mirror of
https://github.com/gristlabs/grist-core.git
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616 lines
21 KiB
Python
616 lines
21 KiB
Python
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# pylint: disable=redefined-builtin, line-too-long, unused-argument
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from math import _chain, _chain_numeric, _chain_numeric_a
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from info import ISNUMBER, ISLOGICAL
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from date import DATE # pylint: disable=unused-import
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def _average(iterable):
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total, count = 0.0, 0
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for value in iterable:
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total += value
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count += 1
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return total / count
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def _default_if_empty(iterable, default):
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"""
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Yields all values from iterable, except when it is empty, yields just the single default value.
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"""
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empty = True
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for value in iterable:
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empty = False
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yield value
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if empty:
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yield default
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def AVEDEV(value1, value2):
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"""Calculates the average of the magnitudes of deviations of data from a dataset's mean."""
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raise NotImplementedError()
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def AVERAGE(value, *more_values):
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"""
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Returns the numerical average value in a dataset, ignoring non-numerical values.
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Each argument may be a value or an array. Values that are not numbers, including logical
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and blank values, and text representations of numbers, are ignored.
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>>> AVERAGE([2, -1.0, 11])
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4.0
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>>> AVERAGE([2, -1, 11, "Hello"])
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4.0
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>>> AVERAGE([2, -1, "Hello", DATE(2015,1,1)], True, [False, "123", "", 11])
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4.0
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>>> AVERAGE(False, True)
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Traceback (most recent call last):
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...
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ZeroDivisionError: float division by zero
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"""
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return _average(_chain_numeric(value, *more_values))
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def AVERAGEA(value, *more_values):
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"""
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Returns the numerical average value in a dataset, counting non-numerical values as 0.
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Each argument may be a value of an array. Values that are not numbers, including dates and text
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representations of numbers, are counted as 0 (zero). Logical value of True is counted as 1, and
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False as 0.
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>>> AVERAGEA([2, -1.0, 11])
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4.0
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>>> AVERAGEA([2, -1, 11, "Hello"])
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3.0
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>>> AVERAGEA([2, -1, "Hello", DATE(2015,1,1)], True, [False, "123", "", 11.5])
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1.5
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>>> AVERAGEA(False, True)
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0.5
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"""
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return _average(_chain_numeric_a(value, *more_values))
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# Note that Google Sheets offers a similar function, called AVERAGE.WEIGHTED
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# (https://support.google.com/docs/answer/9084098?hl=en)
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def AVERAGE_WEIGHTED(pairs):
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"""
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Given a list of (value, weight) pairs, finds the average of the values weighted by the
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corresponding weights. Ignores any pairs with a non-numerical value or weight.
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If you have two lists, of values and weights, use the Python built-in zip() function to create a
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list of pairs.
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>>> AVERAGE_WEIGHTED(((95, .25), (90, .1), ("X", .5), (85, .15), (88, .2), (82, .3), (70, None)))
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87.7
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>>> AVERAGE_WEIGHTED(zip([95, 90, "X", 85, 88, 82, 70], [25, 10, 50, 15, 20, 30, None]))
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87.7
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>>> AVERAGE_WEIGHTED(zip([95, 90, False, 85, 88, 82, 70], [.25, .1, .5, .15, .2, .3, True]))
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87.7
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"""
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sum_value, sum_weight = 0.0, 0.0
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for value, weight in pairs:
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# The type-checking here is the same as used by _chain_numeric.
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if ISNUMBER(value) and not ISLOGICAL(value) and ISNUMBER(weight) and not ISLOGICAL(weight):
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sum_value += value * weight
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sum_weight += weight
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return sum_value / sum_weight
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def AVERAGEIF(criteria_range, criterion, average_range=None):
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"""Returns the average of a range depending on criteria."""
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raise NotImplementedError()
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def AVERAGEIFS(average_range, criteria_range1, criterion1, *args):
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"""Returns the average of a range depending on multiple criteria."""
