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Paris Remarkable Trees from 1700 to 2000





In [1]:



    


import pandas
from pandas.io.json import json_normalize
import json

local_file_path = '../files/arbres_remarquables_paris.json'
with open(local_file_path, 'r') as json_file:
    data_json = json.load(json_file)

df = pandas.DataFrame(json_normalize(data_json['records']))
df.head()



    


















    
Out[1]:











  
    

      
      datasetid
      recordid
      record_timestamp
      fields.geom_x_y
      fields.libellefrancais
      fields.objectid
      fields.idemplacement
      fields.arrondissement
      fields.circonferenceencm
      fields.hauteurenm
      ...
      fields.stadedeveloppement
      fields.remarquable
      fields.idbase
      fields.genre
      fields.complementadresse
      fields.typeemplacement
      fields.dateplantation
      geometry.type
      geometry.coordinates
      fields.varieteoucultivar
    

  
  
    

      0
      arbresremarquablesparis
      a236960f295288698ca57fdccfd9825547ffb4d0
      2019-10-04T10:00:41.063000+00:00
      [48.8630096006, 2.2417737512]
      Pin
      49884
      000901001
      BOIS DE BOULOGNE
      241.0
      30.0
      ...
      M
      1
      2002349.0
      Pinus
      16-08
      Arbre
      1882-01-01T00:09:21+00:00
      Point
      [2.2417737512, 48.8630096006]
      NaN
    

    

      1
      arbresremarquablesparis
      d7635937c3636051c99963676d3a97dd4d9dd8c9
      2019-10-04T10:00:41.063000+00:00
      [48.8731110898, 2.24886478886]
      Hêtre
      52761
      000701004
      BOIS DE BOULOGNE
      231.0
      16.0
      ...
      M
      1
      2002354.0
      Fagus
      16-17
      Arbre
      1868-01-01T00:09:21+00:00
      Point
      [2.24886478886, 48.8731110898]
      ''Pendula''
    

    

      2
      arbresremarquablesparis
      460e8eccd3ebc816f16317702223bf406e7d75e6
      2019-10-04T10:00:41.063000+00:00
      [48.8448875264, 2.35366916319]
      Hêtre
      62456
      00000075
      PARIS 5E ARRDT
      85.0
      3.0
      ...
      M
      1
      132669.0
      Fagus
      NaN
      Arbre
      1905-01-01T00:09:21+00:00
      Point
      [2.35366916319, 48.8448875264]
      ''Tortuosa''
    

    

      3
      arbresremarquablesparis
      b60b80e0f8327afba6d0c513b0b6b85639e516b6
      2019-10-04T10:00:41.063000+00:00
      [48.8787029872, 2.30647417371]
      Erable
      68801
      00NO0144
      PARIS 8E ARRDT
      450.0
      28.0
      ...
      M
      1
      305507.0
      Acer
      08-01
      Arbre
      1700-01-01T00:09:21+00:00
      Point
      [2.30647417371, 48.8787029872]
      NaN
    

    

      4
      arbresremarquablesparis
      47e163d6f0536bc9e26ba2993447c6f50b14a6f6
      2019-10-04T10:00:41.063000+00:00
      [48.8671184424, 2.25294211329]
      Platane
      82341
      001201001
      BOIS DE BOULOGNE
      534.0
      45.0
      ...
      M
      1
      2002389.0
      Platanus
      16-10
      Arbre
      1872-01-01T00:09:21+00:00
      Point
      [2.25294211329, 48.8671184424]
      NaN
    

  



5 rows × 24 columns



















In [2]:



    


df.columns = df.columns.str.replace(".", "_")
df[['lon','lat']] = pandas.DataFrame(df['geometry_coordinates'].values.tolist(), index=df.index)
df.loc[:, ['geometry_coordinates', 'lon', 'lat']]



    


















    
Out[2]:











  
    

      
      geometry_coordinates
      lon
      lat
    

  
  
    

      0
      [2.2417737512, 48.8630096006]
      2.241774
      48.863010
    

    

      1
      [2.24886478886, 48.8731110898]
      2.248865
      48.873111
    

    

      2
      [2.35366916319, 48.8448875264]
      2.353669
      48.844888
    

    

      3
      [2.30647417371, 48.8787029872]
      2.306474
      48.878703
    

    

      4
      [2.25294211329, 48.8671184424]
      2.252942
      48.867118
    

    

      ...
      ...
      ...
      ...
    

    

      177
      [2.38368012582, 48.8814687366]
      2.383680
      48.881469
    

    

      178
      [2.35069308175, 48.8330296265]
      2.350693
      48.833030
    

    

      179
      [2.32633191622, 48.8940926568]
      2.326332
      48.894093
    

    

      180
      [2.43334109188, 48.8395251386]
      2.433341
      48.839525
    

    

      181
      [2.33940394029, 48.857749011]
      2.339404
      48.857749
    

  



182 rows × 3 columns



















In [3]:



    


from geopandas import GeoDataFrame, points_from_xy

gdf = GeoDataFrame(df, geometry=points_from_xy(df['lon'], df['lat']))
gdf.head()



    


















    
Out[3]:











  
    

      
      datasetid
      recordid
      record_timestamp
      fields_geom_x_y
      fields_libellefrancais
      fields_objectid
      fields_idemplacement
      fields_arrondissement
      fields_circonferenceencm
      fields_hauteurenm
      ...
      fields_genre
      fields_complementadresse
      fields_typeemplacement
      fields_dateplantation
      geometry_type
      geometry_coordinates
      fields_varieteoucultivar
      lon
      lat
      geometry
    

