"""
Provide basic components for groupby. These defintiions
hold the whitelist of methods that are exposed on the
SeriesGroupBy and the DataFrameGroupBy objects.
"""
import types
from pandas.util._decorators import make_signature
from pandas.core.dtypes.common import is_list_like, is_scalar
class GroupByMixin(object):
"""
Provide the groupby facilities to the mixed object.
"""
@staticmethod
def _dispatch(name, *args, **kwargs):
"""
Dispatch to apply.
"""
def outer(self, *args, **kwargs):
def f(x):
x = self._shallow_copy(x, groupby=self._groupby)
return getattr(x, name)(*args, **kwargs)
return self._groupby.apply(f)
outer.__name__ = name
return outer
def _gotitem(self, key, ndim, subset=None):
"""
Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on
"""
# create a new object to prevent aliasing
if subset is None:
subset = self.obj
# we need to make a shallow copy of ourselves
# with the same groupby
kwargs = {attr: getattr(self, attr) for attr in self._attributes}
# Try to select from a DataFrame, falling back to a Series
try:
groupby = self._groupby[key]
except IndexError:
groupby = self._groupby
self = self.__class__(subset,
groupby=groupby,
parent=self,
**kwargs)
self._reset_cache()
if subset.ndim == 2:
if is_scalar(key) and key in subset or is_list_like(key):
self._selection = key
return self
# special case to prevent duplicate plots when catching exceptions when
# forwarding methods from NDFrames
plotting_methods = frozenset(['plot', 'hist'])
common_apply_whitelist = frozenset([
'quantile', 'fillna', 'mad', 'take',
'idxmax', 'idxmin', 'tshift',
'skew', 'corr', 'cov', 'diff'
]) | plotting_methods
series_apply_whitelist = ((common_apply_whitelist |
{'nlargest', 'nsmallest',
'is_monotonic_increasing',
'is_monotonic_decreasing'})
) | frozenset(['dtype', 'unique'])
dataframe_apply_whitelist = ((common_apply_whitelist |
frozenset(['dtypes', 'corrwith'])))
cython_transforms = frozenset(['cumprod', 'cumsum', 'shift',
'cummin', 'cummax'])
cython_cast_blacklist = frozenset(['rank', 'count', 'size'])
def whitelist_method_generator(base, klass, whitelist):
"""
Yields all GroupBy member defs for DataFrame/Series names in whitelist.
Parameters
----------
base : class
base class
klass : class
class where members are defined.
Should be Series or DataFrame
whitelist : list
list of names of klass methods to be constructed
Returns
-------
The generator yields a sequence of strings, each suitable for exec'ing,
that define implementations of the named methods for DataFrameGroupBy
or SeriesGroupBy.
Since we don't want to override methods explicitly defined in the
base class, any such name is skipped.
"""
method_wrapper_template = \
"""def %(name)s(%(sig)s) :
\"""
%(doc)s
\"""
f = %(self)s.__getattr__('%(name)s')
return f(%(args)s)"""
property_wrapper_template = \
"""@property
def %(name)s(self) :
\"""
%(doc)s
\"""
return self.__getattr__('%(name)s')"""
for name in whitelist:
# don't override anything that was explicitly defined
# in the base class
if hasattr(base, name):
continue
# ugly, but we need the name string itself in the method.
f = getattr(klass, name)
doc = f.__doc__
doc = doc if type(doc) == str else ''
if isinstance(f, types.MethodType):
wrapper_template = method_wrapper_template
decl, args = make_signature(f)
# pass args by name to f because otherwise
# GroupBy._make_wrapper won't know whether
# we passed in an axis parameter.
args_by_name = ['{0}={0}'.format(arg) for arg in args[1:]]
params = {'name': name,
'doc': doc,
'sig': ','.join(decl),
'self': args[0],
'args': ','.join(args_by_name)}
else:
wrapper_template = property_wrapper_template
params = {'name': name, 'doc': doc}
yield wrapper_template % params