Distributions

This can be visually with the help of seaborn and numpy random.

Look at the following sample code:


from numpy import random
import matplotlib.pyplot as plt
import seaborn as sns

# normal Distribuition
sns.distplot(random.normal(size=1000), hist=False)
plt.title(' Normal Distribuition')
plt.savefig('nd.jpg')
plt.show()

#binomial
sns.distplot(random.binomial(n=10, p=0.5, size=100), hist=True, kde=False)
plt.title(' Binomial Distribuition')
plt.savefig('bnd.jpg')
plt.show()


# poisson distribuition
sns.distplot(random.poisson(lam=2, size=100), kde=False)
plt.title('Poisson Distribuition')
plt.savefig('pois.jpg')
plt.show()


# All together
sns.distplot(random.normal(loc=50, scale=5, size=100), hist=False, label='normal')
sns.distplot(random.binomial(n=100, p=0.5, size=100), hist=False, label='binomial')
plt.title('Comparisosn between Normal and Binomial Distribuition')
plt.savefig('n-bnd.jpg')
plt.show()

sns.distplot(random.poisson(lam=2, size=100), kde=False)
plt.title('Poisson Distribuition')
plt.savefig('poisson.jpg')
plt.show()


sns.distplot(random.uniform(size=100), hist=False)
plt.title('Uniform Distribuition')
plt.savefig('uniform.jpg')
plt.show()

sns.distplot(random.logistic(size=1000), hist=False)
plt.title('Logistic Distribuition ')
plt.savefig('logistic.jpg')
plt.show()

sns.distplot(random.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6]))
plt.title('Multimodal Distribuition ')
plt.savefig('multimodal.jpg')
plt.show()


sns.distplot(random.exponential(size=1000), hist=False)
plt.title('Exponential Distribuition ')
plt.savefig('exponential.jpg')
plt.show()


sns.distplot(random.chisquare(df=1, size=1000), hist=False)
plt.title('Chisquare Distribuition ')
plt.savefig('chisquare.jpg')
plt.show()

sns.distplot(random.rayleigh(size=1000), hist=False)
plt.title('Rayleigh Distribuition')
plt.savefig('Rayleigh.jpg')
plt.show()

sns.distplot(random.pareto(a=2, size=1000), kde=False)
plt.title('Pareto Distribuition')
plt.savefig('Pareto.jpg')
plt.show()


x = random.zipf(a=2, size=1000)
sns.distplot(x[x<10], kde=False)
plt.title('Zipf Distribuition')
plt.savefig('zipf.jpg')
plt.show()