def qplot(x=None,
y=None,
data=None,
facets=None,
margins=False,
geom='auto',
xlim=None,
ylim=None,
log='',
main=None,
xlab=None,
ylab=None,
asp=None,
**kwargs):
"""
Quick plot
Parameters
----------
x : str | array_like
x aesthetic
y : str | array_like
y aesthetic
data : dataframe
Data frame to use (optional). If not specified,
will create one, extracting arrays from the
current environment.
geom : str | list
*geom(s)* to do the drawing. If ``auto``, defaults
to 'point' if ``x`` and ``y`` are specified or
'histogram' if only ``x`` is specified.
xlim : tuple
x-axis limits
ylim : tuple
y-axis limits
log : str in ``{'x', 'y', 'xy'}``
Which variables to log transform.
main : str
Plot title
xlab : str
x-axis label
ylab : str
y-axis label
asp : str | float
The y/x aspect ratio.
**kwargs : dict
Arguments passed on to the geom.
Returns
-------
p: ggplot
ggplot object
"""
# Extract all recognizable aesthetic mappings from the parameters
# String values e.g "I('red')", "I(4)" are not treated as mappings
environment = EvalEnvironment.capture(1)
aesthetics = {} if x is None else {'x': x}
if y is not None:
aesthetics['y'] = y
def is_mapping(value):
"""
Return True if value is not enclosed in I() function
"""
with suppress(AttributeError):
return not (value.startswith('I(') and value.endswith(')'))
return True
def I(value):
return value
I_env = EvalEnvironment([{'I': I}])
for ae in kwargs.keys() & all_aesthetics:
value = kwargs[ae]
if is_mapping(value):
aesthetics[ae] = value
else:
kwargs[ae] = I_env.eval(value)
# List of geoms
if is_string(geom):
geom = [geom]
elif isinstance(geom, tuple):
geom = list(geom)
if data is None:
data = pd.DataFrame()
# Work out plot data, and modify aesthetics, if necessary
def replace_auto(lst, str2):
"""
Replace all occurences of 'auto' in with str2
"""
for i, value in enumerate(lst):
if value == 'auto':
lst[i] = str2
return lst
if 'auto' in geom:
if 'sample' in aesthetics:
replace_auto(geom, 'qq')
elif y is None:
# If x is discrete we choose geom_bar &
# geom_histogram otherwise. But we need to
# evaluate the mapping to find out the dtype
env = environment.with_outer_namespace({'factor': pd.Categorical})
if isinstance(aesthetics['x'], str):
try:
x = env.eval(aesthetics['x'], inner_namespace=data)
except Exception:
msg = "Could not evaluate aesthetic 'x={}'"
raise PlotnineError(msg.format(aesthetics['x']))
elif not hasattr(aesthetics['x'], 'dtype'):
x = np.asarray(aesthetics['x'])
if array_kind.discrete(x):
replace_auto(geom, 'bar')
else:
replace_auto(geom, 'histogram')
else:
if x is None:
if pdtypes.is_list_like(aesthetics['y']):
aesthetics['x'] = range(len(aesthetics['y']))
xlab = 'range(len(y))'
ylab = 'y'
else:
# We could solve the issue in layer.compute_asthetics
# but it is not worth the extra complexity
raise PlotnineError("Cannot infer how long x should be.")
replace_auto(geom, 'point')
p = ggplot(aes(**aesthetics), data=data, environment=environment)
def get_facet_type(facets):
with suppress(PlotnineError):
parse_grid_facets(facets)
return 'grid'
with suppress(PlotnineError):
parse_wrap_facets(facets)
return 'wrap'
warn(
"Could not determine the type of faceting, "
"therefore no faceting.", PlotnineWarning)
return 'null'
if facets:
facet_type = get_facet_type(facets)
if facet_type == 'grid':
p += facet_grid(facets, margins=margins)
elif facet_type == 'wrap':
p += facet_wrap(facets)
else:
p += facet_null()
# Add geoms
for g in geom:
geom_name = 'geom_{}'.format(g)
geom_klass = Registry[geom_name]
stat_name = 'stat_{}'.format(geom_klass.DEFAULT_PARAMS['stat'])
stat_klass = Registry[stat_name]
# find params
recognized = (
kwargs.keys() &
(geom_klass.DEFAULT_PARAMS.keys() | geom_klass.aesthetics()
| stat_klass.DEFAULT_PARAMS.keys() | stat_klass.aesthetics()))
recognized = recognized - aesthetics.keys()
params = {ae: kwargs[ae] for ae in recognized}
p += geom_klass(**params)
