Cdf empírica python

# fit an empirical cdf to a bimodal dataset
from matplotlib import pyplot
from numpy.random import normal
from numpy import hstack
from statsmodels.distributions.empirical_distribution import ECDF
# generate a sample
sample1 = normal(loc=20, scale=5, size=300)
sample2 = normal(loc=40, scale=5, size=700)
sample = hstack((sample1, sample2))
# fit a cdf
ecdf = ECDF(sample)
# get cumulative probability for values
print('P(x<20): %.3f' % ecdf(20))
print('P(x<40): %.3f' % ecdf(40))
print('P(x<60): %.3f' % ecdf(60))
# plot the cdf
pyplot.plot(ecdf.x, ecdf.y)
pyplot.show()

Source: machinelearningmastery.com

Cdf empírica python

# example of a bimodal data sample
from matplotlib import pyplot
from numpy.random import normal
from numpy import hstack
# generate a sample
sample1 = normal(loc=20, scale=5, size=300)
sample2 = normal(loc=40, scale=5, size=700)
sample = hstack((sample1, sample2))
# plot the histogram
pyplot.hist(sample, bins=50)
pyplot.show()

Source: machinelearningmastery.com

Cdf empírica python

...
# fit a cdf
ecdf = ECDF(sample)

Source: machinelearningmastery.com

Cdf empírica python

...
# get cumulative probability for values
print('P(x<20): %.3f' % ecdf(20))
print('P(x<40): %.3f' % ecdf(40))
print('P(x<60): %.3f' % ecdf(60))

Source: machinelearningmastery.com

Cdf empírica python

P(x<20): 0.149
P(x<40): 0.654
P(x<60): 1.000

Source: machinelearningmastery.com

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