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adding ddof parameter back into std and var #965

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Merged
merged 1 commit into from
Apr 2, 2012
Merged

adding ddof parameter back into std and var #965

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12 changes: 6 additions & 6 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3585,21 +3585,21 @@ def mad(self, axis=0, skipna=True, level=None):
@Substitution(name='unbiased variance', shortname='var',
na_action=_doc_exclude_na, extras='')
@Appender(_stat_doc)
def var(self, axis=0, skipna=True, level=None):
def var(self, axis=0, skipna=True, level=None, ddof=1):
if level is not None:
return self._agg_by_level('var', axis=axis, level=level,
skipna=skipna)
return self._reduce(nanops.nanvar, axis=axis, skipna=skipna,
numeric_only=None)
numeric_only=None, ddof=ddof)

@Substitution(name='unbiased standard deviation', shortname='std',
na_action=_doc_exclude_na, extras='')
@Appender(_stat_doc)
def std(self, axis=0, skipna=True, level=None):
def std(self, axis=0, skipna=True, level=None, ddof=1):
if level is not None:
return self._agg_by_level('std', axis=axis, level=level,
skipna=skipna)
return np.sqrt(self.var(axis=axis, skipna=skipna))
return np.sqrt(self.var(axis=axis, skipna=skipna, ddof=ddof))

@Substitution(name='unbiased skewness', shortname='skew',
na_action=_doc_exclude_na, extras='')
Expand All @@ -3619,8 +3619,8 @@ def _agg_by_level(self, name, axis=0, level=0, skipna=True):
applyf = lambda x: method(x, axis=axis, skipna=skipna)
return grouped.aggregate(applyf)

def _reduce(self, op, axis=0, skipna=True, numeric_only=None):
f = lambda x: op(x, axis=axis, skipna=skipna)
def _reduce(self, op, axis=0, skipna=True, numeric_only=None, **kwds):
f = lambda x: op(x, axis=axis, skipna=skipna, **kwds)
labels = self._get_agg_axis(axis)
if numeric_only is None:
try:
Expand Down
15 changes: 9 additions & 6 deletions pandas/core/nanops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,10 @@ def _bottleneck_switch(bn_name, alt, zero_value=None, **kwargs):
bn_func = getattr(bn, bn_name)
except (AttributeError, NameError): # pragma: no cover
bn_func = None
def f(values, axis=None, skipna=True):
def f(values, axis=None, skipna=True, **kwds):
if len(kwargs) > 0:
for k, v in kwargs.iteritems():
kwds[k] = v
try:
if zero_value is not None and values.size == 0:
if values.ndim == 1:
Expand All @@ -29,14 +32,14 @@ def f(values, axis=None, skipna=True):
return result

if _USE_BOTTLENECK and skipna and values.dtype != np.object_:
result = bn_func(values, axis=axis, **kwargs)
result = bn_func(values, axis=axis, **kwds)
# prefer to treat inf/-inf as NA
if _has_infs(result):
result = alt(values, axis=axis, skipna=skipna, **kwargs)
result = alt(values, axis=axis, skipna=skipna, **kwds)
else:
result = alt(values, axis=axis, skipna=skipna, **kwargs)
result = alt(values, axis=axis, skipna=skipna, **kwds)
except Exception:
result = alt(values, axis=axis, skipna=skipna, **kwargs)
result = alt(values, axis=axis, skipna=skipna, **kwds)

return result

Expand Down Expand Up @@ -180,7 +183,7 @@ def nanargmin(values, axis=None, skipna=True):
nansum = _bottleneck_switch('nansum', _nansum, zero_value=0)
nanmean = _bottleneck_switch('nanmean', _nanmean)
nanmedian = _bottleneck_switch('nanmedian', _nanmedian)
nanvar = _bottleneck_switch('nanvar', _nanvar, ddof=1)
nanvar = _bottleneck_switch('nanvar', _nanvar)
nanmin = _bottleneck_switch('nanmin', _nanmin)
nanmax = _bottleneck_switch('nanmax', _nanmax)

Expand Down
4 changes: 2 additions & 2 deletions pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1012,7 +1012,7 @@ def std(self, axis=None, dtype=None, out=None, ddof=1, skipna=True,
level=None):
if level is not None:
return self._agg_by_level('std', level=level, skipna=skipna)
return np.sqrt(nanops.nanvar(self.values, skipna=skipna))
return np.sqrt(nanops.nanvar(self.values, skipna=skipna, ddof=ddof))

@Substitution(name='unbiased variance', shortname='var',
na_action=_doc_exclude_na, extras='')
Expand All @@ -1021,7 +1021,7 @@ def var(self, axis=None, dtype=None, out=None, ddof=1, skipna=True,
level=None):
if level is not None:
return self._agg_by_level('var', level=level, skipna=skipna)
return nanops.nanvar(self.values, skipna=skipna)
return nanops.nanvar(self.values, skipna=skipna, ddof=ddof)

@Substitution(name='unbiased skewness', shortname='skew',
na_action=_doc_exclude_na, extras='')
Expand Down