def default(self, 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
-------
None
Adds requested View to the Stack, storing it under the full
view name notation key.
"""
view = View(link, name, kwargs)
pos, relation, rel_to, weights, text = view.get_std_params()
meta = link.get_meta()
categorical = ['single', 'delimited set']
numeric = ['int', 'float']
string = ['string']
categorizable = categorical + numeric
x_type, y_type, transpose = self._get_method_types(link)
q = qp.Quantity(link, weight=weights)
if q.type == 'array' and not q.y == '@':
pass
else:
if link.y == '@':
if x_type in categorical or x_type == 'array':
view_df = q.count().result
elif x_type in numeric:
view_df = q.summarize().result
view_df.drop((link.x, 'All'), axis=0, inplace=True)
elif x_type in string:
view_df = tools.view.agg.make_default_str_view(data,
x=link.x)
elif link.x == '@':
if y_type in categorical:
view_df = q.count().result
elif y_type in numeric:
view_df = q.summarize().result
view_df.drop((link.y, 'All'), axis=1, inplace=True)
else:
if x_type in categorical and y_type in categorizable:
view_df = q.count().result
elif x_type in numeric and y_type in categorizable:
view_df = q.summarize().result
view_df.drop((link.x, 'All'), axis=0, inplace=True)
view_df.drop((link.y, 'All'), axis=1, inplace=True)
notation = view.notation('default', ':')
view.dataframe = view_df
view._notation = notation
link[notation] = view
def default(self, 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
-------
None
Adds requested View to the Stack, storing it under the full
view name notation key.
"""
view = View(link, kwargs)
pos, relation, rel_to, weights, text = view.std_params()
meta = link.get_meta()
categorical = ['single', 'delimited set']
numeric = ['int', 'float']
string = ['string']
categorizable = categorical + numeric
x_type, y_type, transpose = self._get_method_types(link)
q = qp.Quantity(link, weight=weights)
if link.y == '@':
if x_type in categorical:
view_df = q.count()
elif x_type in numeric:
view_df = q.describe()
elif x_type in string:
view_df = tools.view.agg.make_default_str_view(data, x=link.x)
elif link.x == '@':
if y_type in categorical:
view_df = q.count()
elif y_type in numeric:
view_df = q.describe()
else:
if x_type in categorical and y_type in categorizable:
view_df = q.count()
elif x_type in numeric and y_type in categorizable:
view_df = q.describe()
relation = view.spec_relation()
notation = view.notation('default', name, relation)
view.dataframe = view_df.result
view.name = notation
link[notation] = view
def coltests(self, 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
Keyword arguments (specific):
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.
"""
view = View(link, name, kwargs=kwargs)
axis, condition, rel_to, weights, text = view.get_std_params()
cache = self._cache = link.get_cache()
metric = kwargs.get('metric', 'props')
mimic = kwargs.get('mimic', 'Dim')
level = kwargs.get('level', 'low')
flags = kwargs.get('flag_bases', None)
test_total = kwargs.get('test_total', False)
stack = link.stack
get = 'count' if metric == 'props' else 'mean'
views = self._get_view_names(cache, stack, weights, get=get)
for in_view in views:
try:
view = View(link, name, kwargs=kwargs)
condition = in_view.split('|')[2]
test = qp.Test(link, in_view, test_total)
if mimic == 'Dim':
test.set_params(level=level, flag_bases=flags)
elif mimic == 'askia':
test.set_params(testtype='unpooled',
level=level,
mimic=mimic,
use_ebase=False,
ovlp_correc=False,
cwi_filter=True)
view_df = test.run()
notation = view.notation(
't.{}.{}.{}{}'.format(metric, mimic,
'{:.2f}'.format(test.level)[2:],
'+@' if test_total else ''),
condition)
view.dataframe = view_df
view._notation = notation
link[notation] = view
except:
pass
def descriptives(self, 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
Keyword arguments (specific)
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
-------
None
Adds requested View to the Stack, storing it under the full
view name notation key.
