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DOC: update the Rolling.var docstring #20233

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Merged
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Mar 12, 2018
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DOC: update the Rolling.var docstring #20233

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DOC: update the Rolling.var docstring
mmaybeno Mar 10, 2018
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mmaybeno Mar 10, 2018
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Addressing comments
mmaybeno Mar 10, 2018
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Addressing comments 2
mmaybeno Mar 11, 2018
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args, kwargs
TomAugspurger Mar 11, 2018
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57 changes: 53 additions & 4 deletions pandas/core/window.py
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Original file line number Diff line number Diff line change
Expand Up @@ -879,13 +879,64 @@ def f(arg, *args, **kwargs):
ddof=ddof, **kwargs)

_shared_docs['var'] = dedent("""
%(name)s variance
Calculate unbiased %(name)s variance.

Normalized by N-1 by default. This can be changed using the `ddof`
argument.

Parameters
----------
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of elements.""")
is ``N - ddof``, where ``N`` represents the number of elements.
*args
Under Review.
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@jorisvandenbossche ?

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It was mentioned that the exact content for what should be documented for args/kwargs was to be determined. If you'd rather me add the documentation I can do that too.

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I think we're noting that they're accepted, but not used.

**kwargs
Under Review.
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*args and **kwargs should have the stars. We changed the convention last minute, and the validation script is wrong, ignore the messages.


Returns
-------
Series or DataFrame
Returns the same object type as the caller of the %(name)s calculation.

See Also
--------
Series.%(name)s : Calling object with Series data
DataFrame.%(name)s : Calling object with DataFrames
Series.var : Equivalent method for Series
DataFrame.var : Equivalent method for DataFrame
numpy.var : Equivalent method for Numpy array

Notes
-----
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so in both this one and the std one, should put in the Notes that the ddof default is different than numpy. (we use 1, they use 0). This is in the Series.var/std strings already.

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👍

The default `ddof` of 1 used in Series.var is different than the default
`ddof` of 0 in numpy.var.

A minimum of 1 periods is required for the rolling calculation.

Examples
--------
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
>>> s.rolling(3).var()
0 NaN
1 NaN
2 0.333333
3 1.000000
4 1.000000
5 1.333333
6 0.000000
dtype: float64

>>> s.expanding(3).var()
0 NaN
1 NaN
2 0.333333
3 0.916667
4 0.800000
5 0.700000
6 0.619048
dtype: float64
""")
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Can you show s.expanding(3).var(1) as well?


def var(self, ddof=1, *args, **kwargs):
nv.validate_window_func('var', args, kwargs)
Expand Down Expand Up @@ -1257,7 +1308,6 @@ def std(self, ddof=1, *args, **kwargs):
return super(Rolling, self).std(ddof=ddof, **kwargs)

@Substitution(name='rolling')
@Appender(_doc_template)
@Appender(_shared_docs['var'])
def var(self, ddof=1, *args, **kwargs):
nv.validate_rolling_func('var', args, kwargs)
Expand Down Expand Up @@ -1496,7 +1546,6 @@ def std(self, ddof=1, *args, **kwargs):
return super(Expanding, self).std(ddof=ddof, **kwargs)

@Substitution(name='expanding')
@Appender(_doc_template)
@Appender(_shared_docs['var'])
def var(self, ddof=1, *args, **kwargs):
nv.validate_expanding_func('var', args, kwargs)
Expand Down