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)
Binom_info = binom.rvs(n=20,p=0.8,loc=0,size=1000)
ax = sb.distplot(Binom_info,
kde=True,
color='blue',
hist_kws={"linewidth": 25,'alpha':1})
ax.set(xlabel='Binomial', ylabel='Frequency')Its output is as follows −