[3.8] bpo-36018: Address more reviewer feedback (GH-15733) (GH-15734)
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 @@
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 @@
"""
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 @@
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 @@
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