QuantipyViews

class quantipy.QuantipyViews(views=None, template=None)

A collection of extendable MR aggregation and statistic methods.

View methods are used to generate various numerical or categorical data aggregations. Their behaviour is controlled via kwargs.

coltests(link, name, kwargs)

Will test appropriate views from a Stack for stat. sig. differences.

Tests can be performed on frequency aggregations (generated by frequency) and means (from summarize) and will compare all unique column pair combinations.

Parameters:
  • link (Quantipy Link object.) –
  • name (str) – The shortname applied to the view.
  • kwargs (dict) –
  • arguments (specific) (Keyword) –
  • text (str, optional, default None) – Sets an optional label in the meta component of the view that is used when the view is passed into a Quantipy build (e.g. Excel, Powerpoint).
  • metric ({'props', 'means'}, default 'props') – Determines whether a proportion or means test algorithm is performed.
  • test_total (bool, deafult False) – If True, the each View’s y-axis column will be tested against the uncoditional total of its x-axis.
  • mimic ({'Dim', 'askia'}, default 'Dim') – It is possible to mimic the test logics used in other statistical software packages by passing them as instructions. The method will then choose the appropriate test parameters.
  • level ({'high', 'mid', 'low'} or float) – Sets the level of significance to which the test is carried out. Given as str the levels correspond to 'high' = 0.01, 'mid' = 0.05 and 'low' = 0.1. If a float is passed the specified level will be used.
  • flags (list of two int, default None) – Base thresholds for Dimensions-like tests, e.g. [30, 100]. First int is minimum base for reported results, second int controls small base indication.
Returns:

  • None – Adds requested View to the Stack, storing it under the full view name notation key.
  • .. note:: – Mimicking the askia software (mimic = 'askia') restricts the values to be one of 'high', 'low', 'mid'. Any other value passed will make the algorithm fall back to 'low'. Mimicking Dimensions (mimic = 'Dim') can use either the str or float version.
default(link, name, kwargs)

Adds a file meta dependent aggregation to a Stack.

Checks the Link definition against the file meta and produces either a numerical or categorical summary tabulation including marginal the results.

Parameters:
  • link (Quantipy Link object.) –
  • name (str) – The shortname applied to the view.
  • kwargs (dict) –
Returns:

Adds requested View to the Stack, storing it under the full view name notation key.
Return type:

None
descriptives(link, name, kwargs)

Adds num. distribution statistics of a Link defintion to the Stack.

descriptives views can apply a range of summary statistics. Measures include statistics of centrality, dispersion and mass.

Parameters:
  • link (Quantipy Link object.) –
  • name (str) – The shortname applied to the view.
  • kwargs (dict) –
  • arguments (specific) (Keyword) –
  • text (str, optional, default None) – Sets an optional label suffix for the meta component of the view which will be appended to the statistic name and used when the view is passed into a Quantipy build (e.g. Excel, Powerpoint).
  • stats (str, default 'mean') – The measure to compute.
  • exclude (list of int) – Codes that will not be considered calculating the result.
  • rescale (dict) –

    A mapping of {old code: new code}, e.g.:

    {
 1: 0,
 2: 25,
 3: 50,
 4: 75,
 5: 100
}
  • drop (bool) – If rescale provides a new scale defintion, drop will remove all codes that are not transformed. Acts as a shorthand for manually passing any remaining codes in exclude.
Returns:

Adds requested View to the Stack, storing it under the full view name notation key.
Return type:

None
frequency(link, name, kwargs)

Adds count-based views on a Link defintion to the Stack object.

frequency is able to compute several aggregates that are based on the count of code values in uni- or bivariate Links. This includes bases / samples sizes, raw or normalized cell frequencies and code summaries like simple and complex nets.

Parameters:
  • link (Quantipy Link object.) –
  • name (str) – The shortname applied to the view.
  • kwargs (dict) –
  • arguments (specific) (Keyword) –
  • text (str, optional, default None) – Sets an optional label in the meta component of the view that is used when the view is passed into a Quantipy build (e.g. Excel, Powerpoint).
  • logic (list of int, list of dicts or core.tools.view.logic operation) –

    If a list is passed this instructs a simple net of the codes given as int. Multiple nets can be generated via a list of dicts that map names to lists of ints. For complex logical statements, expression are parsed to identify the qualifying rows in the data. For example:

    # simple net
'logic': [1, 2, 3]

# multiple nets/code groups
'logic': [{'A': [1, 2]}, {'B': [3, 4]}, {'C', [5, 6]}]

# code logic
'logic': has_all([1, 2, 3])
  • calc (TODO) –
  • calc_only (TODO) –
Returns:

  • None – Adds requested View to the Stack, storing it under the full view name notation key.
  • .. note:: Net codes take into account if a variable is – multi-coded. The net will therefore consider qualifying cases and not the raw sum of the frequencies per category, i.e. no multiple counting of cases.