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.

Notes

Array API Standard Support

cumfreq has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	⛔
PyTorch	⛔	⛔
JAX	⛔	⛔
Dask	⛔	n/a

See Support for the array API standard for more information.

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()