Consistency within a project is more important. Consistency within one module or function is the most important. ... However, know when to be inconsistent -- sometimes style guide recommendations just aren't applicable. When in doubt, use your best judgment. Look at other examples and decide what looks best. And don't hesitate to ask!
Getting started
-
With this in mind, we've created a data science cookiecutter template for projects in Python. Your analysis doesn't have to be in Python, but the template does provide some Python boilerplate that you'd want to remove (in the src folder for example, and the Sphinx documentation skeleton in docs).
+
With this in mind, we've created a data science cookiecutter template for projects in Python. Your analysis doesn't have to be in Python, but the template does provide some Python boilerplate that you'd want to remove (in the {{ cookiecutter.module_name }} folder for example, and the Sphinx documentation skeleton in docs).
Requirements
Python 2.7 or 3.5
@@ -161,14 +182,12 @@
Starting a new project
Starting a new project is as easy as running this command at the command line. No need to create a directory first, the cookiecutter will do it for you.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
@@ -194,8 +213,8 @@
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- Make this project pip installable with `pip install -e`
-├── src <- Source code for use in this project.
-│ ├── __init__.py <- Makes src a Python module
+├── {{ cookiecutter.module_name }} <- Source code for use in this project.
+│ ├── __init__.py <- Makes {{ cookiecutter.module_name }} a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
@@ -211,13 +230,12 @@
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
-└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
+└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
-
Opinions
There are some opinions implicit in the project structure that have grown out of our experience with what works and what doesn't when collaborating on data science projects. Some of the opinions are about workflows, and some of the opinions are about tools that make life easier. Here are some of the beliefs which this project is built on—if you've got thoughts, please contribute or share them.
Data is immutable
-
Don't ever edit your raw data, especially not manually, and especially not in Excel. Don't overwrite your raw data. Don't save multiple versions of the raw data. Treat the data (and its format) as immutable. The code you write should move the raw data through a pipeline to your final analysis. 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.
+
Don't ever edit your raw data, especially not manually, and especially not in Excel. Don't overwrite your raw data. Don't save multiple versions of the raw data. Treat the data (and its format) as immutable. The code you write should move the raw data through a pipeline to your final analysis. 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 {{ cookiecutter.module_name }} and the data in data/raw.
Also, if data is immutable, it doesn't need source control in the same way that code does. Therefore, by default, the data folder is included in the .gitignore file. If you have a small amount of data that rarely changes, you may want to include the data in the repository. Github currently warns if files are over 50MB and rejects files over 100MB. Some other options for storing/syncing large data include AWS S3 with a syncing tool (e.g., s3cmd), Git Large File Storage, Git Annex, and dat. Currently by default, we ask for an S3 bucket and use AWS CLI to sync data in the data folder with the server.
Notebooks are for exploration and communication
Notebook packages like the Jupyter notebook, Beaker notebook, Zeppelin, and other literate programming tools are very effective for exploratory data analysis. However, these tools can be less effective for reproducing an analysis. When we use notebooks in our work, we often subdivide the notebooks folder. For example, notebooks/exploratory contains initial explorations, whereas notebooks/reports is more polished work that can be exported as html to the reports directory.
@@ -227,22 +245,21 @@
Follow a naming convention that shows the owner and the order the analysis was done in. We use the format <step>-<ghuser>-<description>.ipynb (e.g., 0.3-bull-visualize-distributions.ipynb).
-
Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at src/data/make_dataset.py and load data from data/interim. If it's useful utility code, refactor it to src.
+
Refactor the good parts. Don't write code to do the same task in multiple notebooks. If it's a data preprocessing task, put it in the pipeline at {{ cookiecutter.module_name }}/data/make_dataset.py and load data from data/interim. If it's useful utility code, refactor it to {{ cookiecutter.module_name }}.
Now by default we turn the project into a Python package (see the setup.py file). You can import your code and use it in notebooks with a cell like the following:
# OPTIONAL: Load the "autoreload" extension so that code can change
%load_ext autoreload
-# OPTIONAL: always reload modules so that as you change code in src, it gets loaded
+# OPTIONAL: always reload modules so that as you change code in {{ cookiecutter.module_name }}, it gets loaded
%autoreload 2
-from src.data import make_dataset
+from {{ cookiecutter.module_name }}.data import make_dataset
-
Analysis is a DAG
Often in an analysis you have long-running steps that preprocess data or train models. If these steps have been run already (and you have stored the output somewhere like the data/interim directory), you don't want to wait to rerun them every time. We prefer make for managing steps that depend on each other, especially the long-running ones. Make is a common tool on Unix-based platforms (and is available for Windows). Following the make documentation, Makefile conventions, and portability guide will help ensure your Makefiles work effectively across systems. Here are someexamples to get started. A number of data folks use make as their tool of choice, including Mike Bostock.
-
There are other tools for managing DAGs that are written in Python instead of a DSL (e.g., Paver, Luigi, Airflow, Snakemake, Ruffus, or Joblib). Feel free to use these if they are more appropriate for your analysis.
+
There are other tools for managing DAGs that are written in Python instead of a DSL (e.g., Paver, Luigi, Airflow, Snakemake, Ruffus, or Joblib). Feel free to use these if they are more appropriate for your analysis.
Build from the environment up
The first step in reproducing an analysis is always reproducing the computational environment it was run in. You need the same tools, the same libraries, and the same versions to make everything play nicely together.
One effective approach to this is use virtualenv (we recommend virtualenvwrapper for managing virtualenvs). By listing all of your requirements in the repository (we include a requirements.txt file) you can easily track the packages needed to recreate the analysis. Here is a good workflow:
@@ -257,16 +274,15 @@ from src.data import make_dataset
You really don't want to leak your AWS secret key or Postgres username and password on Github. Enough said — see the Twelve Factor App principles on this point. Here's one way to do this:
Store your secrets and config variables in a special file
Create a .env file in the project root folder. Thanks to the .gitignore, this file should never get committed into the version control repository. Here's an example:
-
# example .env file
+
# example .env file
DATABASE_URL=postgres://username:password@localhost:5432/dbname
AWS_ACCESS_KEY=myaccesskey
AWS_SECRET_ACCESS_KEY=mysecretkey
OTHER_VARIABLE=something
-
Use a package to load these variables automatically.
-
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:
-
# src/data/dotenv_example.py
+
If you look at the stub script in {{ cookiecutter.module_name }}/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. However, managing mutiple sets of keys on a single machine (e.g. when working on multiple projects) it is best to use a credentials file, typically located in ~/.aws/credentials. A typical file might look like:
You can add the profile name when initialising a project; assuming no applicable environment variables are set, the profile credentials will be used be default.
Be conservative in changing the default folder structure
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.
Finally, a huge thanks to the Cookiecutter project (github), which is helping us all spend less time thinking about and writing boilerplate and more time getting things done.
+
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
Search
-
-
-
- From here you can search these documents. Enter your search terms below.
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
Search
+
+
+
+ From here you can search these documents. Enter
+ your search terms below.
+