"""
view = View(link, name, kwargs=kwargs)
if not view._x['is_multi'] or kwargs.get('source'):
view = View(link, name, kwargs=kwargs)
axis, condition, rel_to, weights, text = view.get_std_params()
logic, expand, complete, calc, exclude, rescale = view.get_edit_params(
)
stat = kwargs.get('stats', 'mean')
view._kwargs['calc_only'] = True
w = weights if weights is not None else None
q = qp.Quantity(link, w)
if kwargs.get('source', None):
q = self._swap_and_rebase(q, kwargs['source'])
if q.type == 'array' and not q.y == '@':
pass
else:
if exclude is not None:
q.exclude(exclude, axis=axis)
if rescale is not None:
drop = kwargs.get('drop', False)
q.rescale(rescale, drop)
if drop:
view._kwargs['exclude'] = q.miss_x
condition = view.spec_condition(link)
q.summarize(stat=stat, margin=False, as_df=True)
if calc:
q.calc(calc, result_only=True)
method_nota = 'd.' + stat + '.c:f'
else:
method_nota = 'd.' + stat
notation = view.notation(method_nota, condition)
view.cbases = q.cbase
view.rbases = q.rbase
if q.type == 'array':
view.dataframe = q.result.T if link.y == '@' else q.result
else:
view.dataframe = q.result
view._notation = notation
view.translate_metric(set_value='meta')
view._kwargs['exclude'] = q.miss_x
link[notation] = view
def frequency(self, 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
Keyword arguments (specific)
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.
"""
view = View(link, name, kwargs=kwargs)
axis, condition, rel_to, weights, text = view.get_std_params()
logic, expand, complete, calc, exclude, rescale = view.get_edit_params(
)
# ====================================================================
# this block of kwargs should be removed
# parameter overwriting should be done using the template
# NOT QP core code!
if kwargs.get('combine', False):
view._kwargs['expand'], expand = None, None
view._kwargs['complete'], complete = False, False
if logic is not None:
for no, logic_def in enumerate(logic):
if 'expand' in logic_def.keys():
logic_def['expand'] = None
logic[no] = logic_def
view._kwargs['logic'] = logic
# ====================================================================
w = weights if weights is not None else None
ignore = True if name == 'cbase_gross' else False
q = qp.Quantity(link, w, ignore_flags=ignore)
if q.type == 'array' and not q.y == '@':
pass
else:
if logic is not None:
try:
q.group(groups=logic,
axis=axis,
expand=expand,
complete=complete)
except NotImplementedError, e:
warnings.warn('NotImplementedError: {}'.format(e))
return None
q.count(axis=None, as_df=False, margin=False)
condition = view.spec_condition(link, q.logical_conditions,
expand)
else:
def descriptives(self, 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
Keyword arguments (specific)
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).
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
}
stats : str, default 'mean'
The measure to compute.
Returns
-------
None
Adds requested View to the Stack, storing it under the full
view name notation key.
"""
view = View(link, kwargs=kwargs)
if not view._x['is_multi']:
func_name = 'descriptives'
func_type = 'distribution statistics'
pos, relation, rel_to, weights, text = view.std_params()
stat = kwargs.get('stats', 'mean')
exclude = view.missing()
rescale = view.rescaling()
q = qp.Quantity(link, weights)
if exclude is not None:
q = q.missingfy(exclude, keep_base=False)
if rescale is not None:
q = q.rescale(rescale)
view.fulltext_for_stat(stat)
relation = view.spec_relation(link)
view_df = q.describe(show=stat, margin=False, as_df=True)
notation = view.notation(stat, name, relation)
view.cbases = view_df.cbase
view.rbases = view_df.rbase
view.dataframe = view_df.result
view.name = notation
link[notation] = view
def coltests(self, 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 ``descriptives``) and will compare all
unique column pair combinations.
Parameters
----------
link : Quantipy Link object.
name : str
The shortname applied to the view.
kwargs : dict
Keyword arguments (specific):
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.
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.
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.
"""
func_name = 'coltests'
func_type = 'column differences tests'
view = View(link, kwargs=kwargs)
pos, relation, rel_to, weights, text = view.std_params()
metric = kwargs.get('metric', 'props')
mimic = kwargs.get('mimic', 'Dim')
level = kwargs.get('level', 'low')
stack = link.stack
get = 'count' if metric == 'props' else 'mean'
views = self._get_view_names(stack, weights, get=get)
for in_view in views:
try:
view = View(link, kwargs=kwargs)
relation = in_view.split('|')[2]
test = qp.Test(link, in_view)
if mimic == 'Dim':
test.set_params(level=level)
elif mimic == 'askia':
test.set_params(testtype='unpooled',
level=level, mimic=mimic,
use_ebase=False,
ovlp_correc=False,
cwi_filter=True)
view_df = test.run()
siglevel = test.level
notation = tools.view.query.set_fullname(
pos,
'%s.%s.%s.%s' % ('tests',
metric,
mimic,
"{:.2f}".format(siglevel)[2:]),
relation, rel_to, weights, name)
view.dataframe = view_df
view.name = notation
link[notation] = view
except:
pass
def frequency(self, 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
Keyword arguments (specific)
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.
