Python_data_science_第二课

####我主要看的书叫 Hands-On Data Science and Python Machine Learning. 作者Frank kane

今天是PMF 和PDF, 其实就是Data Distribution. 单纯看代码比较不直观，但是一用matplotlib 把图绘出来，就会变的非常直观。

Terminology difference: A probability density function is a solid curve that describes the probability of a range of values happening with continuous data. A probability mass function is the probabilities of given discrete values occurring in a dataset

    PDF=Probability Density Function
    PMF=Probility Mass function.

1. import packages

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

2. Uniform Distribution: Flat

    value= np.random.uniform(-10.0, 10.0, 100000)
    plt.hist(value, 50)
    plt.show()
   

3. Normal / Gaussian Distribution:

    from scipy.stats import norm
    x= np.arange(-3, 3, 0.001)
    plt.plot(x, norm.pdf(x))
   

4. Exponential PDF / “Power Law”

   from scipy.stats import expon
   x=np.arange(0, 10, 0.001)
   plt.plot(x, expon.pdf(x))
   plt.show()
   

5. Binomial Probability Mass function.

    from scipy.stats import binom
    x=np.arange(0, 10, 0.001)
    plt.plot(x, binom.pmf(x, 10, 0.5))
    plt.show()
   

6. Poisson Probability Mass Function:

   from scipy.stats import poisson
   mu=500
   x=np.arange(400, 600, 0.5)
   plt.plot(x, poisson.pmf(x, mu))
   plt.show()