`scipy.stats._stats_py:relfreq`

source: /scipy/stats/_stats_py.py :2424

Signature

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

Summary

Return a relative frequency histogram, using the histogram function.

Extended Summary

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

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

frequency : ndarray: Binned values of relative frequency.
lowerlimit : float: Lower real limit.
binsize : float: Width of each bin.
extrapoints : int: Extra points.

Notes

Array API Standard Support

relfreq 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()
a = np.array([2, 4, 1, 2, 3, 2])
res = stats.relfreq(a, numbins=4)

✓

res.frequency
np.sum(res.frequency)  # relative frequencies should add up to 1

✗

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()

✓

Aliases

scipy.stats.relfreq