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

Similar codes

1 vote187 views1 answer
UnavailableInvalidChannel error in conda
Python
3 votes211 views2 answers
Variance calculation python manually
Python
0 vote182 views1 answer
Dict to attr python
Python
55 votes169 views4 answers
List len python
Python
0 vote178 views1 answer
Generic rectangle
Python
3 votes190 views3 answers
Dot product python
Python
44 votes187 views6 answers
Python ascii
Python
2 votes199 views1 answer
Python unchain list
Python
0 vote193 views1 answer
Pandas set one column equal to another
Python
5 votes175 views2 answers
Python fetch url
Python