This is data science different topic explanation conversation series.
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Data Science different topic’s explanation – Part11 – Poisson Distribution
from scipy.stats import poisson
import seaborn as sb
import numpy as np
import matplotlib.pyplot as plt
def poisson_dist(): > None
plt.figure(figsize=(15,15))
data_binom = poisson.rvs(mu=4, size=10000)
ax = sb.distplot(data_binom, kde=True, color='b',
bins=np.arange(data_binom.min(), data_binom.max() + 1),
kde_kws={'color': 'r', 'lw': 3, 'label': 'KDE'})
ax.set(xlabel='Poisson', ylabel='Frequency')
poisson_dist()
The Poisson distribution is obtained as a limiting case of the Bernoulli distribution, if we push p
to zero and n
to infinity, but so that their product remains constant: n * p  a
. Formally, such transition leads to the formula

x
is a random variable (the number of occurrences of event A);  λ is the event rate (average number of events in an interval) so called the rate parameter. It is also equal to mean and variance.