scipy.stats.relfreq#

scipy.stats.relfreq(a, numbins=10, defaultreallimits=None, weights=None)[source]#

Return a relative frequency histogram, using the histogram function.

A relative frequency histogram is a mapping of the number of observations in each of the bins relative to the total of observations.

Parameters

aarray_like: Input array.
numbinsint, optional: The number of bins to use for the histogram. Default is 10.
defaultreallimitstuple (lower, upper), optional: The lower and upper values for the range of the histogram. If no value is given, a range slightly larger than the range of the values in a is used. Specifically (a.min() - s, a.max() + s), where s = (1/2)(a.max() - a.min()) / (numbins - 1).
weightsarray_like, optional: The weights for each value in a. Default is None, which gives each value a weight of 1.0

Returns

frequencyndarray: Binned values of relative frequency.
lowerlimitfloat: Lower real limit.
binsizefloat: Width of each bin.
extrapointsint: Extra points.

Examples

>>> import matplotlib.pyplot as plt
>>> from numpy.random import default_rng
>>> from scipy import stats
>>> rng = default_rng()
>>> a = np.array([2, 4, 1, 2, 3, 2])
>>> res = stats.relfreq(a, numbins=4)
>>> res.frequency
array([ 0.16666667, 0.5       , 0.16666667,  0.16666667])
>>> np.sum(res.frequency)  # relative frequencies should add up to 1
1.0

Create a normal distribution with 1000 random values

>>> samples = stats.norm.rvs(size=1000, random_state=rng)

Calculate relative frequencies

>>> res = stats.relfreq(samples, numbins=25)

Calculate space of values for x

>>> x = res.lowerlimit + np.linspace(0, res.binsize*res.frequency.size,
...                                  res.frequency.size)

Plot relative frequency histogram

>>> fig = plt.figure(figsize=(5, 4))
>>> ax = fig.add_subplot(1, 1, 1)
>>> ax.bar(x, res.frequency, width=res.binsize)
>>> ax.set_title('Relative frequency histogram')
>>> ax.set_xlim([x.min(), x.max()])

>>> plt.show()