human
I am working on the file "08-dados_alunos.ipynb".
The first task is:
Generalized Extreme Value distribution
Can you write Python program for it?

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Give me a the Python program you requested?
assistant
#Here is the Python program you requested.

import wafo.kdetools as wk

fig, ax = plt.subplots(figsize=(9, 9))

wk.TKDE(df['Hsig'], L2=0.5)(output='plot').plot('c--')
gev.plotepdf(symb1='g-')
wei.plotepdf(symb1='r-')

from scipy import stats
import matplotlib.pyplot as plt

def make_plot():
    fig, ax = plt.subplots(figsize=(9, 9))
    ax.hist(df['Hsig'], bins=10, normed=True, alpha=0.5);

    x = np.linspace(df['Hsig'].min(), df['Hsig'].max(), 1000)

    # Exponential Weibull
    params = stats.exponweib.fit(df['Hsig'])
    weib = stats.exponweib.pdf(x, *params)
    ax.plot(x, weib, label='exponweib')

    # Weibull minimum (Frechet right)
    params = stats.weibull_min.fit(df['Hsig'])
    weib = stats.weibull_min.pdf(x, *params)
    ax.plot(x, weib, label='weibull_min')

    # By hand!
    def weib(x, c):
        return c*x**(c-1)*np.exp(-x**c)
    c, loc, scale = params
    y = (x-loc) / scale
    w = weib(y, c) / scale
    ax.plot(x, w, '--', label='Manual')

    leg = ax.legend()

make_plot()