Normal/Gaussian in datascience in python

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

x=np.arange(-3,3,0.001)
plt.plot(x,norm.pdf(x))

output:

import matplotlib.pyplot as plt
import numpy as np

mu= 5.0
sigma = 2.0

values = np.random.normal(mu,sigma,10000)
#10000 is data points
plt.hist(values,50)
#50 is number of bars

plt.show()

output:

uniform distribution in datascience uisng python

import numpy as np
import matplotlib.pyplot as plt

values=np.random.uniform(-10.0,10.0,10000)

plt.hist(values,50)
plt.show()

output:

standard deviation in datascience using python matplot lib

import numpy as np
import matplotlib.pyplot as plt

income= np.random.normal(100.0,20.0,10000)

plt.hist(income,50)
plt.show()

output:

income.std()

19.99095786583713

import numpy as np 
import matplotlib.pyplot as plt

income= np.random.normal(100.0,30.0,10000)

plt.hist(income,50)
plt.show()

Mean ,Median and Mode in datascience using python programming

Example1:
import numpy as np
income= np.random.normal(27000,15000,10000)
np.mean(income)

output:

26908.89269950194

%matplotlib inline
import matplotlib.pyplot as plt
plt.hist(income,50)
plt.show()

np.median(income)

output:

26735.667830776893

Mode:
ages=np.random.randint(18,high=90,size=500)
ages




output:

array([62, 66, 21, 39, 74, 56, 20, 35, 29, 37, 53, 88, 39, 54, 45, 34, 22,
       29, 46, 82, 38, 29, 36, 56, 87, 35, 39, 42, 67, 50, 60, 58, 64, 19,
       80, 18, 22, 84, 56, 65, 60, 38, 77, 21, 45, 88, 45, 49, 71, 72, 31,
       77, 41, 41, 27, 50, 51, 81, 29, 22, 41, 71, 78, 69, 41, 35, 43, 21,
       81, 80, 31, 73, 46, 44, 61, 78, 39, 72, 50, 28, 60, 19, 66, 73, 40,
       77, 42, 43, 80, 81, 86, 87, 37, 61, 53, 28, 74, 55, 53, 35, 35, 29,
       49, 82, 75, 19, 57, 53, 19, 55, 34, 45, 80, 62, 43, 56, 86, 69, 33,
       70, 52, 49, 32, 77, 73, 18, 50, 51, 73, 34, 50, 89, 48, 28, 35, 64,
       64, 35, 69, 53, 62, 63, 38, 80, 51, 67, 53, 72, 68, 50, 36, 80, 19,
       74, 39, 59, 53, 83, 82, 70, 63, 78, 25, 59, 22, 47, 39, 36, 60, 35,
       47, 87, 69, 54, 28, 51, 80, 43, 68, 61, 79, 61, 63, 36, 82, 42, 88,
       66, 71, 73, 27, 50, 68, 20, 74, 50, 55, 86, 87, 72, 76, 79, 76, 43,
       74, 19, 27, 60, 40, 61, 82, 26, 52, 62, 32, 20, 20, 25, 20, 84, 83,
       54, 56, 74, 68, 83, 68, 38, 86, 25, 26, 81, 58, 57, 68, 58, 20, 71,
       28, 51, 22, 63, 51, 19, 89, 89, 37, 46, 27, 77, 78, 83, 70, 38, 39,
       67, 18, 52, 85, 37, 31, 27, 85, 81, 86, 59, 49, 22, 26, 44, 32, 58,
       63, 21, 60, 35, 70, 39, 54, 52, 33, 18, 67, 44, 74, 31, 22, 60, 78,
       27, 68, 40, 59, 53, 20, 21, 26, 32, 86, 82, 54, 61, 64, 27, 64, 26,
       51, 55, 70, 30, 18, 40, 31, 44, 40, 64, 73, 89, 75, 39, 20, 85, 20,
       68, 29, 37, 83, 23, 28, 51, 82, 23, 26, 39, 36, 41, 57, 76, 27, 89,
       23, 42, 25, 44, 44, 41, 64, 32, 24, 27, 68, 52, 39, 19, 76, 40, 87,
       68, 66, 30, 53, 54, 32, 63, 28, 85, 36, 87, 66, 59, 80, 88, 53, 66,
       58, 40, 69, 53, 59, 74, 64, 71, 58, 69, 74, 37, 88, 31, 72, 66, 34,
       49, 80, 71, 75, 41, 40, 89, 49, 63, 86, 78, 34, 68, 21, 65, 61, 73,
       49, 35, 84, 23, 79, 64, 79, 65, 54, 75, 25, 82, 22, 73, 89, 58, 66,
       76, 53, 29, 27, 32, 33, 57, 81, 31, 43, 76, 46, 38, 47, 49, 61, 42,
       49, 28, 50, 54, 49, 22, 81, 81, 85, 55, 51, 20, 42, 52, 68, 47, 62,
       29, 75, 55, 55, 57, 21, 43, 52, 57, 47, 63, 46, 38, 73, 46, 72, 54,
       49, 54, 39, 63, 42, 59, 70, 81, 55, 74, 62, 71, 23, 26, 21, 25, 85,
       50, 38, 78, 68, 24, 38, 50])