Provides utilities and functions for managing data projects in python. Requires use of IPython and Pandas.
A quick workflow example:
from gloo import interactive interactive.create_project("MyProject") #now if we have some some scripts to use and some data in the data folder we #can load the project interactive.load_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.
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 whenload_project()
is called. Defaults to empty.logging
A boolean to dictate if logging is started whenload_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.
load_project()
- The config is loaded into a dictionary.
- 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. - Files in the
munge
directory are run. This folder is where you would put files necessary for preprocessing the data. - Files in the
lib
directory are imported. This folder is where you would put files that you would like to load as a module. - Packages specified in the config are loaded into the environment.
- 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.
This project is a bit of a rip-off or port (however nice you're feeling) of Project Template, which if you're using R I would highly recommend. It's fantastic.