Some reformatting (suggested by Black) and minor factoring. (GH-20865)
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Raymond Hettinger
GitHub
parent
d71ab4f738
commit
5aad027db9
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@ -163,7 +163,7 @@ def _sum(data, start=0):
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T = _coerce(int, type(start))
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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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for n,d in map(_exact_ratio, values):
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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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if None in partials:
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@ -261,7 +261,7 @@ def _convert(value, T):
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return T(value)
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except TypeError:
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if issubclass(T, Decimal):
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return T(value.numerator)/T(value.denominator)
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return T(value.numerator) / T(value.denominator)
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else:
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raise
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@ -277,8 +277,8 @@ def _find_lteq(a, x):
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def _find_rteq(a, l, x):
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'Locate the rightmost value exactly equal to x'
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i = bisect_right(a, x, lo=l)
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if i != (len(a)+1) and a[i-1] == x:
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return i-1
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if i != (len(a) + 1) and a[i - 1] == x:
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return i - 1
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raise ValueError
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@ -315,7 +315,7 @@ def mean(data):
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raise StatisticsError('mean requires at least one data point')
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T, total, count = _sum(data)
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assert count == n
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return _convert(total/n, T)
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return _convert(total / n, T)
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def fmean(data):
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@ -403,11 +403,11 @@ def harmonic_mean(data):
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else:
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raise TypeError('unsupported type')
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try:
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T, total, count = _sum(1/x for x in _fail_neg(data, errmsg))
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T, total, count = _sum(1 / x for x in _fail_neg(data, errmsg))
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except ZeroDivisionError:
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return 0
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assert count == n
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return _convert(n/total, T)
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return _convert(n / total, T)
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# FIXME: investigate ways to calculate medians without sorting? Quickselect?
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@ -428,11 +428,11 @@ def median(data):
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n = len(data)
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if n == 0:
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raise StatisticsError("no median for empty data")
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if n%2 == 1:
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return data[n//2]
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if n % 2 == 1:
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return data[n // 2]
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else:
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i = n//2
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return (data[i - 1] + data[i])/2
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i = n // 2
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return (data[i - 1] + data[i]) / 2
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def median_low(data):
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@ -451,10 +451,10 @@ def median_low(data):
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n = len(data)
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if n == 0:
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raise StatisticsError("no median for empty data")
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if n%2 == 1:
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return data[n//2]
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if n % 2 == 1:
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return data[n // 2]
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else:
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return data[n//2 - 1]
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return data[n // 2 - 1]
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def median_high(data):
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@ -473,7 +473,7 @@ def median_high(data):
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n = len(data)
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if n == 0:
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raise StatisticsError("no median for empty data")
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return data[n//2]
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return data[n // 2]
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def median_grouped(data, interval=1):
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@ -510,15 +510,15 @@ def median_grouped(data, interval=1):
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return data[0]
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# Find the value at the midpoint. Remember this corresponds to the
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# centre of the class interval.
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x = data[n//2]
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x = data[n // 2]
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for obj in (x, interval):
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if isinstance(obj, (str, bytes)):
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raise TypeError('expected number but got %r' % obj)
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try:
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L = x - interval/2 # The lower limit of the median interval.
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L = x - interval / 2 # The lower limit of the median interval.
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except TypeError:
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# Mixed type. For now we just coerce to float.
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L = float(x) - float(interval)/2
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L = float(x) - float(interval) / 2
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# Uses bisection search to search for x in data with log(n) time complexity
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# Find the position of leftmost occurrence of x in data
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@ -528,7 +528,7 @@ def median_grouped(data, interval=1):
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l2 = _find_rteq(data, l1, x)
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cf = l1
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f = l2 - l1 + 1
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return L + interval*(n/2 - cf)/f
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return L + interval * (n / 2 - cf) / f
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def mode(data):
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@ -554,8 +554,7 @@ def mode(data):
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If *data* is empty, ``mode``, raises StatisticsError.
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"""
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data = iter(data)
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pairs = Counter(data).most_common(1)
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pairs = Counter(iter(data)).most_common(1)
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try:
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return pairs[0][0]
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except IndexError:
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@ -597,7 +596,7 @@ def multimode(data):
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# For sample data where there is a positive probability for values
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# beyond the range of the data, the R6 exclusive method is a
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# reasonable choice. Consider a random sample of nine values from a
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# population with a uniform distribution from 0.0 to 100.0. The
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# population with a uniform distribution from 0.0 to 1.0. The
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# distribution of the third ranked sample point is described by
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# betavariate(alpha=3, beta=7) which has mode=0.250, median=0.286, and
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# mean=0.300. Only the latter (which corresponds with R6) gives the
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@ -643,9 +642,8 @@ def quantiles(data, *, n=4, method='exclusive'):
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m = ld - 1
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result = []
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for i in range(1, n):
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j = i * m // n
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delta = i*m - j*n
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interpolated = (data[j] * (n - delta) + data[j+1] * delta) / n
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j, delta = divmod(i * m, n)
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interpolated = (data[j] * (n - delta) + data[j + 1] * delta) / n
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result.append(interpolated)
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return result
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if method == 'exclusive':
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@ -655,7 +653,7 @@ def quantiles(data, *, n=4, method='exclusive'):
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j = i * m // n # rescale i to m/n
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j = 1 if j < 1 else ld-1 if j > ld-1 else j # clamp to 1 .. ld-1
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delta = i*m - j*n # exact integer math
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interpolated = (data[j-1] * (n - delta) + data[j] * delta) / n
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interpolated = (data[j - 1] * (n - delta) + data[j] * delta) / n
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result.append(interpolated)
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return result
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raise ValueError(f'Unknown method: {method!r}')
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@ -689,9 +687,9 @@ def _ss(data, c=None):
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T, total, count = _sum((x-c)**2 for x in data)
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# The following sum should mathematically equal zero, but due to rounding
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# error may not.
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U, total2, count2 = _sum((x-c) for x in data)
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U, total2, count2 = _sum((x - c) for x in data)
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assert T == U and count == count2
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total -= total2**2/len(data)
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total -= total2 ** 2 / len(data)
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assert not total < 0, 'negative sum of square deviations: %f' % total
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return (T, total)
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@ -740,7 +738,7 @@ def variance(data, xbar=None):
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if n < 2:
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raise StatisticsError('variance requires at least two data points')
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T, ss = _ss(data, xbar)
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return _convert(ss/(n-1), T)
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return _convert(ss / (n - 1), T)
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def pvariance(data, mu=None):
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@ -784,7 +782,7 @@ def pvariance(data, mu=None):
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if n < 1:
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raise StatisticsError('pvariance requires at least one data point')
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T, ss = _ss(data, mu)
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return _convert(ss/n, T)
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return _convert(ss / n, T)
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def stdev(data, xbar=None):
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@ -993,7 +991,7 @@ class NormalDist:
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if not isinstance(other, NormalDist):
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raise TypeError('Expected another NormalDist instance')
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X, Y = self, other
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if (Y._sigma, Y._mu) < (X._sigma, X._mu): # sort to assure commutativity
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if (Y._sigma, Y._mu) < (X._sigma, X._mu): # sort to assure commutativity
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X, Y = Y, X
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X_var, Y_var = X.variance, Y.variance
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if not X_var or not Y_var:
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