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 Crunch Cubes

Introduction

This package contains the implementation of the Crunch Cube API. It is used to extract useful information from Crunch Cube 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 Crunch Cube package can be installed by using the pip install:

pip install cr.cube

For developers

For development mode, Crunch Cube needs to be installed from the local checkout of the crunch-cube repository. Navigate to the top-level folder of the repo, on the local file system, and run:

python setup.py develop

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

1.0 Initial release

1.1 Fix stray ipdb.

1.2 Support exporter

1.3 Implement Headers & Subtotals

1.4 Update based on tabbook tests from cr.lib

1.4.1 Update based on deck tests from cr.server

1.4.2 Fix bugs discovered by first cr.exporter deploy to alpha

1.4.3 Fix bug (exporting 2D crtab with H&S on row only)

1.4.4 Implement obtaining labels with category ids (useful for H&S in exporter)

1.4.5 Fix MR x MR proportions calculation

1.5.0 Start implementing index table functionality

1.5.1 Implement index for MR x MR

1.5.2 Fix bugs with anchor: 0 for H&S

1.5.3 Fix bugs with invalid input data for H&S

1.6.0 Z-Score and bug fixes.

1.6.1 standardized_residuals are now included.

1.6.2 support "Before" and "After" in variable transformations since they exist in zz9 data.

1.6.4 Fixes for 3d Pruning.

1.6.5 Fixes for Pruning and Headers and subtotals.

  • Population size support.
  • Fx various calculations in 3d cubes.

1.6.6 Added support for CubeSlice, which always represents a

  • 2D cube (even if they're the slices of a 3D cube).
  • Various fixes for support of wide-export

1.6.7 Population fraction

  • Various bugfixes and optimizations.

  • Add property population_fraction. This is needed for the exporter to be able to calculate the correct population counts, based on weighted/unweighted and filtered/unfiltered states of the cube.

  • Apply newly added population_fraction to the calculation of population_counts.

  • Modify API for scale_means. It now accepts additional parameters hs_dims (defaults to None) and prune (defaults to False). Also, the format of the return value is slightly different in nature. It is a list of lists of numpy arrrays. It functions like this:

    • The outermost list corresponds to cube slices. If cube.ndim < 3, then it's a single-element list
    • Inner lists have either 1 or 2 elements (if they're a 1D cube slice, or a 2D cube slice, respectively).
    • If there are scale means defined on the corresponding dimension of the cube slice, then the inner list element is a numpy array with scale means. If it doesn't have scale means defined (numeric values), then the element is None.

  • Add property ca_dim_ind to CubeSlice.

  • Add property is_double_mr to CubeSlice (which is needed since it differs from the interpretation of the cube. E.g. MR x CA x MR will render slices which are not double MRs).

  • Add shape, ndim, and scale_means to CubeSlice, for accessibility.

  • index now also operates on slices (no api change).

1.6.8 Scale Means Marginal

  • Add capability to calculate the scale means marginal. This is used when analysing a 2D cube, and obtaining a sort of a "scale mean total" for each of the variables constituting a cube.

1.6.9 Bugfix

  • When Categorical Array variable is selected in multitable export, and Scale Means is selected, the cube fails, because it tries to access the non-existing slice (the CA is only interpreted as multiple slices in tabbooks). This fix makes sure that the export cube doesn't fail in such case.

1.6.10 Fix README on pypi

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

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

This version

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-1.6.10.tar.gz (769.6 kB view details)

Uploaded Source

Built Distribution

cr.cube-1.6.10-py2-none-any.whl (33.4 kB view details)

Uploaded Python 2

File details

Details for the file cr.cube-1.6.10.tar.gz.

File metadata

  • Download URL: cr.cube-1.6.10.tar.gz
  • Upload date:
  • Size: 769.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/40.4.1 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/2.7.10

File hashes

Hashes for cr.cube-1.6.10.tar.gz
Algorithm Hash digest
SHA256 ecc8bddbaba88e53333fad914e9f52fd264dabc57f408f5d99c4952cde0b10ff
MD5 31a4c40bb7fbe70c79b91015b803e39c
BLAKE2b-256 95106dcecf6f949b944f91f34d8bdecd1e7d294564a1155e29479ec222dbf029

See more details on using hashes here.

File details

Details for the file cr.cube-1.6.10-py2-none-any.whl.

File metadata

  • Download URL: cr.cube-1.6.10-py2-none-any.whl
  • Upload date:
  • Size: 33.4 kB
  • Tags: Python 2
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/40.4.1 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/2.7.10

File hashes

Hashes for cr.cube-1.6.10-py2-none-any.whl
Algorithm Hash digest
SHA256 5c1b7544cc898f3658674601fc9a4faf13b614f67de2cc85a9022c672ddc0557
MD5 dfff6e67fd127c0c722ac0c2815463cb
BLAKE2b-256 6681745ee26f30306849efd86d1a52994161e5f720fec43203b30fcd492c89e5

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