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https://github.com/crunch-io/crunch-cube
Python tools for Cubes in the Crunch API
https://github.com/crunch-io/crunch-cube
Last synced: about 1 month ago
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Python tools for Cubes in the Crunch API
- Host: GitHub
- URL: https://github.com/crunch-io/crunch-cube
- Owner: Crunch-io
- License: mit
- Created: 2017-10-16T22:56:51.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-03-19T15:21:37.000Z (10 months ago)
- Last Synced: 2024-04-27T06:20:49.372Z (8 months ago)
- Language: Python
- Homepage:
- Size: 6.4 MB
- Stars: 3
- Watchers: 30
- Forks: 2
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- License: LICENSE
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README
# 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.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.
---
## Changes
### 3.1.1
- Bug fix for subtotal diff (wave difference) for categorical date dimensions
### 3.1.0
- Subtotal diff (wave difference) for categorical date dimensions
### 3.0.45
- Enumerator refactoring
### 3.0.44
- Bug fix median measure for exporter
### 3.0.43
- Median measure
For a complete list of changes see [history](https://github.com/Crunch-io/crunch-cube/blob/master/HISTORY.md).