Gloo
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Data Project Management for IPython
============== A Data Project Manager for IPython
Provides utilities and functions for managing data projects in python. Requires use of IPython and Pandas.
A quick workflow example::
from gloo import Gloo
proj = Gloo('My Project', full_structure, packages=['scipy',
('numpy', 'np')])
proj.create_project()
Introduction
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 and more. These are things that have to be done often, but aren't the fun part.
proj.create_project() Options
project_name
: This is a string that is the name of your project.
Current Config Options:
full_structure
If True the folder structure outline below. By default
creates smaller project, i.e. False.
packages
A list of strings of python packages to load when
load_project()
is called. Defaults to empty. If you want to alias your
package you can pass a tuple to the list. ['scipy', ('numpy', 'np')]
will import scipy as scipy and numpy as np.
logging
A boolean to dictate if logging is started when
load_project()
is called. Defaults to False.
svn
Pass a list or a string to init version control. Currently supports
git and bzr. svn = ['git', 'bzr']
will init both.
Those options are saved into a pickled file called .gloo at the root of the project directory.
What Happens When You Call load_project()
proj.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. So if you have utility.py in the lib directory. When you load the project you'll have utility availble in the namespace. - Packages specified in the config are loaded into the environment.
- Logging starts
Folder Structure
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
Other things you can do
You can update the config. Say you have packages = ['numpy']
but once
you've worked on the project you realize you need pandas and you want to load
it as pd. It's easy to update this of the future::
> proj.packages
['numpy']
> proj.packages.append(('pandas', 'pd'))
> proj.save_project()
So next time you load the project pandas as pd will be available.
Installing Gloo
-
pip install Gloo
is available. - There is also an ubuntu package available on
LaunchPad <https://code.launchpad.net/~pythonxy/+archive/pythonxy-devel>
_ - Gloo currently isn't supported on Windows
Contributing
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.
Thanks
This project is a bit of a rip-off or port (however nice you're feeling) of
Project Template <http://www.projecttemplate.net>
_, which if
you're using R I would highly recommend. It's fantastic.