scipy.stats.cumfreq#

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

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 numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy import stats
>>> rng = np.random.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()