 # Binomial Distribution in Python ( cdf and pmf )

Jul 1st, 2022 (edited)
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1. import numpy as np
2. import matplotlib.pyplot as pl
3. import seaborn as sn
4. from scipy.stats import binom
5.
6. # A manifactures has 12% defects rate in production. The buyer decides to test 20 random pieces and will accept the supplier if there # are 2 or less defectives. What is the probability of getting accepted?
7.
8. production = np.random.binomial(20,0.12,1000)
9. production
10.
11. sn.countplot(x=production)
12. sn.set_theme(style = 'darkgrid')
13. pl.title("Binomial Probability")
14. pl.xlabel("Number of Defectives")
15. pl.show()
16.
17.
18. exact_two = binom.pmf(2,20,0.12)
19. exact_two
20.
21. P_0_1_2 = binom.cdf(2,20,0.12)
22. P_0_1_2
23.
24. more_than_two = 1 - binom.cdf(2,20,0.12)
25. more_than_two
26.
27. binom.mean(20,0.12)
28.
29. binom.std(20,0.12)
30.
31. binom.var(20,0.12)
32.
33. # Plotting for exact number
34. x_ax = np.arange(0,21)
35. y_ax = binom.pmf(x_ax,20,0.12)
36. sns.barplot(x = x_ax, y = y_ax)
37.
38. # Plotting with cumulative func
39. y_ax2 = binom.cdf(x_ax,20,0.12)
40. sns.barplot(x = x_ax, y= y_ax2)
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