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 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 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
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
