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BUG: fixing ewma() for adjust=False and ignore_na=False #7896

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Merged
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Aug 1, 2014
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BUG: fixing ewma() for adjust=False and ignore_na=False #7896

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5 changes: 4 additions & 1 deletion pandas/algos.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -1018,7 +1018,10 @@ def ewma(ndarray[double_t] input, double_t com, int adjust, int ignore_na):
if cur == cur:
old_wt *= old_wt_factor
weighted_avg = ((old_wt * weighted_avg) + (new_wt * cur)) / (old_wt + new_wt)
old_wt += new_wt
if adjust:
old_wt += new_wt
else:
old_wt = 1.
elif not ignore_na:
old_wt *= old_wt_factor
else:
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17 changes: 17 additions & 0 deletions pandas/stats/moments.py
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Expand Up @@ -107,6 +107,23 @@
:math:`c = (s - 1) / 2`

So a "20-day EWMA" would have center 9.5.

When adjust is True (default), weighted averages are calculated using weights
(1-alpha)**(n-1), (1-alpha)**(n-2), ..., 1-alpha, 1.

When adjust is False, weighted averages are calculated recursively as:
weighted_average[0] = arg[0];
weighted_average[i] = (1-alpha)*weighted_average[i-1] + alpha*arg[i].

When ignore_na is False (default), weights are based on absolute positions.
For example, the weights of x and y used in calculating the final weighted
average of [x, None, y] are (1-alpha)**2 and 1 (if adjust is True), and
(1-alpha)**2 and alpha (if adjust is False).

When ignore_na is True (reproducing pre-0.15.0 behavior), weights are based on
relative positions. For example, the weights of x and y used in calculating
the final weighted average of [x, None, y] are 1-alpha and 1 (if adjust is
True), and 1-alpha and alpha (if adjust is False).
"""

_expanding_kw = """min_periods : int, default None
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29 changes: 17 additions & 12 deletions pandas/stats/tests/test_moments.py
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Expand Up @@ -551,25 +551,30 @@ def test_ewma_nan_handling(self):
s0 = Series([np.nan, 1., 101.])
s1 = Series([1., np.nan, 101.])
s2 = Series([np.nan, 1., np.nan, np.nan, 101., np.nan])
s3 = Series([1., np.nan, 101., 50.])
com = 2.
alpha = 1. / (1. + com)

def simple_wma(s, w):
return (s.multiply(w).cumsum() / w.cumsum()).fillna(method='ffill')

for (s, adjust, ignore_na, w) in [
(s0, True, False, [np.nan, (1.0 - alpha), 1.]),
(s0, True, True, [np.nan, (1.0 - alpha), 1.]),
(s0, False, False, [np.nan, (1.0 - alpha), alpha]),
(s0, False, True, [np.nan, (1.0 - alpha), alpha]),
(s1, True, False, [(1.0 - alpha)**2, np.nan, 1.]),
(s1, True, True, [(1.0 - alpha), np.nan, 1.]),
(s1, False, False, [(1.0 - alpha)**2, np.nan, alpha]),
(s1, False, True, [(1.0 - alpha), np.nan, alpha]),
(s2, True, False, [np.nan, (1.0 - alpha)**3, np.nan, np.nan, 1., np.nan]),
(s2, True, True, [np.nan, (1.0 - alpha), np.nan, np.nan, 1., np.nan]),
(s2, False, False, [np.nan, (1.0 - alpha)**3, np.nan, np.nan, alpha, np.nan]),
(s2, False, True, [np.nan, (1.0 - alpha), np.nan, np.nan, alpha, np.nan]),
(s0, True, False, [np.nan, (1. - alpha), 1.]),
(s0, True, True, [np.nan, (1. - alpha), 1.]),
(s0, False, False, [np.nan, (1. - alpha), alpha]),
(s0, False, True, [np.nan, (1. - alpha), alpha]),
(s1, True, False, [(1. - alpha)**2, np.nan, 1.]),
(s1, True, True, [(1. - alpha), np.nan, 1.]),
(s1, False, False, [(1. - alpha)**2, np.nan, alpha]),
(s1, False, True, [(1. - alpha), np.nan, alpha]),
(s2, True, False, [np.nan, (1. - alpha)**3, np.nan, np.nan, 1., np.nan]),
(s2, True, True, [np.nan, (1. - alpha), np.nan, np.nan, 1., np.nan]),
(s2, False, False, [np.nan, (1. - alpha)**3, np.nan, np.nan, alpha, np.nan]),
(s2, False, True, [np.nan, (1. - alpha), np.nan, np.nan, alpha, np.nan]),
(s3, True, False, [(1. - alpha)**3, np.nan, (1. - alpha), 1.]),
(s3, True, True, [(1. - alpha)**2, np.nan, (1. - alpha), 1.]),
(s3, False, False, [(1. - alpha)**3, np.nan, (1. - alpha) * alpha, alpha * ((1. - alpha)**2 + alpha)]),
(s3, False, True, [(1. - alpha)**2, np.nan, (1. - alpha) * alpha, alpha]),
]:
expected = simple_wma(s, Series(w))
result = mom.ewma(s, com=com, adjust=adjust, ignore_na=ignore_na)
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