cr-cube 3.3.6

Crunch.io Cube library

crunch-cube

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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.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]])

Complete API Doc

Please visit https://crunch-cube.readthedocs.io/en/latest for the API reference.

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Changes

3.3.6

• Rebuild

3.3.5

• Fix bug in _Strand.table_base and _Strand.table_missing for MRs.

3.3.4

• Fix bug in _Slice.rows_disaggregated_missing_unweighted_counts for Numeric Array grouped by categorical
• Fix bug in _Strand.table_base_scalar and _Strand.table_base_range when a variable is entirely missing.

3.3.3

• Add information about missing values to partitions:
  • _Nub: .table_missing
  • _Strand: .table_missing, .disaggregated_missing_unweighted_counts, .disaggregated_missing_labels, & .disaggregated_missing_element_ids
  • _Slice: .rows_disaggregated_missing_unweighted_counts, .rows_disaggregated_missing_labels, .rows_disaggregated_missing_element_ids .columns_disaggregated_missing_unweighted_counts, .columns_disaggregated_missing_labels, & .columns_disaggregated_missing_element_ids

3.3.2

• Bug fix column numeric ranges for _Slice

3.3.1

• Add numeric ranges

3.2.1

• Fix translate_element_id when is is None

For a complete list of changes see history.