Open Source Python implementation of the API for working with CrunchCubes

This package contains the implementation of the CrunchCube API. It is used to extract useful information from CrunchCube responses (we'll refer to them as cubes in the subsequent text). Cubes are obtained from the Crunch.io platform, as JSON responses to the specific queries created by the user. These queries specify which data the user wants to extract from the Crunch.io system. The most common usage is to obtain the following:

• Cross correlation between different variable
• Margins of the cross tab cube
• Proportions of the cross tab cube (e.g. proportions of each single element to the entire sample size)
• Percentages

When the data is obtained from the Crunch.io platform, it needs to be interpreted to the form that's convenient for a user. The actual shape of the cube JSON contains many internal details, which are not of essence to the end-user (but are still necessary for proper cube functionality).

The job of this library is to provide a convenient API that handles those intricacies, and enables the user to quickly and easily obtain (extract) the relevant data from the cube. Such data is best represented in a table-like format. For this reason, the most of the API functions return some form of the ndarray type, from the numpy package. Each function is explained in greater detail, uner its own section, under the API subsection of this document.

The cr.cube package can be installed by using the pip install:

pip install cr.cube

For development mode, cr.cube needs to be installed from the local checkout of the crunch-cube repository. It is strongly advised to use virtualenv. Assuming you've created and activated a virtual environment venv, navigate to the top-level folder of the repo, on the local file system, and run:

pip install -e .

or

python setup.py develop

To setup and run tests, you will need to install cr.cube as well as testing dependencies. To do this, from the root directory, simply run:

pip install -e .[testing]

And then tests can be run using py.test in the root directory:

pytest

After the cr.cube package has been successfully installed, the usage is as simple as:

>>> from cr.cube.cube import Cube

>>> ### Obtain the crunch cube JSON payload using app.crunch.io, pycrunch, rcrunch or scrunch
>>> ### And store it in the 'cube_JSON_response' variable

>>> cube = Cube(cube_JSON_response)
>>> print(cube)
Cube(name='MyCube', dimension_types='CAT x CAT')
>>> cube.counts
np.array([[1169, 547],
          [1473, 1261]])

Tabular, or matrix, representation of the cube. The detailed description can be found here.

Calculates margins of the cube. The detailed description can be found here.

Calculates proportions of single variable elements to the whole sample size. The detailed description can be found here.

Calculates percentages of single variable elements to the whole sample size. The detailed description can be found here.

• Inflate cubes that are single column filters

• Remove deepcopy from dimension module due to a performance issue

• Fix bug with weighted vs unweighted in pairwise effect calculation

• Remove cube response deepcopy due to a performance issue

• Improve calculation of DoF for pairwise comparison
• Use effective counts as column bases for DoF

• Add squared counts as a cube measure
• Enable calculating pairwise stats with effective denominator

• Fix bug in pairwise sig values for means.

• Fix bug where categorical dimension would sometimes be interpreted as MR_CATS.

For a complete list of changes see history.