perhaps the most easy way of plotting the cumilative distribution function in python:
import numpy as np
import statsmodels.api as sm # recommended import according to the docs
import matplotlib.pyplot as plt
sample = np.random.uniform(0, 1, 50)
sample=[1,2,2,3,2,3,3,3,3,3,3,4,4,4,4,4,60,3,3,3,10]
ecdf = sm.distributions.ECDF(sample)
dsum = sum(sample);
normalized_data = sample/dsum;
x = np.linspace(min(sample), max(sample),10000)
print x
y = ecdf(x)
plt.step(x, y)
plt.show()
Here is the plot:
import numpy as np
import statsmodels.api as sm # recommended import according to the docs
import matplotlib.pyplot as plt
sample = np.random.uniform(0, 1, 50)
sample=[1,2,2,3,2,3,3,3,3,3,3,4,4,4,4,4,60,3,3,3,10]
ecdf = sm.distributions.ECDF(sample)
dsum = sum(sample);
normalized_data = sample/dsum;
x = np.linspace(min(sample), max(sample),10000)
print x
y = ecdf(x)
plt.step(x, y)
plt.show()
Here is the plot:
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