the binomial distribution model deals with finding the probability of success of an event which has only two possible outcomes in a series of experiments. for example, tossing of a coin always gives a head or a tail. the probability of finding exactly 3 heads in tossing a coin repeatedly for 10 times is estimated during the binomial distribution.

we use the seaborn python library which has in-built functions to create such probability distribution graphs. also, the scipy package helps is creating the binomial distribution.

from scipy.stats import binom
import seaborn as sb

binom.rvs(size=10,n=20,p=0.8)

data_binom = binom.rvs(n=20,p=0.8,loc=0,size=1000)
ax = sb.distplot(data_binom,
                  kde=true,
                  color='blue',
                  hist_kws={"linewidth": 25,'alpha':1})
ax.set(xlabel='binomial', ylabel='frequency')

its output is as follows −