"""
func_name = 'frequency'
func_type = 'countbased'
view = View(link, kwargs=kwargs)
pos, relation, rel_to, weights, text = view.std_params()
q = qp.Quantity(link, weights)
logic = kwargs.get('logic', None)
calc = kwargs.get('calc', None)
val_name = None
if name in ['ebase', 'cbase', 'rbase']:
freq = q.count(name, margin=False, as_df=False)
elif name in ['counts', 'c%', 'r%']:
freq = q.count('freq', margin=False, as_df=False)
elif logic:
if isinstance(logic, list):
if not isinstance(logic[0], dict):
val_name = name
if calc:
calc_only = kwargs.get('calc_only', False)
else:
calc_only = False
freq = q.combine(logic, op=calc, op_only=calc_only,
margin=False, as_df=False)
relation = view.spec_relation()
else:
val_name = name
casedata = link.get_data().copy()
idx, relation = tools.view.logic.get_logic_index(
casedata[link.x], logic, casedata)
filtered_q = qp.Quantity(link, weights, idx)
freq = filtered_q.combine(margin=False, as_df=False)
view.cbases = freq.cbase
view.rbases = freq.rbase
if rel_to is not None:
base = 'col' if rel_to == 'y' else 'row'
freq = freq.normalize(base)
view_df = freq.to_df(val_name).result
notation = view.notation(func_name, name, relation)
view.name = notation
view.dataframe = view_df
link[notation] = view
def frequency(self, 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
Keyword arguments (specific)
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.
"""
view = View(link, name, kwargs=kwargs)
axis, condition, rel_to, weights, text = view.get_std_params()
logic, expand, complete, calc, exclude, rescale = view.get_edit_params(
)
# ====================================================================
# This block of kwargs should be removed
# parameter overwriting should be done using the template
# NOT QP core code!
if kwargs.get('combine', False):
view._kwargs['expand'], expand = None, None
view._kwargs['complete'], complete = False, False
if logic is not None:
for no, logic_def in enumerate(logic):
if 'expand' in list(logic_def.keys()):
logic_def['expand'] = None
logic[no] = logic_def
view._kwargs['logic'] = logic
# --------------------------------------------------------------------
# This block of code resolves the rel_to arg. in order to be able to use
# rebased % computations. We are also adjusting for the regular notation
# string here...
# We need to avoid the forced overwriting of the kwarg and use the actual
# rel_to != 'x', 'y', 'counts_sum' string...
per_cell = False
if not rel_to in ['', None, 'x', 'y', 'counts_sum']:
view._kwargs['rel_to'] = 'y'
rel_to_kind = rel_to.split('.')
if len(rel_to_kind) == 2:
rel_to = rel_to_kind[0]
if rel_to_kind[1] == 'cells':
per_cell = True
elif rel_to_kind[1] == 'y':
per_cell = False
try:
link['x|f|:||{}|counts'.format(
weights)]._kwargs['rebased'] = True
except:
pass
# ====================================================================
w = weights if weights is not None else None
ignore = True if name == 'cbase_gross' else False
q = qp.Quantity(link, w, ignore_flags=ignore)
if q.type == 'array' and not q.y == '@':
pass
else:
if q.leveled:
leveled = Level(q)
if rel_to is not None:
leveled.percent()
elif axis == 'x':
leveled.base()
else:
leveled.count()
view.dataframe = leveled.lvldf
elif logic is not None:
try:
q.group(groups=logic,
axis=axis,
expand=expand,
complete=complete)
except NotImplementedError as e:
warnings.warn('NotImplementedError: {}'.format(e))
return None
q.count(axis=None, as_df=False, margin=False)
condition = view.spec_condition(link, q.logical_conditions,
expand)
else:
eff = True if name == 'ebase' else False
raw = True if name in ['counts_sum', 'c%_sum'] else False
cum_sum = True if name in ['counts_cumsum', 'c%_cumsum'
] else False
if cum_sum: axis = None
if eff: axis = 'x'
q.count(axis=axis,
raw_sum=raw,
effective=eff,
cum_sum=cum_sum,
margin=False,
as_df=False)
if rel_to is not None:
if q.type == 'array':
rel_to = 'y'
q.normalize(rel_to, per_cell=per_cell)
q.to_df()
view.cbases = q.cbase
view.rbases = q.rbase
if calc is not None:
calc_only = kwargs.get('calc_only', False)
q.calc(calc, axis, result_only=calc_only)
if calc is not None or name in [
'counts_sum', 'c%_sum', 'counts_cumsum', 'c%_cumsum'
]:
method_nota = 'f.c:f'
else:
method_nota = 'f'
notation = view.notation(method_nota, condition)
view._notation = notation
if not q.leveled:
if q.type == 'array':
view.dataframe = q.result.T if link.y == '@' else q.result
else:
view.dataframe = q.result
view._kwargs['exclude'] = q.miss_x
link[notation] = view
def coltests(self, 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 ``descriptives``) and will compare all
unique column pair combinations.
