stats_visualizing_linePlots.py

#!/usr/bin/env python
# coding: utf-8

# # COURSE: Master statistics and machine learning: Intuition, Math, code
# ##### COURSE URL: udemy.com/course/statsml_x/?couponCode=202006 
# ## SECTION: Visualizing data
# ### VIDEO: Line plots
# #### TEACHER: Mike X Cohen, sincxpress.com
# 

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# import libraries
import matplotlib.pyplot as plt
import numpy as np


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## create data for the plot

# number of data points
n = 1000

# generate log-normal distribution
data1 = np.exp( np.random.randn(n)/2 )
data2 = np.exp( np.random.randn(n)/10 )
data3 = np.exp( np.random.randn(n)/2 + 1 )


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## plots of their histograms

# number of histogram bins
k = 20

plt.hist(data1,bins=k)
plt.hist(data2,bins=k)
plt.hist(data3,bins=k)

plt.show()


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# histogram discretization for the datasets
y1,x1 = np.histogram(data1,bins=k)
xx1 = (x1[0:-1] + x1[1:]) / 2
y1 = y1 / sum(y1) # convert to probability

y2,x2 = np.histogram(data2,bins=k)
xx2 = (x2[0:-1] + x2[1:]) / 2
y2 = y2 / sum(y2) # convert to probability

y3,x3 = np.histogram(data3,bins=k)
xx3 = (x3[0:-1] + x3[1:]) / 2
y3 = y3 / sum(y3) # convert to probability



# show the plots
plt.plot(xx1,y1,'s-',label='data1')
plt.plot(xx2,y2,'o-',label='data2')
plt.plot(xx3,y3,'^-',label='data3')

plt.legend()
plt.xlabel('Value')
plt.ylabel('Probability')
plt.show()