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DOC: Update Kurt Docstr #20044

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Merged
merged 9 commits into from
Mar 9, 2018
Merged

DOC: Update Kurt Docstr #20044

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ab1b1ad
Continued updates to kurt docstr
WillAyd Mar 7, 2018
d08faa7
Merge remote-tracking branch 'upstream/master' into kurt-docstr2
WillAyd Mar 7, 2018
471499b
Minor docstring updates
WillAyd Mar 7, 2018
01ecb99
Merge remote-tracking branch 'upstream/master' into kurt-docstr2
WillAyd Mar 8, 2018
2fb4011
Added See Also descriptions
WillAyd Mar 8, 2018
efa7026
Updated **kwargs Parameter
WillAyd Mar 8, 2018
adf81ea
Merge remote-tracking branch 'upstream/master' into kurt-docstr2
WillAyd Mar 9, 2018
ffaa01f
Updated See Also
WillAyd Mar 9, 2018
083d0e5
Update window.py
jorisvandenbossche Mar 9, 2018
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26 changes: 14 additions & 12 deletions pandas/core/window.py
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Original file line number Diff line number Diff line change
Expand Up @@ -910,8 +910,9 @@ def skew(self, **kwargs):

Returns
-------
Series or DataFrame (matches input)
Like-indexed object containing the result of function application
Series or DataFrame
Returned object type is dictated by the caller of the %(name)s
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As a non-native speaker, I found your original "determined" better than "dictated" (but maybe not more correct :-)). Or simpler "Returned object is of the same type as the caller ..." ?

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Well I was trying to be as literal as possible so I didn't want to say caller, since that's technically either the Rolling or Expanding object. I'll revert to determined - not that semantically different

calculation
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align with the "Returned .." from above


See Also
--------
Expand All @@ -932,19 +933,20 @@ def skew(self, **kwargs):
four matching the equivalent function call using `scipy.stats`.

>>> arr = [1, 2, 3, 4, 999]
>>> fmt = "{0:.6f}" # limit the printed precision to 6 digits
>>> import scipy.stats
>>> print("{0:.6f}".format(scipy.stats.kurtosis(arr[:-1], bias=False)))
>>> print(fmt.format(scipy.stats.kurtosis(arr[:-1], bias=False)))
-1.200000
>>> print("{0:.6f}".format(scipy.stats.kurtosis(arr[1:], bias=False)))
>>> print(fmt.format(scipy.stats.kurtosis(arr[1:], bias=False)))
3.999946
>>> df = pd.DataFrame(arr)
>>> df.rolling(4).kurt()
0
0 NaN
1 NaN
2 NaN
3 -1.200000
4 3.999946
>>> ser = pd.Series(arr)
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let's use s by default (following the guide)

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Hmm OK I didn't see that. I would actually prefer ser to s but I'll bring that up as a separate discussion point with the docs. Will change for now

>>> ser.rolling(4).kurt()
0 NaN
1 NaN
2 NaN
3 -1.200000
4 3.999946
dtype: float64
""")

def kurt(self, **kwargs):
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