diff options
Diffstat (limited to 'Lib/statistics.py')
| -rw-r--r-- | Lib/statistics.py | 32 |
1 files changed, 27 insertions, 5 deletions
diff --git a/Lib/statistics.py b/Lib/statistics.py index 4b17266..70c48d6 100644 --- a/Lib/statistics.py +++ b/Lib/statistics.py @@ -624,9 +624,8 @@ def quantiles(data, /, *, n=4, method='exclusive'): Set *n* to 100 for percentiles which gives the 99 cuts points that separate *data* in to 100 equal sized groups. - The *data* can be any iterable containing sample data or it can be - an instance of a class that defines an inv_cdf() method. For sample - data, the cut points are linearly interpolated between data points. + The *data* can be any iterable containing sample. + The cut points are linearly interpolated between data points. If *method* is set to *inclusive*, *data* is treated as population data. The minimum value is treated as the 0th percentile and the @@ -634,8 +633,6 @@ def quantiles(data, /, *, n=4, method='exclusive'): """ if n < 1: raise StatisticsError('n must be at least 1') - if hasattr(data, 'inv_cdf'): - return [data.inv_cdf(i / n) for i in range(1, n)] data = sorted(data) ld = len(data) if ld < 2: @@ -955,6 +952,17 @@ class NormalDist: raise StatisticsError('cdf() not defined when sigma at or below zero') return _normal_dist_inv_cdf(p, self._mu, self._sigma) + def quantiles(self, n=4): + """Divide into *n* continuous intervals with equal probability. + + Returns a list of (n - 1) cut points separating the intervals. + + Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles. + Set *n* to 100 for percentiles which gives the 99 cuts points that + separate the normal distribution in to 100 equal sized groups. + """ + return [self.inv_cdf(i / n) for i in range(1, n)] + def overlap(self, other): """Compute the overlapping coefficient (OVL) between two normal distributions. @@ -995,6 +1003,20 @@ class NormalDist: return self._mu @property + def median(self): + "Return the median of the normal distribution" + return self._mu + + @property + def mode(self): + """Return the mode of the normal distribution + + The mode is the value x where which the probability density + function (pdf) takes its maximum value. + """ + return self._mu + + @property def stdev(self): "Standard deviation of the normal distribution." return self._sigma |