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Choropleth Maps Exercise - Solutions

Welcome to the Choropleth Maps Exercise! In this exercise we will give you some simple datasets and ask you to create Choropleth Maps from them. Due to the Nature of Plotly we can't show you examples embedded inside the notebook.

Full Documentation Reference

Plotly Imports





In [13]:



    


import plotly.graph_objs as go 
from plotly.offline import init_notebook_mode,iplot,plot
init_notebook_mode(connected=True)

Import pandas and read the csv file: 2014_World_Power_Consumption





In [2]:



    


import pandas as pd



    











In [3]:



    


df = pd.read_csv('2014_World_Power_Consumption')

Check the head of the DataFrame. 





In [4]:



    


df.head()



    


















    
Out[4]:










  
    

      
      Country
      Power Consumption KWH
      Text
    

  
  
    

      0
      China
      5.523000e+12
      China 5,523,000,000,000
    

    

      1
      United States
      3.832000e+12
      United 3,832,000,000,000
    

    

      2
      European
      2.771000e+12
      European 2,771,000,000,000
    

    

      3
      Russia
      1.065000e+12
      Russia 1,065,000,000,000
    

    

      4
      Japan
      9.210000e+11
      Japan 921,000,000,000

Referencing the lecture notes, create a Choropleth Plot of the Power Consumption for Countries using the data and layout dictionary. 





In [19]:



    


data = dict(
        type = 'choropleth',
        colorscale = 'Viridis',
        reversescale = True,
        locations = df['Country'],
        locationmode = "country names",
        z = df['Power Consumption KWH'],
        text = df['Country'],
        colorbar = {'title' : 'Power Consumption KWH'},
      ) 

layout = dict(title = '2014 Power Consumption KWH',
                geo = dict(showframe = False,projection = {'type':'Mercator'})
             )



    











In [20]:



    


choromap = go.Figure(data = [data],layout = layout)
plot(choromap,validate=False)



    


















    
Out[20]:








'file:///Users/marci/Pierian-Data-Courses/Udemy-Python-Data-Science-Machine-Learning/Python-Data-Science-and-Machine-Learning-Bootcamp/Python-for-Data-Visualization/Geographical Plotting/temp-plot.html'

USA Choropleth

Import the 2012_Election_Data csv file using pandas. 





In [7]:



    


usdf = pd.read_csv('2012_Election_Data')

Check the head of the DataFrame. 





In [8]:



    


usdf.head()



    


















    
Out[8]:










  
    

      
      Year
      ICPSR State Code
      Alphanumeric State Code
      State
      VEP Total Ballots Counted
      VEP Highest Office
      VAP Highest Office
      Total Ballots Counted
      Highest Office
      Voting-Eligible Population (VEP)
      Voting-Age Population (VAP)
      % Non-citizen
      Prison
      Probation
      Parole
      Total Ineligible Felon
      State Abv
    

  
  
    

      0
      2012
      41
      1
      Alabama
      NaN
      58.6%
      56.0%
      NaN
      2,074,338
      3,539,217
      3707440.0
      2.6%
      32,232
      57,993
      8,616
      71,584
      AL
    

    

      1
      2012
      81
      2
      Alaska
      58.9%
      58.7%
      55.3%
      301,694
      300,495
      511,792
      543763.0
      3.8%
      5,633
      7,173
      1,882
      11,317
      AK
    

    

      2
      2012
      61
      3
      Arizona
      53.0%
      52.6%
      46.5%
      2,323,579
      2,306,559
      4,387,900
      4959270.0
      9.9%
      35,188
      72,452
      7,460
      81,048
      AZ
    

    

      3
      2012
      42
      4
      Arkansas
      51.1%
      50.7%
      47.7%
      1,078,548
      1,069,468
      2,109,847
      2242740.0
      3.5%
      14,471
      30,122
      23,372
      53,808
      AR
    

    

      4
      2012
      71
      5
      California
      55.7%
      55.1%
      45.1%
      13,202,158
      13,038,547
      23,681,837
      28913129.0
      17.4%
      119,455
      0
      89,287
      208,742
      CA

Now create a plot that displays the Voting-Age Population (VAP) per state. If you later want to play around with other columns, make sure you consider their data type. VAP has already been transformed to a float for you. 





In [16]:



    


data = dict(type='choropleth',
            colorscale = 'Viridis',
            reversescale = True,
            locations = usdf['State Abv'],
            z = usdf['Voting-Age Population (VAP)'],
            locationmode = 'USA-states',
            text = usdf['State'],
            marker = dict(line = dict(color = 'rgb(255,255,255)',width = 1)),
            colorbar = {'title':"Voting-Age Population (VAP)"}
            )



    











In [17]:



    


layout = dict(title = '2012 General Election Voting Data',
              geo = dict(scope='usa',
                         showlakes = True,
                         lakecolor = 'rgb(85,173,240)')
             )



    











In [18]:



    


choromap = go.Figure(data = [data],layout = layout)
plot(choromap,validate=False)



    


















    
Out[18]:








'file:///Users/marci/Pierian-Data-Courses/Udemy-Python-Data-Science-Machine-Learning/Python-Data-Science-and-Machine-Learning-Bootcamp/Python-for-Data-Visualization/Geographical Plotting/temp-plot.html'

Great Job!

Content source: AtmaMani/pyChakras

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