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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Examples of Data Distributions
Uniform Distribution
Step1:  Normal / Gaussian
Visualize the probability density function
Step2:  Generate some random numbers with a normal distribution. "mu" is the desired mean, "sigma" is the standard deviation
Step3:  Exponential PDF / "Power Law"
Step4:  Binomial Probability Mass Function
Step5:  Poisson Probability Mass Function
Example
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Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
values = np.random.uniform(-10.0, 10.0, 100000)
plt.hist(values, 50)
plt.show()
Explanation: Examples of Data Distributions
Uniform Distribution
End of explanation
from scipy.stats import norm
import matplotlib.pyplot as plt
x = np.arange(-3, 3, 0.001)
plt.plot(x, norm.pdf(x))
Explanation: Normal / Gaussian
Visualize the probability density function:
End of explanation
import numpy as np
import matplotlib.pyplot as plt
mu = 5.0
sigma = 2.0
values = np.random.normal(mu, sigma, 10000)
plt.hist(values, 50)
plt.show()
Explanation: Generate some random numbers with a normal distribution. "mu" is the desired mean, "sigma" is the standard deviation:
End of explanation
from scipy.stats import expon
import matplotlib.pyplot as plt
x = np.arange(0, 10, 0.001)
plt.plot(x, expon.pdf(x))
Explanation: Exponential PDF / "Power Law"
End of explanation
from scipy.stats import binom
import matplotlib.pyplot as plt
n, p = 10, 0.5
x = np.arange(0, 10, 0.001)
plt.plot(x, binom.pmf(x, n, p))
Explanation: Binomial Probability Mass Function
End of explanation
from scipy.stats import poisson
import matplotlib.pyplot as plt
mu = 500
x = np.arange(400, 600, 0.5)
plt.plot(x, poisson.pmf(x, mu))
Explanation: Poisson Probability Mass Function
Example: My website gets on average 500 visits per day. What's the odds of getting 550?
End of explanation
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