Parameters
----------
link : Quantipy Link object.
name : str
The shortname applied to the view.
kwargs : dict
Keyword arguments (specific):
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.
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.
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.
"""
func_name = 'coltests'
func_type = 'column differences tests'
view = View(link, kwargs=kwargs)
pos, relation, rel_to, weights, text = view.std_params()
metric = kwargs.get('metric', 'props')
mimic = kwargs.get('mimic', 'Dim')
level = kwargs.get('level', 'low')
stack = link.stack
get = 'count' if metric == 'props' else 'mean'
views = self._get_view_names(stack, weights, get=get)
for in_view in views:
try:
view = View(link, kwargs=kwargs)
relation = in_view.split('|')[2]
test = qp.Test(link, in_view)
if mimic == 'Dim':
test.set_params(level=level)
elif mimic == 'askia':
test.set_params(testtype='unpooled',
level=level,
mimic=mimic,
use_ebase=False,
ovlp_correc=False,
cwi_filter=True)
view_df = test.run()
siglevel = test.level
notation = tools.view.query.set_fullname(
pos, '%s.%s.%s.%s' %
('tests', metric, mimic, "{:.2f}".format(siglevel)[2:]),
relation, rel_to, weights, name)
view.dataframe = view_df
view.name = notation
link[notation] = view
except:
pass
def descriptives(self, 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
Keyword arguments (specific)
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).
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
}
stats : str, default 'mean'
The measure to compute.
Returns
-------
None
Adds requested View to the Stack, storing it under the full
view name notation key.
"""
view = View(link, kwargs=kwargs)
if not view._x['is_multi']:
func_name = 'descriptives'
func_type = 'distribution statistics'
pos, relation, rel_to, weights, text = view.std_params()
stat = kwargs.get('stats', 'mean')
exclude = view.missing()
rescale = view.rescaling()
q = qp.Quantity(link, weights)
if exclude is not None:
q = q.missingfy(exclude, keep_base=False)
if rescale is not None:
q = q.rescale(rescale)
view.fulltext_for_stat(stat)
relation = view.spec_relation(link)
view_df = q.describe(show=stat, margin=False, as_df=True)
notation = view.notation(stat, name, relation)
view.cbases = view_df.cbase
view.rbases = view_df.rbase
view.dataframe = view_df.result
view.name = notation
link[notation] = view
def frequency(self, 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
Keyword arguments (specific)
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.
"""
func_name = 'frequency'
func_type = 'countbased'
view = View(link, kwargs=kwargs)
pos, relation, rel_to, weights, text = view.std_params()
q = qp.Quantity(link, weights)
logic = kwargs.get('logic', None)
calc = kwargs.get('calc', None)
val_name = None
if name in ['ebase', 'cbase', 'rbase']:
freq = q.count(name, margin=False, as_df=False)
elif name in ['counts', 'c%', 'r%']:
freq = q.count('freq', margin=False, as_df=False)
elif logic:
if isinstance(logic, list):
if not isinstance(logic[0], dict):
val_name = name
if calc:
calc_only = kwargs.get('calc_only', False)
else:
calc_only = False
freq = q.combine(logic,
op=calc,
op_only=calc_only,
margin=False,
as_df=False)
relation = view.spec_relation()
else:
val_name = name
casedata = link.get_data().copy()
idx, relation = tools.view.logic.get_logic_index(
casedata[link.x], logic, casedata)
filtered_q = qp.Quantity(link, weights, idx)
freq = filtered_q.combine(margin=False, as_df=False)
view.cbases = freq.cbase
view.rbases = freq.rbase
if rel_to is not None:
base = 'col' if rel_to == 'y' else 'row'
freq = freq.normalize(base)
view_df = freq.to_df(val_name).result
notation = view.notation(func_name, name, relation)
view.name = notation
view.dataframe = view_df
link[notation] = view
