Hướng dẫn ecdf python

We can create the ECDF with

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Nội dung chính

• Python: inverse empirical cumulative distribution function [ECDF]?
• Some Code Answers
• More Answers Related Python Inverse Empirical Cumulative Distribution Function Ecdf
• How to Use an Empirical Distribution Function in Python
• Python Empirical distribution function [ecdf] …
• Empirical cumulative distribution plots in Python
• Empirical Cumulative Distribution Function [ECDF] in Python
• How to Generate ECDF Plot using Python and R - Medium
• Calculate ECDF in Python | Codementor
• Using an Empirical Distribution Function in Python
• Empirical cumulative distribution function [ECDF] in Python
• statsmodels.distributions.empirical_distribution.ECDF

import numpy as np
from statsmodels.distributions.empirical_distribution import ECDF
ecdf = ECDF[[3, 3, 1, 4]]

and obtain then ECDF at point with

ecdf[x]

However, what if I want to know the x for percentile 97.5% ?

From //www.statsmodels.org/stable/generated/statsmodels.distributions.empirical_distribution.ECDF.html?highlight=ecdf, it seems like not been implemented.

Is there any way to do this? Or any other libraries?

asked May 23, 2017 at 10:42

5

Since the empirical CDF just places mass of 1/n at each data point, the 97.5th quantile is just the data point that is bigger than 97.5% of all the other points. To find this value, you can simply sort the data in ascending order and find the 0.975n-th largest value.

sample = [1, 5, 2, 10, -19, 4, 7, 2, 0, -1]
n = len[sample]
sort = sorted[sample]
print sort[int[n * 0.975]]

Which produces:

10

Since we remember than for discrete distributions [like the empirical cdf], the quantile function is defined here , we realize that we have to take the 0.975n-th [rounded up] largest value.

MattR

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answered Aug 28, 2017 at 3:32

This is my suggestion. Linear interpolation because dfs are only effectively estimated from fairly large samples anyway. The interpolating line segments can be obtained because their endpoints occur at distinct values in the sample.

import statsmodels.distributions.empirical_distribution as edf
from scipy.interpolate import interp1d
import numpy as np
import matplotlib.pyplot as plt

sample = [1,4,2,6,5,5,3,3,5,7]
sample_edf = edf.ECDF[sample]

slope_changes = sorted[set[sample]]

sample_edf_values_at_slope_changes = [ sample_edf[item] for item in slope_changes]
inverted_edf = interp1d[sample_edf_values_at_slope_changes, slope_changes]

x = np.linspace[0.1, 1]
y = inverted_edf[x]
plt.plot[x, y, 'ro', x, y, 'b-']
plt.show[]

print ['97.5 percentile:', inverted_edf[0.975]]

It produces the following output,

97.5 percentile: 6.75

and this graph.

answered May 24, 2017 at 15:46

Bill BellBill Bell

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Python: inverse empirical cumulative distribution function [ECDF]?

Tags: python , numpy , statsmodels Answers: | Viewed 8,654 times

We can create the ECDF with

import numpy as np
 from statsmodels.distributions.empirical_distribution import ECDF
 ecdf = ECDF[[3, 3, 1, 4]]
 

and obtain then ECDF at point with

ecdf[x]
 

However, what if I want to know the x for percentile 97.5% ?

From //www.statsmodels.org/stable/generated/statsmodels.distributions.empirical_distribution.ECDF.html?highlight=ecdf, it seems like not been implemented.

Is there any way to do this? Or any other libraries?

Some Code Answers

import numpy as np from statsmodels.distributions.empirical_distribution import ECDF ecdf = ECDF[[3, 3, 1, 4]] 

sample = [1, 5, 2, 10, -19, 4, 7, 2, 0, -1] n = len[sample] sort = sorted[sample] print sort[int[n * 0.975]] 

import statsmodels.distributions.empirical_distribution as edf from scipy.interpolate import interp1d import numpy as np import matplotlib.pyplot as plt  sample = [1,4,2,6,5,5,3,3,5,7] sample_edf = edf.ECDF[sample]  slope_changes = sorted[set[sample]]  sample_edf_values_at_slope_changes = [ sample_edf[item] for item in slope_changes] inverted_edf = interp1d[sample_edf_values_at_slope_changes, slope_changes]  x = np.linspace[0.1, 1] y = inverted_edf[x] plt.plot[x, y, 'ro', x, y, 'b-'] plt.show[]  print ['97.5 percentile:', inverted_edf[0.975]] 

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