Write me a python code to use weibull distribution. To fit a group of data and calculate the quantile values

Sure, here's an example Python code using the `scipy.stats` module to fit a group of data to a Weibull distribution and calculate the quantile values:

import numpy as np
from scipy.stats import weibull_min

# generate some sample data
data = weibull_min.rvs(1.5, size=1000)

# fit the data to a Weibull distribution
shape, loc, scale = weibull_min.fit(data)

# calculate the 90th percentile (i.e. the value below which 90% of the data falls)
pct = 0.9
q = weibull_min.ppf(pct, shape, loc, scale)

print("Shape parameter:", shape)
print("Location parameter:", loc)
print("Scale parameter:", scale)
print("90th percentile:", q)

In this example, we first generate a sample of 1000 data points from a Weibull distribution with shape parameter 1.5. We then fit these data to a Weibull distribution using the `weibull_min.fit()` function, which returns the estimated shape, location, and scale parameters of the distribution. Finally, we calculate the 90th percentile of the distribution using the `weibull_min.ppf()` function, which takes as input the desired percentile (in this case, 0.9), as well as the shape, location, and scale parameters of the distribution.