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raise NotImplementedError()
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def BINOMDIST(num_successes, num_trials, prob_success, cumulative):
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"""
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Calculates the probability of drawing a certain number of successes (or a maximum number of
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successes) in a certain number of tries given a population of a certain size containing a
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certain number of successes, with replacement of draws.
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"""
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raise NotImplementedError()
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def CONFIDENCE(alpha, standard_deviation, pop_size):
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"""Calculates the width of half the confidence interval for a normal distribution."""
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raise NotImplementedError()
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def CORREL(data_y, data_x):
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"""Calculates r, the Pearson product-moment correlation coefficient of a dataset."""
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raise NotImplementedError()
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def COUNT(value, *more_values):
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"""
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Returns the count of numerical values in a dataset, ignoring non-numerical values.
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Each argument may be a value or an array. Values that are not numbers, including logical
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and blank values, and text representations of numbers, are ignored.
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>>> COUNT([2, -1.0, 11])
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3
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>>> COUNT([2, -1, 11, "Hello"])
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3
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>>> COUNT([2, -1, "Hello", DATE(2015,1,1)], True, [False, "123", "", 11.5])
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3
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>>> COUNT(False, True)
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0
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"""
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return sum(1 for v in _chain_numeric(value, *more_values))
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def COUNTA(value, *more_values):
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"""
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Returns the count of all values in a dataset, including non-numerical values.
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Each argument may be a value or an array.
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>>> COUNTA([2, -1.0, 11])
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3
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>>> COUNTA([2, -1, 11, "Hello"])
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4
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>>> COUNTA([2, -1, "Hello", DATE(2015,1,1)], True, [False, "123", "", 11.5])
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9
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>>> COUNTA(False, True)
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2
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"""
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return sum(1 for v in _chain(value, *more_values))
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def COVAR(data_y, data_x):
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"""Calculates the covariance of a dataset."""
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raise NotImplementedError()
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def CRITBINOM(num_trials, prob_success, target_prob):
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"""Calculates the smallest value for which the cumulative binomial distribution is greater than or equal to a specified criteria."""
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raise NotImplementedError()
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def DEVSQ(value1, value2):
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"""Calculates the sum of squares of deviations based on a sample."""
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raise NotImplementedError()
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def EXPONDIST(x, lambda_, cumulative):
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"""Returns the value of the exponential distribution function with a specified lambda at a specified value."""
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raise NotImplementedError()
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def F_DIST(x, degrees_freedom1, degrees_freedom2, cumulative):
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"""
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Calculates the left-tailed F probability distribution (degree of diversity) for two data sets
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with given input x. Alternately called Fisher-Snedecor distribution or Snedecor's F
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distribution.
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"""
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raise NotImplementedError()
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def F_DIST_RT(x, degrees_freedom1, degrees_freedom2):
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"""
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Calculates the right-tailed F probability distribution (degree of diversity) for two data sets
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with given input x. Alternately called Fisher-Snedecor distribution or Snedecor's F
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distribution.
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"""
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raise NotImplementedError()
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def FDIST(x, degrees_freedom1, degrees_freedom2):
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"""
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Calculates the right-tailed F probability distribution (degree of diversity) for two data sets
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with given input x. Alternately called Fisher-Snedecor distribution or Snedecor's F
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distribution.
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"""
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raise NotImplementedError()
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def FISHER(value):
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"""Returns the Fisher transformation of a specified value."""
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raise NotImplementedError()
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def FISHERINV(value):
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"""Returns the inverse Fisher transformation of a specified value."""
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raise NotImplementedError()
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def FORECAST(x, data_y, data_x):
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"""Calculates the expected y-value for a specified x based on a linear regression of a dataset."""
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raise NotImplementedError()
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def GEOMEAN(value1, value2):
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"""Calculates the geometric mean of a dataset."""
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raise NotImplementedError()
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def HARMEAN(value1, value2):
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"""Calculates the harmonic mean of a dataset."""
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raise NotImplementedError()
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def HYPGEOMDIST(num_successes, num_draws, successes_in_pop, pop_size):
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"""Calculates the probability of drawing a certain number of successes in a certain number of tries given a population of a certain size containing a certain number of successes, without replacement of draws."""
