cr.cube 1.6.3

Crunch.io Cube library

# crunch-cube

Open Source Python implementation of the API for working with Crunch Cubes

## Introduction

This package contains the implementation of the Crunch Cube API. It is used to
extract useful information from Crunch Cube 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.

## Installation

The Crunch Cube package can be installed by using the `pip install`:

pip install cr.cube


### For developers

For development mode, Crunch Cube needs to be installed from the local checkout
of the `crunch-cube` repository. Navigate to the top-level folder of the repo,
on the local file system, and run:

python setup.py develop

## Usage

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


from cr.cube.crunch_cube import CrunchCube

### Obtain the crunch cube JSON from the Crunch.io
### And store it in the 'cube_JSON_response' variable

cube = CrunchCube(cube_JSON_response)
cube.as_array()

### Outputs:
#
# np.array([
# [5, 2],
# [5, 3]
# ])

## API

### `as_array`

Tabular, or matrix, representation of the _cube_. The detailed description can
be found
[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).

### `margin`

Calculates margins of the _cube_. The detailed description can be found
[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).

### `proportions`

Calculates proportions of single variable elements to the whole sample size.
The detailed description can be found
[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).

### `percentages`

Calculates percentages of single variable elements to the whole sample size.
The detailed description can be found
[here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).

[](https://travis-ci.org/Crunch-io/crunch-cube)
[](https://coveralls.io/github/Crunch-io/crunch-cube?branch=master)
[](http://crunch-cube.readthedocs.io/en/latest/?badge=latest)


## Changes

1.0 Initial release

1.1 *Fix stray ipdb.

1.2 Support exporter

1.3 Implement Headers & Subtotals

1.4 Update based on tabbook tests from `cr.lib`

1.4.1 Update based on deck tests from `cr.server`

1.4.2 Fix bugs discovered by first `cr.exporter` deploy to alpha

1.4.3 Fix bug (exporting 2D crtab with H&S on row only)

1.4.4 Implement obtaining labels with category ids (useful for H&S in exporter)

1.4.5 Fix MR x MR proportions calculation

1.5.0 Start implementing index table functionality

1.5.1 Implement index for MR x MR

1.5.2 Fix bugs with `anchor: 0` for H&S

1.5.3 Fix bugs with invalid input data for H&S

1.6.0 Z-Score and bug fixes.

1.6.1 standardized_residuals are now included.

1.6.2 support "Before" and "After" in variable transformations since they exist in zz9 data.

1.6.4 Fixes for 3d Pruning.