[Python-checkins] Some reformatting (suggested by Black) and minor factoring. (GH-20865)
Raymond Hettinger
webhook-mailer at python.org
Sat Jun 13 22:17:36 EDT 2020
https://github.com/python/cpython/commit/5aad027db9618f22f6fa2274e05dd50f928d2ed7
commit: 5aad027db9618f22f6fa2274e05dd50f928d2ed7
branch: master
author: Raymond Hettinger <rhettinger at users.noreply.github.com>
committer: GitHub <noreply at github.com>
date: 2020-06-13T19:17:28-07:00
summary:
Some reformatting (suggested by Black) and minor factoring. (GH-20865)
files:
M Lib/statistics.py
diff --git a/Lib/statistics.py b/Lib/statistics.py
index 93a46334649df..f9d3802ec5f83 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -163,7 +163,7 @@ def _sum(data, start=0):
T = _coerce(int, type(start))
for typ, values in groupby(data, type):
T = _coerce(T, typ) # or raise TypeError
- for n,d in map(_exact_ratio, values):
+ for n, d in map(_exact_ratio, values):
count += 1
partials[d] = partials_get(d, 0) + n
if None in partials:
@@ -261,7 +261,7 @@ def _convert(value, T):
return T(value)
except TypeError:
if issubclass(T, Decimal):
- return T(value.numerator)/T(value.denominator)
+ return T(value.numerator) / T(value.denominator)
else:
raise
@@ -277,8 +277,8 @@ def _find_lteq(a, x):
def _find_rteq(a, l, x):
'Locate the rightmost value exactly equal to x'
i = bisect_right(a, x, lo=l)
- if i != (len(a)+1) and a[i-1] == x:
- return i-1
+ if i != (len(a) + 1) and a[i - 1] == x:
+ return i - 1
raise ValueError
@@ -315,7 +315,7 @@ def mean(data):
raise StatisticsError('mean requires at least one data point')
T, total, count = _sum(data)
assert count == n
- return _convert(total/n, T)
+ return _convert(total / n, T)
def fmean(data):
@@ -403,11 +403,11 @@ def harmonic_mean(data):
else:
raise TypeError('unsupported type')
try:
- T, total, count = _sum(1/x for x in _fail_neg(data, errmsg))
+ T, total, count = _sum(1 / x for x in _fail_neg(data, errmsg))
except ZeroDivisionError:
return 0
assert count == n
- return _convert(n/total, T)
+ return _convert(n / total, T)
# FIXME: investigate ways to calculate medians without sorting? Quickselect?
@@ -428,11 +428,11 @@ def median(data):
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
- if n%2 == 1:
- return data[n//2]
+ if n % 2 == 1:
+ return data[n // 2]
else:
- i = n//2
- return (data[i - 1] + data[i])/2
+ i = n // 2
+ return (data[i - 1] + data[i]) / 2
def median_low(data):
@@ -451,10 +451,10 @@ def median_low(data):
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
- if n%2 == 1:
- return data[n//2]
+ if n % 2 == 1:
+ return data[n // 2]
else:
- return data[n//2 - 1]
+ return data[n // 2 - 1]
def median_high(data):
@@ -473,7 +473,7 @@ def median_high(data):
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
- return data[n//2]
+ return data[n // 2]
def median_grouped(data, interval=1):
@@ -510,15 +510,15 @@ class 3.5-4.5, and interpolation is used to estimate it.
return data[0]
# Find the value at the midpoint. Remember this corresponds to the
# centre of the class interval.
- x = data[n//2]
+ x = data[n // 2]
for obj in (x, interval):
if isinstance(obj, (str, bytes)):
raise TypeError('expected number but got %r' % obj)
try:
- L = x - interval/2 # The lower limit of the median interval.
+ L = x - interval / 2 # The lower limit of the median interval.
except TypeError:
# Mixed type. For now we just coerce to float.
- L = float(x) - float(interval)/2
+ L = float(x) - float(interval) / 2
# Uses bisection search to search for x in data with log(n) time complexity
# Find the position of leftmost occurrence of x in data
@@ -528,7 +528,7 @@ class 3.5-4.5, and interpolation is used to estimate it.
l2 = _find_rteq(data, l1, x)
cf = l1
f = l2 - l1 + 1
- return L + interval*(n/2 - cf)/f
+ return L + interval * (n / 2 - cf) / f
def mode(data):
@@ -554,8 +554,7 @@ def mode(data):
If *data* is empty, ``mode``, raises StatisticsError.
"""
- data = iter(data)
- pairs = Counter(data).most_common(1)
+ pairs = Counter(iter(data)).most_common(1)
try:
return pairs[0][0]
except IndexError:
@@ -597,7 +596,7 @@ def multimode(data):
# For sample data where there is a positive probability for values
# beyond the range of the data, the R6 exclusive method is 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
# 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
@@ -643,9 +642,8 @@ def quantiles(data, *, n=4, method='exclusive'):
m = ld - 1
result = []
for i in range(1, n):
- j = i * m // n
- delta = i*m - j*n
- interpolated = (data[j] * (n - delta) + data[j+1] * delta) / n
+ j, delta = divmod(i * m, n)
+ interpolated = (data[j] * (n - delta) + data[j + 1] * delta) / n
result.append(interpolated)
return result
if method == 'exclusive':
@@ -655,7 +653,7 @@ def quantiles(data, *, n=4, method='exclusive'):
j = i * m // n # rescale i to m/n
j = 1 if j < 1 else ld-1 if j > ld-1 else j # clamp to 1 .. ld-1
delta = i*m - j*n # exact integer math
- interpolated = (data[j-1] * (n - delta) + data[j] * delta) / n
+ interpolated = (data[j - 1] * (n - delta) + data[j] * delta) / n
result.append(interpolated)
return result
raise ValueError(f'Unknown method: {method!r}')
@@ -689,9 +687,9 @@ def _ss(data, c=None):
T, total, count = _sum((x-c)**2 for x in data)
# The following sum should mathematically equal zero, but due to rounding
# error may not.
- U, total2, count2 = _sum((x-c) for x in data)
+ U, total2, count2 = _sum((x - c) for x in data)
assert T == U and count == count2
- total -= total2**2/len(data)
+ total -= total2 ** 2 / len(data)
assert not total < 0, 'negative sum of square deviations: %f' % total
return (T, total)
@@ -740,7 +738,7 @@ def variance(data, xbar=None):
if n < 2:
raise StatisticsError('variance requires at least two data points')
T, ss = _ss(data, xbar)
- return _convert(ss/(n-1), T)
+ return _convert(ss / (n - 1), T)
def pvariance(data, mu=None):
@@ -784,7 +782,7 @@ def pvariance(data, mu=None):
if n < 1:
raise StatisticsError('pvariance requires at least one data point')
T, ss = _ss(data, mu)
- return _convert(ss/n, T)
+ return _convert(ss / n, T)
def stdev(data, xbar=None):
@@ -993,7 +991,7 @@ def overlap(self, other):
if not isinstance(other, NormalDist):
raise TypeError('Expected another NormalDist instance')
X, Y = self, other
- if (Y._sigma, Y._mu) < (X._sigma, X._mu): # sort to assure commutativity
+ if (Y._sigma, Y._mu) < (X._sigma, X._mu): # sort to assure commutativity
X, Y = Y, X
X_var, Y_var = X.variance, Y.variance
if not X_var or not Y_var:
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