scipy.stats.cumfreq

scipy.stats.cumfreq(a, numbins=10, defaultreallimits=None, weights=None)

Return a cumulative frequency histogram, using the histogram function.

A cumulative histogram is a mapping that counts the cumulative number of observations in all of the bins up to the specified bin.

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

cumcountndarray

Binned values of cumulative 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()
>>> x = [1, 4, 2, 1, 3, 1]
>>> res = stats.cumfreq(x, numbins=4, defaultreallimits=(1.5, 5))
>>> res.cumcount
array([ 1.,  2.,  3.,  3.])
>>> res.extrapoints
3

Create a normal distribution with 1000 random values

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

Calculate cumulative frequencies

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

Calculate space of values for x

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

Plot histogram and cumulative histogram

>>> fig = plt.figure(figsize=(10, 4))
>>> ax1 = fig.add_subplot(1, 2, 1)
>>> ax2 = fig.add_subplot(1, 2, 2)
>>> ax1.hist(samples, bins=25)
>>> ax1.set_title('Histogram')
>>> ax2.bar(x, res.cumcount, width=res.binsize)
>>> ax2.set_title('Cumulative histogram')
>>> ax2.set_xlim([x.min(), x.max()])

>>> plt.show()