Dockerfile and Vagrantfile respectively) you can easily add to source control to describe how to create a virtual machine with the requirements you need.

Here's an example: If you look at the stub script in src/data/make_dataset.py, it uses a package called python-dotenv to load up all the entries in this file as environment variables so they are accessible with os.environ.get.

Here's an example snippet adapted from the python-dotenv documentation: When using Amazon S3 to store data, a simple method of managing AWS access is to set your access keys to environment variables.

To keep this structure broadly applicable for many different kinds of projects, we think the best approach is to be liberal in changing the folders around for your project, but be conservative in changing the default structure for all projects.

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