`scipy.stats._stats_py:cumfreq`

source: /scipy/stats/_stats_py.py :2341

Signature

   def     cumfreq (    a    ,    numbins    =  10   ,    defaultreallimits    =  None   ,    weights    =  None     )

Summary

Return a cumulative frequency histogram, using the histogram function.

Extended Summary

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

Parameters

a : array_like: Input array.
numbins : int, optional: The number of bins to use for the histogram. Default is 10.
defaultreallimits : tuple (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).
weights : array_like, optional: The weights for each value in a. Default is None, which gives each value a weight of 1.0

Returns

cumcount : ndarray: Binned values of cumulative frequency.
lowerlimit : float: Lower real limit
binsize : float: Width of each bin.
extrapoints : int: 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 dev-arrayapi 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

✗

res.extrapoints

✓

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 + 1)

✓

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[:-1], res.cumcount, width=res.binsize, align='edge')
ax2.set_title('Cumulative histogram')
ax2.set_xlim([x.min(), x.max()])

✗

plt.show()

✓

Aliases

scipy.stats.cumfreq