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raise NotImplementedError()
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def INTERCEPT(data_y, data_x):
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"""Calculates the y-value at which the line resulting from linear regression of a dataset will intersect the y-axis (x=0)."""
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raise NotImplementedError()
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def KURT(value1, value2):
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"""Calculates the kurtosis of a dataset, which describes the shape, and in particular the "peakedness" of that dataset."""
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raise NotImplementedError()
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def LARGE(data, n):
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"""Returns the nth largest element from a data set, where n is user-defined."""
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raise NotImplementedError()
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def LOGINV(x, mean, standard_deviation):
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"""Returns the value of the inverse log-normal cumulative distribution with given mean and standard deviation at a specified value."""
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raise NotImplementedError()
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def LOGNORMDIST(x, mean, standard_deviation):
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"""Returns the value of the log-normal cumulative distribution with given mean and standard deviation at a specified value."""
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raise NotImplementedError()
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def MAX(value, *more_values):
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"""
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Returns the maximum value in a dataset, ignoring non-numerical values.
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Each argument may be a value or an array. Values that are not numbers, including logical
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and blank values, and text representations of numbers, are ignored. Returns 0 if the arguments
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contain no numbers.
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>>> MAX([2, -1.5, 11.5])
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11.5
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>>> MAX([2, -1.5, "Hello", DATE(2015, 1, 1)], True, [False, "123", "", 11.5])
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11.5
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>>> MAX(True, -123)
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-123
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>>> MAX("123", -123)
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-123
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>>> MAX("Hello", "123", DATE(2015, 1, 1))
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0
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"""
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return max(_default_if_empty(_chain_numeric(value, *more_values), 0))
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def MAXA(value, *more_values):
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"""
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Returns the maximum numeric value in a dataset.
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Each argument may be a value of an array. Values that are not numbers, including dates and text
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representations of numbers, are counted as 0 (zero). Logical value of True is counted as 1, and
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False as 0. Returns 0 if the arguments contain no numbers.
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>>> MAXA([2, -1.5, 11.5])
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11.5
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>>> MAXA([2, -1.5, "Hello", DATE(2015, 1, 1)], True, [False, "123", "", 11.5])
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11.5
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>>> MAXA(True, -123)
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1
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>>> MAXA("123", -123)
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0
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>>> MAXA("Hello", "123", DATE(2015, 1, 1))
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0
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"""
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return max(_default_if_empty(_chain_numeric_a(value, *more_values), 0))
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def MEDIAN(value, *more_values):
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"""
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Returns the median value in a numeric dataset, ignoring non-numerical values.
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Each argument may be a value or an array. Values that are not numbers, including logical
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and blank values, and text representations of numbers, are ignored.
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Produces an error if the arguments contain no numbers.
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The median is the middle number when all values are sorted. So half of the values in the dataset
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are less than the median, and half of the values are greater. If there is an even number of
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values in the dataset, returns the average of the two numbers in the middle.
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>>> MEDIAN(1, 2, 3, 4, 5)
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3
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>>> MEDIAN(3, 5, 1, 4, 2)
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3
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>>> MEDIAN(xrange(10))
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4.5
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>>> MEDIAN("Hello", "123", DATE(2015, 1, 1), 12.3)
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12.3
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>>> MEDIAN("Hello", "123", DATE(2015, 1, 1))
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Traceback (most recent call last):
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...
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ValueError: MEDIAN requires at least one number
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"""
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values = sorted(_chain_numeric(value, *more_values))
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if not values:
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raise ValueError("MEDIAN requires at least one number")
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count = len(values)
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if count % 2 == 0:
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return (values[count / 2 - 1] + values[count / 2]) / 2.0
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else:
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return values[(count - 1) / 2]
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def MIN(value, *more_values):
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"""
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Returns the minimum value in a dataset, ignoring non-numerical values.
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Each argument may be a value or an array. Values that are not numbers, including logical
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and blank values, and text representations of numbers, are ignored. Returns 0 if the arguments
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contain no numbers.
