Choropleth Maps Exercise

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

Full Documentation Reference

Plotly Imports



In [38]:

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

Import pandas and read the csv file: 2014_World_Power_Consumption



In [1]:



In [152]:

Check the head of the DataFrame.



In [156]:









    Out[156]:






  
    
      
      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.



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

USA Choropleth

Import the 2012_Election_Data csv file using pandas.



In [109]:

Check the head of the DataFrame.



In [110]:









    Out[110]:






  
    
      
      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 [120]:



In [121]:



In [ ]:

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

	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

	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

Choropleth Maps Exercise

Plotly Imports

USA Choropleth

Great Job!