  
  
    

      0
      arbresremarquablesparis
      a236960f295288698ca57fdccfd9825547ffb4d0
      2019-10-04T10:00:41.063000+00:00
      [48.8630096006, 2.2417737512]
      Pin
      49884
      000901001
      BOIS DE BOULOGNE
      241.0
      30.0
      ...
      Pinus
      16-08
      Arbre
      1882-01-01T00:09:21+00:00
      Point
      [2.2417737512, 48.8630096006]
      NaN
      2.241774
      48.863010
      POINT (2.24177 48.86301)
    

    

      1
      arbresremarquablesparis
      d7635937c3636051c99963676d3a97dd4d9dd8c9
      2019-10-04T10:00:41.063000+00:00
      [48.8731110898, 2.24886478886]
      Hêtre
      52761
      000701004
      BOIS DE BOULOGNE
      231.0
      16.0
      ...
      Fagus
      16-17
      Arbre
      1868-01-01T00:09:21+00:00
      Point
      [2.24886478886, 48.8731110898]
      ''Pendula''
      2.248865
      48.873111
      POINT (2.24886 48.87311)
    

    

      2
      arbresremarquablesparis
      460e8eccd3ebc816f16317702223bf406e7d75e6
      2019-10-04T10:00:41.063000+00:00
      [48.8448875264, 2.35366916319]
      Hêtre
      62456
      00000075
      PARIS 5E ARRDT
      85.0
      3.0
      ...
      Fagus
      NaN
      Arbre
      1905-01-01T00:09:21+00:00
      Point
      [2.35366916319, 48.8448875264]
      ''Tortuosa''
      2.353669
      48.844888
      POINT (2.35367 48.84489)
    

    

      3
      arbresremarquablesparis
      b60b80e0f8327afba6d0c513b0b6b85639e516b6
      2019-10-04T10:00:41.063000+00:00
      [48.8787029872, 2.30647417371]
      Erable
      68801
      00NO0144
      PARIS 8E ARRDT
      450.0
      28.0
      ...
      Acer
      08-01
      Arbre
      1700-01-01T00:09:21+00:00
      Point
      [2.30647417371, 48.8787029872]
      NaN
      2.306474
      48.878703
      POINT (2.30647 48.87870)
    

    

      4
      arbresremarquablesparis
      47e163d6f0536bc9e26ba2993447c6f50b14a6f6
      2019-10-04T10:00:41.063000+00:00
      [48.8671184424, 2.25294211329]
      Platane
      82341
      001201001
      BOIS DE BOULOGNE
      534.0
      45.0
      ...
      Platanus
      16-10
      Arbre
      1872-01-01T00:09:21+00:00
      Point
      [2.25294211329, 48.8671184424]
      NaN
      2.252942
      48.867118
      POINT (2.25294 48.86712)
    

  



5 rows × 27 columns



















In [4]:



    


from cartoframes.viz import Layer

Layer(gdf)



    


















    
Out[4]:







  
    
    

      

        

        
        
      
 
    
 
  

  

  

  

    :
    

      
      
    

  


  

    StackTrace
    
    
  
    

    





">



    


    


    

    


    

    

    In [5]:
    

    
    
    

    gdf['plantation_date'] = pandas.to_datetime(gdf['fields_dateplantation'], errors='coerce')
gdf = gdf[gdf['plantation_date'].notnull()]
gdf['plantation_year'] = pandas.to_numeric(gdf['plantation_date'].dt.strftime('%Y'))

gdf.loc[:, ['plantation_date', 'plantation_year']]

    

    
    

    

    


    


    

    



    

    
    Out[5]:
    




    

    


    
  
    
    
      
      plantation_date
      plantation_year
    
    
      
  
    
    
      0
      1882-01-01 00:09:21+00:00
      1882
    
    
    
    
      1
      1868-01-01 00:09:21+00:00
      1868
    
    
    
    
      2
      1905-01-01 00:09:21+00:00
      1905
    
    
    
    
      3
      1700-01-01 00:09:21+00:00
      1700
    
    
    
    
      4
      1872-01-01 00:09:21+00:00
      1872
    
    
    
    
      ...
      ...
      ...
    
    
    
    
      177
      1700-01-01 00:09:21+00:00
      1700
    
    
    
    
      178
      1894-01-01 00:09:21+00:00
      1894
    
    
    
    
      179
      1700-01-01 00:09:21+00:00
      1700
    
    
    
    
      180
      1918-01-01 00:00:00+00:00
      1918
    
    
    
    
      181
      1700-01-01 00:09:21+00:00
      1700
    
    
      

    

    181 rows × 2 columns
    

    

    


    


    

    


    

    

    In [7]:
    

    
    
    

    from cartoframes.viz import Layer, color_bins_style

Layer(gdf, color_bins_style('plantation_year', breaks=[1700, 1800, 1900, 2000], palette='Mint'))

    

    
    

    

    


    


    

    



    

    
    Out[7]:
    




    

  
    
    
    
      
    
        
    
        
        
          
    
            
    
              


    
  
    
      
        
        
        
    
          
            
              
                
                
              
            
          
        
    
      
    
      

    
            
     
          
     
        
      
     
    
     
      

  

  
    
  
    
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      Content source: CartoDB/cartoframes

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