/ Python And R Data science skills: 99 Real Data US Map Choropleth(1)

Sunday 18 February 2018

99 Real Data US Map Choropleth(1)

99 Real Data US Map Choropleth
In [10]:
import plotly.plotly as py
import plotly.graph_objs as go 
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
In [11]:
init_notebook_mode(connected=True) 
In [14]:
import pandas as pd
In [15]:
d = dict(type = 'choropleth',
            locations = ['AZ','CA','NY'],
            locationmode = 'USA-states',
            colorscale= 'Portland',
            text= ['text1','text2','text3'],
            z=[1.0,2.0,3.0],
            colorbar = {'title':'my titile'})
l = dict(geo = {'scope':'usa'})
In [16]:
choromap = go.Figure(data = [d],layout = l)
iplot(choromap)
In [5]:
df = pd.read_csv('2011_US_AGRI_Exports')
df.head()
Out[5]:
code state category total exports beef pork poultry dairy fruits fresh fruits proc total fruits veggies fresh veggies proc total veggies corn wheat cotton text
0 AL Alabama state 1390.63 34.4 10.6 481.0 4.06 8.0 17.1 25.11 5.5 8.9 14.33 34.9 70.0 317.61 Alabama<br>Beef 34.4 Dairy 4.06<br>Fruits 25.1...
1 AK Alaska state 13.31 0.2 0.1 0.0 0.19 0.0 0.0 0.00 0.6 1.0 1.56 0.0 0.0 0.00 Alaska<br>Beef 0.2 Dairy 0.19<br>Fruits 0.0 Ve...
2 AZ Arizona state 1463.17 71.3 17.9 0.0 105.48 19.3 41.0 60.27 147.5 239.4 386.91 7.3 48.7 423.95 Arizona<br>Beef 71.3 Dairy 105.48<br>Fruits 60...
3 AR Arkansas state 3586.02 53.2 29.4 562.9 3.53 2.2 4.7 6.88 4.4 7.1 11.45 69.5 114.5 665.44 Arkansas<br>Beef 53.2 Dairy 3.53<br>Fruits 6.8...
4 CA California state 16472.88 228.7 11.1 225.4 929.95 2791.8 5944.6 8736.40 803.2 1303.5 2106.79 34.6 249.3 1064.95 California<br>Beef 228.7 Dairy 929.95<br>Frui...
In [18]:
data = dict(type='choropleth',
            colorscale = 'YIOrRd',
            locations = df['code'],
            z = df['total exports'],
            locationmode = 'USA-states',
            text = df['text'],
            marker = dict(line = dict(color = 'rgb(255,255,255)',width = 2)),
            colorbar = {'title':"Millions USD"}
            ) 
In [22]:
layout = dict(title = '2011 US Agriculture Exports by State',
              geo = dict(scope='usa',
                         showlakes = True,
                         lakecolor = 'rgb(85,173,240)')
             )
In [23]:
choromap = go.Figure(data = [data],layout = layout)
In [24]:
iplot(choromap)

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