Crunch.io Cube library
Project description
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
2.0.3
- Fix mean measure for CubeSet
2.0.2
- Expose
cube.valid_countsandcube.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.
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