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Load data from a CARTO Table

This example illustrates how to load data from a CARTO table.

Use this when you need to see or modify the data locally. If you only need to visualize the data, just use the name of the dataset in the Layer: Layer('dataset_name')

Note: You'll need CARTO Account credentials to reproduce this example.





In [1]:



    


from cartoframes.auth import set_default_credentials

set_default_credentials('cartoframes')



    











In [2]:



    


from cartoframes import read_carto

gdf = read_carto('global_power_plants', limit=100)
gdf.head()



    


















    
Out[2]:











  
    

      
      cartodb_id
      the_geom
      fuel4
      country_abbrev
      country
      name
      gppd_idnr
      capacity_mw
      latitude
      longitude
      ...
      owner
      source
      url
      geolocation_source
      year_of_capacity_data
      generation_gwh_2013
      generation_gwh_2014
      generation_gwh_2015
      generation_gwh_2016
      estimated_generation_gwh
    

  
  
    

      0
      8465
      POINT (10.84410 48.58820)
      
      DEU
      Germany
      Nordendorf Solar Power Plant
      WRI1030340
      7.4
      48.5882
      10.8441
      ...
      
      Sinosol AG
      http://www.sinosol.de
      Industry About
      NaN
      NaN
      
      NaN
      NaN
      68.537617
    

    

      1
      8410
      POINT (11.20140 48.32320)
      
      DEU
      Germany
      Miegersbach Solar Power Plant
      WRI1030364
      5.2
      48.3232
      11.2014
      ...
      
      Phoenix Solar AG
      http://www.phoenixsolar-group.com
      Industry About
      NaN
      NaN
      
      NaN
      NaN
      48.161569
    

    

      2
      11314
      POINT (14.50530 -22.95750)
      
      NAM
      Namibia
      Anixas
      WRI1023132
      24.3
      -22.9575
      14.5053
      ...
      
      African Development Bank Group
      http://powerafrica.opendataforafrica.org
      Power Africa
      NaN
      NaN
      
      NaN
      NaN
      5.273790
    

    

      3
      25385
      POINT (-70.09890 8.83630)
      
      VEN
      Venezuela
      Masparro
      WRI1018659
      25.0
      8.8363
      -70.0989
      ...
      
      Venezuela Electricity Sector
      http://www.mppee.gob.ve/download/anuario_estad...
      GEODB
      NaN
      165.0
      
      NaN
      NaN
      123.392392
    

    

      4
      13042
      POINT (126.51120 34.40910)
      
      KOR
      South Korea
      Gochang
      WRI1029915
      15.0
      34.4091
      126.5112
      ...
      
      CDM
      https://cdm.unfccc.int/Projects/DB/KEMCO125427...
      WRI
      NaN
      NaN
      
      NaN
      NaN
      405.443975
    

  



5 rows × 24 columns



















In [3]:



    


from cartoframes.viz import Layer

Layer(gdf)



    


















    
Out[3]:







  
    
    

      

        

        
        
      
 
    
 
  

  

  

  

    :
    

      
      
    

  


  

    StackTrace
    
    
  
    

    





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    Content source: CartoDB/cartoframes

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