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Some reformatting (suggested by Black) and minor factoring. (GH-20865)

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Raymond Hettinger 2020-06-13 19:17:28 -07:00 • committed by GitHub
parent d71ab4f738
commit 5aad027db9
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1 changed files with 29 additions and 31 deletions
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8
Lib/statistics.py
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@ -554,8 +554,7 @@ def mode(data):
If *data* is empty, ``mode``, raises StatisticsError. If *data* is empty, ``mode``, raises StatisticsError.
""" """
data = iter(data) pairs = Counter(iter(data)).most_common(1)
pairs = Counter(data).most_common(1)
try: try:
return pairs[0][0] return pairs[0][0]
except IndexError: except IndexError:
@ -597,7 +596,7 @@ def multimode(data):
# For sample data where there is a positive probability for values # For sample data where there is a positive probability for values
# beyond the range of the data, the R6 exclusive method is a # beyond the range of the data, the R6 exclusive method is a
# reasonable choice. Consider a random sample of nine values from a # reasonable choice. Consider a random sample of nine values from a
# population with a uniform distribution from 0.0 to 100.0. The # population with a uniform distribution from 0.0 to 1.0. The
# distribution of the third ranked sample point is described by # distribution of the third ranked sample point is described by
# betavariate(alpha=3, beta=7) which has mode=0.250, median=0.286, and # betavariate(alpha=3, beta=7) which has mode=0.250, median=0.286, and
# mean=0.300. Only the latter (which corresponds with R6) gives the # mean=0.300. Only the latter (which corresponds with R6) gives the
@ -643,8 +642,7 @@ def quantiles(data, *, n=4, method='exclusive'):
m = ld - 1 m = ld - 1
result = [] result = []
for i in range(1, n): for i in range(1, n):
j = i * m // n j, delta = divmod(i * m, n)
delta = i*m - j*n
interpolated = (data[j] * (n - delta) + data[j + 1] * delta) / n interpolated = (data[j] * (n - delta) + data[j + 1] * delta) / n
result.append(interpolated) result.append(interpolated)
return result return result