A Choropleth Map is a map composed of colored polygons. It is used to represent spatial variations of a quantity. The following simple exercises help in getting familiar with visualizing geographical data.
Refer to Plotly documentation for upto date usage and best practices guides.
import chart_studio.plotly as py
from plotly.offline import download_plotlyjs, init_notebook_mode,plot,iplot
import plotly.graph_objs as go
init_notebook_mode(connected = True)
Data is in JSON dictionary format. The following example shows how a data dictionary is setup.
The keyword arg z contains the data to be visualized. Here is a simple example below.
data = dict(type = 'choropleth',
locations = ['AZ','CA','NY'],
locationmode = 'USA-states',
colorscale= 'Jet',
text= ['Arizona','California','NewYork'],
z=[1.0,2.0,3.0],
colorbar = {'title':'Values'})
layout = dict(geo = {'scope':'usa'})
choromap = go.Figure(data = [data],layout = layout)
iplot(choromap)
import pandas as pd
# Read in the data
df = pd.read_csv('2011_US_AGRI_Exports')
# Get basic information on the data
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 code 50 non-null object
1 state 50 non-null object
2 category 50 non-null object
3 total exports 50 non-null float64
4 beef 50 non-null float64
5 pork 50 non-null float64
6 poultry 50 non-null float64
7 dairy 50 non-null float64
8 fruits fresh 50 non-null float64
9 fruits proc 50 non-null float64
10 total fruits 50 non-null float64
11 veggies fresh 50 non-null float64
12 veggies proc 50 non-null float64
13 total veggies 50 non-null float64
14 corn 50 non-null float64
15 wheat 50 non-null float64
16 cotton 50 non-null float64
17 text 50 non-null object
dtypes: float64(14), object(4)
memory usage: 7.2+ KB
Create data variable from the dataset.
Note: Observe locations as passed in via the code column and locationmode is set to USA-states
exports = dict(type='choropleth',
colorscale='ylorbr',
locations = df['code'],
locationmode = 'USA-states',
z = df['total exports'],
text = df['text'],
colorbar = {'title': 'Millions USD'},
marker = dict(line = dict(color = 'rgb(82, 82, 82)',width = 1))
)
Create layout variable for the dataset
Note: The layout scope is set to usa
layout = dict(title = '2011 US Agriculture Exports by State',
geo = dict(scope='usa',
showlakes = True,
lakecolor = 'rgb(85,173,240)')
)
choromap = go.Figure(data = [exports],layout = layout)
iplot(choromap)
# Read in data
df = pd.read_csv('2014_World_GDP')
# Get basic info of the data
df.info
<bound method DataFrame.info of COUNTRY GDP (BILLIONS) CODE
0 Afghanistan 21.71 AFG
1 Albania 13.40 ALB
2 Algeria 227.80 DZA
3 American Samoa 0.75 ASM
4 Andorra 4.80 AND
.. ... ... ...
217 Virgin Islands 5.08 VGB
218 West Bank 6.64 WBG
219 Yemen 45.45 YEM
220 Zambia 25.61 ZMB
221 Zimbabwe 13.74 ZWE
[222 rows x 3 columns]>
Create data variable from the dataset.
Note: Observe locations as passed in via the code column and the default locationmode is global
gdp = dict(type='choropleth',
locations = df['CODE'],
z = df['GDP (BILLIONS)'],
text = df['COUNTRY'],
colorscale='ylorbr',
colorbar = {'title': 'GDP in Billions USD'})
Create layout variable for the dataset
layout = dict(title = '2014 Global GDP',
geo = dict(showframe = False,
projection = {'type': 'natural earth'}
))
choromap = go.Figure(data=[gdp],layout = layout)
iplot(choromap)
# Read in the data
power = pd.read_csv('2014_World_Power_Consumption.csv')
# Get some basic info on the data
power.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 212 entries, 0 to 211
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Code 212 non-null object
1 Country 212 non-null object
2 Power Consumption KWH 212 non-null float64
3 Text 212 non-null object
dtypes: float64(1), object(3)
memory usage: 6.8+ KB
Create data variable from the dataset.
gdp = dict(type='choropleth',
locations = power['Code'],
z = power['Power Consumption KWH'],
text = power['Text'],
colorscale='ylorbr',
colorbar = {'title': 'Global Power Consumption 2014 (WH)'})
Create layout variable for the dataset
layout = dict(title = 'Global Power Consumption 2014',
geo = dict(showframe = False,
projection = {'type': 'natural earth'}
))
choromap = go.Figure(data=[gdp],layout = layout)
iplot(choromap)
Reference jupyter notebook.
Choropleth - Geographical Plotting