import matplotlib.pyplot as plt import pandas as pd import numpy as np import seaborn plt.style.use('seaborn') from numpy import random

data = np.random.randint(1, 7, size=(1, 100000)) data=data[0] seaborn.histplot(pd.DataFrame(data), discrete=True, shrink=0.9, legend=False, stat='probability');

seaborn.ecdfplot(data=data);

data = random.uniform(low=0.0, high=1.0, size=100000) pd.DataFrame(data).hist(bins=10);

seaborn.ecdfplot(data=data);

lançamentos_1 = np.random.randint(1, 7, size=(1, 10000))[0] lançamentos_1

n, p = 1, .5 # number of trials, probability of each trial s = np.random.binomial(n, p, 1000) pd.DataFrame(s).hist();

pd.DataFrame(random.normal(loc=0.0, scale=1.0, size=100000)).hist(bins=100);

seaborn.ecdfplot(data=data);

data = np.random.randint(1, 7, size=(2, 100000)) data = data[0] + data[1] seaborn.histplot(pd.DataFrame(data), discrete=True, shrink=0.9, legend=False, stat='probability');