python-scipyHow do I fit a Gaussian distribution using Python and SciPy?

To fit a Gaussian distribution using Python and SciPy, you can use the scipy.stats.norm.fit() function. This function takes an array of data samples and returns the mean and standard deviation of the best fitting Gaussian distribution.

Example code

from scipy.stats import norm

# Generate random data
data = np.random.randn(10000)

# Fit a normal distribution to the data
mu, std = norm.fit(data)

print('mu:', mu)
print('std:', std)

Output example

mu: 0.007817331827256067
std: 0.9908788996850786

The code above does the following:

1. Imports the norm function from the scipy.stats module.
2. Generates an array of random data using np.random.randn().
3. Fits a normal distribution to the data using norm.fit().
4. Prints the mean and standard deviation of the best fitting Gaussian distribution.

Helpful links

• scipy.stats.norm.fit() documentation
• numpy.random.randn() documentation

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