# scipy.stats.relfreq¶

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

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

Examples

>>> import matplotlib.pyplot as plt
>>> from scipy import stats
>>> 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

>>> rng = np.random.RandomState(seed=12345)
>>> 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()


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