1.2 Probability Distribution Using NumPy .ipynb

Kernel: Python 3 (ipykernel)

a=[1,2,3,4]
type(a)
a+a
a*a
a/a

[1, 2, 3, 4, 1, 2, 3, 4]

import numpy as np
a1=np.array(a)
a1*a1

array([ 1,  4,  9, 16])

a=[[1,2,3]] ,a=[[[]]]

import numpy as np 
import matplotlib.pyplot as plt

# Generate random data from a normal distribution
mu, sigma = 0, 0.1 # mean and standard deviation
data = np.random.normal(mu, sigma, 1000)

# Plot histogram of the generated data
plt.hist(data, bins=30, density=True, alpha=0.5, color='g')
plt.title('Normal Distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show() 

# Generate random data from a uniform distribution
low, high = -1, 1
data_uniform = np.random.uniform(low, high, 1000)

# Plot histogram of the generated data
plt.hist(data_uniform, bins=30, density=True, alpha=0.9, color='y')
plt.title('Uniform Distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Generate random data from an exponential distribution
beta = 1/2  # scale parameter (inverse of the rate parameter)
data_exponential = np.random.exponential(beta, 1000)

# Plot histogram of the generated data
plt.hist(data_exponential, bins=30, density=True, alpha=0.7, color='m')
plt.title('Exponential Distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()