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>>> MIN([2, -1.5, 11.5])
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-1.5
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>>> MIN([2, -1.5, "Hello", DATE(2015, 1, 1)], True, [False, "123", "", 11.5])
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-1.5
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>>> MIN(True, 123)
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123
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>>> MIN("-123", 123)
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123
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>>> MIN("Hello", "123", DATE(2015, 1, 1))
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0
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"""
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return min(_default_if_empty(_chain_numeric(value, *more_values), 0))
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def MINA(value, *more_values):
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"""
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Returns the minimum numeric value in a dataset.
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Each argument may be a value of an array. Values that are not numbers, including dates and text
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representations of numbers, are counted as 0 (zero). Logical value of True is counted as 1, and
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False as 0. Returns 0 if the arguments contain no numbers.
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>>> MINA([2, -1.5, 11.5])
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-1.5
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>>> MINA([2, -1.5, "Hello", DATE(2015, 1, 1)], True, [False, "123", "", 11.5])
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-1.5
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>>> MINA(True, 123)
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1
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>>> MINA("-123", 123)
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0
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>>> MINA("Hello", "123", DATE(2015, 1, 1))
|
||
|
|
0
|
||
|
|
"""
|
||
|
|
return min(_default_if_empty(_chain_numeric_a(value, *more_values), 0))
|
||
|
|
|
||
|
|
|
||
|
|
def MODE(value1, value2):
|
||
|
|
"""Returns the most commonly occurring value in a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def NEGBINOMDIST(num_failures, num_successes, prob_success):
|
||
|
|
"""Calculates the probability of drawing a certain number of failures before a certain number of successes given a probability of success in independent trials."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def NORMDIST(x, mean, standard_deviation, cumulative):
|
||
|
|
"""
|
||
|
|
Returns the value of the normal distribution function (or normal cumulative distribution
|
||
|
|
function) for a specified value, mean, and standard deviation.
|
||
|
|
"""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def NORMINV(x, mean, standard_deviation):
|
||
|
|
"""Returns the value of the inverse normal distribution function for a specified value, mean, and standard deviation."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def NORMSDIST(x):
|
||
|
|
"""Returns the value of the standard normal cumulative distribution function for a specified value."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def NORMSINV(x):
|
||
|
|
"""Returns the value of the inverse standard normal distribution function for a specified value."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def PEARSON(data_y, data_x):
|
||
|
|
"""Calculates r, the Pearson product-moment correlation coefficient of a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def PERCENTILE(data, percentile):
|
||
|
|
"""Returns the value at a given percentile of a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def PERCENTRANK(data, value, significant_digits=None):
|
||
|
|
"""Returns the percentage rank (percentile) of a specified value in a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def PERCENTRANK_EXC(data, value, significant_digits=None):
|
||
|
|
"""Returns the percentage rank (percentile) from 0 to 1 exclusive of a specified value in a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def PERCENTRANK_INC(data, value, significant_digits=None):
|
||
|
|
"""Returns the percentage rank (percentile) from 0 to 1 inclusive of a specified value in a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def PERMUT(n, k):
|
||
|
|
"""Returns the number of ways to choose some number of objects from a pool of a given size of objects, considering order."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def POISSON(x, mean, cumulative):
|
||
|
|
"""
|
||
|
|
Returns the value of the Poisson distribution function (or Poisson cumulative distribution
|
||
|
|
function) for a specified value and mean.
