cr.cube 2.1.3

Crunch.io Cube library

crunch-cube

Open Source Python implementation of the API for working with CrunchCubes

Introduction

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.

Installation

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

pip install cr.cube

For developers

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

Running tests

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

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.

margin

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

proportions

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

percentages

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

Changes

2.1.3

• Transpose dimension for numeric arrays

2.1.2

• Handle numeric array explicit order

2.1.1

• Custom column bases for Numeric Array matrix types

2.1.0

• Measure Consolidation

2.0.3

• Fix mean measure for CubeSet

2.0.2

• Expose cube.valid_counts and cube.valid_counts_summary

2.0.1

• Fix row standard error for MR x MR

2.0.0

• De-vectorize matrix.py and add sort-by-value
• Remove old api interface

1.12.11

• Numeric array measures available

1.12.10

• Selected category labels partition interface

1.12.9

• Margin of error for row %
• Margin of error for population
• Std deviation and std error for row %

1.12.8

• Fix pairwise t-test for scale means
• Fix UserWarning for smoothing measures
• Move cr.cube.enum -> cr.cube.enums

1.12.7

• Margin of error for 1D cubes
• Allow pairwise significance for CA_SUBVAR

1.12.6

• T-stats scale means for multiple response
• Margin of error for column percentages

1.12.4

• Measure expression evaluation method
• Multiple response allowed for pairwise comparison

1.12.3

• Bug fix for t_stats scale means

1.12.2

• Smoothing on scale means

1.12.1

• Smoothing on column percentages and column index

For a complete list of changes see history.