# scipy.stats.relfreq#

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

Return a relative frequency histogram, using the histogram function.

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

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:
frequencyndarray

Binned values of relative 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()
>>> a = np.array([2, 4, 1, 2, 3, 2])
>>> res = stats.relfreq(a, numbins=4)
>>> res.frequency
array([ 0.16666667, 0.5       , 0.16666667,  0.16666667])
>>> np.sum(res.frequency)  # relative frequencies should add up to 1
1.0
```

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()
```