from IPython.display import Image
from IPython.core.display import HTML 
Image(url= "https://upload.wikimedia.org/wikipedia/commons/b/ba/Data_visualization_process_v1.png" 
      ,height=400, width=550)


"""
Explanation: Visualization of Data
Visual communication of results from analysis.
The story told by the images should enhance the understanding of the viewer versus viewing raw data or equations.
Visual representation of data:
"information that has been abstracted in some schematic form, including attributes or variables for the units of information"

Michael Friendly (2008). <a href="http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf">
"Milestones in the history of thematic cartography, statistical graphics, and data visualization".</a>

Data visualization is both an art and a science.  The are many good and bad examples of what should be done.
End of explanation
"""


%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.plot(np.arange(10))

#Plots in matplotlib reside in a figure object. Create a new figure object with 
plt.figure()

fig = plt.figure()
fig.clear

ax1 = fig.add_subplot(2,2,1)
ax2 = fig.add_subplot(2,2,2)
ax3 = fig.add_subplot(2,2,3)
#Empty Matplotlib figure with three subplots
fig

plt.plot(randn(50).cumsum(),'k--')

"""
Explanation: The are a number of packages that we could use to present the data.
In this case we will be exploring the use of matplotlib. Use '%matplotlib inline' line in your ipython notebook to activate the inline viewing of the plots
End of explanation
"""


_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)
ax2.scatter(np.arange(30), np.arange(30) +3 * randn(30))

fig

"""
Explanation: k-- is a style option for black dashed lines
End of explanation
"""


