I have a histogram (see below) and I am trying to find the mean and standard deviation along with code which fits a curve to my histogram. I think there is something in SciPy or matplotlib that can help, but every example I've tried doesn't work.

```
import matplotlib.pyplot as plt
import numpy as np
with open('gau_b_g_s.csv') as f:
v = np.loadtxt(f, delimiter= ',', dtype="float", skiprows=1, usecols=None)
fig, ax = plt.subplots()
plt.hist(v, bins=500, color='#7F38EC', histtype='step')
plt.title("Gaussian")
plt.axis([-1, 2, 0, 20000])
plt.show()
```

Take a look at this answer for fitting arbitrary curves to data. Basically you can use `scipy.optimize.curve_fit`

to fit any function you want to your data. The code below shows how you can fit a Gaussian to some random data (credit to this SciPy-User mailing list post).

```
import numpy
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
# Define some test data which is close to Gaussian
data = numpy.random.normal(size=10000)
hist, bin_edges = numpy.histogram(data, density=True)
bin_centres = (bin_edges[:-1] + bin_edges[1:])/2
# Define model function to be used to fit to the data above:
def gauss(x, *p):
A, mu, sigma = p
return A*numpy.exp(-(x-mu)**2/(2.*sigma**2))
# p0 is the initial guess for the fitting coefficients (A, mu and sigma above)
p0 = [1., 0., 1.]
coeff, var_matrix = curve_fit(gauss, bin_centres, hist, p0=p0)
# Get the fitted curve
hist_fit = gauss(bin_centres, *coeff)
plt.plot(bin_centres, hist, label='Test data')
plt.plot(bin_centres, hist_fit, label='Fitted data')
# Finally, lets get the fitting parameters, i.e. the mean and standard deviation:
print 'Fitted mean = ', coeff[1]
print 'Fitted standard deviation = ', coeff[2]
plt.show()
```

You can try sklearn gaussian mixture model estimation as below :

```
import numpy as np
import sklearn.mixture
gmm = sklearn.mixture.GMM()
# sample data
a = np.random.randn(1000)
# result
r = gmm.fit(a[:, np.newaxis]) # GMM requires 2D data as of sklearn version 0.16
print("mean : %f, var : %f" % (r.means_[0, 0], r.covars_[0, 0]))
```

Reference : http://scikit-learn.org/stable/modules/mixture.html#mixture

Note that in this way, you don't need to estimate your sample distribution with an histogram.

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