# pd.Series objects have name attributes. In a dataframe, the
# series have the name of the column.
labels = {}
for ae in scaled_aesthetics & kwargs.keys():
with suppress(AttributeError):
labels[ae] = kwargs[ae].name
with suppress(AttributeError):
labels['x'] = xlab if xlab is not None else x.name
with suppress(AttributeError):
labels['y'] = ylab if ylab is not None else y.name
if main is not None:
labels['title'] = main
if 'x' in log:
p += scale_x_log10()
if 'y' in log:
p += scale_y_log10()
if labels:
p += labs(**labels)
if asp:
p += theme(aspect_ratio=asp)
return p
def subset(self, which_terms):
"""Create a new :class:`DesignMatrixBuilder` that includes only a
subset of the terms that this object does.
For example, if `builder` has terms `x`, `y`, and `z`, then::
builder2 = builder.subset(["x", "z"])
will return a new builder that will return design matrices with only
the columns corresponding to the terms `x` and `z`. After we do this,
then in general these two expressions will return the same thing (here
we assume that `x`, `y`, and `z` each generate a single column of the
output)::
build_design_matrix([builder], data)[0][:, [0, 2]]
build_design_matrix([builder2], data)[0]
However, a critical difference is that in the second case, `data` need
not contain any values for `y`. This is very useful when doing
prediction using a subset of a model, in which situation R usually
forces you to specify dummy values for `y`.
If using a formula to specify the terms to include, remember that like
any formula, the intercept term will be included by default, so use
`0` or `-1` in your formula if you want to avoid this.
:arg which_terms: The terms which should be kept in the new
:class:`DesignMatrixBuilder`. If this is a string, then it is parsed
as a formula, and then the names of the resulting terms are taken as
the terms to keep. If it is a list, then it can contain a mixture of
term names (as strings) and :class:`Term` objects.
.. versionadded: 0.2.0
"""
factor_to_evaluators = {}
for evaluator in self._evaluators:
factor_to_evaluators[evaluator.factor] = evaluator
design_info = self.design_info
term_name_to_term = dict(zip(design_info.term_names,
design_info.terms))
if isinstance(which_terms, basestring):
# We don't use this EvalEnvironment -- all we want to do is to
# find matching terms, and we can't do that use == on Term
# objects, because that calls == on factor objects, which in turn
# compares EvalEnvironments. So all we do with the parsed formula
# is pull out the term *names*, which the EvalEnvironment doesn't
# effect. This is just a placeholder then to allow the ModelDesc
# to be created:
env = EvalEnvironment({})
desc = ModelDesc.from_formula(which_terms, env)
if desc.lhs_termlist:
raise PatsyError("right-hand-side-only formula required")
which_terms = [term.name() for term in desc.rhs_termlist]
terms = []
evaluators = set()
term_to_column_builders = {}
for term_or_name in which_terms:
if isinstance(term_or_name, basestring):
if term_or_name not in term_name_to_term:
raise PatsyError("requested term %r not found in "
"this DesignMatrixBuilder" %
(term_or_name, ))
term = term_name_to_term[term_or_name]
else:
term = term_or_name
if term not in self._termlist:
raise PatsyError("requested term '%s' not found in this "
"DesignMatrixBuilder" % (term, ))
for factor in term.factors:
evaluators.add(factor_to_evaluators[factor])
terms.append(term)
column_builder = self._term_to_column_builders[term]
term_to_column_builders[term] = column_builder
return DesignMatrixBuilder(terms, evaluators, term_to_column_builders)
