Data Visualization

Component of effective Visualization:

• Visual cues
• Position
• Length
• Angle

• Visual Encoding
• Direction
• Shape
• Area
• Volume
• Color
• Hue
• Saturation

• Coordinate systems

• Scale / Data Types
• Numeric
• Categorical
• Time Series

• Context

Visual Cues Accuracy:

Python Plotting Package:

• MatPlotLib
• GGPloyt ( Grammar of Graphics)  
•  ggplot ( data,  aes (xVar, yVar)) + geom _point(color='red') + geom_line(color='blue') + xlab ('x axis label') + ylab ( 'y axis label')
• print ggplot ( data,  aes (xVar, yVar))

Plotting in Python

from pandas import *
from ggplot import *

import pandas

def lineplot(hr_year_csv):
    # A csv file will be passed in as an argument which
    # contains two columns -- 'HR' (the number of homerun hits)
    # and 'yearID' (the year in which the homeruns were hit).
    #
    # Fill out the body of this function, lineplot, to use the
    # passed-in csv file, hr_year_csv, and create a
    # chart with points connected by lines, both colored 'red',
    # showing the number of HR by year.
    #
    # You will want to first load the csv file into a pandas dataframe
    # and use the pandas dataframe along with ggplot to create your visualization
    #
    # You can check out the data in the csv file at the link below:
    # https://www.dropbox.com/s/awgdal71hc1u06d/hr_year.csv
    #
    # You can read more about ggplot at the following link:
    # https://github.com/yhat/ggplot/
    
    inData = pandas.read_csv(hr_year_csv)
    gg = ggplot(inData, aes('yearID', 'HR')) + geom_point(color = 'red') + geom_line(color = 'red') + ggtitle('title') + xlab('x') + ylab('y')    
    

    return gg

Data Types:

• Numeric
• Discrete
• Continuous

• Categorical Data

• Time Series

Plotting Line Chart:
from pandas import *
from ggplot import *

import pandas

def lineplot_compare(hr_by_team_year_sf_la_csv):
    # Write a function, lineplot_compare, that will read a csv file
    # called hr_by_team_year_sf_la_csv and plot it using pandas and ggplot2.
    #
    # This csv file has three columns -- yearID, HR, and teamID, 
    # representing the total number of HR hit each year by the SF Giants 
    # and LA Dodgers. Produce a visualization comparing the total HR by 
    # year of the two teams. 
    # 
    # You can see the data in hr_by_team_year_sf_la_csv
    # at the link below:
    # https://www.dropbox.com/s/wn43cngo2wdle2b/hr_by_team_year_sf_la.csv
    #
    # Note that to differentiate between multiple categories on the 
    # same plot in ggplot, we can pass color in with the other arguments
    # to aes, rather than in our geometry functions.
    # 
    # For example, ggplot(data, aes(xvar, yvar, color=category_var)).  This
    # should help you.
    
    inData = pandas.read_csv(hr_by_team_year_sf_la_csv)
    gg = ggplot(inData, aes(x='yearID', y='HR', color = 'teamID')) + geom_point() + geom_line()

    return gg