Gloo provides utilities and functions for managing data projects in Python. Requires use of IPython and Pandas.
A quick workflow example:
from gloo import interactive
#now if we have some some scripts to use and some data in the data folder we
#can load the project
Gloo's goal is to tie together a lot of the data analysis actions that happen regularly and make that processes easy. Automatically loading data into the ipython environment, running scripts, making utitlity functions available. These are things that have to be done often, but aren't the fun part.
What Happens When You Call create_project("MyProject")
create_project(project_name = "MyProject", **kwds)
project_name: This is a string that is the name of your project.
Current Config Options:
full_structure A boolean that if true creates a full folder structure. If True the folder structure outline below. Defaults to True.
packages A list of strings of python packages to load when load_project() is called. Defaults to empty.
logging A boolean to dictate if logging is started when load_project() is called. Defaults to False.
git A boolean to dictate if a git repo is init'd. Defaults to False.
Those options are saved into a json file called .config.json at the root of the project directory.
What Happens When You Call load_project()
1. The config is loaded into a dictionary.
2. Data is the data directory is loaded into the environment. This is done recursively so you can have subdirectories. If you do, the parent folder of the data file will be prepended to data file, folder_file. The plan is to make the prepending optional.
3. Files in the munge directory are run. This folder is where you would put files necessary for preprocessing the data.
4. Files in the lib directory are imported. This folder is where you would put files that you would like to load as a module.
5. Packages specified in the config are loaded into the environment.
6. Logging starts
The full structure is as follows:
data/ : data
doc/ : documentation
diagnostics/ : automatically check for data issues
graphs/ : graph domicile
lib/ : utility functions
munge/ : preprocessing scripts
profiling/ : benchmark performance
reports/ : reports you'll produce
tests/ : tests
Because this project is in such an early state I would love for anybody and everybody to help contribute. I think this could be very valuable for those working with python for data projets.