Crunch.io Cube library

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for earbitrio from gravatar.com earbitrio Avatar for gergness from gravatar.com gergness Avatar for operation from gravatar.com operation Avatar for percious from gravatar.com percious Avatar for slobodan.ilic from gravatar.com slobodan.ilic

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

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.

Build Status Coverage Status Documentation Status

Changes

1.11.25

  • Expose two-level pairwise-t-test

1.11.24

  • Bug fix for scale_median calculation

1.11.23

  • Expose population fraction in cube partitions

1.11.22

  • Additional summary measures for scale (std_dev, std_error, median)

1.11.21

  • Fix slicing for CA + single col filter

1.11.20

  • Fix cube title payload discrepancy

1.11.19

  • Fix problem where pre-ordering anchor-idx was used for locating inserted subtotal vectors
  • Enable handling of filter-only multitable-template placeholders.
  • New measures: table and columns standard deviation and standard error

1.11.18

  • Fix wrong proportions and base values when explicit order is expressed

1.11.17

  • Fix incorrect means values after hiding

For a complete list of changes see history.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for earbitrio from gravatar.com earbitrio Avatar for gergness from gravatar.com gergness Avatar for operation from gravatar.com operation Avatar for percious from gravatar.com percious Avatar for slobodan.ilic from gravatar.com slobodan.ilic

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

3.0.45

3.0.44

3.0.43

3.0.42

3.0.41

3.0.40

3.0.39

3.0.38

3.0.37

3.0.36

3.0.35

3.0.34

3.0.33

3.0.32

3.0.31

3.0.30

3.0.29

3.0.28

3.0.27

3.0.26

3.0.25

3.0.24

3.0.23

3.0.22

3.0.21

3.0.20

3.0.19

3.0.18

3.0.17

3.0.16

3.0.15

3.0.14

3.0.13

3.0.12

3.0.11

3.0.10

3.0.9

3.0.8

3.0.7

3.0.6

3.0.5

3.0.4

3.0.3

3.0.2

3.0.1

3.0.0

2.3.9

2.3.8

2.3.7

2.3.6

2.3.5

2.3.4

2.3.3

2.3.2

2.3.1

2.3.0

2.2.3

2.2.2

2.2.1

2.2.0

2.1.34

2.1.33

2.1.32

2.1.31

2.1.30

2.1.29

2.1.28

2.1.27

2.1.26

2.1.25

2.1.24

2.1.23

2.1.22

2.1.21

2.1.20

2.1.19

2.1.18

2.1.17

2.1.16

2.1.15

2.1.14

2.1.13

2.1.12

2.1.11

2.1.10

2.1.9

2.1.8

2.1.7

2.1.6

2.1.5

2.1.4

2.1.3

2.1.2

2.1.1

2.1.0

2.0.3

2.0.2

2.0.1

2.0.0

1.12.11

1.12.11rc0 pre-release

1.12.11b0 pre-release

1.12.10

1.12.9

1.12.8

1.12.7

1.12.6

1.12.5

1.12.4

1.12.4b0 pre-release

1.12.3

1.12.2

1.12.1

1.12.1a0 pre-release

1.12.0a0 pre-release

1.11.37

1.11.36

1.11.35

1.11.34

1.11.33

1.11.32

1.11.31

1.11.30

1.11.29

1.11.28

1.11.27

1.11.26

This version

1.11.25

1.11.24

1.11.23

1.11.22

1.11.21

1.11.20

1.11.19

1.11.18

1.11.17

1.11.16

1.11.15

1.11.14

1.11.13

1.11.12

1.11.11

1.11.10

1.11.9

1.11.8

1.11.7

1.11.6

1.11.5

1.11.4

1.11.3

1.11.2

1.11.1

1.11.0

1.10.6

1.10.5

1.10.4

1.10.3

1.10.2

1.10.1

1.10.0

1.9.19

1.9.18

1.9.17

1.9.16

1.9.15

1.9.14

1.9.13

1.9.12

1.9.11

1.9.10

1.9.9

1.9.8

1.9.7

1.9.6

1.9.5

1.9.4

1.9.3

1.9.2

1.9.1

1.9.0

1.8.6

1.8.5

1.8.4

1.8.3

1.8.2

1.8.1

1.8.0

1.7.3

1.7.2

1.7.0

1.6.11

1.6.10

1.6.8

1.6.7

1.6.6

1.6.5

1.6.3

1.6.1

1.6.0

1.5.6

1.5.3

1.5.2

1.5.1

1.5.0

1.4.5

1.4.4

1.4.3

1.4.2

1.4.1

1.4

1.3

1.2

1.1

1.0

0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cr.cube-1.11.25.tar.gz (1.0 MB view details)

Uploaded Source

Built Distribution

cr.cube-1.11.25-py2-none-any.whl (95.8 kB view details)

Uploaded Python 2

File details

Details for the file cr.cube-1.11.25.tar.gz.

File metadata

  • Download URL: cr.cube-1.11.25.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.15.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/44.0.0 requests-toolbelt/0.9.1 tqdm/4.44.1 CPython/2.7.16

File hashes

Hashes for cr.cube-1.11.25.tar.gz
Algorithm Hash digest
SHA256 d4efe798a96d3ff1330a716f4dc89476e4db081000648a0b4c5d0298c06ba726
MD5 6cbab787cd3a4744b5073095a006a356
BLAKE2b-256 17fdc54a402a40be293382039e37088f70724861b563763fcd4d98aa059710af

See more details on using hashes here.

File details

Details for the file cr.cube-1.11.25-py2-none-any.whl.

File metadata

  • Download URL: cr.cube-1.11.25-py2-none-any.whl
  • Upload date:
  • Size: 95.8 kB
  • Tags: Python 2
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.15.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/44.0.0 requests-toolbelt/0.9.1 tqdm/4.44.1 CPython/2.7.16

File hashes

Hashes for cr.cube-1.11.25-py2-none-any.whl
Algorithm Hash digest
SHA256 7318ed965ffacf0eab80bf69173595f459703b021152dca1452d3f004f3e614e
MD5 d3b230837158e1d145e66273a9f597ad
BLAKE2b-256 f777163ee446506fa7ccbedaf2b0b6bdfe1538ad9e5e1ae4f8a4d7449082c0be

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page