|
||
|
|
"""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def PROB(data, probabilities, low_limit, high_limit=None):
|
||
|
|
"""Given a set of values and corresponding probabilities, calculates the probability that a value chosen at random falls between two limits."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def QUARTILE(data, quartile_number):
|
||
|
|
"""Returns a value nearest to a specified quartile of a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def RANK(value, data, is_ascending=None):
|
||
|
|
"""Returns the rank of a specified value in a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def RANK_AVG(value, data, is_ascending=None):
|
||
|
|
"""Returns the rank of a specified value in a dataset. If there is more than one entry of the same value in the dataset, the average rank of the entries will be returned."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def RANK_EQ(value, data, is_ascending=None):
|
||
|
|
"""Returns the rank of a specified value in a dataset. If there is more than one entry of the same value in the dataset, the top rank of the entries will be returned."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def RSQ(data_y, data_x):
|
||
|
|
"""Calculates the square of r, the Pearson product-moment correlation coefficient of a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def SKEW(value1, value2):
|
||
|
|
"""Calculates the skewness of a dataset, which describes the symmetry of that dataset about the mean."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def SLOPE(data_y, data_x):
|
||
|
|
"""Calculates the slope of the line resulting from linear regression of a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def SMALL(data, n):
|
||
|
|
"""Returns the nth smallest element from a data set, where n is user-defined."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def STANDARDIZE(value, mean, standard_deviation):
|
||
|
|
"""Calculates the normalized equivalent of a random variable given mean and standard deviation of the distribution."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
# This should make us all cry a little. Because the sandbox does not do Python3 (which has
|
||
|
|
# statistics package), and because it does not do numpy (because it's native and hasn't been built
|
||
|
|
# for it), we have to implement simple stats functions by hand.
|
||
|
|
# TODO: switch to use the statistics package instead, once we upgrade to Python3.
|
||
|
|
#
|
||
|
|
# The following implementation of stdev is taken from https://stackoverflow.com/a/27758326/328565
|
||
|
|
def _mean(data):
|
||
|
|
return sum(data) / float(len(data))
|
||
|
|
|
||
|
|
def _ss(data):
|
||
|
|
"""Return sum of square deviations of sequence data."""
|
||
|
|
c = _mean(data)
|
||
|
|
return sum((x-c)**2 for x in data)
|
||
|
|
|
||
|
|
def _stddev(data, ddof=0):
|
||
|
|
"""Calculates the population standard deviation
|
||
|
|
by default; specify ddof=1 to compute the sample
|
||
|
|
standard deviation."""
|
||
|
|
n = len(data)
|
||
|
|
ss = _ss(data)
|
||
|
|
pvar = ss/(n-ddof)
|
||
|
|
return pvar**0.5
|
||
|
|
|
||
|
|
# The examples in the doctests below come from https://support.google.com/docs/answer/3094054 and
|
||
|
|
# related articles, which helps ensure correctness and compatibility.
|
||
|
|
def STDEV(value, *more_values):
|
||
|
|
"""
|
||
|
|
Calculates the standard deviation based on a sample, ignoring non-numerical values.
|
||
|
|
|
||
|
|
>>> STDEV([2, 5, 8, 13, 10])
|
||
|
|
4.277849927241488
|
||
|
|
>>> STDEV([2, 5, 8, 13, 10, True, False, "Test"])
|
||
|
|
4.277849927241488
|
||
|
|
>>> STDEV([2, 5, 8, 13, 10], 3, 12, 15)
|
||
|
|
4.810702354423639
|
||
|
|
>>> STDEV([2, 5, 8, 13, 10], [3, 12, 15])
|
||
|
|
4.810702354423639
|
||
|
|
>>> STDEV([5])
|
||
|
|
Traceback (most recent call last):
|
||
|
|
...
|
||
|
|
ZeroDivisionError: float division by zero
|
||
|
|
"""
|
||
|
|
return _stddev(list(_chain_numeric(value, *more_values)), 1)
|
||
|
|
|
||
|
|
def STDEVA(value, *more_values):
|
||
|
|
"""
|
||
|
|
Calculates the standard deviation based on a sample, setting text to the value `0`.
|
||
|
|
|
||
|
|
>>> STDEVA([2, 5, 8, 13, 10])
|
||
|
|
4.277849927241488
|
||
|
|
>>> STDEVA([2, 5, 8, 13, 10, True, False, "Test"])
|
||
|
|
4.969550137731641
|
||
|
|
>>> STDEVA([2, 5, 8, 13, 10], 1, 0, 0)
|
||
|
|
4.969550137731641
|
||
|
|
>>> STDEVA([2, 5, 8, 13, 10], [1, 0, 0])
|
||
|
|
4.969550137731641
|
||
|
|
>>> STDEVA([5])
|
||
|
|
Traceback (most recent call last):
|
||
|
|
...
