SciPy skewnormal fitting

Question

I am trying to fit data into a skew normal distribution using the SciPy Skewnorm package.

However, I am failing to understand the usage properly as I cannot find proper documentation or examples on this matter.

On the help section I found Documentation and trying to use skewnorm.fit() along with skewnorm.pdf() to fit data into a model and use that model to output a distribution and compare with the original data.

Please let me know if anyone can help with this.

from scipy import stats
import matplotlib.pyplot as plt
import numpy as np

# choose some parameters
a, loc, scale = 5.3, -0.1, 2.2
# draw a sample
data = stats.skewnorm(a, loc, scale).rvs(1000)
# estimate parameters from sample
ae, loce, scalee = stats.skewnorm.fit(data)
# Plot the PDF.
plt.figure()
plt.hist(data, bins=100, normed=True, alpha=0.6, color='g')
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = stats.skewnorm.pdf(x,ae, loce, scalee)#.rvs(100)
plt.plot(x, p, 'k', linewidth=2)

Output:

Answer 1

Here is an example to get you started.

>>> from scipy import stats

# choose some parameters
>>> a, loc, scale = 1.3, -0.1, 2.2
# draw a sample
>>> sample = stats.skewnorm(a, loc, scale).rvs(1000)

# estimate parameters from sample
>>> ae, loce, scalee = stats.skewnorm.fit(sample)
>>> ae
1.2495366661560348
>>> loce
-0.039775813819310835
>>> scalee
2.1126121580965536