from scipy.stats import skewnorm
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('ggplot')
fig, ax = plt.subplots(facecolor="w", figsize=(10, 10))

skews = [-4, 0, 4]

for i, skew in enumerate(skews):
    plt.subplot(3, 1, i+1)
    x = np.linspace(skewnorm.ppf(0.01, skew),
                    skewnorm.ppf(0.99, skew), 100)
    plt.plot(x, skewnorm.pdf(x, skew), lw=5, alpha=0.5, label=f'norm dist (skew={skew})')
    r = skewnorm.rvs(skew, size=1000)
    plt.hist(r, density=True, histtype='stepfilled', alpha=0.2)
    plt.legend(loc='best', frameon=False)
plt.show()

import matplotlib.pyplot as plt
import scipy.stats as stats
from scipy.stats import kurtosis

plt.style.use('ggplot')
fig, ax = plt.subplots(facecolor="w", figsize=(10, 5))

x = np.linspace(-5, 5, 100)
ax = plt.subplot()
distnames = ['laplace', 'norm', 'uniform']

for distname in distnames:
    if distname == 'uniform':
        dist = getattr(stats, distname)(loc=-2, scale=4)
    else:
        dist = getattr(stats, distname)
    data = dist.rvs(size=1000)
    kur = kurtosis(data, fisher=True)
    y = dist.pdf(x)
    ax.plot(x, y, lw=5, alpha=0.5, label="{} dist (kurtosis={})".format(distname, round(kur, 3)))
    ax.legend()