|
||
|
|
ZeroDivisionError: float division by zero
|
||
|
|
"""
|
||
|
|
return _stddev(list(_chain_numeric_a(value, *more_values)), 1)
|
||
|
|
|
||
|
|
def STDEVP(value, *more_values):
|
||
|
|
"""
|
||
|
|
Calculates the standard deviation based on an entire population, ignoring non-numerical values.
|
||
|
|
|
||
|
|
>>> STDEVP([2, 5, 8, 13, 10])
|
||
|
|
3.8262252939417984
|
||
|
|
>>> STDEVP([2, 5, 8, 13, 10, True, False, "Test"])
|
||
|
|
3.8262252939417984
|
||
|
|
>>> STDEVP([2, 5, 8, 13, 10], 3, 12, 15)
|
||
|
|
4.5
|
||
|
|
>>> STDEVP([2, 5, 8, 13, 10], [3, 12, 15])
|
||
|
|
4.5
|
||
|
|
>>> STDEVP([5])
|
||
|
|
0.0
|
||
|
|
"""
|
||
|
|
return _stddev(list(_chain_numeric(value, *more_values)), 0)
|
||
|
|
|
||
|
|
def STDEVPA(value, *more_values):
|
||
|
|
"""
|
||
|
|
Calculates the standard deviation based on an entire population, setting text to the value `0`.
|
||
|
|
|
||
|
|
>>> STDEVPA([2, 5, 8, 13, 10])
|
||
|
|
3.8262252939417984
|
||
|
|
>>> STDEVPA([2, 5, 8, 13, 10, True, False, "Test"])
|
||
|
|
4.648588495446763
|
||
|
|
>>> STDEVPA([2, 5, 8, 13, 10], 1, 0, 0)
|
||
|
|
4.648588495446763
|
||
|
|
>>> STDEVPA([2, 5, 8, 13, 10], [1, 0, 0])
|
||
|
|
4.648588495446763
|
||
|
|
>>> STDEVPA([5])
|
||
|
|
0.0
|
||
|
|
"""
|
||
|
|
return _stddev(list(_chain_numeric_a(value, *more_values)), 0)
|
||
|
|
|
||
|
|
def STEYX(data_y, data_x):
|
||
|
|
"""Calculates the standard error of the predicted y-value for each x in the regression of a dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def T_INV(probability, degrees_freedom):
|
||
|
|
"""Calculates the negative inverse of the one-tailed TDIST function."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def T_INV_2T(probability, degrees_freedom):
|
||
|
|
"""Calculates the inverse of the two-tailed TDIST function."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def TDIST(x, degrees_freedom, tails):
|
||
|
|
"""Calculates the probability for Student's t-distribution with a given input (x)."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def TINV(probability, degrees_freedom):
|
||
|
|
"""Calculates the inverse of the two-tailed TDIST function."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def TRIMMEAN(data, exclude_proportion):
|
||
|
|
"""Calculates the mean of a dataset excluding some proportion of data from the high and low ends of the dataset."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def TTEST(range1, range2, tails, type):
|
||
|
|
"""Returns the probability associated with t-test. Determines whether two samples are likely to have come from the same two underlying populations that have the same mean."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def VAR(value1, value2):
|
||
|
|
"""Calculates the variance based on a sample."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def VARA(value1, value2):
|
||
|
|
"""Calculates an estimate of variance based on a sample, setting text to the value `0`."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def VARP(value1, value2):
|
||
|
|
"""Calculates the variance based on an entire population."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def VARPA(value1, value2):
|
||
|
|
"""Calculates the variance based on an entire population, setting text to the value `0`."""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def WEIBULL(x, shape, scale, cumulative):
|
||
|
|
"""
|
||
|
|
Returns the value of the Weibull distribution function (or Weibull cumulative distribution
|
||
|
|
function) for a specified shape and scale.
|
||
|
|
"""
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def ZTEST(data, value, standard_deviation):
|
||
|
|
"""Returns the two-tailed P-value of a Z-test with standard distribution."""
|
||
|
|
raise NotImplementedError()
|