Optimize fmean() weighted average (#102626)

Author: rhettinger

Lib/statistics.py

@@ -136,9 +136,9 @@
 from decimal import Decimal
 from itertools import count, groupby, repeat
 from bisect import bisect_left, bisect_right
-from math import hypot, sqrt, fabs, exp, erf, tau, log, fsum
+from math import hypot, sqrt, fabs, exp, erf, tau, log, fsum, sumprod
 from functools import reduce
-from operator import mul, itemgetter
+from operator import itemgetter
 from collections import Counter, namedtuple, defaultdict
 
 _SQRT2 = sqrt(2.0)
@@ -496,28 +496,26 @@ def fmean(data, weights=None):
  >>> fmean([3.5, 4.0, 5.25])
  4.25
  """
- try:
- n = len(data)
- except TypeError:
- # Handle iterators that do not define __len__().
- n = 0
- def count(iterable):
- nonlocal n
- for n, x in enumerate(iterable, start=1):
- yield x
- data = count(data)
  if weights is None:
+ try:
+ n = len(data)
+ except TypeError:
+ # Handle iterators that do not define __len__().
+ n = 0
+ def count(iterable):
+ nonlocal n
+ for n, x in enumerate(iterable, start=1):
+ yield x
+ data = count(data)
  total = fsum(data)
  if not n:
  raise StatisticsError('fmean requires at least one data point')
  return total / n
- try:
- num_weights = len(weights)
- except TypeError:
+ if not isinstance(weights, (list, tuple)):
  weights = list(weights)
-  num_weights = len(weights)
- num = fsum(map(mul, data, weights))
- if n != num_weights:
+ try:
+  num = sumprod(data, weights)
+ except ValueError:
  raise StatisticsError('data and weights must be the same length')
  den = fsum(weights)
  if not den: