In [1]:
import pandas as pd
In [2]:
exp = pd.read_csv("data/sankey.csv")
In [3]:
exp.head()
Out[3]:
In [4]:
import jupyterviz
In [5]:
jupyterviz.viz(type="sankey",datadf=exp)
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In [6]:
routes = pd.read_csv("data/continent-route.csv",header=None)
In [7]:
routes.head()
Out[7]:
In [8]:
routes["source"]=routes.apply(lambda row: row[0].split("-")[0]+"-Departure",axis=1)
routes["target"]=routes.apply(lambda row: row[0].split("-")[1]+"-Arrival",axis=1)
routes = routes.drop(0,1)
routes.columns = ["value","source","target"]
In [9]:
routes.head()
Out[9]:
In [10]:
jupyterviz.viz(type="sankey",datadf=routes)
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In [12]:
import json
energyJson = json.loads("""
{"nodes":[
{"name":"Agricultural 'waste'"},
{"name":"Bio-conversion"},
{"name":"Liquid"},
{"name":"Losses"},
{"name":"Solid"},
{"name":"Gas"},
{"name":"Biofuel imports"},
{"name":"Biomass imports"},
{"name":"Coal imports"},
{"name":"Coal"},
{"name":"Coal reserves"},
{"name":"District heating"},
{"name":"Industry"},
{"name":"Heating and cooling - commercial"},
{"name":"Heating and cooling - homes"},
{"name":"Electricity grid"},
{"name":"Over generation / exports"},
{"name":"H2 conversion"},
{"name":"Road transport"},
{"name":"Agriculture"},
{"name":"Rail transport"},
{"name":"Lighting & appliances - commercial"},
{"name":"Lighting & appliances - homes"},
{"name":"Gas imports"},
{"name":"Ngas"},
{"name":"Gas reserves"},
{"name":"Thermal generation"},
{"name":"Geothermal"},
{"name":"H2"},
{"name":"Hydro"},
{"name":"International shipping"},
{"name":"Domestic aviation"},
{"name":"International aviation"},
{"name":"National navigation"},
{"name":"Marine algae"},
{"name":"Nuclear"},
{"name":"Oil imports"},
{"name":"Oil"},
{"name":"Oil reserves"},
{"name":"Other waste"},
{"name":"Pumped heat"},
{"name":"Solar PV"},
{"name":"Solar Thermal"},
{"name":"Solar"},
{"name":"Tidal"},
{"name":"UK land based bioenergy"},
{"name":"Wave"},
{"name":"Wind"}
],
"links":[
{"source":0,"target":1,"value":124.729},
{"source":1,"target":2,"value":0.597},
{"source":1,"target":3,"value":26.862},
{"source":1,"target":4,"value":280.322},
{"source":1,"target":5,"value":81.144},
{"source":6,"target":2,"value":35},
{"source":7,"target":4,"value":35},
{"source":8,"target":9,"value":11.606},
{"source":10,"target":9,"value":63.965},
{"source":9,"target":4,"value":75.571},
{"source":11,"target":12,"value":10.639},
{"source":11,"target":13,"value":22.505},
{"source":11,"target":14,"value":46.184},
{"source":15,"target":16,"value":104.453},
{"source":15,"target":14,"value":113.726},
{"source":15,"target":17,"value":27.14},
{"source":15,"target":12,"value":342.165},
{"source":15,"target":18,"value":37.797},
{"source":15,"target":19,"value":4.412},
{"source":15,"target":13,"value":40.858},
{"source":15,"target":3,"value":56.691},
{"source":15,"target":20,"value":7.863},
{"source":15,"target":21,"value":90.008},
{"source":15,"target":22,"value":93.494},
{"source":23,"target":24,"value":40.719},
{"source":25,"target":24,"value":82.233},
{"source":5,"target":13,"value":0.129},
{"source":5,"target":3,"value":1.401},
{"source":5,"target":26,"value":151.891},
{"source":5,"target":19,"value":2.096},
{"source":5,"target":12,"value":48.58},
{"source":27,"target":15,"value":7.013},
{"source":17,"target":28,"value":20.897},
{"source":17,"target":3,"value":6.242},
{"source":28,"target":18,"value":20.897},
{"source":29,"target":15,"value":6.995},
{"source":2,"target":12,"value":121.066},
{"source":2,"target":30,"value":128.69},
{"source":2,"target":18,"value":135.835},
{"source":2,"target":31,"value":14.458},
{"source":2,"target":32,"value":206.267},
{"source":2,"target":19,"value":3.64},
{"source":2,"target":33,"value":33.218},
{"source":2,"target":20,"value":4.413},
{"source":34,"target":1,"value":4.375},
{"source":24,"target":5,"value":122.952},
{"source":35,"target":26,"value":839.978},
{"source":36,"target":37,"value":504.287},
{"source":38,"target":37,"value":107.703},
{"source":37,"target":2,"value":611.99},
{"source":39,"target":4,"value":56.587},
{"source":39,"target":1,"value":77.81},
{"source":40,"target":14,"value":193.026},
{"source":40,"target":13,"value":70.672},
{"source":41,"target":15,"value":59.901},
{"source":42,"target":14,"value":19.263},
{"source":43,"target":42,"value":19.263},
{"source":43,"target":41,"value":59.901},
{"source":4,"target":19,"value":0.882},
{"source":4,"target":26,"value":400.12},
{"source":4,"target":12,"value":46.477},
{"source":26,"target":15,"value":525.531},
{"source":26,"target":3,"value":787.129},
{"source":26,"target":11,"value":79.329},
{"source":44,"target":15,"value":9.452},
{"source":45,"target":1,"value":182.01},
{"source":46,"target":15,"value":19.013},
{"source":47,"target":15,"value":289.366}
]}
""")
In [13]:
energyData = map(lambda obj: {'source': energyJson["nodes"][obj["source"]]["name"],"target": energyJson["nodes"][obj["target"]]["name"],"value": obj["value"]} ,energyJson["links"])
In [14]:
energydf = pd.DataFrame(energyData)
In [15]:
energydf.head()
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In [16]:
jupyterviz.viz(type="sankey",datadf=energydf)
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