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.

0. import packages

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

1.  Uniform Distribution: Flat

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

2. Normal / Gaussian Distribution: 

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

3.  Exponential PDF / "Power Law"

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

4.  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()

5.  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()