Fitting distribution functions to dataset in Python 3 - python-3.x

Question

I'm trying to find the find the probability distribution that better fits my data. I've tried with the code I've found in different threads, but the results are not what I'm expecting.
The descriptive statistics and histogram for my data are as follows:
Data Histogram
count 865.000000
mean 43.476713
std 12.486362
min 4.075682
25% 34.934609
50% 41.917304
75% 51.271708
max 88.843940
I tried to find a proper distribution function using the following code, but the results are not what I expected.
size = 865
kappa=99
x = scipy.arange(size)
y = scipy.int_(scipy.round_(st.vonmises.rvs(kappa,size=size)*100))
h = plt.hist(df['spreadMaizChicagoAtlantico'],bins=100,color='b')
dist_names = ['gamma', 'beta', 'rayleigh', 'norm', 'pareto']
for dist_name in dist_names:
dist = getattr(scipy.stats, dist_name)
param = dist.fit(y)
pdf_fitted = dist.pdf(x, *param[:-2], loc=param[-2], scale=param[-1]) * size
plt.plot(pdf_fitted, label=dist_name)
plt.xlim(0,100)
plt.legend(loc='upper right')
plt.show()
Data histogram with functions
Can Anyone please tell me what I'm doing wrong and guide me through a better understanding of this solutions.

Answer 1

Thanks to the reply from before I found my mistake.
I got all the values from the DataFrame and made a numpy array.
ser=df.values
Then I ran a similar code from before correcting the fitting of the distribution to the proper data
size = 867
x = scipy.arange(size)
y = scipy.int_(scipy.round_(scipy.stats.vonmises.rvs(5,size=size)*60))
h = plt.hist(ser, bins=range(80))
dist_names = ['beta', 'rayleigh', 'norm']
for dist_name in dist_names:
dist = getattr(scipy.stats, dist_name)
param = dist.fit(ser)
pdf_fitted = dist.pdf(x, *param[:-2], loc=param[-2], scale=param[-1]) * size
plt.plot(pdf_fitted, label=dist_name)
plt.xlim(0,100)
plt.legend(loc='upper right')
plt.show()
The result is as follows, showing the histogram and three probability density functions.

Answer 2

The distfit library can do this job as it searches for the best fit among 89 theoretical distributions.
pip install distfit
import numpy as np
from distfit import distfit
# Example data
X = np.random.normal(10, 3, 2000)
# Initialize
dfit = distfit()
# Search for best theoretical fit on your empirical data
dfit.fit_transform(X)
# The plot function will now also include the predictions of y
dfit.plot(chart='PDF',
emp_properties={'linewidth': 4, 'color': 'k'},
bar_properties={'edgecolor':'k', 'color':'g'},
pdf_properties={'linewidth': 4, 'color': 'r'})