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Statistics internals: Make fewer calls to _coerce() when data types are mixed (GH-31619)
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@ -137,6 +137,7 @@ from decimal import Decimal
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from itertools import groupby, repeat
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from bisect import bisect_left, bisect_right
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from math import hypot, sqrt, fabs, exp, erf, tau, log, fsum
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from functools import reduce
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from operator import mul
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from collections import Counter, namedtuple, defaultdict
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@ -183,11 +184,12 @@ def _sum(data):
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allowed.
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"""
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count = 0
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types = set()
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types_add = types.add
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partials = {}
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partials_get = partials.get
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T = int
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for typ, values in groupby(data, type):
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T = _coerce(T, typ) # or raise TypeError
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types_add(typ)
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for n, d in map(_exact_ratio, values):
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count += 1
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partials[d] = partials_get(d, 0) + n
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@ -199,6 +201,7 @@ def _sum(data):
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else:
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# Sum all the partial sums using builtin sum.
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total = sum(Fraction(n, d) for d, n in partials.items())
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T = reduce(_coerce, types, int) # or raise TypeError
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return (T, total, count)
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@ -214,11 +217,12 @@ def _ss(data, c=None):
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T, total, count = _sum((d := x - c) * d for x in data)
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return (T, total, count)
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count = 0
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types = set()
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types_add = types.add
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sx_partials = defaultdict(int)
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sxx_partials = defaultdict(int)
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T = int
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for typ, values in groupby(data, type):
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T = _coerce(T, typ) # or raise TypeError
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types_add(typ)
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for n, d in map(_exact_ratio, values):
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count += 1
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sx_partials[d] += n
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@ -236,6 +240,7 @@ def _ss(data, c=None):
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# This formula has poor numeric properties for floats,
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# but with fractions it is exact.
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total = (count * sxx - sx * sx) / count
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T = reduce(_coerce, types, int) # or raise TypeError
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return (T, total, count)
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