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. 
 
 - 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() 